# A Survey on Exploratory Spatiotemporal Visual Analytics Approaches for
Climate Science

Abdullah-Al-Raihan Nayeem†, Dongyun Han†, Huikyo Lee, Donghoon Kim,
Daniel Feldman, William J. Tolone, Daniel Crichton, and Isaac Cho, Member,
IEEE AAR. Nayeem and W.J. Tolone are with the College of Computing and
Informatics, University of North Carolina at Charlotte, Charlotte, NC 28223,
United States. E-mail: {anayeem<EMAIL_ADDRESS>D. Han, D. Kim, and I. Cho
are with the Department of Computer Science, Utah State University, Logan, UT
84322, United States. Email: {dongyun.han, donghoon.kim<EMAIL_ADDRESS>H.
Lee, D. Crichton are with Jet Propulsion Laboratory, California Institute of
Technology, Pasadena, CA 91109, United States. Email: {huikyo.lee,
<EMAIL_ADDRESS>D. Feldman is with the Earth and Environmental
Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA 94720,
United States. Email<EMAIL_ADDRESS>† These authors contributed equally
Manuscript received April 19, 2005; revised August 26, 2015.

###### Abstract

Climate science produces a wealth of complex, high-dimensional, multivariate
data from observations and numerical models. These data are critical for
understanding human-induced climate change and its socioeconomic impacts, as
well as for effective decision-making for potential adaptations. Climate
scientists are continuously evaluating output from numerical models against
observations. This model evaluation process provides useful guidance to
improve the numerical models and subsequent climate projections. Exploratory
visual analytics systems possess the potential to significantly reduce the
burden on scientists for traditional spatiotemporal analyses. In addition,
technology and infrastructure advancements are further facilitating broader
access to climate data. Climate scientists today can access climate data in
distributed analytic environments and render exploratory visualizations for
analyses. Efforts are ongoing to optimize the computational efficiency of
spatiotemporal analyses to enable efficient exploration of massive data. These
advances present further opportunities for the visualization community to
innovate over the full landscape of challenges and requirements raised by
scientists. In this report, we provide a comprehensive review of the
challenges, requirements, and current approaches for exploratory
spatiotemporal visual analytics solutions for climate data. We categorize the
visual analytic techniques, systems, and tools presented in the relevant
literature based on task requirements, data sources, statistical techniques,
interaction methods, visualization techniques, performance evaluation methods,
and application domains. Moreover, our analytic review identifies trends,
limitations, and key challenges in visual analysis. This report will advance
future research activities in climate visualizations and enables the end-users
of climate data to identify effective climate change mitigation strategies.

###### Index Terms:

Visualization, spatiotemporal data, exploratory analysis, visual analytics,
climate science, survey.

##  1 Introduction

In recent years, climate science and visualization researchers have made
significant advances in the spatiotemporal visual analysis of climate data.
Interactive visualizations, today, enable scientists to hypothesize,
investigate, and compare spatiotemporal fields from various observations and
numerical models in innovative ways while leading to deeper, more insightful
understandings. Nevertheless, the breadth of climate science and its diversity
with numerous branches and sub-disciplines [1, 2] continuously demand visual
analytics (VA) capabilities that can flexibly meet diverse sets of
requirements [3, 4]. Climate data are mostly spatiotemporal, high-dimensional,
and complex. As such, to create cognitively efficient VA solutions for climate
data, researchers often propose use-case specific approaches for
visualizations and user interactions [5] rather than building versatile
approaches to explore data.

There are many spatiotemporal VA systems customized for various exploratory
and predictive analyses in climate science. For example, several VA systems
facilitate analyses of long-term climatology [6, 7, 8, 9] and focus on
analyzing weather data [10, 11, 12]. In addition, some systems feature
specific aspects of climate data analysis such as uncertainty quantification
[13, 14], anomaly detection [15], and adaptation and mitigation of climate
change [16, 17]. Moreover, spatiotemporal visualizations are often leveraged
to highlight the temporal evolution of a data field by visualizing data
trajectories. [18, 19, 20]. General purpose visualization systems
encapsulating data formats and analysis pipelines [21, 22, 23] for
spatiotemporal data are also receiving attention in the earth science research
community.

The aforementioned research efforts illuminate the challenges and requirements
when developing spatiotemporal VA systems for climate data. The requirements
commonly include designing a pipeline that supports data cleaning, data
integration, data transformation, and dimensional reduction of data [24]. In
addition, data are often resampled into a structured grid to facilitate visual
presentation [25]. All of the challenges and requirements are framed by
various types of analysis tasks [26]. Furthermore, interactive features are
introduced in VA systems to facilitate filtering, exploration, and hypothesis
generation of climate data [27]. The complexity of this landscape and the
diversity of research fields in climate science motivate the comprehensive
review of spatiotemporal VA systems.

To date, a comprehensive review of the challenges, requirements, and current
approaches to exploratory spatiotemporal VA solutions for climate science is
missing. This report, however, presents a comprehensive survey and organizes
relevant domain knowledge and VA solutions for climate science and
visualization researchers. This report also outlines a VA pipeline adapted to
exploratory analysis tasks to render interactive visualization over
distributed spatiotemporal climate data. To promote innovation within the
visualization community, this report serves as a guide to visualization
experts by identifying challenges, requirements, trends, limitations, and gaps
in existing spatiotemporal VA systems for climate science. At the same time,
this report advances climate science by informing scientists and decision-
makers of relevant spatiotemporal VA solutions, including their applicability
and limitations, that could be used in support of policymaking and the
identification of effective climate change mitigation strategies.

To achieve these goals, we reviewed current literature to summarize recent
research advances in exploratory spatiotemporal VA solutions for climate
science. Based on the literature review, we identified and characterized
exploratory spatiotemporal VA systems along the following key dimensions [28,
27]:

Data Source & Definition – Climate data are commonly high-dimensional and
often generated as high-resolution geospatial projections [29, 30]. We
organize the data source & definition dimension of the exploratory
spatiotemporal VA domain according to the data structures and formats utilized
by climate scientists [31, 32].

Statistical Analysis – Climate scientists apply novel statistical analysis
techniques to explore the potential impacts of climate change. The statistical
analysis of variables such as precipitation, temperature, and those associated
with air quality [33, 34] is often accompanied by visualization. Along this
dimension of the exploratory spatiotemporal VA domain, we organize solutions
based on the method by which statistical analyses are incorporated into the
solution.

Visualization Techniques – Novel exploratory visualization approaches [35, 36,
37] have been leveraged to create spatiotemporal representations for climate
model evaluations relative to climate data observations and corresponding
analysis tasks. Throughout this paper, we highlight the visualization
techniques employed by current exploratory spatiotemporal VA for climate
science. In addition, we assess visualization techniques for other
spatiotemporal data [38, 20, 39] that may address requirements from
underexplored areas of climate science.

Interaction Methods – Interactive features facilitate scientists’ efforts to
explore, reconfigure, and compare the visual renderings of climate data. This
dimension emphasizes the potential of each method to unfold knowledge from
multivariate attributes (e.g., [25, 40]).

Analytics Systems – Novel analytics pipelines have been introduced and tested
for VA systems (e.g. [41, 42, 43]). We identify key features and limitations
of the present pipelines in supporting climate data analysis.

Performance Evaluation – It is challenging to define quantitative and
qualitative metrics to measure the performance of VA systems [44, 45].
Nevertheless, we organize this dimension of our review according to the
respective categorization techniques, data types, and analysis tasks [46, 47]
to close the research gap between usability and task efficiency in climate
science VA.

This paper is organized as follows. Section 2 discusses the previous surveys
related to visualizing climate data and spatiotemporal VA. Section 3 presents
our survey methodology, including how we collected and filtered peer-reviewed
publications relevant to our topic. Section 4 summarizes the landscape of task
requirements for exploratory spatiotemporal VA solutions. Section 5 presents a
summary of spatiotemporal data and exploratory analysis tasks in climate
science. Section 6 discusses the visualization techniques and interaction
methods employed for spatiotemporal datasets. Section 7 examines the interface
designs, tools, and infrastructures leveraged in existing systems and a
pipeline for state-of-the-art VA approaches. Section 8 discusses the
qualitative and quantitative metrics to measure the performance of VA systems
and close the research gaps between usability and task efficiency Section 9
overviews current trends in VA research related to climate science and
reflects on existing research gaps and potential future research directions to
address these gaps and the ongoing challenges of understanding spatiotemporal
climate data.

##  2 Previous Surveys

We identify nine previous surveys that appear relevant to this report. In the
following, we summarize these studies and highlight their significance. We
also identify how this report advances the work of these previous studies.

Andrienko et al. [4] reported an analytical review on exploratory
spatiotemporal visualization. This work focuses on two important capabilities
of visualization techniques - 1) exploratory analysis and 2) compatible data
types. To relate the visualization tools and techniques to specific tasks and
data, this report provides a categorization of exploratory analysis tasks
based on 3W: where (space), when (time), and what (event) [48]. Moreover, the
user interaction techniques and the concept of a reconfigurable interface are
discussed to support visual querying. The visualization techniques reported
for long-term trend analysis are generally applicable to long-term climate
data.

Tominski et al. [44] reported a survey interviewing 76 participants working in
multiple fields of climate science. The authors collected information about
the usage of visualization tools and analytical tasks of the participants to
outline how an interactive visualization tool can benefit climate science
research. They also inquired about the participants and discussed the reasons
for the low pervasion of the state-of-the-art visualization tools to perform
analysis tasks.

Rautenhaus et al. [49] surveyed visualization tools and techniques for
meteorological data analysis tasks. Their work highlights the categorization
and summary of visualization approaches for operational forecasting with
large-scale meteorological data. In this survey, a map visualization is
identified as vital in visualizing both observational and numerical model
data. This paper approaches the existing techniques and tools from two domain
perspectives, visualization and meteorological, to assess the support for
studying meteorological data.

Yang et al. [50] classified visualization tools and analytical methods. Their
paper focuses on enhancing human perception and improving cognition efficiency
for spatiotemporal data analysis. It characterizes visualization as a series
of conversion processes (raw data to visual artifacts) that recognize the
human eye’s perception and the brain’s cognition abilities to gain knowledge
visually. In this paper, spatiotemporal visualization methods are further
categorized using statistical charts combined with map-based visualization
methods, methods that are further classified according to five approaches - 1)
Aggregation Charts; 2) Hierarchical Color Maps; 3) Heatmaps; 4) Origin-
Destination Maps; and, 5) 3D Virtual Visualization.

He et al. [51] illustrated trajectory analysis to extract patterns from
multivariate spatiotemporal data. This paper conceptualizes the visualization
of spatiotemporal trajectory on both geographic and abstract domains. They
reported five encoding techniques (coordinated multiple views, juxtaposing,
superimposing, cutting, and explicit encoding) to support visual comparison of
trajectory data along with their corresponding weakness and strength. The
interaction techniques are categorized based on the target objects such as
view manipulation, data manipulation, transition, etc. rather than the
interaction types.

Chen et al. [52] reported the state-of-the-art multivariate VA techniques for
simulation data. The visualization techniques are characterized into three
categories. The first category is the visual designs in simulation space that
covers the visual mapping and summarizing of the spatiotemporal data. The
second category is the parameter space analysis for reasoning the model
simulation that covers the visual steering, parameter space projection, etc.
The third category is the data processing in feature space that covers the
spatiotemporal feature definition, extraction, clustering, etc.

Rudenko et al. [53] conducted a brief review of the 4D visualization tools
specialized for weather data. Their review summarizes the tools and techniques
that support multi-format data from the processing and visualization
phenomenon. However, their discussion reflected solely on the present
advancements in geovisual environments while there are existing knowledge gaps
of climate scientists’ needs for exploratory analytics.

Andrienko et al. [54] reported state-of-the-art spatiotemporal VA that
summarizes the advancements of VA concerning spatiotemporal analysis. Their
review focuses on the research efforts behind spatiotemporal visualization of
trajectory movement, data filtering, aggregation, and spatial event clustering
and distribution. A time-based data querying and filtering technique ‘Time
Mask’ is illustrated where temporal data are displayed in a space-time cube
visualization. Moreover, this paper reflects on the shortcomings of the VA
approaches in dealing with robustness and limited human cognition factor
instead of overly focusing on large data volumes.

Alam et al. [5] conducted a survey on spatiotemporal data analytics systems.
They categorized their contributions into three groups - 1) spatial databases,
2) spatiotemporal data processing infrastructures, and 3) tools to support
spatiotemporal analysis. The scope of this survey does not include specific
visualization techniques to perform spatiotemporal analysis tasks. Rather,
this work illustrates the spatiotemporal data management approaches, available
processing infrastructures, and libraries/packages for VA.

The spatiotemporal VA approaches discussed in those surveys are applicable to
climate science research, thus, have some overlaps with our paper. However,
except for [44, 49], the other surveys do not scope any specific analytical
use cases from the perspective of climate scientists. In contrast, our paper
surveys the current progress of spatiotemporal visualization techniques and
analytics approaches from the perspective of climate scientists. By
incorporating this perspective, our survey makes two important contributions.
First, this report advances climate science by informing scientists and
decision-makers in a manner that aligns with use cases of relevant
spatiotemporal VA solutions, including their applicability and limitations,
that may be used in support of policymaking and the identification of
appropriate and effective climate change mitigation strategies. Second, this
report serves as a guide to visualization researchers by identifying
challenges, requirements, trends, limitations, and gaps in existing
spatiotemporal VA systems in the context of their use in climate science.

##  3 Methodology

This section presents our survey methodology. To conduct a survey of the
state-of-art spatiotemporal VA in climate science, we reviewed literature from
geographical science, climate science, visualization, and other related
application domains. In doing so, we utilized a rich set of search terms,
including the synonyms and variants of our topic. The search terms used
included but were not limited to ‘climate’, ‘spatiotemporal’, ‘visualization’,
‘exploration’, and ‘analytics’. VA researchers on our team searched
spatiotemporal visualizations-related literature from reputed peer-reviewed
visualization and visual analytic publication venues. Similarly, climate
scientists on our team searched for relevant climate science analytics
literature from reputed peer-reviewed climate science journals and
conferences. In addition, we jointly identified relevant literature based on
spatiotemporal analysis published in reputed visualization venues (e.g., [21,
9, 43]). This approach provided us with a rich set of literature focused on
spatiotemporal visualization and analytics in climate science.

To collect the papers, we used the search term method previously described in
different combinations using IEEE Xplore and ACM digital libraries. Our
initial searches focused on papers published during the last decade. After
constructing an initial collection, we began reviewing the titles and
abstracts to filter the papers specific to our topic. Next, we reviewed the
previously identified surveys and state-of-the-art reports (see section 2).
These efforts expanded our pool of related research according to data types
and climate science use cases. Then, we widened our search scope further to
include additional relevant literature from the journals and conferences that
previously had not been included in the initial list. At each stage, filtering
was conducted as described above.

After thoroughly filtering the collected papers, our resulting collection of
relevant literature included 108 papers for further review. Once we reviewed
these papers, we organized the papers using the taxonomy presented in section
4. Next, we extracted the visualization techniques, analysis tasks, user
interactions, and evaluation methods to organize the collection further. In
addition, the climate scientists on our team identified, reviewed, and
validated the climate science task requirements while the visualization
experts on our team performed a similar task for the identified visualization
requirements for exploratory spatiotemporal analysis. Research gaps and
potential future research directions are presented in section 9.

##  4 Survey of Task and Visualization Requirements

In addition to reviewing the recent advances in spatiotemporal visualization
and VA approaches in climate science, we have studied analytical reviews and
previously conducted domain experts’ surveys related to our topic. Climate
scientists advance the scientific understanding of how the Earth system
achieves a climate state, and how that state has changed, is changing, or will
change in the future, and consequently analyze large volumes of data, both
produced by models and collected from observations. These datasets can be very
large, can be homogeneous or heterogeneous, and can contain spatial and
temporal dimensions, so visualization requirements for using these datasets to
support and augment climate science must first recognize the specific data
science challenges that climate science data present. These requirements must
also recognize fundamental features that are expressed across observational
and modeling climate datasets and also recognize that these datasets can
expand at geometric rates.

Additionally, massive climate data are often fragmented across distributed
data servers [41]. Alam et al. [5] addressed the challenges of extracting and
analyzing knowledge from the spatiotemporal data due to the heterogeneity and
implicit spatial and temporal dependencies. It is widely popular among climate
scientists to leverage high-performance clusters or supercomputers to enable
exploratory analyses [55]. Therefore, VA systems should address these storage
and computational requirements. Moreover, generalized and reproducible
workflows are being developed in order to filter, aggregate, classify, and
analyze the movement of the spatiotemporal variables [54, 56]. To explore both
raw and processed climate data, various visualization tools and techniques are
utilized such as time series charts, bar plots, scatter plots, map
visualizations, etc. [44, 50]. Nonetheless, several previous studies reported
the difficulty in performing analysis tasks running back and forth between the
data analytics and visualization tools (e.g., [55]). We recognize the need for
an interactive VA interface, which can obtain an enormous potential to ease
climate scientists in conducting exploratory tasks.

Based on our literature review and collaboration with the climate scientists
in our team, we identified the following task requirements for a
spatiotemporal VA system to support a wide range of workflows and
investigations currently performed by climate scientists. Massive climate
datasets are typically not analyzed in an unguided manner, rather, there are
very specific questions that scientists seek to explore in large datasets. The
guided and targeted approach to scientific inquiries where visualization is an
important, and generally necessary tool, enables the identification of more
generalized findings regarding climate data. Because of this, there are
interrelated data management, analysis feature, and VA requirements that a
literature review and scientist’s surveys have revealed. Briefly, these are:

Data Management Requirements

  1. R1

To enable efficient large-scale data encoding and retrieval to support
analyses and interactive visualizations.

  2. R2

To provide access to disparate and fragmented data from distributed data hosts
to support cross-relation and impact study.

  3. R3

To provide a cloud environment and features for data extraction, pre-
processing, and transformation to support data preparation and visual
exploration.

  4. R4

To enable, via standardized APIs, interactive query-based data accessibility
and mapping across disparate, multivariate datasets.

Analysis Feature Requirements

  1. R5

To enable the identification of underlying trends, patterns, and anomalies in
spatiotemporal data.

  2. R6

To support multivariate analysis to explore associations among the variables
related to climate change effects and impacts.

  3. R7

To enable comparative analysis and aggregation based on spatiotemporal
granularity, clustering, and ensemble analysis.

VA Requirements

  1. R8

To mediate user interaction with the data servers and high-performance
computing clusters to run analytical workflows.

  2. R9

To provide customized interactive visualization techniques and analytics
interface to explore and analyze climate data.

  3. R10

To enable exploratory analysis at various scopes and scales with rich user
interactions to understand multivariate climate data at different levels of
abstraction.

If these requirements are achieved, then visualization approaches can be
powerful tools for enabling scientific investigations by streamlining
hypothesis generation and testing. Often, they are not, however, visualization
approaches developed years to decades ago where computational capabilities
were far inferior to the present, are still being used. Supplanting vestigial
solutions with modern, improved solutions can take time, or in some cases,
does not happen. With established workflows, even on old or sub-optimal
visualization solutions, there is a high barrier to entry for novel solutions,
even if they are superior to those already being used. These challenges may
also explain the low pervasion of VA tools in climate science research
reported in previous studies [44]. We discuss adoption challenges in the
potential future research directions in our discussion.

##  5 Spatiotemporal Data in Climate Science

In this section, we discuss climate science analytics from the aspect of
spatiotemporal data management, analysis techniques, and applications. We
present the definition of spatiotemporal data and its different
transformations in the context of climate science. We scope the exploratory
spatiotemporal analyses that are employed to study climate change, its impact,
and possible adaptations. In turn, we introduce the analysis techniques and
different applications of spatiotemporal climate variables.

###  5.1 Data Definition

Events in spatiotemporal data consist of space and time. In simple terms,
spatiotemporal data denotes events related to location that occurs over time.
Climate science produces diverse spatiotemporal data based on the dimensions,
events, and structure. We categorize different data types and sources employed
in the VA interfaces according to the data type taxonomy discussed by
Shneiderman [57]. This taxonomy was later utilized by Afzal et al. [55] for
the ocean and atmospheric datasets. The data type taxonomy proposed by
Shneiderman includes 1-2-3 dimensional, temporal, multivariate, tree, and
network data [57]. He et al. [51] presented a conceptual illustration of
multidimensional and multivariate data among other types as shown in Fig. 1a
and b. In addition, we leveraged the data representation diagram presented by
Tominski et al. [40] to illustrate the tree and network data types as shown in
Fig. 1c and d.

Figure 1: A conceptual illustration of different data types classified in
[57]. He et al. [51] illustrated the concept of the data type classification:
(a) Multidimensional: 0D, 1D, 2D, and 3D; (b) Multivariate: scalar, 2-tuple,
and n-tuple. (c) Network and (d) Tree data types are illustrated from the view
diagram presented by Tominski et al. [40].

1-Dimensional (1D): This data type represents linear data such as textual
documents, a list of names, and source codes. The data are organized in a
sequential layout, generally consisting of unstructured information. As a
result, it is particularly challenging to design visualizations for this data
type. We did not find any instance of this type in context to spatiotemporal
climate variables.

2-Dimensional (2D): This data type represents planar data (i.e., x and y) that
include a geographic map, floorplans, or layouts. Datasets of this type
generally have additional attributes such as name, value, etc. 2D climate data
often represents the spatial distribution of a variable [21, 8]. VA interfaces
in climate science visualize these datasets with a multi-layer approach on a
2D map. Users can toggle layers of the map based on the variable of interest
[14, 58, 59].

3-Dimensional (3D): This data type includes 3D models of real-world objects
(e.g., buildings, Computer-Aided Design (CAD) models) and scientific datasets
(e.g., computed tomography scan). The data often contain attributes with
volume and complex interrelation with other data items. Volumetric rendering
is a popular technique to visualize scientific data [60, 61, 62]. Most modern
climate models produce 3D data to study cross-correlation maps among climate
variables [63]. Climate change impact and adaption-related analysis also
produce this type of data [64].

Multidimensional: Multidimensional data denotes multiple dimensions in the
dataset whereas multivariate data denotes the multiple attributes associated
with each dimension. Climate scientists study a wealth of multivariate data
[40] to analyze variable association [64, 65], distribution [66], path
ensemble [8], etc. Multidimensional data is usually visualized using different
2D techniques such as integrated multiple visualization setup [23, 67, 65],
small multiples [66, 41, 42], color/glyph stylization [68, 37], or summarized
form with fewer dimension [69]. Parallel coordinates are widely used for
visualizing multidimensional and multivariate datasets [64, 8].

Temporal: This data type denotes datasets that contain time-varying variables.
Temporal data denotes the changes in climate variables over time such as
temperature, precipitation, humidity, etc. Data sources utilized in most of
the VA interfaces discussed in this paper relate to this data type [43] as the
temporal dimension contains an essential portion of spatiotemporal data.
Temporal data is generally visualized using a standard 2D visualization
technique such as area plot [9], scatter plot [70], animation [11], time
sliders [16, 17], etc. based on the data transformation.

Tree: This data type represents hierarchically structured data where each item
in the collection has a link to the parent item except for the root. The data
items are often linked based on multiple attributes. These datasets are
visualized using the different representations of tree/hierarchical
visualizations (e.g., edge-bundling, treemap). Kappe et al. [35] detailed a
hierarchical clustering based on the climate states and time-dependent
ensembles (Fig. 7D). They used dendrogram and hierarchical clustering to
visualize the data. Nayeem et al. [41] summarized spatiotemporal precipitation
and temperature data to produce a hierarchical seasonal-regional mean.
However, we understand that tree-structured data are not common in climate
science as we found only a few examples.

Network: This data type includes the links among the data attributes that
cannot be represented using a tree structure. Network data items often obtain
an arbitrary number of relationships with other items that are commonly
visualized using node-link diagrams and matrix representations. Kalo et al.
[43] studied spatiotemporal interpolation of air pollution based on the data
collected at measurement sites across the United States (US). They used a
node-link diagram to visualize the triangulation of air pollution data (Fig.
6A). Analysis of path ensemble [71], spatiotemporal trajectory [72], variable
similarity [66, 65], data flow [73] etc. derive network data for climate
variables.

###  5.2 Data Storage

Storage of spatiotemporal data has been a challenge for the analyst and
research community due to its volume and complex nature [31]. Climate data are
characterized as complex because of high dimensionality, mutlivariability, and
multi-resolution factors [64]. Advancement in infrastructure and equipment has
made it possible to acquire a more granular level of spatiotemporal data,
which demands vast and faster storage units. For faster transactions,
spatiotemporal data are frequently stored in structured relational databases
(e.g., PostgreSQL [74], PostGIS [75]). However, a large volume of data from
multiple sources are often stored in a non-structured format, known as NoSQL
(e.g., Redis [76], MongoDB [77]). Storage is still a concern for
spatiotemporal data for efficient data encoding and retrieval.

In climate science, increasing resolution of geospatial data and frequent
temporal records make Network Common Data Form (NetCDF) [78] a well-accepted
data storing format in the research community for multidimensional climate
variables such as temperature, precipitation, and air quality [31, 79, 80].
NetCDF provides an encoding convention for the researchers to store
multidimensional data in a relatively smaller space [32]. Moreover, it allows
streaming data without copying or defining the structure from scratch.
Individual data files are simultaneously accessible by one writer and multiple
readers [78]. Climate scientists established metadata conventions for storing
climate variables in NetCDF files [81]. The conventions include acceptable
data types, naming conventions, dimensions, units of measurement, etc. The
metadata container should also describe the title, data source, institute, and
necessary references for the data [81]. Hence, NetCDF has become one of the
widely used formats for storing earth science data [82].

Besides, based on general usage and data characteristics, spatiotemporal
datasets are also stored as fragmented, partitioned clusters across
distributed servers [73, 41]. As a result, exploratory analysis performed by
researchers and scientists does not always reside in a single data server.
Important insights and knowledge often reside in distributed and disparate
datasets. Spatiotemporal data fragmented across multiple remote data hosts are
identified as distributed spatiotemporal datasets. Big data computing
infrastructure such as Hadoop [83] supports large-scale data encodingto enable
distributed analysis [11]. Other big data infrastructures are being explored
(e.g., Virtual Infrastructure and Virtual Information-Fabric Infrastructure
(VIFI) [84]), that mitigate analyses of distributed data.

TABLE I: Distribution of exploratory spatiotemporal analysis tasks supported
by the current VA systems.

###  5.3 Analysis Tasks

A core element of future weather (or air quality) forecasts and climate
projections are numerical models that not only provide a foretelling of
physical indicators of future climate but also indirectly provide information
on societal impacts and thus provide a key resource for addressing adaptation
and mitigation questions. Because of the critical spatiotemporal data such
models offer, it is a high priority to bring as much observational scrutiny to
the output from the numerical models as possible. This requires the systematic
application of observational datasets from various sources. As such, enabling
spatiotemporal analysis of observational datasets and evaluation of the
spatial-temporal output from numerical models are all necessary for visual
analytic systems to provide a reliable characterization of future weather/air
quality/climate that can lead to an informed decision-making process [56].

Data Exploration: Uncovers valuable insights from the spatiotemporal data as
one of the earlier stages in the analysis process to identify a subset/area of
interest. Piltner et al. primarily focused on spatiotemporal interpolation of
irregularly spaced air quality observations for the contiguous US [43]. Their
spatiotemporal interpolation and its validation are conducted in the stage of
data processing, so users of the visual analytic system explore the
interpolated data via a web interface. Similarly, DDLVis [9] applies three
advanced statistical techniques (a peak-based kernel density estimation, a
dictionary learning method, and a peak-based variation generation model) to
store, visualize, and query climate data efficiently. Climate Engine [23]
provisions calculation of basic statistics including mean, median, maximum,
minimum, and total only. Moreover, with special emphasis on the US National
Climate Assessment (NCA), the National Climate Change Viewer (NCCV [7])
provides various summary statistics for various geographical regions, such as
counties, states, and NCA regions, over the contiguous US. The summary
statistics include spatially averaged time series and percentile tables of
temperature and precipitation from 30 models for present and future climate.

Multivariate Analysis: In most analysis tasks, climate scientists analyze
multiple variables which are associated with certain phenomena. For example,
concentrations of ozone, nitrous oxides, and particulate matter from
observations and numerical models are analyzed in air quality studies. In
multivariate analyses of climate data, it is always important to figure out a
normal state, which is usually defined as averages of individual variables
over a certain period. Wang et al. [64] applied an association rule-learning
algorithm to study the relationship of multiple variables in climate data. To
facilitate the rule-learning algorithm and parallel coordinate plotting, they
also applied a categorization algorithm to group each variable’s values into
five categories. Moreover, Poco et al. [66] demonstrated an inter-comparison
among simulated climate models leveraging multifaceted data. CrossVis [36]
provides a VA system to explore large-scale heterogeneous multivariate data
with a use case of historical hurricane observations.

Anomaly Detection: Anomalous events, such as heatwaves, cold surges, heavy
precipitation, drought, and severe air pollution, can be detected by analyzing
the deviation of spatiotemporal data from their averages. Using the Scalable
Online Visual Analytic System (SOVAS; [8]), climate scientists can detect
anomalous events such as extreme heat events. SOVAS also enables the
calculation of correlation coefficients between two meteorological variables
and spatial means. Voila [15] presented a tensor-based analysis algorithm for
interactively detecting anomalies in spatiotemporal data. Moreover, the
phenomena portal [70] provides a map-based user interface to visualize certain
anomalous events detected by their convolutional neural network (CNN) model.
Users of the portal can provide feedback on the detected events.

Uncertainty Analysis: The evaluation of numerical models against observations
aims to quantify uncertainty for future projections from the models. This
uncertainty quantification process is key to informing climate model
development and providing actionable climate information to support decision-
making [85]. To visualize forecast output from multiple simulations and their
uncertainty, Noodles [13] applies two visualization techniques, uncertainty
glyphs, and an uncertainty ribbon. The authors compared these two techniques
with conventional spaghetti plots and show the advantages of the new
techniques. Herring et al. [16] present a context-switching technique in
ClimateData.US to explore low and high-emission scenarios of climate variables
such as temperature and precipitation. ClimateData.US essentially enables the
uncertainty analysis of climate risks.

Trajectory & Flow Analysis: Trajectory analysis are widely used to track
hurricanes or air pollutants including volcanic ashes and dust storms. Kim et
al. [73] developed a novel flow analysis technique to extract the flow map
from the non-directional spatiotemporal data. EnsembleGraph [86] utilizes
graph visualization of ensemble simulations. In the graph, nodes are
subregions with similar simulation output and edges represent spatial overlap.
The Hawaii Rainfall Analysis and Mapping Application (HI-RAMA, [6]) applies a
random forest model for quality control of rainfall station data and two
different weighting approaches for the gap-filling of missing data. Again,
these statistical techniques are not applied to users’ data exploration.

TABLE II: Categories for the visualization techniques reviewed in this paper.
Standard 2D charts and geospatial contours have been popular choices in visual
analysis. Coordinated Multi-Views are widely used in recent years to visualize
spatiotemporal data.

##  6 Visualization Techniques

In this section, we discuss various visualization techniques that have been
employed to visualize the spatiotemporal climate data. We investigated these
techniques from different aspects to report a categorization and mapping to
spatiotemporal data types and analysis tasks discussed in section 5. We
reviewed the taxonomy for visualization techniques defined by Keim [28] and
extended further by Afzal et al. [55]. In their work, the visualization
techniques are classified as standard 2D/3D displays, geometrically
transformed displays, icon-based displays, dense pixel displays, and stacked
displays. However, climate visualizations, in many instances, leverage
multiple visualization techniques to illustrate the spatiotemporal data due to
its high dimensional and multivariate attributes. Hence, display design is
also a crucial aspect of data visualization in climate science. In our review,
we provide a classification of the visualization techniques inspired by [28],
albeit based on the display design instead of visual encoding. The
classification focuses more on the different approaches of integrated
visualization methods for spatiotemporal data as depicted in Table II.

To characterize the visualization techniques for spatiotemporal climate data,
we first discuss the different standard 2D/3D visualizations such as time
series plots [70], bar charts, parallel-coordinates [66, 36], volumetric
rendering [64, 62], map visualizations [59, 87], etc. Then, we discuss
different display designs for integrated spatiotemporal visualizations for
climate science such as temporal transition, path ensembles [88, 89],
trajectory [62], space-time cube [90, 91], radial map [92], and coordinated
multiple views [41, 9]. These integrated visual representations assist climate
scientists in performing exploratory visual analysis such as sensemaking [6],
anomaly detection [15], trajectory analysis [73], and visual query [9].

###  6.1 Standard Visualization Techniques

Climate variables generally contain timestamps and geolocation, so climate
scientists widely use geospatial visualization and time series charts to
analyze their data [93]. Geospatial visualization provides an interface to
inspect and explore spatial variables with geographic location [94, 7, 41]
whereas time series charts are essential to perceive the temporal trends of
the spatial features. The most commonly used techniques to visualize
spatiotemporal climate data are a combination of standard charts, such as a
line chart, a scatter plot, and their variations, [49, 55] along with a map
visualization (i.e., satellite [87], choropleth [14]). These standard
visualizations are illustrated in 2D and 3D. The basic idea of 2D
visualizations in this context is to present the data variable against time in
x-y axes. In contrast, 3D visualization includes another dimension essentially
supporting the analysis of trends and patterns in spatiotemporal data with
x-y-z axes. However, leveraging standard charts often create occlusion on the
visual interface that challenges cognition efficiency [45]. In this section,
we discuss different 2D and 3D visualization techniques for the visual
analysis of spatiotemporal climate data.

####  6.1.1 2D Techniques

In climate science VA, 2D visualizations are the most commonly used technique
to illustrate spatiotemporal data. This category includes basic charts such as
line graphs, scatter plots, bar charts, etc. [14, 13, 92, 23, 15, 70]. Li et
al. [8] used a time-series plot to present temporal trends and correlations
between temperature and precipitation. Sharma et al. [11] leveraged a
combination of a line graph and a scatter plot in their work to study the
temperature anomalies in the ocean and land surfaces.

Figure 2: CrossVis demonstrates interactive axes selection of categorical
cells and numerical range in parallel coordinates to explore large-scale
multivariate data [36].

Other 2D charts besides line and scatter plots are also utilized to visualize
the spatiotemporal climate variables. Parallel coordinates are popularly
leveraged to analyze the association multivariate climate data (Task
requirement R6). Poco et al. [66] studied the multi-model distribution of
spatiotemporal climate variables in a parallel coordinates visualization.
Steed et al. [36] demonstrated interactive parallel coordinates consisting of
categorical and numerical axes to explore large-scale heterogeneous
multivariate data as shown in Fig. 2. Wang et al. [64] and Li et al. [8] also
used a multifaced parallel coordinate plot to illustrate the association of
climate variables near a cyclone center. Bar charts are also leveraged to
compare the multi-model, seasonal, and yearly distribution of climate
variables in [7, 23, 41]. Various designs of statistical bar visualizations
are depicted in Fig. 2 and 7. In addition, Shu et al. [86] introduced a 2D
temporal graph that provides spatiotemporal behaviors in ensemble simulation
data. Similarly, there are other 2D visualization techniques utilized for
exploring spatiotemporal climate variables such as node-link diagram [40],
area plot [95], and hierarchical clustering [35].

The standard 2D charts are frequently presented as small multiples to allow
the users to easily compare the climate variables against a timeline. Small
multiples combine a set of basic visualizations (e.g., line graphs, bar
charts) and encode data using the same scale to ease the visual comparison
tasks [96]. A specific representation from the basic charts and an arrangement
method such as grid or radial need to be determined to implement small
multiples [97]. Small multiples are also leveraged to form a correlation
matrix generally using scatter plots [8] or color-coded glyphs [40]. Examples
of small multiples for comparing spatiotemporal climate variables are
presented in [66, 41].

In addition, we reviewed various implementations of 2D map visualization for a
spatial portion of the climate data. Geospatial maps and choropleths are
popularly leveraged to visualize the spatial intensity or projection of
climate variables [98, 23, 70, 40]. Sliding animation and display transitions
are generally implemented to support browsing the temporal data variables
through the spatial visualizations [13, 16]. Zahan et al. [37] introduce 4
contour-line stylization techniques to study multivariate information such as
temperature, elevation, etc. The encoding techniques include parallel lines,
color blending, pie, and thickness-shade. Heatmap, contour, and glyph-based
representations are also extensively used over geospatial maps as an
additional layer to present the spatial intensity [17, 6] (Fig. 6C.)

Area maps are used in NASA’s Giovanni and VAPOR to plot the area of modeled
impact [99, 62]. Histogram and scatter plot have been leveraged in National
Climate Change Viewer (NCCV) to compare the climate models by predicted max
temperature [7] In the spatiotemporal VA interfaces for climate science, 2D
standard charts are frequently implemented in an integrated fashion to encode
high dimensional data and reduce occlusion [7, 15, 9].

####  6.1.2 3D Techniques

3D visualizations are also standard techniques demonstrated in many climate
science VA interfaces [44, 21, 11]. These visualizations often cases provide
more context than 2D techniques. Geospatial maps are widely used 3D
visualizations for analyzing climate variables. Li et al. [8] utilized a 3D
globe heatmap to present the worldwide heatwave. Rober et al. [100], in
another work, discusses a 3D globe to demonstrate 21st-century projections of
the climate variables. In addition, the use of variable raytracing and depth
of field are demonstrated in [100, 87] to direct the user’s attention to a
specific region on the 3D map (Fig. 3b).

Moreover, volume rendering is another 3D visualization technique used in
climate science VA [63, 101, 64]. Helbig et al. [60] present a volume
rendering to visualize the elevation, simulated precipitation, and mass
fraction of snow. 3D glyphs are used in their encoding to illustrate the
direction. Rober et al. [100] discuss the visualization of 3D liquid cloud
water and cloud ice using OSPRay volume rendering [102]. In addition, Zhang et
al. [61] provide a rendering comparison to report an efficient dynamic
volumetric encoding for meteorological data in a 3D globe. Li et al. [62]
demonstrated an exploratory volumetric visualization in VAPOR, to
interactively analyze the simulation data. Wang et al. [12] illustrated volume
rendering over map visualization to study multidimensional meteorological
data.

Figure 3: CORNEA [87] demonstrates an immersive virtual environment to explore
simulated climate models using (a) globe and (b) surface display.

An immersive environment allows the user to interactively explore the
spatiotemporal variables on a spherical shell. Castruccio et al. [87]
presented a virtual reality environment, CORNEA, to visualize and compare the
simulated climate models as shown in Fig. 3. Helbig et al. [60] also presented
their volume rendering in a virtual reality environment using Autodesk VRED
[103].

###  6.2 Integrated Visualization Techniques

The display design for climate science VA is crucial as the complex
spatiotemporal data often challenges human cognition. Several studies
investigated the design factors that impact the human ability to comprehend
the illustrated data [104, 49, 47]. As a result of the multidimensional and
multiresolution characteristics of the spatiotemporal data, VA environments
often leverage multiple visual components integrated with interactive features
to serve exploratory purposes. In this section, we discuss the integrated
visualization techniques employed to explore the climate variables.

####  6.2.1 Temporal Transition

The transition technique in spatiotemporal visualization frequently consists
of a geospatial or contour-based visualization to plot the spatial intensity
along with a secondary visualization to stream through the temporal dimension.
It provides a dynamic representation as the temporal events appear and
disappear on the spatial map controlled by time or user selection [47, 11].
The spatial dimension illustrated in 2D/3D geospatial maps is often layered
with a base map or choropleth [16, 42, 65]. The temporal progression of data
variables is summarized using standard visualization techniques such as line
graphs, area charts, scatter plots, or a series of geospatial depictions of
the temporal data [95, 70, 41, 9]. Time sliders are also a popular choice to
demonstrate the temporal transition on a geospatial visualization for
spatiotemporal data [105, 13, 30, 16, 8]. Display transition and animation
have been employed in CORNEA [87] where sliders are implemented to control the
time speed, rotation, and altitude on the 3D globe. This technique is
particularly useful to explore smaller slices from large-scale spatiotemporal
data. The user can interfere in the transition at a time and select a location
to observe the changes/characteristics of the particular variable (Fig. 3.)

Maskey et al. [70] demonstrated a spatiotemporal depiction using the display
transition of geospatial visualization. Pahins et al. [95] designed an
integrated visualization to explore large-scale spatiotemporal data using a
geospatial view and area chart. The transition view allows the user to
perceive the subtle temporal change of events in space. However, the fast
progression of events can overwhelm the user when exploring the data
variables. User attention is also split between the geospatial and timeline
views which might lead to missing the non-salient changes over time. Mayr et
al. [47] discussed several studies to compare the animation speed and user
interactions for spatiotemporal transition to find cognitive efficiency.

Temporal transitions are also utilized in flow visualizations. Lu et al. [72]
demonstrated the use of temporal transition to visualize the flow of sea level
pressure. Liu et al. [71] visualized the hourly storm path ensembles
transitioning the display. Li et al. [62] illustrate the animation of volume
rendering to present a simulated progression of the hurricane path.

The transition technique is usually mapped to the time dimension to depict the
spatial change of events over time. This technique fosters human cognition to
detect subtle temporal changes. However, a fast transition on a large spatial
dimension can deprive the user to miss non-salient changes. User-preferred
transition speed and time step selection can resolve this issue.

####  6.2.2 Space-time Cube

Figure 4: Ensemble of time-varying iso-contours in a weather forecast to
produce interactive space-time volume rendering through time-hierarchical
clustering [90].

Space-time cube is an integrated 3D visualization technique leveraged in
visualizing various spatiotemporal climate variables. To answer the question
of where (space) and when (time) from the spatiotemporal climate data, Kraak
[106] presented this technique combining specific visualizations for space and
time. In a space-time cube, the cube’s bottom represents space whereas the
height represents time with temporal cutting operations. A spatial map is
generally used to denote the space followed by multiple layers of standard
visualizations to illustrate the climate variables in different timestamps in
the dataset [107, 108, 54]. We review the use of bar plots, scatter plots,
volume rendering, and spatial maps on the temporal axis in space-time cube
visualizations.

Schroth et al. [105] conducted a case study on the existing tools for
understanding the spatiotemporal climate scenarios where they presented a
space-time cube for resolving the multi-dimensional climate data visualization
problem. Eaglin et al. [69] provided a web-based interface illustrating a
space-time cube where the temporal axis consists of a volume rendering. The
user can interact with the 3D view to visualize a time layer from the volume
in a 2D heatmap. Poco et al. [66] demonstrated the use of a space-time cube in
the SimilarityExplorer to visualize the complexity of multifaceted climate
data variables. Hadlak et al. [68] presented various illustrations of space-
time cubes using colored links (Fig. 5), pencil, and helix glyphs to visualize
temporal patterns in spatiotemporal variables. Visual encodings for the
temporal axis are able to include multivariate climate data in a space-time
cube visualization. Rober et al. [109] utilized a space-time cube
representation while demonstrating in-situ visualization [101] processing with
climate data. Ferstl et al. [90] presented an approach to visualize time-
varying iso-contours in a time-hierarchical clustering using juxtaposition as
shown in Fig. 4. The stacked iso-contours in the space-time cluster
essentially form a volumetric rendering of the weather forecast ensembles.

Figure 5: Visual hierarchy of multivariate spatiotemporal data demonstrated by
Hadlak et al. [68] using glyph-based space-time illustration.

Hewagamage et al. [110] illustrates a spiral view on the geospatial map to
visualize the spatiotemporal pattern. This concept was later extended by
Tominski et al. [108] where they capitalized the spiral helix to visualize
multivariate spatiotemporal data to explore trends and correlations among
attributes as shown in Fig. 5.

Space-time cube visualizations are able to encode high-dimensional
spatiotemporal data in an integrated display design. Hence, this visualization
is utilized to provide an overview of the spatiotemporal events to the users.
However, with an increasing number of time steps and variables in the data,
the visual occlusion can challenge user cognition when conducting the
exploratory analysis. Select interaction aligned with the temporal axis is
discussed as one of the potential solutions in [69, 54].

Figure 6: Examples of the color-coding technique leveraged in climate
visualizations. (A) A geospatial heatmap and color-coded hotspots are merged
with Delaunay triangulation to visualize the air pollution data. [43]. (B)
Glyphs and color-coded hotspots are utilized to visualize data flow [73]. (C)
HI-RAMA [6] uses color schemes to present the rasterized station-wide rainfall
data.

####  6.2.3 Trajectory Visualization

A trajectory visualization approach generally plots multiple temporal
layers/glyphs into an integrated map where transparency and intensity derive
the position of the object or event. This technique encodes the temporal
dimension with different variations of glyph-based objects or colors as shown
in Fig. 6. To denote the spatial boundary, the trajectory technique is often
merged with a map visualization [73, 43] or choropleth [14, 98]. Various
transformations of this technique are leveraged to visualize the data flow,
path ensembles, and trajectory in the spatiotemporal data.

Trajectory visualizations in climate science illustrate the temporal movement
of climate attributes in geographical or abstract space. These visualizations
can assist climate scientists to identify valuable patterns and trends from
the spatiotemporal data. Various illustrations are employed to explore the
underlying patterns in the moving object/event data [38]. Liu et al. [71]
demonstrated trajectory visualizations for ensemble representations and
position prediction of storm paths. They employed the ensemble path
visualization to denote the hurricane direction and smoothly interpolated
hotspots to denote the storm-strike position (Task requirement R7). Kim et al.
[73] leveraged similar techniques to visually interpret the flow map in
spatiotemporal data without trajectory information as shown in Fig. 6B. Wang
[10] published a software suite for meteorological data visualization,
MeteoInfo, where the wind trajectory is demonstrated with ensemble path
visualization over spatial contour from the station data.

Path ensemble simulations often leverage explicit encoding techniques to
present high-dimensional and multivariate climate data. The spatial dimensions
are usually encoded in a map, parallel coordinates, or hierarchical edge-
bundling. In addition, the path ensemble information is commonly visualized
using glyphs [13], hotspots [71], flow maps [73], volume rendering [60], etc.

####  6.2.4 Coordinated Multiple Views

Coordinated multiple views (CMV) is an exploratory visualization technique
that enables the user to explore the data using multiple visualizations
integrated into a window [111]. CMVs are employed to interactively visualize
the complex high dimensional spatiotemporal data [112]. In spatiotemporal
climate visualizations, CMV visualize the spatial and temporal aspect of the
data using two different visualization techniques. A geospatial map is often
used to visualize the spatial portion and a linear representation is used to
visualize the temporal portion of the climate data [47]. The goal is to allow
the users to identify insights from complex spatiotemporal data by providing
an integrated set of standard visualizations without cluttering the window.
Visualizations in CMV are integrated through rich user interaction and view
manipulation [111].

Figure 7: Examples of CMV demonstrated in climate science VA to explore
spatiotemporal data. (A) CMV leveraged to explore downscaled climate
projections [41]. (B) DDLVis [9] facilitates real-time visual query of
spatiotemporal data distribution. (C) Rule-based association in multivariate
climate data is presented using a coordinated 3D globe and parallel
coordinates [64]. (D) User-guided time-dependent hierarchical clustering to
visualize the ensemble simulation [35].

Wang et al. [64] demonstrated CMV for conducting association rule-based
multivariate analysis of spatiotemporal climate data. They employed a 3D
volume rendering and parallel coordinates (Fig. 7C) to identify multivariate
correlations among climate variables. Parallel coordinates are leveraged with
geospatial spatial heatmap to compare multifaceted climate variables in
SimilarityExplorer [66]. Li et al. [92] presented CMV in Vismate to visualize
the station-based observation data on climate change. Moreover, Voila is a
visual anomaly detection and monitoring system where Cao et al. [15] presented
a CMV compatible for visualizing the spatiotemporal climate variables. Li et
al. [9] recently published a VA system, DDLVis, for the real-time visual
queries of spatiotemporal data. They designed the interface with a geospatial
map and area chart to navigate through the summarized spatiotemporal data.
Based on their interaction with these visualizations the results are presented
in a streaming view side-by-side, as shown in Fig. 7B.

CMV represents the spatiotemporal data using multiple visualization techniques
that allow the user to split their focus into time and space dimensions [47].
CMV often cases leverages the standard visualizations that fall under the
visual literacy of the domain users. With rich user interactions and view
manipulations, CMV overcomes the shortcomings of standard visualizations in
extracting underlying knowledge from the data. However, depending on the
visual encoding techniques, a hierarchical number of view manipulations, and
user interactions, CMV can be overwhelming for the users. Several analytical
reviews [113, 112, 111] reported diversity in visual encoding and consistency
in context switching are essential in visualizing spatiotemporal attributes in
CMV.

###  6.3 Interaction Methods

TABLE III: Categories for the interaction methods in spatiotemporal climate
visualizations.

User interaction in the visual analysis approaches is essential to facilitate
the user’s exploratory functionalities [114]. What user interactions mean for
visual analysis differs based on systems, tasks, and user intentions [115]. In
VA systems, user interactions affect the pipeline, transform raw and processed
data, and alter the mapping and view [115]. In this section, we discuss
different user interaction methods demonstrated in climate science VA. We
categorize the interaction techniques based on the interaction methods
taxonomy discussed in [114, 116]. We also label the reviewed literature based
on that taxonomy shown in Table III. These user interactions are often
correlated and overlapped while implementing the visualizations.

Select interaction enables users to mark or tag specific data based on their
point of interest. This interaction method can be essential to remove outliers
from the viewport. In climate science VA, select is the most common
interaction method. Panoply and NCCV [7, 22] both render visualizations for
climate projections where they offer selecting parameters for altering the
time entity. NASA’s Giovanni [99] provides interaction within the interface to
select variables, time range and an analytical plot that essentially renders a
static visualization. Select interaction is extensively leveraged in the CMVs
as it allows the user to select an area of interest to update other
visualizations accordingly [92, 35, 41]. McLean et al. [6] employ select
interaction on the map markers to display information about rainfall station
and volume. Select interaction enables other interaction methods such as
explore, connect, or abstract [13, 66, 69].

Explore interaction provides users the control to include new items in the
view, usually by superimposing them on the interactive view. Spatiotemporal
climate variables are often multi-variate and multidimensional such that it is
particularly challenging to display all the details in a single window. This
interaction in such cases is very useful for the exploration of high-
dimensional feature spaces. Explore is a commonly used interaction method for
integrated visualizations, especially for the map visualizations to support
the identification of anomalies [15], uncertainties [13], or patterns [35] in
the data. Quinan et al. [98] leveraged explore interactions to enable
isocontour features on the ensemble map visualization. Johansson et al. [17]
used this interaction to explore the risks for flooding and sea level rise in
the context of climate scenarios for a selected location.

Reconfigure interaction enables the user to change the arrangement of
visualizations by sorting or rearranging, useful for removing occlusion from
the window. Reconfigure interaction is utilized to support time-varying large-
scale multivariate analysis using parallel coordinates [66, 8]. Steed et al.
[36] demonstrated parallel coordinates visualization reconfigured with focus
and context range selection for the temporal and numerical axes, as shown in
Fig. 2. Wang et al. [64] leveraged reconfigure in 3D spatial volume rendering
to demonstrate the rule-based multivariate analysis.

Encode  interaction fundamentally alters the visual representation by changing
the number of dimensions, colors, and sizes in visualizations. Encode
interaction can be particularly essential in multivariate analysis where the
user can map the attributes to different colors and shapes. Eaglin et al. [69]
employed encode interaction to allow the user to select a 2D temporal slice
from the space-time cube. Li et al. [62] allow users to colorize the
trajectories based on the variable and length of the trajectory. Moreover,
interfaces employing parallel coordinates also demonstrate the use of encode
interaction [36] as they often change the color based on the selected range of
multifaceted attributes.

Filter interaction helps the user to analyze data by applying conditions to
the rendering. This interaction allows the user to specify a range or
condition to visualize a subset of the data. We reviewed a handful of VA
systems employed in climate research that supports interaction to filter the
sample space (Task requirement R4). DDLVis [9] employed filter interaction for
the user to investigate an area of interest on the map visualization. Li et
al. [8] allowed the user to write a textual query to visualize spatial
heatmaps and temporal trends. Moreover, climate science produces complex and
high dimensional data that often create occlusion in the view. The filter
interaction is utilized in those scenarios to allow the user to focus on the
data of interest [14, 117, 15, 70]. Sanyal et al. [13] allowed the user to
filter the glyphs and ribbons presenting ensemble uncertainty in the
geospatial visualizations. Climate Data Analysis Tool (CDAT) provides scope
for user interaction to filter the data range that essentially reflects on the
visualization [118].

Abstract (or elaborate) interaction allows the user to view data from various
levels of granularity. User interactions with the data points to see details
in a tooltip or by zooming over the map are the common use cases of abstract
interaction. Huntington et al. [23] employed abstract interaction in Climate
Engine to allow the user to zoom on the time series and view values at the
data points. Castruccio et al. [87] utilized abstract interaction in a
portable virtual reality environment to explore climate variables across the
globe and selected surfaces [11].

Connect interaction enables the user to find relationships between the views
and highlight features that are similar or relevant. This interaction is
applicable for both single-view and multiple views visualizations. In a single
view, connect interaction highlights the related nodes or data points within
the visualization. In multiple views, the user interacts with a record to
effectively highlight all the related records across multiple views. Climate
science VA interface usually consists of multiple views [62, 70] as
spatiotemporal data are often split into spatial and temporal dimensions to
separate plots. VisAdapt [17] presents connect interaction where users
interact with the geospatial contour to visualize the area detail on the other
coordinated views. Wang et al. [64, 117] demonstrate connect interaction where
the spatial volume rendering updates based on the user interactions with the
multivariate parallel coordinate plot.

Shepherd is the interaction method that allows users to guide the modeling
process. Such guidance can be direct or indirect. Direct Guiding enables the
user to set or update different parameters for modeling whereas Indirect
Guiding includes providing constraints and thresholds. This interaction is not
a common method. Kappe et al. [35] demonstrated decadal climate prediction
where they allowed the user to toggle the parameters to refine the ground
truth in order to get the most accurate prediction. Cao et al. [15] enable
users to provide context-guided input for ranking anomalous patterns in
streaming spatiotemporal data. Li et al. [9] allow users to guide real-time
queries in spatiotemporal data distributions using density dictionary
learning.

##  7 Visual Analytics Systems

In this paper, we identify the task requirements of climate scientists for
exploratory analyses with spatiotemporal data and review a comprehensive set
of literature published in the visualization community addressing such
requirements with a VA approach. VA includes the aspect of data preparation,
pre-processing, analytical workflow, interactive visualization, and usability
metrics of exploratory analysis. We review the VA approaches from the aspects
of tools that are leveraged in the implementation process, system
accessibility, and exploratory analysis workflow.

Figure 8: Spatiotemporal VA workflow for exploratory analysis [119]. We
contextualized the workflow for climate science by labeling it with our
identified task requirements for exploratory spatiotemporal VA.

Cashman et al. [120] presented a user-based VA workflow for exploratory
analysis that enables the users to discover underlying knowledge from the
given dataset. The workflow consists of several fundamental steps such as data
interaction, problem exploration, problem specification generation, and model
exploration. Cui [121] provided a comprehensive analytical review of the VA
systems and summarizes the analytics process in 6 steps - data pre-processing,
analysis method, visualization, knowledge generation, interactive hypothesis
building, and visual reflection of user perception. Moreover, Nayeem et al.
[41] demonstrated a VA framework for distributed analysis where users engage
in a sensemaking loop by iteratively hypothesizing, analyzing, and foraging
through data exploration, analyses, and interactive visualizations. Sacha et
al. [122] illustrated a knowledge generation model for VA that represents both
computer and human-centric approaches through exploration, verification, and
sensemaking loop. Andrienko et al. [119] described a VA framework for
spatiotemporal analysis and modeling. In addition to the conventional
exploratory VA workflow, this research addressed the user’s perception of data
and control flow from a spatiotemporal analysis and visualization perspective.
Based on our extensive literature review, we believe Andrienko’s VA
illustration [119] more accurately outlines the VA workflow for spatiotemporal
exploratory analysis in climate science. We have labeled their illustration of
the main VA components with our identified task requirements (Section 4) as
depicted in Figure 8.

While reviewing the implementation process, we found that VA systems often
leverage other data analytics tools to run exploratory analyses. These tools
mostly enable climate scientists to run analytical scripts and visualize the
result with basic visualizations. It is popular among climate scientists to
leverage tools such as MATLAB [123], GrADS [124], or NCAR Command Language
(NCL) [125]. Moreover, exploring spatiotemporal data frequently leverages the
capabilities of distributed computing [7, 73, 15, 126, 41], to overcome the
complex and time-consuming analysis. Several systems are widely used by
analysts and research communities that provide shared computing resources to
run analyses in the spatiotemporal data. The Ultra-scale Visualization Climate
Data Analysis Tool (UV-CDAT; [21]) is one of the oldest systems among those
reviewed in this report. However, UV-CDAT includes statistical analysis tools
to support the specific needs of the climate science community (Task
requirement R5), such as those used in the Assessment Reports from the
Intergovernmental Panel on Climate Change (IPCC). DataONE is another system
for the earth and environmental science data repositories to facilitate easier
access, search, and discovery [127, 128]. The Open Science Grid serves the
scientific research community with its large-scale distributed computing
resources, on more than 120 institutes [129]. In addition, SciServer is a
cyberinfrastructure that provides access to a suite of tools and services.
SciServer infrastructure includes storage, query, and processing for big data
analyses from different domains with diverse data formats and structure [130].
We have not found implementations of DataONE, Open Science Grid, and SciServer
in climate science VA. Virtual Information Fabric Infrastructure (VIFI) is
another distributed data analysis system that enables computation without
moving the massive data [84]. Within VIFI, the analytic scripts move over to
the data hosts, produce the resulting data, and return to the user site. The
potential of VIFI in analyses of complex spatiotemporal data has been
demonstrated in [41]. These analysis tools usually lack interactive
exploratory visualizations and are unable to satisfy climate scientists’
custom visualization requirements. In contrast, VA systems enable climate
scientists to perform their analysis tasks and explore complex spatiotemporal
data with the support of an interactive interface.

We label the climate science VA systems in context to exploratory
spatiotemporal analysis into two categories - general purpose and case-
specific (Table I). General-purpose VA systems support analysis for numerous
use cases and datasets. These systems generally demand the dataset be
organized according to a pre-defined structure. Climate Engine [23], SOVAS
[8], and WebGlobe [11] are some of the leading examples of general-purpose VA
systems we reviewed in this paper. However, these systems often fall short to
satisfy specific analytics or visualization requirements for case-specific VA
systems. Climate scientists work with heterogeneous data that frequently
demand custom features to analyze and explore the underlying trends and
patterns. Most special purpose systems have been developed by domain experts
with their specific requirements [131]. In our review, we also observed
climate scientists collaborating with visualization researchers to develop VA
systems that support case-specific exploratory analysis of climate research
(Task requirement R5, R9). Noodles [13], VisAdapt [17], DDLVis [9] demonstrate
examples of case-specific VA systems.

The modern web platform enables visualization researchers to develop large-
scale web-based VA systems that efficiently integrate data management,
transformation, hypothesis testing, and knowledge discovery with interactive
visualizations [132]. In contrast, native VA systems execute the analytics and
render visualizations within a common computational infrastructure without the
movement of the data. While the native approach is built on a relatively less
complicated application design and ensure real-time access to the data, it is
often challenging in the context of complex spatiotemporal analysis to move
massive data and leverage high-performance computing resource. On the other
hand, web-based systems generally utilize cloud-based high-performance
clusters to execute the analytics. Climate scientists also exploit the
advantages of cloud-based shared computing resources to run their analysis
[133, 84, 33]. Hence, web-based systems obtain the potential to support a
modular architecture and protocol for simultaneous accessibility to analysis
infrastructure without the movement of a massive volume of data (Task
requirements R1, R2, R4, R8). In our survey, we also coded the current VA
approaches in terms of native and web-based accessibility and presented them
in Table I.

We identify the tools and libraries that are commonly used in the development
of visualizations and analytics interfaces. We found several examples where
native applications are built using Visual C++ and visualizations are
developed utilizing OpenGL [110, 98]. In recent years, the trend progressed
more towards web-based VA systems as visualization researchers leverage
JavaScript-based libraries such as AngularJS, and ReactJS to develop an
interactive interface [17, 6]. Standard 2D/3D Visualizations are developed
using WebGL, D3.js, ThreeJS in several analytics interfaces [73, 11, 8]. It is
evident from our discussion in section 6 that map visualizations hold a
crucial aspect in exploratory spatiotemporal visual analysis. Google Earth,
ArcGIS, and LeafletJS are commonly leveraged to develop map visualizations
[23, 7, 41] for the VA interfaces.

TABLE IV: Categories for evaluation methods. Use cases are found as the most
preferred method in climate science research.

##  8 Evaluation Methods

Evaluation of the visualization techniques and analytics systems is an
essential task as it can validate the contribution of the proposed tool in
climate science research. Johansson et al. [134] reflected on the approaches
for evaluating climate visualization in order to support advanced interactive
visual analysis. They combined three aspects of climate visualizations -
analysis, communication of research results, and decision support to evaluate
their utility in understanding the complex data. These aspects provide
actionable insights to consider more qualitative and quantitative evaluation
metrics for scientific evaluation. Kristensson et al. [135] discussed the
evaluation approach of spatiotemporal representations taking user performance,
task completion time, and user experience into account. In terms of
quantitative evaluation, Mayr et al. [47] acknowledged that the conventional
method of evaluating spatiotemporal visualizations with time and error
indicators often falls short. Comparative evaluations, usability with
multivariate information, and participants’ visual literacy are all crucial
for a justifiable evaluation [45]. Moreover, Elmqvist et al. [136] discussed
several patterns for visualization evaluation including exploration, control,
generalization, validation, and presentation. The anatomy of each pattern
consists of five basic components including evaluation name, problem context,
proposed solution, consequences, and illustrative examples.

We take the evaluation patterns [136] into account to categorize the
evaluation methods of the reviewed VA approaches based on expert feedback,
case study, use cases, performance, and the user’s ability to comprehend
without extensive training (_do-it-yourself_). The case study describes the
scenario where the system is demonstrated with the help of domain experts
conducting specific analysis tasks. In contrast, use cases include a number of
hypothetical usage scenarios where the proposed system might be leveraged in
performing analysis tasks. Table IV presents an overview of the evaluation
methods utilized in the VA interfaces related to spatiotemporal climate
research.

MacEachren et al. [137] conducted a quantitative evaluation to assess the
temporally varying geographic visualization. Participants are provided with 3
sets of tasks to analyze spatiotemporal patterns. A timeline map is presented
for each participant that marks the illustration of every interactive feature
utilized to perform the analysis tasks. Herring et al. [16] provided an online
portal to conduct a quantitative performance evaluation of a geospatial
interface for US climate data with 55 participants. The proposed interface was
evaluated based on each participant’s time of assessment and accuracy which
entails the perceived reality of climate change. Mayr et al. [47] conducted a
comparative quantitative analysis of the 4 different integrated visualizations
in performing spatiotemporal analysis. Inferential statistical techniques such
as t-tests, and analysis of variance (ANOVA) are widely used in the
comparative assessment of the performance metrics [16, 69, 37].

As we discussed in Section 7, the domain expert involvement in the design and
development process [87] of case-specific VA systems is valuable. Their
feedback is influential in the evaluation process as well. Sanyal et al.
presented a visualization tool, Noodle [13], for exploring ensemble
uncertainty in numerical weather models. Two meteorologists were included in
the evaluation process where they used the tool to perform ensemble analysis
and provided qualitative feedback. Several research works have taken similar
steps to evaluate proposed systems [98, 36]. Moreover, domain experts are also
invited to participate in the evaluation and present pre-defined use cases to
validate the usability and novelty of the VA interfaces [15, 23].

In our review, we found that use cases are the most popular method for
evaluation, followed by case studies. In contrast, we did not find any
examples where a user’s ability to comprehend the analytics interface on their
own (_do-it-yourself_ [136]) is considered as an evaluation metric. Johansson
et al. conducted the evaluation for VisAdapt [17] using an iterative process
including both climate scientists and end-users from diverse backgrounds.
According to their report, familiarity with the interface is essential for the
users to leverage the VA system to explore the climate change impact and
possible adaptive action.

Figure 9: A web interface to browse the literature we surveyed in this report
related to VA approaches for climate science.

##  9 Summary & Discussion

We surveyed the progress in exploratory spatiotemporal visualization and VA
approaches in climate science to date and intend to outline the potential
future direction to address the present research gaps. To establish the scope
of our survey, we first reviewed the previous surveys related to our topic
[50, 52, 54, 5]. These compiled surveys and analytical reviews show the
overall advancements in spatiotemporal visualization and VA. However, they
lack context for advanced data analytics for climate scientists. The existing
surveys specific to the climate science visualizations [44, 49] are quite
outdated as they include papers published on or before 2018. Since then,
significant advancements have been made in exploratory spatiotemporal VA in
climate science. Moreover, surveys related to climate science reported their
analytical review mainly in the context of data analysis methods and
visualization techniques. In contrast, we report the exploratory
spatiotemporal analysis in climate science more from the VA perspective. By
doing so, we can identify the key aspects of spatiotemporal VA to present a
taxonomy that includes the data sources, analysis tasks, statistical
techniques, visualization techniques, interaction methods, and evaluation
methods, all of which facilitate exploratory data analysis in climate science.

We implemented an interactive web interface to present the papers that are
included in the current report. In our interface, we placed each paper as a
thumbnail with the figure that represents the paper best. The user can hover
over the thumbnail to see the paper’s title. To see more details about the
paper such as authors, abstract, keywords, publication venue, and year, the
user can click on the thumbnail to open a popup window. We tagged the papers
based on the categorizations we discussed in this paper. The interface
leverages the tags to allow the user to filter the paper based on their
selection on the left side panel. Figure 9 shows an illustration of our web
interface 111The interface for spatiotemporal climate VA browser:
http://vizus.cs.usu.edu/app/climatescience

###  9.1 Knowledge Gaps & Future Scope

While we reflect on the current trends in exploratory spatiotemporal VA
research related to climate science, we identified existing gaps and potential
future research directions to address these knowledge gaps and the ongoing
challenges of understanding spatiotemporal climate data. We summarize some of
the key aspects below:

Hierarchical clustering for visualizing spatiotemporal granularities. In our
data type classification, we found a majority of climate data falls into a
multidimensional type due to the high dimensionality, multi-variability, and
multi-resolution factors in climate data. In contrast, tree-type datasets are
not much utilized in climate VA. However, we understand interpreting
spatiotemporal data to tree data type holds enormous potential to observe
patterns and identify correlations among climate variables through several
spatial (e.g., region, state, and county) and temporal granularities (e.g.,
annual, seasonal, and monthly) (Task requirement R7). Consequently,
hierarchical clustering [35] (depicted in Fig. 7D) and treemap [138]
visualization techniques are not extensively utilized in spatiotemporal
climate data exploration and have the potential to be a new researchdirection
for climate science visualizations.

Growing trend of integrated visualizations in spatiotemporal exploration. In
recent years, integrated visualization techniques have become widely utilized
as tools to visualize spatiotemporal climate data. Integrated visualizations
are essential for the exploratory analysis of climate variables because of
their usability and existing and potential future new interactive features.
Visualizations that enable scientists to perceive salient data features
quickly and efficiently are key. For example, distributing the spatial and
temporal dimensions across multiple visual components allows users to inspect
the data by focusing on individual dimensions. Consequently, such a capability
not only helps scientists to identify underlying trends and patterns from the
data but also helps them to maximize cognitive efficiency. Table II suggests
CMV has become a trending approach for the interactive exploration of large-
scale spatiotemporal climate data. However, significant challenges remain in
terms of choosing relevant visualization techniques, display formation, and
interactive features for exploratory visual analysis. Roberts [111] reflected
on the view generation, interaction, and manipulation aspect to achieve state-
of-the-art in CMV.

Underexplored immersive visual environments. Climate scientists often use
point cloud data from observations and numerical models which can be analyzed
in a fully immersive environment [139] applying Virtual and Augmented Reality
technologies. Such an immersive analytic environment would provide better
spatial perception to climate scientists for exploratory spatial analysis with
other climate variables and a collaborative environment to discuss and share
their findings with other scientists and researchers.

Interactive shepherding of exploratory models. Table III suggests that
shepherd interactions are underutilized in climate science VA. This entails a
shortcoming of these systems in facilitating user-driven exploratory analysis.
Systems such as UV-CDAT [21] and MeteoInfo [12] comprise active participation
in building the exploratory analysis model. However, these systems are heavily
code dependent and often lack interactive features (Task requirement R10).
This unfolds a research opportunity to enable interactive user-driven
exploratory analysis from the VA interface (Task requirement R3).

Case-specific approaches over general-purpose VA. We grouped the
spatiotemporal VA systems for climate science into general-purpose and case-
specific systems. While the general purpose systems [140, 21, 23] are well-
commended for diverse usability and rich visualization library, these systems
often lack the domain or use-case-specific custom visualizations and user
interactions to support analysis tasks or demand high levels of programming
expertise from the user. In addition, we reviewed VA systems developed to
serve specific analysis tasks or data sources [6, 43, 9, 42]. In contrast to
the general purpose systems, these systems are more oriented to employ a use-
case-specific analytics pipeline, visualization techniques, and user
interaction. Climate scientists and visualization researchers usually engage
in a joint collaboration to develop case-specific systems to satisfy the
analysis requirements. Table I indicates a significant number of VA systems
developed for climate science focus on specific use cases (Task requirement
R9).

Surge of web-based VA systems. As climate scientists work with diversified
datasets to understand climate change and its social impact and derive
effective decision-making for adaptation, they often obtain data from
disparate sources to perform distributed decision support and sensemaking
analysis [141]. To conduct such analyses, they commonly transfer the data from
distributed sources to their local workstations, apply data processing and
transformations on the workstations, then generate visual illustrations. To
mediate this inefficient workflow, a web-based VA approach can address these
challenges of accessing disparate remote data sources, performing case-
specific analyses on high-performance clusters, and providing an interactive
interface for exploratory spatiotemporal visualizations [67, 42] (R2, R8,
R10). With advancements in modern web technology, VA systems are widely built
on the web-based platform in recent years as shown in Table I.

Low pervasion among climate scientists. A comprehensive review of several
analytical reports and surveys [44, 55], as well as, our discussion with the
atmospheric and environmental scientists identify that most climate scientists
do not leverage state-of-the-art VA solutions to conduct their exploratory
analyses. This gap between VA capabilities and climate science advancement
must be bridged. Traditional approaches that incidentally rely on
visualizations are becoming increasingly insufficient to match the scope of
the climate science community’s scientific challenges. Much of the information
contained in the ever-growing observational and modeling datasets remains
untapped. For example, two-dimensional plots, that are central to publications
and anchor scientific paper development are highly reductive. Training of
climate scientists and collaboration with VA domain experts will help bridge
these persistent gaps.

Unexploited potential of progressive visualization. There are growing numbers
of observational platforms with increased data rates, growing numbers of
climate models that exhibit increasing complexity, and a growing
sophistication of experiments undertaken with climate models [79]. This rapid
growth of data also challenges VA systems to support on-demand exploratory
capabilities over large volumes of data. Conventional methods in VA systems
often leverage background computation based on user queries [101]. They, then,
display the computed result at once upon completion. Zgraggen et al. [142]
labeled such a method as a ‘blocking approach’ where the user’s analytical
visions are blocked until the computation is fully completed. To address this
challenge, climate research can benefit from the potential for progressive
visualizations where data are incrementally processed in smaller chunks as the
analytics systems interactively update the interface, working from an
approximation that is refined over time [142, 143] (Task requirements R1, R4).
As the computations take place in chunks, shepherd interaction [114] can be
utilized to build the exploratory analysis model. In our survey, we have not
found any progressive VA systems to explore spatiotemporal climate data. This
indicates another opportunity for visualization researchers to develop
progressive systems in collaboration with climate scientists.

##  10 Conclusion

Our paper presents a survey on the state-of-art exploratory spatiotemporal VA
approaches for climate science. We identified the task requirements, reviewed
a comprehensive set of literature based on the taxonomies, and presented the
advancements in the exploratory analysis of climate science from the VA
perspective. We also uncover potential research opportunities and the need for
collaboration among climate scientists and visualization researchers to bridge
gaps between the use of visualizations in traditional climate science and the
capabilities of visualization researchers. The collaboration will likely
become ever more urgent in the face of growing data volumes where the lack of
efficient means to extract information from datasets will result in that
information being unused. Consequently, we believe that our survey provides
useful guidance to enable bridge-building between VA researchers and climate
scientists to produce systems that enable deeper explorations of climate
change data that will be increasingly necessary to advance the scientific
understanding of these domains.

## Acknowledgements

This paper was supported by the US National Science Foundation (NSF) Data
Infrastructure Building Blocks (DIBBs) Program (Award #1640818).

## References

  * [1] W. Von Engelhardt, J. Zimmermann, and J. Zimmerman, _Theory of earth science_. CUP Archive, 1988.
  * [2] S. S. Board, N. R. Council _et al._ , _Earth science and applications from space: national imperatives for the next decade and beyond_. National Academies Press, 2007.
  * [3] G. J. Borradaile, _Statistics of earth science data: their distribution in time, space and orientation_. Springer Science & Business Media, 2003.
  * [4] N. Andrienko, G. Andrienko, and P. Gatalsky, “Exploratory spatio-temporal visualization: An analytical review,” _Journal of Visual Languages and Computing_ , vol. 14, pp. 503–541, 2003.
  * [5] M. M. Alam, L. Torgo, and A. Bifet, “A survey on spatio-temporal data analytics systems,” _ACM Comput. Surv._ , vol. 54, no. 10s, nov 2022.
  * [6] J. H. McLean, S. B. Cleveland, M. Lucas, R. Longman, T. W. Giambelluca, J. Leigh, and G. A. Jacobs, “The hawai‘i rainfall analysis and mapping application (hi-rama): Decision support and data visualization for statewide rainfall data.” Association for Computing Machinery, 7 2020, pp. 239–245.
  * [7] J. R. Alder and S. W. Hostetler, “Web based visualization of large climate data sets,” _Environmental Modelling & Software_, vol. 68, pp. 175–180, 2015.
  * [8] Z. Li, Q. Huang, Y. Jiang, and F. Hu, “Sovas: a scalable online visual analytic system for big climate data analysis,” _International Journal of Geographical Information Science_ , vol. 34, no. 6, pp. 1188–1209, 2020.
  * [9] C. Li, G. Baciu, Y. Wang, J. Chen, and C. Wang, “Ddlvis: Real-time visual query of spatiotemporal data distribution via density dictionary learning,” _IEEE Transactions on Visualization and Computer Graphics_ , pp. 1–1, 2021.
  * [10] Y. Q. Wang, “Meteoinfo: Gis software for meteorological data visualization and analysis,” _Meteorological Applications_ , vol. 21, no. 2, pp. 360–368, 2014.
  * [11] A. Sharma, S. M. A. Zaidi, V. Chandola, M. R. Allen, and B. L. Bhaduri, “Webglobe - a cloud-based geospatial analysis framework for interacting with climate data.” Association for Computing Machinery, Inc, 11 2018, pp. 42–46.
  * [12] Y. Wang, “An open source software suite for multi-dimensional meteorological data computation and visualisation,” _Journal of Open Research Software_ , vol. 7, 07 2019.
  * [13] J. Sanyal, S. Zhang, J. Dyer, A. Mercer, P. Amburn, and R. Moorhead, “Noodles: A tool for visualization of numerical weather model ensemble uncertainty,” _IEEE Transactions on Visualization and Computer Graphics_ , vol. 16, pp. 1421–1430, 2010.
  * [14] R. MacIejewski, S. Rudolph, R. Hafen, A. Abusalah, M. Yakout, M. Ouzzani, W. S. Cleveland, S. J. Grannis, and D. S. Ebert, “A visual analytics approach to understanding spatiotemporal hotspots,” vol. 16, 3 2010, pp. 205–220.
  * [15] N. Cao, C. Lin, Q. Zhu, Y. R. Lin, X. Teng, and X. Wen, “Voila: Visual anomaly detection and monitoring with streaming spatiotemporal data,” _IEEE Transactions on Visualization and Computer Graphics_ , vol. 24, pp. 23–33, 1 2018.
  * [16] J. Herring, M. S. VanDyke, R. G. Cummins, and F. Melton, “Communicating local climate risks online through an interactive data visualization,” _Environmental Communication_ , vol. 11, pp. 90–105, 1 2017.
  * [17] J. Johansson, T. Opach, E. Glaas, T.-S. Neset, C. Navarra, B.-O. Linnér, and J. K. Rød, “Visadapt: A visualization tool to support climate change adaptation,” _IEEE Computer Graphics and Applications_ , vol. 37, no. 2, pp. 54–65, 2017.
  * [18] S. Kutty, R. Nayak, P. Turnbull, R. Chernich, G. Kennedy, and K. Raymond, “Paperminer—a real-time spatiotemporal visualization for newspaper articles,” _Digital Scholarship in the Humanities_ , vol. 35, no. 1, pp. 83–100, 01 2019.
  * [19] B. A. Peterson, M. T. Brownlee, J. C. Hallo, J. A. Beeco, D. L. White, R. L. Sharp, and T. W. Cribbs, “Spatiotemporal variables to understand visitor travel patterns: A management-centric approach,” _Journal of Outdoor Recreation and Tourism_ , vol. 31, p. 100316, 2020.
  * [20] H. Wang, Y. Lu, S. T. Shutters, M. Steptoe, F. Wang, S. Landis, and R. Maciejewski, “A visual analytics framework for spatiotemporal trade network analysis,” _IEEE Transactions on Visualization and Computer Graphics_ , vol. 25, no. 1, pp. 331–341, 2019.
  * [21] E. Santos, J. Poco, Y. Wei, S. Liu, B. Cook, D. N. Williams, and C. T. Silva, “Uv-cdat: Analyzing climate datasets from a user‘s perspective,” _Computing in Science & Engineering_, vol. 15, no. 1, pp. 94–103, 2013.
  * [22] J. G. Acker, G. T. Alcott, M. Ventura, J. C. Wei, and D. J. Meyer, “The advantages of synergy - quantitative earth science data visualization and analysis with giovanni, panoply, and excel,” in _AGU Fall Meeting Abstracts_ , vol. 2018, Dec. 2018, pp. IN21B–35.
  * [23] J. L. Huntington, K. C. Hegewisch, B. Daudert, C. G. Morton, J. T. Abatzoglou, D. J. McEvoy, and T. Erickson, “Climate engine: Cloud computing and visualization of climate and remote sensing data for advanced natural resource monitoring and process understanding,” _Bulletin of the American Meteorological Society_ , vol. 98, pp. 2397–2409, 11 2017.
  * [24] D. A. Keim, F. Mansmann, J. Schneidewind, J. Thomas, and H. Ziegler, “Visual analytics: Scope and challenges,” Berlin, Heidelberg, pp. 76–90, 2008.
  * [25] R. Fuchs and H. Hauser, “Visualization of multi-variate scientific data,” _Computer Graphics Forum_ , vol. 28, pp. 1670–1690, 2009.
  * [26] D. Keim, G. Andrienko, J.-D. Fekete, C. Görg, J. Kohlhammer, and G. Melançon, “Visual analytics: Definition, process, and challenges,” in _Information visualization_. Springer, 2008, pp. 154–175.
  * [27] J. Kehrer, F. Ladstädter, P. Muigg, H. Doleisch, A. Steiner, and H. Hauser, “Hypothesis generation in climate research with interactive visual data exploration,” pp. 1579–1586, 2008.
  * [28] D. Keim, “Information visualization and visual data mining,” _IEEE Transactions on Visualization and Computer Graphics_ , vol. 8, no. 1, pp. 1–8, 2002.
  * [29] G. A. Meehl, W. M. Washington, J. M. Arblaster, A. Hu, H. Teng, J. E. Kay, A. Gettelman, D. M. Lawrence, B. M. Sanderson, and W. G. Strand, “Climate change projections in cesm1 (cam5) compared to ccsm4,” _Journal of Climate_ , vol. 26, no. 17, pp. 6287–6308, 2013.
  * [30] G. A. Schmidt, M. Kelley, L. Nazarenko, R. Ruedy, G. L. Russell, I. Aleinov, M. Bauer, S. E. Bauer, M. K. Bhat, R. Bleck _et al._ , “Configuration and assessment of the giss modele2 contributions to the cmip5 archive,” _Journal of Advances in Modeling Earth Systems_ , vol. 6, no. 1, pp. 141–184, 2014.
  * [31] E. Edward Hartnett and R. Rew, “Experience with an enhanced netcdf data model and interface for scientific data access,” in _24th Conference on IIPS_ , 2008.
  * [32] “NetCDF-CF: supporting earth system science with data access, analysis, and visualization,” _AGUFM_ , vol. 2017, pp. IN33C–0137, 2017.
  * [33] H. Lee, A. Goodman, and K. Gorski, “The Big Climate Data Pipeline (BCDP): A tool to facilitate server-side processing of NASA-NEX data on Amazon Cloud,” _AGUFM_ , vol. 2019, pp. IN12A–03, 2019.
  * [34] R. S. Khan and M. A. E. Bhuiyan, “Artificial intelligence-based techniques for rainfall estimation integrating multisource precipitation datasets,” _Atmosphere_ , vol. 12, no. 10, 2021.
  * [35] C. P. Kappe, M. Böttinger, and H. Leitte, “Analysis of decadal climate predictions with user-guided hierarchical ensemble clustering,” in _Computer Graphics Forum_ , vol. 38, no. 3. Wiley Online Library, 2019, pp. 505–515.
  * [36] C. A. Steed, J. R. Goodall, J. Chae, and A. Trofimov, “Crossvis: A visual analytics system for exploring heterogeneous multivariate data with applications to materials and climate sciences,” _Graphics and Visual Computing_ , vol. 3, p. 200013, 2020.
  * [37] G. M. H. Zahan, D. Mondal, and C. Gutwin, “Contour line stylization to visualize multivariate information,” 2021.
  * [38] M. Sakr, G. Andrienko, T. Behr, N. Andrienko, R. H. Güting, and C. Hurter, “Exploring spatiotemporal patterns by integrating visual analytics with a moving objects database system,” in _Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems_ , ser. GIS ‘11. New York, NY, USA: Association for Computing Machinery, 2011, p. 505–508.
  * [39] F. Kamw, S. Al-Dohuki, Y. Zhao, T. Eynon, D. Sheets, J. Yang, X. Ye, and W. Chen, “Urban structure accessibility modeling and visualization for joint spatiotemporal constraints,” _IEEE Transactions on Intelligent Transportation Systems_ , vol. 21, no. 1, pp. 104–116, 2020.
  * [40] C. Tominski, G. Andrienko, N. Andrienko, S. Bleisch, S. I. Fabrikant, E. Mayr, S. Miksch, M. Pohl, and A. Skupin, “Toward flexible visual analytics augmented through smooth display transitions,” _Visual Informatics_ , vol. 5, pp. 28–38, 9 2021.
  * [41] A.-A.-R. Nayeem, M. Elshambakey, T. Dobbs, H. Lee, D. Crichton, Y. Zhu, C. Chokwitthaya, W. J. Tolone, and I. Cho, “A visual analytics framework for distributed data analysis systems,” in _2021 IEEE International Conference on Big Data (Big Data)_ , Dec 2021, pp. 229–240.
  * [42] A.-A.-R. Nayeem, H. Lee, D. Han, M. Elshambakey, W. J. Tolone, T. Dobbs, D. Crichton, and I. Cho, “Dcpviz: A visual analytics approach for downscaled climate projections,” in _2022 IEEE International Conference on Big Data (Big Data)_ , 2022, pp. 291–300.
  * [43] M. Kalo, X. Zhou, L. Li, W. Tong, and R. Piltner, “Chapter 8 - sensing air quality: Spatiotemporal interpolation and visualization of real-time air pollution data for the contiguous united states,” in _Spatiotemporal Analysis of Air Pollution and Its Application in Public Health_ , L. Li, X. Zhou, and W. Tong, Eds. Elsevier, 2020, pp. 169–196.
  * [44] C. Tominski, J. F. Donges, and T. Nocke, “Information visualization in climate research,” in _2011 15th International Conference on Information Visualisation_ , July 2011, pp. 298–305.
  * [45] T. Nocke, M. Flechsig, and U. Bohm, “Visual exploration and evaluation of climate-related simulation data,” in _2007 Winter Simulation Conference_. IEEE, 2007, pp. 703–711.
  * [46] M. D. Gerst, M. A. Kenney, A. E. Baer, A. Speciale, J. F. Wolfinger, J. Gottschalck, S. Handel, M. Rosencrans, and D. Dewitt, “Using visualization science to improve expert and public understanding of probabilistic temperature and precipitation outlooks,” _Weather, Climate, and Society_ , vol. 12, no. 1, pp. 117–133, 2020.
  * [47] E. Mayr, G. Schreder, S. Salisu, and F. Windhager, “Integrated visualization of space and time: A distributed cognition perspective,” Dec 2018.
  * [48] D. J. Peuquet, “It’s about time: A conceptual framework for the representation of temporal dynamics in geographic information systems,” _Annals of the Association of american Geographers_ , vol. 84, no. 3, pp. 441–461, 1994.
  * [49] M. Rautenhaus, M. Bottinger, S. Siemen, R. Hoffman, R. M. Kirby, M. Mirzargar, N. Rober, and R. Westermann, “Visualization in meteorology - a survey of techniques and tools for data analysis tasks,” pp. 3268–3296, 12 2018.
  * [50] L. Yang, Z. Ma, L. Zhu, and L. Liu, “Research on the visualization of spatio-temporal data,” in _IOP Conference Series: Earth and Environmental Science_ , vol. 234, no. 1. IOP Publishing, 2019, p. 012013.
  * [51] J. He, H. Chen, Y. Chen, X. Tang, and Y. Zou, “Variable-based spatiotemporal trajectory data visualization illustrated,” _IEEE Access_ , vol. 7, pp. 143 646–143 672, 2019.
  * [52] X. Chen, L. Shen, Z. Sha, R. Liu, S. Chen, G. Ji, and C. Tan, “A survey of multi-space techniques in spatio-temporal simulation data visualization,” _Visual Informatics_ , vol. 3, no. 3, pp. 129–139, 2019.
  * [53] R. Rudenko, I. M. Pires, M. Liberato, J. Barroso, and A. Reis, “A brief review on 4d weather visualization,” _Sustainability_ , vol. 14, no. 9, 2022.
  * [54] N. Andrienko and G. Andrienko, “Spatio-temporal visual analytics: a vision for 2020s,” _Journal of Spatial Information Science_ , no. 20, pp. 87–95, 2020.
  * [55] S. Afzal, M. M. Hittawe, S. Ghani, T. Jamil, O. Knio, M. Hadwiger, and I. Hoteit, “The state of the art in visual analysis approaches for ocean and atmospheric datasets,” in _Computer Graphics Forum_ , vol. 38, no. 3. Wiley Online Library, 2019, pp. 881–907.
  * [56] C. Guivarch, T. Le Gallic, N. Bauer, P. Fragkos, D. Huppmann, M. Jaxa-Rozen, I. Keppo, E. Kriegler, T. Krisztin, G. Marangoni _et al._ , “Using large ensembles of climate change mitigation scenarios for robust insights,” _Nature Climate Change_ , vol. 12, no. 5, pp. 428–435, 2022.
  * [57] B. Shneiderman, “The eyes have it: a task by data type taxonomy for information visualizations,” in _Proceedings 1996 IEEE Symposium on Visual Languages_ , Sep. 1996, pp. 336–343.
  * [58] W. Chang, M. L. Stein, J. Wang, V. R. Kotamarthi, and E. J. Moyer, “Changes in spatiotemporal precipitation patterns in changing climate conditions,” _Journal of Climate_ , vol. 29, no. 23, pp. 8355 – 8376, 2016.
  * [59] H. Mohammadi, M. R. Delavar, M. A. Sharifi, and M. D. Pirooz, “Spatiotemporal visualization of tsunami waves using kml on google earth,” vol. 42. International Society for Photogrammetry and Remote Sensing, 9 2017, pp. 1291–1299.
  * [60] C. Helbig, H.-S. Bauer, K. Rink, V. Wulfmeyer, M. Frank, and O. Kolditz, “Concept and workflow for 3d visualization of atmospheric data in a virtual reality environment for analytical approaches,” _Environmental earth sciences_ , vol. 72, no. 10, pp. 3767–3780, 2014.
  * [61] X. Zhang, P. Yue, Y. Chen, and L. Hu, “An efficient dynamic volume rendering for large-scale meteorological data in a virtual globe,” _Computers & Geosciences_, vol. 126, pp. 1–8, 2019.
  * [62] S. Li, S. Jaroszynski, S. Pearse, L. Orf, and J. Clyne, “Vapor: A visualization package tailored to analyze simulation data in earth system science,” _Atmosphere_ , vol. 10, p. 488, 2019.
  * [63] J. Sukharev, C. Wang, K.-L. Ma, and A. T. Wittenberg, “Correlation study of time-varying multivariate climate data sets,” in _2009 IEEE Pacific Visualization Symposium_. IEEE, April 2009, pp. 161–168.
  * [64] F. Wang, W. Li, S. Wang, and C. R. Johnson, “Association rules-based multivariate analysis and visualization of spatiotemporal climate data,” _ISPRS International Journal of Geo-Information_ , vol. 7, no. 7, 2018.
  * [65] A.-A.-R. Nayeem, I. Segovia-Dominguez, H. Lee, D. Han, Y. Chen, Z. Zhen, Y. Gel, and I. Cho, “Learning on health fairness and environmental justice via interactive visualization,” in _2022 IEEE International Conference on Big Data (Big Data)_ , 2022, pp. 784–791.
  * [66] J. Poco, A. Dasgupta, Y. Wei, W. Hargrove, C. Schwalm, R. Cook, E. Bertini, and C. Silva, “Similarityexplorer: A visual inter-comparison tool for multifaceted climate data,” _Computer Graphics Forum_ , vol. 33, no. 3, pp. 341–350, 2014.
  * [67] M. E. Porter, M. C. Hill, T. Harris, A. Brookfield, and X. Li, “The discoverframework freeware toolkit for multivariate spatio-temporal environmental data visualization and evaluation,” _Environmental Modelling & Software_, vol. 143, p. 105104, 2021.
  * [68] S. Hadlak, C. Tominski, H.-J. Schulz, and H. Schumann, “Visualization of attributed hierarchical structures in a spatiotemporal context,” _International Journal of Geographical Information Science_ , vol. 24, no. 10, pp. 1497–1513, 2010.
  * [69] T. Eaglin, I. Cho, and W. Ribarsky, “Space-time kernel density estimation for real-time interactive visual analytics,” in _Proceedings of the 50th Hawaii International Conference on System Sciences_ , 2017.
  * [70] M. Maskey, R. Ramachandran, I. Gurung, M. Ramasubramanian, B. Freitag, A. Kaulfus, G. Priftis, D. Bollinger, R. Mestre, and D. da Silva, “Employing deep learning to enable visual exploration of earth science events,” in _IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium_ , Sep. 2020, pp. 2248–2251.
  * [71] L. Liu, M. Mirzargar, R. M. Kirby, R. Whitaker, and D. H. House, “Visualizing time-specific hurricane predictions, with uncertainty, from storm path ensembles,” in _Computer Graphics Forum_ , vol. 34, no. 3. Wiley Online Library, 2015, pp. 371–380.
  * [72] S. Lu, R. M. Li, W. C. Tjhi, K. K. Lee, L. Wang, X. Li, and D. Ma, “A framework for cloud-based large-scale data analytics and visualization: Case study on multiscale climate data,” in _2011 IEEE Third International Conference on Cloud Computing Technology and Science_ , Nov 2011, pp. 618–622.
  * [73] S. Kim, S. Jeong, I. Woo, Y. Jang, R. Maciejewski, and D. S. Ebert, “Data flow analysis and visualization for spatiotemporal statistical data without trajectory information,” _IEEE Transactions on Visualization and Computer Graphics_ , vol. 24, pp. 1287–1300, 3 2018.
  * [74] R. O. Obe and L. S. Hsu, _PostgreSQL: Up and Running: a Practical Guide to the Advanced Open Source Database_. " O’Reilly Media, Inc.", 2017.
  * [75] R. Obe and L. Hsu, _PostGIS in action_. Simon and Schuster, 2021.
  * [76] M. D. Da Silva and H. L. Tavares, _Redis Essentials_. Packt Publishing Ltd, 2015.
  * [77] K. Chodorow, _MongoDB: the definitive guide: powerful and scalable data storage_. " O’Reilly Media, Inc.", 2013.
  * [78] R. Rew and G. Davis, “Netcdf: an interface for scientific data access,” _IEEE computer graphics and applications_ , vol. 10, pp. 76–82, 1990.
  * [79] V. Eyring, S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor, “Overview of the coupled model intercomparison project phase 6 (cmip6) experimental design and organization,” _Geoscientific Model Development_ , vol. 9, no. 5, pp. 1937–1958, 2016.
  * [80] P. Scarponi, G. Coro, and P. Pagano, “A collection of aquamaps native layers in netcdf format,” _Data in Brief_ , vol. 17, pp. 292–296, 4 2018.
  * [81] B. Eaton, J. Gregory, B. Drach, K. Taylor, S. Hankin, J. Caron, R. Signell, P. Bentley, G. Rappa, H. Höck _et al._ , “Netcdf climate and forecast (cf) metadata conventions,” 2003.
  * [82] J. C. Biard, J. Yu, M. Hedley, S. J. D. Cox, A. Leadbetter, N. J. Car, K. A. Druken, S. Nativi, and E. Davis, “Linking netcdf data with the semantic web-enhancing data discovery across domains,” _AGUFM_ , vol. 2016, p. IN23A–1758, 2016.
  * [83] K. Shvachko, H. Kuang, S. Radia, and R. Chansler, “The hadoop distributed file system,” in _2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST)_ , 2010, pp. 1–10.
  * [84] A. Talukder, M. Elshambakey, S. Wadkar, H. Lee, L. Cinquini, S. Schlueter, I. Cho, W. Dou, and D. J. Crichton, “Vifi: Virtual information fabric infrastructure for data-driven discoveries from distributed earth science data,” in _IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computed, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/ IOP/SCI)_ , Aug 2017, pp. 1–8.
  * [85] E. J. Pebesma, K. de Jong, and D. Briggs, “Interactive visualization of uncertain spatial and spatio-temporal data under different scenarios: an air quality example,” _International Journal of Geographical Information Science_ , vol. 21, no. 5, pp. 515–527, 2007.
  * [86] Q. Shu, H. Guo, J. Liang, L. Che, J. Liu, and X. Yuan, “Ensemblegraph: Interactive visual analysis of spatiotemporal behaviors in ensemble simulation data,” in _2016 IEEE Pacific Visualization Symposium (PacificVis)_ , April 2016, pp. 56–63.
  * [87] S. Castruccio, M. G. Genton, and Y. Sun, “Visualizing spatiotemporal models with virtual reality: from fully immersive environments to applications in stereoscopic view,” _Journal of the Royal Statistical Society: Series A (Statistics in Society)_ , vol. 182, no. 2, pp. 379–387, 2019.
  * [88] S. Papers, M. Hlawitschka, T. Weinkauf, A. Thudt, D. Baur, and S. Carpendale, “Visits: A spatiotemporal visualization of location histories,” 2013.
  * [89] N. Andrienko, G. Andrienko, E. Camossi, C. Claramunt, J. M. Cordero Garcia, G. Fuchs, M. Hadzagic, A.-L. Jousselme, C. Ray, D. Scarlatti, and G. Vouros, “Visual exploration of movement and event data with interactive time masks,” _Visual Informatics_ , vol. 1, no. 1, pp. 25–39, 2017.
  * [90] F. Ferstl, M. Kanzler, M. Rautenhaus, and R. Westermann, “Time-hierarchical clustering and visualization of weather forecast ensembles,” _IEEE Transactions on Visualization and Computer Graphics_ , vol. 23, no. 1, pp. 831–840, Jan 2017.
  * [91] H. Ahmadi, M. Argany, A. Ghanbari, and M. Ahmadi, “Visualized spatiotemporal data mining in investigation of urmia lake drought effects on increasing of pm10 in tabriz using space-time cube (2004-2019),” _Sustainable Cities and Society_ , vol. 76, p. 103399, 2022.
  * [92] J. Li, K. Zhang, and Z.-P. Meng, “Vismate: Interactive visual analysis of station-based observation data on climate changes,” in _2014 IEEE Conference on Visual Analytics Science and Technology (VAST)_. IEEE, Oct 2014, pp. 133–142.
  * [93] T. Nocke, T. Sterzel, M. Böttinger, M. Wrobel _et al._ , “Visualization of climate and climate change data: An overview,” _Digital earth summit on geoinformatics_ , pp. 226–232, 2008.
  * [94] W. Cartwright, J. Crampton, G. Gartner, S. Miller, K. Mitchell, E. Siekierska, and J. Wood, “Geospatial information visualization user interface issues,” _Cartography and Geographic Information Science_ , vol. 28, pp. 45–60, 2000.
  * [95] C. A. L. Pahins, N. Ferreira, and J. L. Comba, “Real-time exploration of large spatiotemporal datasets based on order statistics,” _IEEE Transactions on Visualization and Computer Graphics_ , vol. 26, no. 11, pp. 3314–3326, 2020.
  * [96] E. R. Tufte, N. H. Goeler, and R. Benson, _Envisioning information_. Graphics press Cheshire, CT, 1990, vol. 2.
  * [97] S. Ko, I. Cho, S. Afzal, C. Yau, J. Chae, A. Malik, K. Beck, Y. Jang, W. Ribarsky, and D. S. Ebert, “A survey on visual analysis approaches for financial data,” in _Computer Graphics Forum_ , vol. 35, no. 3. Wiley Online Library, 2016, pp. 599–617.
  * [98] P. S. Quinan and M. Meyer, “Visually comparing weather features in forecasts,” _IEEE Transactions on Visualization and Computer Graphics_ , vol. 22, no. 1, pp. 389–398, Jan 2016.
  * [99] J. G. Acker and G. Leptoukh, “Online Analysis Enhances Use of NASA Earth Science Data,” _EOS Transactions_ , vol. 88, no. 2, pp. 14–17, Jan. 2007.
  * [100] N. Röber, M. Böttinger, and B. Stevens, “Visualization of climate science simulation data,” _IEEE Computer Graphics and Applications_ , vol. 41, no. 1, pp. 42–48, 2021.
  * [101] K.-L. Ma, “In situ visualization at extreme scale: Challenges and opportunities,” _IEEE Computer Graphics and Applications_ , vol. 29, no. 6, pp. 14–19, 2009.
  * [102] I. Wald, G. Johnson, J. Amstutz, C. Brownlee, A. Knoll, J. Jeffers, J. Günther, and P. Navratil, “Ospray - a cpu ray tracing framework for scientific visualization,” _IEEE Transactions on Visualization and Computer Graphics_ , vol. 23, no. 1, pp. 931–940, Jan 2017.
  * [103] A. VRED, “3d visualization and virtual prototyping software for automotive designers,” https://www.autodesk.com/products/vred/, accessed December 21, 2021.
  * [104] T. Nocke, H. Schumann, and U. Böhm, “Methods for the visualization of clustered climate data,” _Comput. Stat._ , vol. 19, no. 1, p. 75–94, feb 2004.
  * [105] O. Schroth, E. Pond, S. Muir-Owen, C. Campbell, and S. R. Sheppard, “Tools for the understanding of spatio-temporal climate scenarios in local planning: Kimberley (bc) case study,” _Zurich: Swiss National Sciences Foundation_ , 2009.
  * [106] M.-J. Kraak, “The space-time cube revisited from a geovisualization perspective,” in _Proc. 21st International Cartographic Conference_. Citeseer, 2003, pp. 1988–1996.
  * [107] M. Kraak, “Timelines, temporal resolution, temporal zoom and time geography,” in _ICC 2005 : Proceedings of the 22nd international cartographic conference_. New Zealand: International Cartographic Association, 2005, 22nd International Cartographic Conference, ICC 2005 : Mapping approaches into a changing world, ICC ; Conference date: 09-07-2005 Through 16-07-2005.
  * [108] C. Tominski, P. Schulze-Wollgast, and H. Schumann, “3d information visualization for time dependent data on maps,” in _Ninth International Conference on Information Visualisation (IV’05)_ , July 2005, pp. 175–181.
  * [109] N. Röber and J. F. Engels, “In-situ processing in climate science,” in _High Performance Computing: ISC High Performance 2019 International Workshops, Frankfurt, Germany, June 16-20, 2019, Revised Selected Papers_. Berlin, Heidelberg: Springer-Verlag, 2019, p. 612–622.
  * [110] K. P. Hewagamage, M. Hirakawa, and T. Ichikawa, “Interactive visualization of spatiotemporal patterns using spirals on a geographical map,” in _Proceedings 1999 IEEE Symposium on Visual Languages_. IEEE, 1999, pp. 296–303.
  * [111] J. C. Roberts, “State of the art: Coordinated amp; multiple views in exploratory visualization,” in _Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization (CMV 2007)_ , July 2007, pp. 61–71.
  * [112] G. Andrienko and N. Andrienko, “Coordinated multiple views: a critical view,” in _Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization (CMV 2007)_. IEEE, 2007, pp. 72–74.
  * [113] G. Convertino, J. Chen, B. Yost, Y.-S. Ryu, and C. North, “Exploring context switching and cognition in dual-view coordinated visualizations,” in _Proceedings International Conference on Coordinated and Multiple Views in Exploratory Visualization - CMV 2003 -_ , July 2003, pp. 55–62.
  * [114] Y. Lu, R. Garcia, B. Hansen, M. Gleicher, and R. Maciejewski, “The state-of-the-art in predictive visual analytics,” in _Computer Graphics Forum_ , vol. 36, no. 3. Wiley Online Library, 2017, pp. 539–562.
  * [115] E. Dimara and C. Perin, “What is interaction for data visualization?” _IEEE Transactions on Visualization and Computer Graphics_ , vol. 26, no. 1, pp. 119–129, Jan 2020.
  * [116] J. S. Yi, Y. ah Kang, J. Stasko, and J. A. Jacko, “Toward a deeper understanding of the role of interaction in information visualization,” _IEEE transactions on visualization and computer graphics_ , vol. 13, no. 6, pp. 1224–1231, 2007.
  * [117] F. Wang, W. Li, and S. Wang, “Polar cyclone identification from 4d climate data in a knowledge-driven visualization system,” _Climate_ , vol. 4, no. 3, 2016.
  * [118] K. Potter, A. Wilson, P.-T. Bremer, D. Williams, C. Doutriaux, V. Pascucci, and C. Johhson, “Visualization of uncertainty and ensemble data: Exploration of climate modeling and weather forecast data with integrated visus-cdat systems,” in _Journal of Physics: Conference Series_ , vol. 180, no. 1. IOP Publishing, 2009, p. 012089.
  * [119] N. Andrienko and G. Andrienko, “A visual analytics framework for spatio-temporal analysis and modelling,” _Data Mining and Knowledge Discovery_ , vol. 27, no. 1, pp. 55–83, 2013.
  * [120] D. Cashman, S. R. Humayoun, F. Heimerl, K. Park, S. Das, J. Thompson, B. Saket, A. Mosca, J. Stasko, A. Endert _et al._ , “A user-based visual analytics workflow for exploratory model analysis,” in _Computer Graphics Forum_ , vol. 38, no. 3. Wiley Online Library, 2019, pp. 185–199.
  * [121] W. Cui, “Visual analytics: A comprehensive overview,” _IEEE Access_ , vol. 7, pp. 81 555–81 573, 2019.
  * [122] D. Sacha, A. Stoffel, F. Stoffel, B. C. Kwon, G. Ellis, and D. A. Keim, “Knowledge generation model for visual analytics,” _IEEE Transactions on Visualization and Computer Graphics_ , vol. 20, no. 12, pp. 1604–1613, 2014.
  * [123] D. Hanselman and B. Littlefield, _Mastering MATLAB: a comprehensive tutorial and reference_. Prentice-Hall, Inc., 1996.
  * [124] F. Berman, A. Chien, K. Cooper, J. Dongarra, I. Foster, D. Gannon, L. Johnsson, K. Kennedy, C. Kesselman, J. Mellor-Crumme, D. Reed, L. Torczon, and R. Wolski, “The grads project: Software support for high-level grid application development,” _The International Journal of High Performance Computing Applications_ , vol. 15, no. 4, pp. 327–344, 2001.
  * [125] D. Brown, R. Brownrigg, M. Haley, and W. Huang, “Ncar command language (ncl),” 2012.
  * [126] H. Lee, A. Goodman, L. McGibbney, D. E. Waliser, J. Kim, P. C. Loikith, P. B. Gibson, and E. C. Massoud, “Regional climate model evaluation system powered by apache open climate workbench v1.3.0: an enabling tool for facilitating regional climate studies,” _Geoscientific Model Development_ , vol. 11, no. 11, pp. 4435–4449, 2018.
  * [127] R. J. Sandusky, “Computational provenance: Dataone and implications for cultural heritage institutions,” in _2016 IEEE International Conference on Big Data (Big Data)_. IEEE, 2016, pp. 3266–3271.
  * [128] J. P. Cohn, “Dataone opens doors to scientists across disciplines,” 2012.
  * [129] R. Pordes, D. Petravick, B. Kramer, D. Olson, M. Livny, A. Roy, P. Avery, K. Blackburn, T. Wenaus, F. Würthwein _et al._ , “The open science grid,” in _Journal of Physics: Conference Series_ , vol. 78, no. 1. IOP Publishing, 2007, p. 012057.
  * [130] D. Medvedev, G. Lemson, and M. Rippin, “Sciserver compute: Bringing analysis close to the data,” in _Proceedings of the 28th international conference on scientific and statistical database management_ , 2016, pp. 1–4.
  * [131] T. Nocke, T. STERZEL, M. Böttinger, and M. Wrobel, “Visualization of climate and climate change data: An overview,” _in Ehlers et al. (Eds.) Digital Earth Summit on Geoinformatics 2008: Tools for Global Change Research (ISDE’08), Wichmann, Heidelberg, pp. 226-232, 2008_ , 01 2008.
  * [132] A. A. Kalinin, S. Palanimalai, and I. D. Dinov, “Socrat platform design: A web architecture for interactive visual analytics applications,” in _Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics_ , ser. HILDA’17. New York, NY, USA: Association for Computing Machinery, 2017.
  * [133] R. R. Nemani, B. L. Thrasher, W. Wang, T. J. Lee, F. S. Melton, J. L. Dungan, and A. Michaelis, “NASA Earth Exchange (NEX) Supporting Analyses for National Climate Assessments,” in _AGU Fall Meeting Abstracts_ , vol. 2015, 2015, pp. GC21E–04.
  * [134] J. Johansson, T.-S. S. Neset, and B.-O. Linnér, “Evaluating climate visualization: An information visualization approach,” in _2010 14th International Conference Information Visualisation_. IEEE, 2010, pp. 156–161.
  * [135] P. O. Kristensson, N. Dahlbck, D. Anundi, M. Bjrnstad, H. Gillberg, J. Haraldsson, I. Mrtensson, M. Nordvall, and J. Sthl, “An evaluation of space time cube representation of spatiotemporal patterns,” _IEEE Transactions on Visualization and Computer Graphics_ , vol. 15, pp. 696–702, 7 2009.
  * [136] N. Elmqvist and J. S. Yi, “Patterns for visualization evaluation,” _Information Visualization_ , vol. 14, no. 3, pp. 250–269, 2015.
  * [137] A. MacEachren, F. Boscoe, D. Haug, and L. Pickle, “Geographic visualization: designing manipulable maps for exploring temporally varying georeferenced statistics,” in _Proceedings IEEE Symposium on Information Visualization (Cat. No.98TB100258)_ , 1998, pp. 87–94.
  * [138] T. Schreck, D. Keim, and F. Mansmann, “Regular treemap layouts for visual analysis of hierarchical data,” in _Proceedings of the 22nd Spring Conference on Computer Graphics_ , ser. SCCG ’06. New York, NY, USA: Association for Computing Machinery, 2006, p. 183–190.
  * [139] K. Marriott, F. Schreiber, T. Dwyer, K. Klein, N. H. Riche, T. Itoh, W. Stuerzlinger, and B. H. Thomas, _Immersive Analytics_. Springer, 2018, vol. 11190.
  * [140] T. Sellis, A. D. Library., and A. for Computing Machinery. Special Interest Group on Management of Data., _An Analytic Data Engine for Visualization in Tableau_. ACM, 2011.
  * [141] D. Cao, J. Zhang, L. Xun, S. Yang, J. Wang, and F. Yao, “Spatiotemporal variations of global terrestrial vegetation climate potential productivity under climate change,” _Science of The Total Environment_ , vol. 770, p. 145320, 2021.
  * [142] E. Zgraggen, A. Galakatos, A. Crotty, J.-D. Fekete, and T. Kraska, “How progressive visualizations affect exploratory analysis,” _IEEE Transactions on Visualization and Computer Graphics_ , vol. 23, no. 8, pp. 1977–1987, 2017.
  * [143] U. U. Turuncoglu, “Toward modular in situ visualization in earth system models: the regional modeling system regesm 1.1,” _Geoscientific Model Development_ , vol. 12, no. 1, pp. 233–259, 2019.

Abdullah-Al-Raihan Nayeem Abdullah-Al-Raihan Nayeem is a Ph.D. candidate in
Computer Science at the University of North Carolina at Charlotte. His
research interests are visual analytics systems, geo-spatiotemporal
visualization, and distributed data analysis.
---
Dongyun Han Dongyun Han is a Ph.D. student in the department of Computer
Science at Utah State University. His research interests include visual
analytics, data visualization, and human-computer interaction.
---
Huikyo Lee Huikyo Lee joined Jet Propulsion Laboratory as a data scientist. He
has a Ph.D. in Atmospheric Sciences from the University of Illinois at Urbana-
Champaign.He has had extensive experience with stratospheric dynamics, neural
network modeling for satellite remote sensing, atmospheric chemistry, and air
quality modeling.
---
Donghoon Kim DongHoon Kim is a PhD student in the Computer Science Department
at Utah State University. His research interests are human perception in
virtual reality and visualization.
---
Daniel Feldman Daniel Feldman is a scientist in the Earth and Environmental
Sciences Area at the Lawrence Berkeley National Laboratory. He has a Ph.D. in
Environmental Science and Engineering from Caltech, and a B.S. in
Environmental Engineering Science from MIT. He research focuses on the
intersection between Earth System Modeling and observations.
---
William J. Tolone William J. Tolone is a professor and associate dean of the
College of Computing and Informatics at the University of North Carolina at
Charlotte. He received his Ph.D. in Computer Science from the University of
Illinois at Urbana-Champaign. His research expertise is in integrated modeling
and simulation, critical infrastructure analytics, visual and data analytics,
and collaborative systems.
---
Daniel Crichton Daniel Crichton is a program manager, principal investigator,
and principal computer scientist at NASA’s Jet Propulsion Laboratory, which he
joined in 1995. He is JPL’s leader of the Center for Data Science and
Technology, a joint center formed with Caltech, focusing on the research,
development and implementation of data science and data intensive system
technologies for science and missions.
---
Isaac Cho Isaac Cho is an assistant professor in the Computer Science
department at Utah State University and an adjunct professor in the Computer
Science department at the University of North Carolina at Charlotte. His main