**Part 2**

### **Precipitation Estimation and Nowcasting**

Doppler Radar Observations – 94 Weather Radar, Wind Profiler, Ionospheric Radar, and Other Advanced Applications

Hinton, D.A., 1993: Airborne derivation of microburst alerts from ground-based Terminal

Labitt, M., 1981: Coordinated radar and aircraft observations of turbulence. Project Rep.

Lee O.S.M. 2004. 'Enhancement of the Anemometer-based System for Windshear Detection

Merritt, M.W., 1987: Automated detection of microburst windshear for Terminal Doppler

Proctor, F.H., D.A. Hinton and R.L. Bowles, 2000: A windshear hazard index, presented at

Shun C.M. and Chan P.W. 2008. Applications of an infrared Doppler Lidar in detection of wind shear. Journal of Atmospheric and Oceanic Technology 25: 637-655.

Memorandum 108990, NASA.

Cambridge, MA, USA.

2000, Orlando, FL., USA.

ATC 108, MIT, Lincoln Lab, 39 pp.

International Civil Aviation Organization.

Doppler Weather Radar information – a flight evaluation, NASA Technical

at the Hong Kong International Airport.' Eighth Meeting of the Communications/Navigation/Surveillance and Meteorology Sub-Group (CNS/MET/SG/8) of APANPIRG, Bangkok, Thailand, 12 - 16 July 2004.

Weather Radar, presented at SPIE Conference on Digital Image Processing and Visual Communications Technologies in Meteorology, 27-28 October 1987,

9th Conference on Aviation, Range, and Aerospace Meteorology, 11-15 September

**4**

*1,3UK 2Australia*

**Nowcasting**

Clive Pierce1, Alan Seed2, Sue Ballard3, David Simonin3 and Zhihong Li3

*2Centre for Australian Weather and Climate Research, Bureau of Meteorology, Melbourne,*

The somewhat inelegant term, *nowcasting*, was devised in the mid-1970s (Browning, 1980). It encapsulates a broad spectrum of observation intensive techniques developed for predicting the weather up to a few hours ahead. These techniques are reliant on the rapid processing of high resolution data sets collected by weather radars and satellites. As such, the evolution of nowcasting as a branch of operational meteorology has been closely bound up with postsecond world war advances in remote sensing, telecommunications and digital computing. A comprehensive treatment of the subject matter is beyond the scope of this Chapter. In a book about Doppler radar the authors make no apology for focusing on radar based

We begin with a brief justification for the use of nowcasts in operational meteorology. This is followed by an overview of nowcasting techniques. A description of some of the key, historical developments in nowcasting is followed by sections on deterministic extrapolation-based nowcasting techniques, errors in precipitation nowcasts and their treatment within nowcasting system frameworks. The remaining sections consider advances in high resolution Numerical Weather Prediction (NWP) model-based nowcasting and review some of the issues and developments surrounding the application of quantitative precipitation nowcasts (QPN) to hydrological forecasting and warning. The Chapter closes with a brief consideration of future prospects for

Operational weather forecasts are produced by primitive equation models known collectively, as Numerical Weather Prediction models. The predictive skill of these models is limited by a number of factors including the accuracy and coverage of routinely available weather observations, the extent to which their model formulations and grid lengths allow the relevant physical and dynamical processes to be modelled accurately, and the non-linear

**1. Introduction**

nowcasts of precipitation.

**2. An overview of nowcasting techniques**

response of the atmospheric system to small perturbations in its state.

nowcasting.

*1Hydro-Meteorological Research, Met Office, FitzRoy Road, Exeter,*

*University of Reading, Earley Gate, Reading,*

*3Joint Centre for Mesoscale Meteorology, Met Office, Meteorology Building,*

### **Nowcasting**

Clive Pierce1, Alan Seed2, Sue Ballard3, David Simonin3 and Zhihong Li3 *1Hydro-Meteorological Research, Met Office, FitzRoy Road, Exeter, 2Centre for Australian Weather and Climate Research, Bureau of Meteorology, Melbourne, 3Joint Centre for Mesoscale Meteorology, Met Office, Meteorology Building, University of Reading, Earley Gate, Reading, 1,3UK*

*2Australia*

#### **1. Introduction**

The somewhat inelegant term, *nowcasting*, was devised in the mid-1970s (Browning, 1980). It encapsulates a broad spectrum of observation intensive techniques developed for predicting the weather up to a few hours ahead. These techniques are reliant on the rapid processing of high resolution data sets collected by weather radars and satellites. As such, the evolution of nowcasting as a branch of operational meteorology has been closely bound up with postsecond world war advances in remote sensing, telecommunications and digital computing. A comprehensive treatment of the subject matter is beyond the scope of this Chapter. In a book about Doppler radar the authors make no apology for focusing on radar based nowcasts of precipitation.

We begin with a brief justification for the use of nowcasts in operational meteorology. This is followed by an overview of nowcasting techniques. A description of some of the key, historical developments in nowcasting is followed by sections on deterministic extrapolation-based nowcasting techniques, errors in precipitation nowcasts and their treatment within nowcasting system frameworks. The remaining sections consider advances in high resolution Numerical Weather Prediction (NWP) model-based nowcasting and review some of the issues and developments surrounding the application of quantitative precipitation nowcasts (QPN) to hydrological forecasting and warning. The Chapter closes with a brief consideration of future prospects for nowcasting.

#### **2. An overview of nowcasting techniques**

Operational weather forecasts are produced by primitive equation models known collectively, as Numerical Weather Prediction models. The predictive skill of these models is limited by a number of factors including the accuracy and coverage of routinely available weather observations, the extent to which their model formulations and grid lengths allow the relevant physical and dynamical processes to be modelled accurately, and the non-linear response of the atmospheric system to small perturbations in its state.

Nowcasting 99

In the following section, we describe some of the key milestones in radar-based precipitation nowcasting and review these in the context of parallel advances in relevant areas of science

Operational weather radar has its origins in the development of military radar during World War Two. The invention of the resonant cavity magnetron by John Randall and Harry Boot at the University of Birmingham in England in 1940 allowed the construction of high powered, centimeter-band radars, suitable for detecting precipitation. The sharing of this technology with American scientists early in the 1940s facilitated its subsequent

Important early papers include those on rain drop size distributions (Marshall & Palmer, 1948) and shapes (Browne & Robinson, 1952; Hunter, 1954; Newell et al., 1955), the measurement of precipitation (Ryde, 1946; Byers, 1948; Bowen, 1951; Twomey, 1953; Battan, 1953; Stout & Neill, 1953), its vertical structure (Langille & Gunn, 1948) and associated estimation errors (Hitschfeld & Bordan, 1954), and those on thunderstorm identification, behaviour and dynamics (Wexler & Swingle, 1947; Byers & Braham, 1949; Wexler, 1951;

The concept of extrapolating radar echoes for the short term prediction of precipitation was first proposed by Ligda (1953). The earliest demonstration of the application of objective extrapolation to radar echoes is described by Hilst and Russo (1960), whilst Noel and Fleischer (1960) were amongst the first radar meteorologists to explore the predictability of precipitation echoes using this approach. Further noteworthy papers are those published by Russo and Bowne (1962) and Kessler and Russo (1963). Kessler (1966) and Wilson (1966) explored the use of cross correlation statistics to diagnose a best estimate of echo pattern average motion. Wilson (1966) used the maximum value of the cross correlation coefficient

Two important conclusions were drawn from these early studies. The first of these was the positive correlation between the predictability of precipitation features and their size: large features tend to be longer lived than small ones. The second conclusion is an adjunct to the first, namely that small scale features are generally short lived – typically a few tens of minutes. These findings are consistent with early investigations into the multi-scaling properties of the

The 1970s saw the further development of cross correlation-based nowcasting algorithms and their automation. Zawadzki (1973) developed an optical device for measuring the space-time statistical properties of radar inferred precipitation fields. Austin and Bellon (1974) evaluated an automated, computerized pattern matching programme for nowcasting precipitation up to 3 hours ahead. They concluded that the useful range of these nowcasts varied with the nature and extent of the precipitation. Nonetheless, this approach was

atmosphere and associated limits on atmospheric predictability (Lorenz, 1963; 1973).

shown to be consistently skilful up to one hour ahead over a wide range of events.

and technology.

**3. Radar-based nowcasting – A brief history 3.1 Origins of weather radar, and early research**

development for meteorological applications.

Ligda, 1951; Battan, 1953).

**3.2 Extrapolation techniques**

as an indicator of pattern development.

Whilst current, operational NWP models are now beginning to resolve important processes such as convection (Lean et al., 2008), their predictive skill generally remains very limited at the convective scales. Furthermore, current computational constraints restrict their operational forecast update cycles to hours, whereas convective phenomena typically exhibit life times of tens of minutes. Thus, NWP-based forecasts of local weather (Browning, 1980) have tended to be rather poor and their use for local forecasting has, until very recently, often been limited to general guidance at the regional scale.

From the 1960s onwards, the availability in near real time of increasingly sophisticated, spatially contiguous, radar and satellite observations, particularly of precipitation or proxies for it, offered the prospect of very short range, local forecasting by extrapolation – the concept of exploiting persistence, either in an Eulerian or Lagrangian reference frame (Germann & Zawadzki, 2002), to make weather predictions with sufficient rapidity to circumvent the perishability of the data. Browning (1980) clarified the relative merits of extrapolation nowcasts and NWP forecasts (see Figure 1), suggesting that the former were of superior accuracy up to 6 hours ahead.

Fig. 1. A schematic diagram after Browning (1980) conceptualizing the relationship between forecasting methodology, skill and forecast range.

The predictability of extrapolation-based precipitation nowcasts and the forecast range at which these nowcasts must hand over to NWP to achieve optimal predictive skill have been explored by a number of authors (e.g. Browning, 1980; Zawadzki et al., 1994; Germann & Zawadzki, 2002; German et al., 2006; Bowler et al., 2006). Recent implementations of convective scale NWP model forecasts are now reducing the useful range of extrapolationbased nowcasts to a few hours ahead, as discussed later in this Chapter.

Doppler Radar Observations – 98 Weather Radar, Wind Profiler, Ionospheric Radar, and Other Advanced Applications

Whilst current, operational NWP models are now beginning to resolve important processes such as convection (Lean et al., 2008), their predictive skill generally remains very limited at the convective scales. Furthermore, current computational constraints restrict their operational forecast update cycles to hours, whereas convective phenomena typically exhibit life times of tens of minutes. Thus, NWP-based forecasts of local weather (Browning, 1980) have tended to be rather poor and their use for local forecasting has, until very

From the 1960s onwards, the availability in near real time of increasingly sophisticated, spatially contiguous, radar and satellite observations, particularly of precipitation or proxies for it, offered the prospect of very short range, local forecasting by extrapolation – the concept of exploiting persistence, either in an Eulerian or Lagrangian reference frame (Germann & Zawadzki, 2002), to make weather predictions with sufficient rapidity to circumvent the perishability of the data. Browning (1980) clarified the relative merits of extrapolation nowcasts and NWP forecasts (see Figure 1), suggesting that the former were of

Fig. 1. A schematic diagram after Browning (1980) conceptualizing the relationship between

The predictability of extrapolation-based precipitation nowcasts and the forecast range at which these nowcasts must hand over to NWP to achieve optimal predictive skill have been explored by a number of authors (e.g. Browning, 1980; Zawadzki et al., 1994; Germann & Zawadzki, 2002; German et al., 2006; Bowler et al., 2006). Recent implementations of convective scale NWP model forecasts are now reducing the useful range of extrapolation-

based nowcasts to a few hours ahead, as discussed later in this Chapter.

recently, often been limited to general guidance at the regional scale.

superior accuracy up to 6 hours ahead.

forecasting methodology, skill and forecast range.

In the following section, we describe some of the key milestones in radar-based precipitation nowcasting and review these in the context of parallel advances in relevant areas of science and technology.

#### **3. Radar-based nowcasting – A brief history**

#### **3.1 Origins of weather radar, and early research**

Operational weather radar has its origins in the development of military radar during World War Two. The invention of the resonant cavity magnetron by John Randall and Harry Boot at the University of Birmingham in England in 1940 allowed the construction of high powered, centimeter-band radars, suitable for detecting precipitation. The sharing of this technology with American scientists early in the 1940s facilitated its subsequent development for meteorological applications.

Important early papers include those on rain drop size distributions (Marshall & Palmer, 1948) and shapes (Browne & Robinson, 1952; Hunter, 1954; Newell et al., 1955), the measurement of precipitation (Ryde, 1946; Byers, 1948; Bowen, 1951; Twomey, 1953; Battan, 1953; Stout & Neill, 1953), its vertical structure (Langille & Gunn, 1948) and associated estimation errors (Hitschfeld & Bordan, 1954), and those on thunderstorm identification, behaviour and dynamics (Wexler & Swingle, 1947; Byers & Braham, 1949; Wexler, 1951; Ligda, 1951; Battan, 1953).

#### **3.2 Extrapolation techniques**

The concept of extrapolating radar echoes for the short term prediction of precipitation was first proposed by Ligda (1953). The earliest demonstration of the application of objective extrapolation to radar echoes is described by Hilst and Russo (1960), whilst Noel and Fleischer (1960) were amongst the first radar meteorologists to explore the predictability of precipitation echoes using this approach. Further noteworthy papers are those published by Russo and Bowne (1962) and Kessler and Russo (1963). Kessler (1966) and Wilson (1966) explored the use of cross correlation statistics to diagnose a best estimate of echo pattern average motion. Wilson (1966) used the maximum value of the cross correlation coefficient as an indicator of pattern development.

Two important conclusions were drawn from these early studies. The first of these was the positive correlation between the predictability of precipitation features and their size: large features tend to be longer lived than small ones. The second conclusion is an adjunct to the first, namely that small scale features are generally short lived – typically a few tens of minutes. These findings are consistent with early investigations into the multi-scaling properties of the atmosphere and associated limits on atmospheric predictability (Lorenz, 1963; 1973).

The 1970s saw the further development of cross correlation-based nowcasting algorithms and their automation. Zawadzki (1973) developed an optical device for measuring the space-time statistical properties of radar inferred precipitation fields. Austin and Bellon (1974) evaluated an automated, computerized pattern matching programme for nowcasting precipitation up to 3 hours ahead. They concluded that the useful range of these nowcasts varied with the nature and extent of the precipitation. Nonetheless, this approach was shown to be consistently skilful up to one hour ahead over a wide range of events.

Nowcasting 101

This same period saw the development of a number of statistical (Krajewski & Georgakakos, 1985; Cox & Isham, 1988) and statistical-dynamical predictive models of precipitation (Georgakakos & Bras, 1984a, b; Lee & Georgakakos, 1990; French & Krajewski, 1994; Bell & Moore, 2000a). The latter were formulated to assimilate radar estimates and surface observations of precipitation and make forecasts using a simplified treatment of the governing atmospheric equations, focusing on the conservation of water mass. These studies were motivated by the operational forecasting requirements of the hydrological community and the limitations of "steady-state" nowcasting techniques and the first generation of

A growing recognition of the need to account for and communicate meteorological forecast uncertainty (Murphy & Carter, 1980; Krzysztofowicz, 1983), particularly in relation to precipitation, led to the development of a range techniques for probabilistic precipitation nowcasting. Andersson and Ivarsson (1991) evaluated an advection-based nowcasting scheme in which the probability of precipitation at a given location is estimated from the areal distribution of precipitation in a neighbourhood surrounding it (see also Schmid et al., 2000) This approach accounts for the impact of extrapolation errors on the location of advected precipitation. Other authors have adopted similar approaches. For example, Germann and Zawadzki (2004) used a local Lagrangian method to produce probabilistic

The previously mentioned theoretical work on multi-fractals, and empirical studies supporting a scaling model representation of precipitation fields, laid the foundations for the development of a number of stochastic precipitation nowcasting schemes exploiting scale decomposition frameworks. Seed (2003) adopted a multi-scale decomposition framework in his S-PROG (Spectral-Prognosis) scheme to nowcast the space-time evolution of high resolution radar derived precipitation fields (see Figure 2); he highlighted the potential application of S-PROG to conditional simulation and design storm modelling.

In a similar vein, the McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE; Turner et al., 2004) exploits a wavelet transform to model the predictability of precipitation as a function of scale. The aim of the scale decomposition is to filter out the unpredictable scales in an extrapolation nowcast, and in so doing, minimize the Root Mean Square nowcast error (typically measured using rain gauge observations and/or

Pegram and Clothier (2001) used a power law model to filter Gaussian distributed random numbers to generate stochastic realizations of radar precipitation fields in their String of Beads Model (SBM). Noise generation techniques similar to these were combined with a stochastic model of extrapolation velocity errors in the Short Term Ensemble Prediction System (STEPS, Bowler et al., 2006) to produce operational precipitation nowcasts quantifying uncertainties in phase as well as amplitude. In STEPS, the noise serves several purposes: it enables ensembles of equally likely nowcast solutions to be generated by perturbing predicted features as they lose skill; it also downscales an NWP forecast,

radar inferred estimates of surface precipitation rate or accumulation).

injecting variance at scales lacking power (variance) relative to the radar.

**3.6 Exploration of statistical and statistical-dynamical models of precipitation**

operational, mesoscale NWP models.

**3.7 Impact of forecast uncertainties**

extrapolation nowcasts.

This latter work led to the operational implementation of an algorithm based upon global cross correlation at McGill University in the mid-1970s. Bellon and Austin (1978) reviewed the operational performance of this scheme, known as SHARP (Short-Term Automated Radar Prediction), on two years' worth of data. The experience gained allowed subsequent enhancement of their cross correlation method to enable independent tracking of different echoes (Austin & Bellon, 1982) using a nine vector motion field. Rinehart (1981) describes a similar, multi-vector, cross correlation approach to determine and extrapolate the motion of individual storms within a multi-storm system.

#### **3.3 Cell tracking**

Algorithms founded on the tracking of radar echo centroids evolved in parallel with fieldbased pattern matching techniques. These were developed specifically for nowcasting thunderstorms, initially in North America. Amongst the earliest of these echo centroid trackers were those described by Wilk and Gray (1970) and Zittel (1976). The extrapolation vectors were diagnosed using a linear least squares fit through successive positions of the echo centroids. Duda and Blackmer (1972) and Blackmer et al. (1973) formulated clustering techniques to resolve difficulties in cases involving the merging and splitting of echoes.

Refinements to these early techniques were subsequently developed and implemented within operational tools during the following decades. Several good examples are the Storm Cell Identification and Tracking (SCIT) algorithm (Witt & Johnson, 1993) and the Thunderstorm Identification, Tracking, Analysis and Nowcasting (TITAN) system (Dixon & Wiener, 1993).

#### **3.4 Steady state versus growth and decay**

The proto-type, operational nowcasting algorithms developed during the 1970s were generally reliant on the steady state assumption. Tsonis and Austin (1981) explored echo size and intensity trending with a view to improving the prediction of long lived convective cells. They found negligible improvement in skill, even using sophisticated non-linear time trending schemes. Wilson et al. (1998) drew similar conclusions in a study involving the use of the TITAN system (Dixon and Wiener, 1993). These results are consistent with the findings of theoretical and NWP modelling experiments showing that the evolution of convective scale features in the atmosphere is non-linear and, to a degree, chaotic (Tsonis, 1989).

#### **3.5 Fractal properties of precipitation**

During the 1980s and 1990s, an improved understanding of the chaotic influence of atmospheric processes such as turbulence on the predictability of precipitation was reflected in a growing number of publications exploring the so called *scaling* or *multi-fractal* attributes of meteorological fields, including those of radar derived precipitation fields. Scaling behaviour or self-similarity implies that similar features can be observed in the atmosphere over a wide range of space and time scales, and that the relationship between certain statistical attributes of a precipitation field measured at different scales can be described by equations which incorporate a scaling factor. The formative papers in this area include those by Lovejoy and Schertzer (1985, 1986).

Doppler Radar Observations – 100 Weather Radar, Wind Profiler, Ionospheric Radar, and Other Advanced Applications

This latter work led to the operational implementation of an algorithm based upon global cross correlation at McGill University in the mid-1970s. Bellon and Austin (1978) reviewed the operational performance of this scheme, known as SHARP (Short-Term Automated Radar Prediction), on two years' worth of data. The experience gained allowed subsequent enhancement of their cross correlation method to enable independent tracking of different echoes (Austin & Bellon, 1982) using a nine vector motion field. Rinehart (1981) describes a similar, multi-vector, cross correlation approach to determine and extrapolate the motion of

Algorithms founded on the tracking of radar echo centroids evolved in parallel with fieldbased pattern matching techniques. These were developed specifically for nowcasting thunderstorms, initially in North America. Amongst the earliest of these echo centroid trackers were those described by Wilk and Gray (1970) and Zittel (1976). The extrapolation vectors were diagnosed using a linear least squares fit through successive positions of the echo centroids. Duda and Blackmer (1972) and Blackmer et al. (1973) formulated clustering techniques to resolve difficulties in cases involving the merging and splitting of echoes.

Refinements to these early techniques were subsequently developed and implemented within operational tools during the following decades. Several good examples are the Storm Cell Identification and Tracking (SCIT) algorithm (Witt & Johnson, 1993) and the Thunderstorm Identification, Tracking, Analysis and Nowcasting (TITAN) system (Dixon &

The proto-type, operational nowcasting algorithms developed during the 1970s were generally reliant on the steady state assumption. Tsonis and Austin (1981) explored echo size and intensity trending with a view to improving the prediction of long lived convective cells. They found negligible improvement in skill, even using sophisticated non-linear time trending schemes. Wilson et al. (1998) drew similar conclusions in a study involving the use of the TITAN system (Dixon and Wiener, 1993). These results are consistent with the findings of theoretical and NWP modelling experiments showing that the evolution of convective scale features in the atmosphere is non-linear and, to a degree, chaotic (Tsonis,

During the 1980s and 1990s, an improved understanding of the chaotic influence of atmospheric processes such as turbulence on the predictability of precipitation was reflected in a growing number of publications exploring the so called *scaling* or *multi-fractal* attributes of meteorological fields, including those of radar derived precipitation fields. Scaling behaviour or self-similarity implies that similar features can be observed in the atmosphere over a wide range of space and time scales, and that the relationship between certain statistical attributes of a precipitation field measured at different scales can be described by equations which incorporate a scaling factor. The formative papers in this area include those

individual storms within a multi-storm system.

**3.4 Steady state versus growth and decay**

**3.5 Fractal properties of precipitation**

by Lovejoy and Schertzer (1985, 1986).

**3.3 Cell tracking**

Wiener, 1993).

1989).

#### **3.6 Exploration of statistical and statistical-dynamical models of precipitation**

This same period saw the development of a number of statistical (Krajewski & Georgakakos, 1985; Cox & Isham, 1988) and statistical-dynamical predictive models of precipitation (Georgakakos & Bras, 1984a, b; Lee & Georgakakos, 1990; French & Krajewski, 1994; Bell & Moore, 2000a). The latter were formulated to assimilate radar estimates and surface observations of precipitation and make forecasts using a simplified treatment of the governing atmospheric equations, focusing on the conservation of water mass. These studies were motivated by the operational forecasting requirements of the hydrological community and the limitations of "steady-state" nowcasting techniques and the first generation of operational, mesoscale NWP models.

#### **3.7 Impact of forecast uncertainties**

A growing recognition of the need to account for and communicate meteorological forecast uncertainty (Murphy & Carter, 1980; Krzysztofowicz, 1983), particularly in relation to precipitation, led to the development of a range techniques for probabilistic precipitation nowcasting. Andersson and Ivarsson (1991) evaluated an advection-based nowcasting scheme in which the probability of precipitation at a given location is estimated from the areal distribution of precipitation in a neighbourhood surrounding it (see also Schmid et al., 2000) This approach accounts for the impact of extrapolation errors on the location of advected precipitation. Other authors have adopted similar approaches. For example, Germann and Zawadzki (2004) used a local Lagrangian method to produce probabilistic extrapolation nowcasts.

The previously mentioned theoretical work on multi-fractals, and empirical studies supporting a scaling model representation of precipitation fields, laid the foundations for the development of a number of stochastic precipitation nowcasting schemes exploiting scale decomposition frameworks. Seed (2003) adopted a multi-scale decomposition framework in his S-PROG (Spectral-Prognosis) scheme to nowcast the space-time evolution of high resolution radar derived precipitation fields (see Figure 2); he highlighted the potential application of S-PROG to conditional simulation and design storm modelling.

In a similar vein, the McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE; Turner et al., 2004) exploits a wavelet transform to model the predictability of precipitation as a function of scale. The aim of the scale decomposition is to filter out the unpredictable scales in an extrapolation nowcast, and in so doing, minimize the Root Mean Square nowcast error (typically measured using rain gauge observations and/or radar inferred estimates of surface precipitation rate or accumulation).

Pegram and Clothier (2001) used a power law model to filter Gaussian distributed random numbers to generate stochastic realizations of radar precipitation fields in their String of Beads Model (SBM). Noise generation techniques similar to these were combined with a stochastic model of extrapolation velocity errors in the Short Term Ensemble Prediction System (STEPS, Bowler et al., 2006) to produce operational precipitation nowcasts quantifying uncertainties in phase as well as amplitude. In STEPS, the noise serves several purposes: it enables ensembles of equally likely nowcast solutions to be generated by perturbing predicted features as they lose skill; it also downscales an NWP forecast, injecting variance at scales lacking power (variance) relative to the radar.

Nowcasting 103

The 1990s saw the first attempts to run convection resolving NWP model forecasts assimilating radar data (Lin et al., 1993). These early experiments were focused on

Some success was demonstrated in cases involving convection strongly forced by the large scale environment. However, other studies showed that convective initiation in a weakly forced environment is difficult to predict because the location and timing of initiation are very sensitive to variations in low level temperature and moisture. It seems likely that the ability of convection resolving NWP models to predict convection is a function of the predominant scale of the associated forcing. In the UK, much of the convection is forced by small-scale orography, as demonstrated by the Convective Storms Initiation Project

Despite these challenges, Lean et al. (2008) found that convection resolving models performed better in terms of convective initiation than a 12 km grid length model with parameterized convection, and a number of national weather services are now running operational, convection resolving NWP models. Indeed, some are trialling configurations with hourly or sub-hourly assimilation of radar data. NWP nowcast experiments in the UK show some improvements in NWP forecast skill in the nowcast time frame. Prospects for

Cell trackers or object-based nowcasting schemes are typically developed in areas where severe convective storms are a significant hazard, and are best suited to the generation of qualitative warnings of severe convective weather. In general, object-based algorithms are used to predict the location of a (convective) object in the future and thereby assign the properties of the object to that location. For example, a storm might be deemed to contain large hail, and therefore a warning of large hail will be issued for the locations on the

1. devise a set of rules that will be used to identify the bounds of an object in either two or

2. analyse current data to identify objects and assign attributes to them (heavy rain,

Most cell tracking algorithms define an object, either as a set of contiguous points that exceed some threshold in radar reflectivity, typically 35, 40 or 45 dBz (e.g. Dixon & Weiner, 1993; Han et al., 2009), or as a small region of increased reflectivity (Crane, 1979), or both (e.g. Handwerker, 2002). Defining the object in three dimensions allows one to compute the volume and height of the cell. This adds value when assigning the elements of severe

3. link the objects to existing tracks and estimate the advection velocity;

NWP nowcasting will be discussed in more detail later in this Chapter.

**3.9 NWP-based nowcasting**

predicting convective storms.

(Morcrette et al., 2007; Lean et al., 2008).

**4. Conventional nowcasting techniques**

**4.1 Deterministic techniques**

The basic elements of cell tracking are:

damaging wind, large hail etc);

4. predict the location of objects in the future.

**4.1.1 Cell tracking**

forecast storm track.

three dimensions;

Fig. 2. The generation of an extrapolation nowcast using the Spectral-Prognosis (Seed, 2003) multi-scale decomposition (cascade) framework (after Berenguer et al., 2005) . The motion of the precipitation field is derived from radar inferred analyses of precipitation rate valid at *t*  and *t-1* (typically a 10 or 15 minute time step). The temporal evolution of the extrapolated field is modelled on the hierarchy of scales produced by the cascade decomposition, using a hierarchy of second order auto-regressive (AR-2) models – one for each scale – and the analyses of precipitation rate valid at *t*, *t-1* and *t-2*.

#### **3.8 Improvements in extrapolation techniques**

The past two decades have also seen further refinements to the extrapolation schemes exploited by precipitation nowcasting algorithms, notably in the form of COTREC (Li et al., 1995; Mecklenburg et al., 2000), Variational Echo Tracking (VET; Germann & Zawadzki, 2002) and optical flow (Bowler et al., 2004, Peura & Hohti, 2004). COTREC constrains the cross correlation diagnosed displacement vectors using the two dimensional continuity equation. This is equivalent to minimizing the divergence of velocities derived for adjacent blocks. The benefits over TREC (Rinehart and Garvey, 1978) were shown to be due to the elimination of spurious motion vectors caused by clutter, beam blockages and rapid changes in the precipitation pattern. In common with optical flow, the VET scheme diagnoses a field of motion by direct application of the optical flow constraint equation. Bowler et al. (2004) solve this equation before applying a smoothness constraint where as Germann and Zawadzki (2002) uses a conjugate gradient method to minimize residuals from two constraints simultaneously.

Doppler Radar Observations – 102 Weather Radar, Wind Profiler, Ionospheric Radar, and Other Advanced Applications

Fig. 2. The generation of an extrapolation nowcast using the Spectral-Prognosis (Seed, 2003) multi-scale decomposition (cascade) framework (after Berenguer et al., 2005) . The motion of the precipitation field is derived from radar inferred analyses of precipitation rate valid at *t*  and *t-1* (typically a 10 or 15 minute time step). The temporal evolution of the extrapolated field is modelled on the hierarchy of scales produced by the cascade decomposition, using a hierarchy of second order auto-regressive (AR-2) models – one for each scale – and the

The past two decades have also seen further refinements to the extrapolation schemes exploited by precipitation nowcasting algorithms, notably in the form of COTREC (Li et al., 1995; Mecklenburg et al., 2000), Variational Echo Tracking (VET; Germann & Zawadzki, 2002) and optical flow (Bowler et al., 2004, Peura & Hohti, 2004). COTREC constrains the cross correlation diagnosed displacement vectors using the two dimensional continuity equation. This is equivalent to minimizing the divergence of velocities derived for adjacent blocks. The benefits over TREC (Rinehart and Garvey, 1978) were shown to be due to the elimination of spurious motion vectors caused by clutter, beam blockages and rapid changes in the precipitation pattern. In common with optical flow, the VET scheme diagnoses a field of motion by direct application of the optical flow constraint equation. Bowler et al. (2004) solve this equation before applying a smoothness constraint where as Germann and Zawadzki (2002) uses a conjugate

gradient method to minimize residuals from two constraints simultaneously.

analyses of precipitation rate valid at *t*, *t-1* and *t-2*.

**3.8 Improvements in extrapolation techniques**

#### **3.9 NWP-based nowcasting**

The 1990s saw the first attempts to run convection resolving NWP model forecasts assimilating radar data (Lin et al., 1993). These early experiments were focused on predicting convective storms.

Some success was demonstrated in cases involving convection strongly forced by the large scale environment. However, other studies showed that convective initiation in a weakly forced environment is difficult to predict because the location and timing of initiation are very sensitive to variations in low level temperature and moisture. It seems likely that the ability of convection resolving NWP models to predict convection is a function of the predominant scale of the associated forcing. In the UK, much of the convection is forced by small-scale orography, as demonstrated by the Convective Storms Initiation Project (Morcrette et al., 2007; Lean et al., 2008).

Despite these challenges, Lean et al. (2008) found that convection resolving models performed better in terms of convective initiation than a 12 km grid length model with parameterized convection, and a number of national weather services are now running operational, convection resolving NWP models. Indeed, some are trialling configurations with hourly or sub-hourly assimilation of radar data. NWP nowcast experiments in the UK show some improvements in NWP forecast skill in the nowcast time frame. Prospects for NWP nowcasting will be discussed in more detail later in this Chapter.

#### **4. Conventional nowcasting techniques**

#### **4.1 Deterministic techniques**

#### **4.1.1 Cell tracking**

Cell trackers or object-based nowcasting schemes are typically developed in areas where severe convective storms are a significant hazard, and are best suited to the generation of qualitative warnings of severe convective weather. In general, object-based algorithms are used to predict the location of a (convective) object in the future and thereby assign the properties of the object to that location. For example, a storm might be deemed to contain large hail, and therefore a warning of large hail will be issued for the locations on the forecast storm track.

The basic elements of cell tracking are:

Most cell tracking algorithms define an object, either as a set of contiguous points that exceed some threshold in radar reflectivity, typically 35, 40 or 45 dBz (e.g. Dixon & Weiner, 1993; Han et al., 2009), or as a small region of increased reflectivity (Crane, 1979), or both (e.g. Handwerker, 2002). Defining the object in three dimensions allows one to compute the volume and height of the cell. This adds value when assigning the elements of severe

Nowcasting 105

Bowler et al. (2004) used the optical flow constraint (Horn & Schunck, 1981) approach that is used for computer vision applications to derive the mean advection vector for tiles with rain. Optical flow uses least squares to find the (u,v) that minimizes the two-dimensional

> <sup>0</sup> *dR R R R u v dt x y t*

Bowler et al. (2004) smoothed the field using a moving average over a (15 x 15) pixel mask before calculating the partial derivatives using a finite difference scheme. The smoothed image was then partitioned into 48 x 48 km2 tiles and least squares used to estimate the mean advection vector within the tile. The resulting vectors were then smoothed so as to minimize <sup>2</sup> *V* . Grecu and Krajewski (2000) used a similar approach over 40 x 40 km2 tiles. Foresti and Pozdnoukhov (2011) used optical flow to track areas with rain rates that exceeded 10 mm/h. Essentially, this represents the application of optical flow to cell

Germann and Zawadzki (2002) used the Variational Echo Tracking (VET) method of Laroche and Zawadzki (1995) to derive the advection velocities. This technique partitions the field into small tiles and then uses the conjugate gradient method to minimize a cost function in one global minimization. The cost function includes a smoothness term. The difference between this approach and optical flow is that optical flow applies the smoothness constraint after the velocity field has been calculated for each tile, thereby avoiding an expensive global minimization (Bowler et al. 2004). Ruzanski et al. (2011) describe another approach using a linear least squares technique in the frequency domain. Cell tracking algorithms assign a velocity to each object and this is advected with a constant velocity during the forecast period. Such an approach is not optimal for field tracking algorithms because it does not allow for changes in direction and speed of motion during the forecast period. Germann and Zawadzki (2002) undertook a detailed analysis of several advection algorithms and found that a modified semi-Lagrangian backward interpolation scheme was optimal. Bowler et al. (2004, 2006) used the simpler semi-Lagrangian scheme that is applied for each time step in the forecast time series. Semi-Lagrangian advection requires a velocity at each pixel in the field and the optical flow technique does not provide advection vectors for tiles that have no rainfall. Therefore the velocity at each pixel must either be interpolated from the tiles with rain, or provided by a hierarchical approach that progressively reduces the size of the tiles that are used in the analysis (e.g. Germann and

Kernel-based methods have been employed for advection by Ruzanski et al. (2011) and Fox

1 2 [ ( , ), ( , ), ,, ( , )]*<sup>T</sup>*

(1)

**y Hy** *t t* <sup>1</sup> (2)

*t n* **y** *y s t ys t ys t* (3)

conservation equation

tracking.

Zawadzki, 2002).

where

and Wikle (2005) using

over a local neighbourhood.

weather or some sort of severity index, but does not necessarily add value to the identification and tracking of the cells. Rigo et al. (2010) used both 2D and 3D radar products and total lightning data to identify and track convective storms. A storm track is defined as a time series of cell positions. Assigning cells to tracks is the most complex aspect of these algorithms.

All cell tracking algorithms have to deal with cell initiation, mergers, splits, and terminations – the hatches, matches, and dispatches as it were – and this is often the point of differentiation between the various approaches. Errors in assigning the correct cell to a track are a major cause of error when estimating the cell velocity. The size of the object depends on the threshold that has been selected. Therefore, the predictability of the object decreases as the threshold is increased since the lifetime of a cell is related to its size. Using a high threshold to define the cell will make it more difficult to assign a cell to a track. This will increase the errors when estimating the track velocity. Using a low threshold will increase the longevity of the tracks, but will tend to limit the ability to forecast the location of the most severe cells within the storm. The concept of an "object" becomes less useful as the precipitation becomes more widespread. At some point (depending on the skill of the tracking algorithm), cell tracking algorithms fail to provide useful forecasts.

TITAN (Dixon & Weiner, 1993) and SCIT (Johnson et al., 1998) are good examples of what can be achieved in the object-tracking paradigm. Both TITAN and SCIT use the threedimensional radar reflectivity data to identify a convective object that is defined by a reflectivity threshold. In TITAN, the current objects are linked to past objects through combinatorial optimization. This minimizes the total advection and change in cell volume between the previous and current time steps. Many other cell tracking algorithms, SCIT for example, assign the cell that is closest to the forecast location of an active track. Han et al. (2009) evaluated several extensions to TITAN including improvements in assigning cells to tracks and using TREC motion vectors to advect the cells. In assigning a cell to a track, they found that the most significant improvements were due to adding a requirement that the forecast cell from the track at the previous time step must overlap with the current cell.

#### **4.1.2 Field-based advection**

Field tracking algorithms generally divide a Cartesian grid of radar reflectivity or rain rate into a number of tiles and then find the advection of the tile that maximizes the cross correlation (or some other measure of similarity) between successive time steps in the data. The mean advection vector for each tile containing rain is then calculated by applying some form of constraint to minimize the divergence of the resulting vectors.

A number of the current field tracking-based nowcasting algorithms use COTREC (Li et al., 1995) as the basis for deriving the advection vectors. Examples include the system that has been developed at the Czech Hydrometeorological Institute (Novak, 2007), the Hong Kong Observatory system, SWIRLS (Li et al., 2000), and the system implemented at the Guangdong Meteorological Observatory system (Liang et al., 2010). Liang et al. (2010) determined that the optimum size of the tile was 30 km. Li et al. (2000) evaluated the performance of an advection scheme on a 93 x 93 grid using a 19 pixel tile: this equates to 20 km on their 256 km x 256 km domain.

Doppler Radar Observations – 104 Weather Radar, Wind Profiler, Ionospheric Radar, and Other Advanced Applications

weather or some sort of severity index, but does not necessarily add value to the identification and tracking of the cells. Rigo et al. (2010) used both 2D and 3D radar products and total lightning data to identify and track convective storms. A storm track is defined as a time series of cell positions. Assigning cells to tracks is the most complex aspect

All cell tracking algorithms have to deal with cell initiation, mergers, splits, and terminations – the hatches, matches, and dispatches as it were – and this is often the point of differentiation between the various approaches. Errors in assigning the correct cell to a track are a major cause of error when estimating the cell velocity. The size of the object depends on the threshold that has been selected. Therefore, the predictability of the object decreases as the threshold is increased since the lifetime of a cell is related to its size. Using a high threshold to define the cell will make it more difficult to assign a cell to a track. This will increase the errors when estimating the track velocity. Using a low threshold will increase the longevity of the tracks, but will tend to limit the ability to forecast the location of the most severe cells within the storm. The concept of an "object" becomes less useful as the precipitation becomes more widespread. At some point (depending on the skill of the

TITAN (Dixon & Weiner, 1993) and SCIT (Johnson et al., 1998) are good examples of what can be achieved in the object-tracking paradigm. Both TITAN and SCIT use the threedimensional radar reflectivity data to identify a convective object that is defined by a reflectivity threshold. In TITAN, the current objects are linked to past objects through combinatorial optimization. This minimizes the total advection and change in cell volume between the previous and current time steps. Many other cell tracking algorithms, SCIT for example, assign the cell that is closest to the forecast location of an active track. Han et al. (2009) evaluated several extensions to TITAN including improvements in assigning cells to tracks and using TREC motion vectors to advect the cells. In assigning a cell to a track, they found that the most significant improvements were due to adding a requirement that the forecast cell from the track at the previous time step must overlap

Field tracking algorithms generally divide a Cartesian grid of radar reflectivity or rain rate into a number of tiles and then find the advection of the tile that maximizes the cross correlation (or some other measure of similarity) between successive time steps in the data. The mean advection vector for each tile containing rain is then calculated by applying some

A number of the current field tracking-based nowcasting algorithms use COTREC (Li et al., 1995) as the basis for deriving the advection vectors. Examples include the system that has been developed at the Czech Hydrometeorological Institute (Novak, 2007), the Hong Kong Observatory system, SWIRLS (Li et al., 2000), and the system implemented at the Guangdong Meteorological Observatory system (Liang et al., 2010). Liang et al. (2010) determined that the optimum size of the tile was 30 km. Li et al. (2000) evaluated the performance of an advection scheme on a 93 x 93 grid using a 19 pixel tile: this equates to

form of constraint to minimize the divergence of the resulting vectors.

tracking algorithm), cell tracking algorithms fail to provide useful forecasts.

of these algorithms.

with the current cell.

**4.1.2 Field-based advection**

20 km on their 256 km x 256 km domain.

Bowler et al. (2004) used the optical flow constraint (Horn & Schunck, 1981) approach that is used for computer vision applications to derive the mean advection vector for tiles with rain. Optical flow uses least squares to find the (u,v) that minimizes the two-dimensional conservation equation

$$\frac{dR}{dt} = \mu \frac{\partial R}{\partial x} + v \frac{\partial R}{\partial y} + \frac{\partial R}{\partial t} = 0 \tag{1}$$

over a local neighbourhood.

Bowler et al. (2004) smoothed the field using a moving average over a (15 x 15) pixel mask before calculating the partial derivatives using a finite difference scheme. The smoothed image was then partitioned into 48 x 48 km2 tiles and least squares used to estimate the mean advection vector within the tile. The resulting vectors were then smoothed so as to minimize <sup>2</sup> *V* . Grecu and Krajewski (2000) used a similar approach over 40 x 40 km2 tiles. Foresti and Pozdnoukhov (2011) used optical flow to track areas with rain rates that exceeded 10 mm/h. Essentially, this represents the application of optical flow to cell tracking.

Germann and Zawadzki (2002) used the Variational Echo Tracking (VET) method of Laroche and Zawadzki (1995) to derive the advection velocities. This technique partitions the field into small tiles and then uses the conjugate gradient method to minimize a cost function in one global minimization. The cost function includes a smoothness term. The difference between this approach and optical flow is that optical flow applies the smoothness constraint after the velocity field has been calculated for each tile, thereby avoiding an expensive global minimization (Bowler et al. 2004). Ruzanski et al. (2011) describe another approach using a linear least squares technique in the frequency domain.

Cell tracking algorithms assign a velocity to each object and this is advected with a constant velocity during the forecast period. Such an approach is not optimal for field tracking algorithms because it does not allow for changes in direction and speed of motion during the forecast period. Germann and Zawadzki (2002) undertook a detailed analysis of several advection algorithms and found that a modified semi-Lagrangian backward interpolation scheme was optimal. Bowler et al. (2004, 2006) used the simpler semi-Lagrangian scheme that is applied for each time step in the forecast time series. Semi-Lagrangian advection requires a velocity at each pixel in the field and the optical flow technique does not provide advection vectors for tiles that have no rainfall. Therefore the velocity at each pixel must either be interpolated from the tiles with rain, or provided by a hierarchical approach that progressively reduces the size of the tiles that are used in the analysis (e.g. Germann and Zawadzki, 2002).

Kernel-based methods have been employed for advection by Ruzanski et al. (2011) and Fox and Wikle (2005) using

$$\mathbf{y}\_{t+1} = \mathbf{H}\mathbf{y}\_t \tag{2}$$

where

$$\mathbf{y}\_t = \begin{bmatrix} y(s\_1, t), y(s\_2, t), \dots, y(s\_n, t) \end{bmatrix}^T \tag{3}$$

Nowcasting 107

Dance et al. (2010) used a year of TITAN tracks to investigate how cell tracking errors varied as a function of lead time, storm intensity, speed and duration. They found that the RMS errors in track speed and direction over the year were about 10 km/h and 30° respectively. Dance et al. (2010) found that tracking errors (both speed and direction) were large when the track speeds were less than 15 km/h; also, errors in track direction decreased with

Mecklenburg et al. (2000) investigated the tracking errors for TREC and COTREC and found that the mean absolute displacement and direction error for a 30-min forecast of convection was about 10 km and 20° respectively. Ebert et al. (2004) showed that TITAN cell tracking algorithms had a median error of about 10 km/h for intense cells. The median tracking error for the baseline field tracking algorithm – a correlation technique finding a single advection vector for the entire field of convective storms – was found to be 20 km/h using the same

Hourly accumulations of rainfall typically have correlation lengths of the order of 10 km (e.g. Anagnostou et al., 1999; Gebremichael & Krajewski, 2004). The tracking error after an hour is at least the same order of magnitude as the correlation length of the accumulations. Therefore, one would expect that enhancements to the current tracking algorithms should lead to improvements in the skill of nowcasts at lead times when tracking errors become a significant fraction of the correlation length of the rainfall field: this is likely to be around

Berenguer et al. (2005) found that the temporal evolution of the advection field was not a significant source of error for nowcasts with lead times less than 60 minutes. Bowler et al. (2006) discovered that forecast errors due to the temporal evolution of the advection field were negligible in the first three hours of a nowcast and accounted for 10% of the total error

It is interesting to note that the probability distribution of cell tracking errors is highly skewed (see, for example, Figure 20 of Ebert et al., 2004) and that the maximum 60 minute location error can be as high as 70 km. The fat tail in the distribution of tracking errors is a significant issue for operational nowcasting systems. Manual editing of the tracks (e.g. Bally, 2004) adds value to the automatic forecasts by eliminating the tracks that are regarded by the forecasters as being, either unimportant from a severe weather perspective, or incorrect. Errors due to the initiation and decay of storms during the forecast period become increasingly dominant as the lead time extends beyond 60 minutes (Wilson et al., 2010). Zawadzki et al. (1994) evaluated the limits of predictability of rainfall fields as a function of space and time and found that the time for a Lagrangian persistence forecast to reach a correlation of 0.5 ranged from 40 to 112 minutes. They also found that these predictability

Roca-Sancho et al. (2009) examined the spatial and temporal structure of forecast errors for MAPLE. They demonstrated that the temporal correlation of forecasts errors was very low after 60 minutes and that the spatial structure of the forecast errors progressively resembled that of rainfall with increasing lead time. The latter effect was due to increasing

times depended on the scales present in the rainfall field.

**4.2.2 Space-time structure of errors and their treatment**

increasing speed.

data.

T + 30 minutes.

after six hours.

is the vector of the *n* pixels in the image and { }*ij* **H** *h* is the *n* x *n* matrix of the advection operator.

Ruzanski et al. (2011) report that their approach is computationally efficient, although the time taken to derive the motion vectors was comparable to that required for optical flow. Furthermore, the advection algorithm was an order of magnitude slower than a simple implementation of a semi-Lagrangian backward interpolation scheme. Both Ruzanski et al. (2011) and Fox and Wikle (2005) demonstrated their methods using small images. The size of the advection operator is likely to become a constraint when using this technique to advect a large (say 106 pixels) image.

#### **4.1.3 Analogues**

Panziera et al. (2011) provide a good introduction on the assumptions and use of analogues in nowcasting. Advection-based tracking techniques rely on the assumption that precipitation fields evolve relatively slowly in Lagrangian coordinates and, therefore, their future state can be predicted largely by extrapolation. The assumption of Lagrangian persistence becomes a major limitation on the accuracy of nowcasts in situations where a field evolves rapidly, for example in situations where new storms are initiated or existing storms grow or decay. Data mining and analogue techniques seek to predict initiation, growth and decay by matching the current weather pattern with similar, past events and then use these past events as the basis for generating a forecast.

The first step in the use of analogues is to, either identify a set of regimes in the historical data, or identify a set of predictors that can be used as measures of similarity. Thereafter, the analogue that is closest to the current situation is selected and used as a basis for the forecast. This implies that the technique must be trained for each location, and that a significant historical record is available. Panziera et al. (2011) used predictors of mesoscale airflow and air-mass stability to select 120 analogues, and then employed two measures from the radar derived rainfall fields to select a set of 12 analogues to use as a forecast ensemble. Foresti and Pozdnoukhov (2011) derived maps of where orographic enhancement was likely to occur for a set of weather types. These could then be used to correct biases in advection forecasts.

#### **4.2 Errors in precipitation nowcasts**

#### **4.2.1 Error sources and attribution**

Sources of forecast errors include errors in the initial quantitative precipitation estimates (QPE), those arising from incorrect diagnosis of the field of motion, and changes in the motion and evolution of precipitation fields during the forecast period.

Approximately half of the total forecast error in the first hour of a forecast is due to errors in the radar derived rainfall analyses (Bellon & Austin, 1984; Fabry & Seed, 2009). This is because radar rainfall estimation errors, arising from variations in the relationship employed to convert the observed radar reflectivity to rainfall, have significant correlations over about an hour in time (Lee et al., 2007) and tens of kilometres in space (Velasco-Forero et al., 2009; Yeung et al., 2011).

Doppler Radar Observations – 106 Weather Radar, Wind Profiler, Ionospheric Radar, and Other Advanced Applications

is the vector of the *n* pixels in the image and { }*ij* **H** *h* is the *n* x *n* matrix of the advection

Ruzanski et al. (2011) report that their approach is computationally efficient, although the time taken to derive the motion vectors was comparable to that required for optical flow. Furthermore, the advection algorithm was an order of magnitude slower than a simple implementation of a semi-Lagrangian backward interpolation scheme. Both Ruzanski et al. (2011) and Fox and Wikle (2005) demonstrated their methods using small images. The size of the advection operator is likely to become a constraint when using this technique to

Panziera et al. (2011) provide a good introduction on the assumptions and use of analogues in nowcasting. Advection-based tracking techniques rely on the assumption that precipitation fields evolve relatively slowly in Lagrangian coordinates and, therefore, their future state can be predicted largely by extrapolation. The assumption of Lagrangian persistence becomes a major limitation on the accuracy of nowcasts in situations where a field evolves rapidly, for example in situations where new storms are initiated or existing storms grow or decay. Data mining and analogue techniques seek to predict initiation, growth and decay by matching the current weather pattern with similar, past events and

The first step in the use of analogues is to, either identify a set of regimes in the historical data, or identify a set of predictors that can be used as measures of similarity. Thereafter, the analogue that is closest to the current situation is selected and used as a basis for the forecast. This implies that the technique must be trained for each location, and that a significant historical record is available. Panziera et al. (2011) used predictors of mesoscale airflow and air-mass stability to select 120 analogues, and then employed two measures from the radar derived rainfall fields to select a set of 12 analogues to use as a forecast ensemble. Foresti and Pozdnoukhov (2011) derived maps of where orographic enhancement was likely to occur for a set of weather types. These could then be used to correct biases in

Sources of forecast errors include errors in the initial quantitative precipitation estimates (QPE), those arising from incorrect diagnosis of the field of motion, and changes in the

Approximately half of the total forecast error in the first hour of a forecast is due to errors in the radar derived rainfall analyses (Bellon & Austin, 1984; Fabry & Seed, 2009). This is because radar rainfall estimation errors, arising from variations in the relationship employed to convert the observed radar reflectivity to rainfall, have significant correlations over about an hour in time (Lee et al., 2007) and tens of kilometres in space (Velasco-Forero

motion and evolution of precipitation fields during the forecast period.

then use these past events as the basis for generating a forecast.

operator.

**4.1.3 Analogues**

advection forecasts.

**4.2 Errors in precipitation nowcasts 4.2.1 Error sources and attribution**

et al., 2009; Yeung et al., 2011).

advect a large (say 106 pixels) image.

Dance et al. (2010) used a year of TITAN tracks to investigate how cell tracking errors varied as a function of lead time, storm intensity, speed and duration. They found that the RMS errors in track speed and direction over the year were about 10 km/h and 30° respectively. Dance et al. (2010) found that tracking errors (both speed and direction) were large when the track speeds were less than 15 km/h; also, errors in track direction decreased with increasing speed.

Mecklenburg et al. (2000) investigated the tracking errors for TREC and COTREC and found that the mean absolute displacement and direction error for a 30-min forecast of convection was about 10 km and 20° respectively. Ebert et al. (2004) showed that TITAN cell tracking algorithms had a median error of about 10 km/h for intense cells. The median tracking error for the baseline field tracking algorithm – a correlation technique finding a single advection vector for the entire field of convective storms – was found to be 20 km/h using the same data.

Hourly accumulations of rainfall typically have correlation lengths of the order of 10 km (e.g. Anagnostou et al., 1999; Gebremichael & Krajewski, 2004). The tracking error after an hour is at least the same order of magnitude as the correlation length of the accumulations. Therefore, one would expect that enhancements to the current tracking algorithms should lead to improvements in the skill of nowcasts at lead times when tracking errors become a significant fraction of the correlation length of the rainfall field: this is likely to be around T + 30 minutes.

Berenguer et al. (2005) found that the temporal evolution of the advection field was not a significant source of error for nowcasts with lead times less than 60 minutes. Bowler et al. (2006) discovered that forecast errors due to the temporal evolution of the advection field were negligible in the first three hours of a nowcast and accounted for 10% of the total error after six hours.

It is interesting to note that the probability distribution of cell tracking errors is highly skewed (see, for example, Figure 20 of Ebert et al., 2004) and that the maximum 60 minute location error can be as high as 70 km. The fat tail in the distribution of tracking errors is a significant issue for operational nowcasting systems. Manual editing of the tracks (e.g. Bally, 2004) adds value to the automatic forecasts by eliminating the tracks that are regarded by the forecasters as being, either unimportant from a severe weather perspective, or incorrect.

Errors due to the initiation and decay of storms during the forecast period become increasingly dominant as the lead time extends beyond 60 minutes (Wilson et al., 2010). Zawadzki et al. (1994) evaluated the limits of predictability of rainfall fields as a function of space and time and found that the time for a Lagrangian persistence forecast to reach a correlation of 0.5 ranged from 40 to 112 minutes. They also found that these predictability times depended on the scales present in the rainfall field.

#### **4.2.2 Space-time structure of errors and their treatment**

Roca-Sancho et al. (2009) examined the spatial and temporal structure of forecast errors for MAPLE. They demonstrated that the temporal correlation of forecasts errors was very low after 60 minutes and that the spatial structure of the forecast errors progressively resembled that of rainfall with increasing lead time. The latter effect was due to increasing

Nowcasting 109

In summary then, the accuracy of a nowcast depends on the accuracy of the initial radar derived rainfall field, the degree of spatial organization of the rain, the rain rate, and forecast lead time. Also, it is likely to be higher in the middle of the storm (in both space and

Given the magnitude of the errors in a 30 minute precipitation nowcast, it is reasonable to adopt a probabilistic approach to nowcasting and attempt to convey to the users the uncertainty that is associated with a particular weather situation. As explained earlier, within the extrapolation nowcast framework, errors can be categorized into those attributable to the radar observations and processing, inaccuracies in the field of motion used to advect the observations, and errors arising from assumptions made about the

A number of techniques have been developed for modelling nowcast errors with a view to producing probabilistic precipitation nowcast products. One of the simplest entails timelagging a consecutive series of deterministic nowcasts using techniques similar to those demonstrated in a NWP post-processing context (Mittermaier, 2007). Each member of the time-lagged ensemble is assigned a weight which is a function of lead time. SWIRLS

Another approach relies on the assumption that errors in the diagnosed advection velocity field predominate. Consequently, the probability of exceeding a chosen precipitation threshold at a given location can be derived from the distribution of precipitation in a neighbourhood surrounding the forecast location. The neighbourhood size increases with lead time to reflect to the growth in advection errors (Andersson & Ivarsson, 1991; Schmid et

Germann and Zawadzki (2004) compared four methods of generating probabilistic precipitation nowcasts based upon radar extrapolation. They concluded that the most skilful method was one based upon the local Lagrangian technique. Essentially, this produces an advection forecast using a semi-Lagrangian backward advection scheme and then uses the probability distribution of forecast rain rates in some search area centred on a pixel to calculate the probability of exceeding a threshold at that location. The size of the search area increases with lead time to reflect the increasing forecast uncertainty. This approach has since been exploited by others, for example Megenhardt et al*.* (2004), and more recently,

Other authors have focused their attentions on modelling errors using stochastic space-time models. Pegram and Clothier (2001) used a power law model to filter Gaussian distributed random numbers to generate stochastic realizations of radar precipitation fields in their String of Beads model (SBM). Noise generation techniques similar to these were combined with a stochastic model of extrapolation velocity errors in the Short Term Ensemble Prediction System (STEPS; Bowler et al., 2006) to produce operational precipitation nowcasts

time) than at the edges.

**4.3.1 Justification**

al., 2000).

Kober et al. (2011).

**4.3 Probabilistic techniques**

Lagrangian evolution of the advected precipitation field.

generates probabilistic nowcasts using this approach (Wang et al., 2009).

**4.3.2 Methods of handling uncertainties**

errors in the location of rainfall. Fabry and Seed (2009) showed that forecasts of high rain rates were generally over-predictions and that the performance of advection forecasts in the recent past is not a good predictor of future performance. The best predictors were found to be raining fraction and the rate of change in mean areal precipitation over the forecast domain.

Germann and Zawadzki (2002) demonstrated that filtering the rainfall analysis field with a 64 km, low-pass filter increased Lagrangian life times by between 40 and 60 minutes, depending on the extent to which small scale features are embedded in larger-scale rain areas. Germann et al. (2006) state that the upper bound for an advection-based nowcasting system that does not include growth and dissipation of rainfall is about six hours. The typical lifetime of a storm is closely related to the scale of the storm, and is often represented as a power law of the scale (e.g. Marsan et al., 1996; Schertzer et al., 1997; Seed et al., 1999). Therefore, some nowcasting systems improve the accuracy (in the RMS error sense) of their predictions by progressively smoothing out the small scale features present in the analysis field (e.g. Seed, 2003; Turner et al., 2004). This removes features from the nowcast that are essentially unpredictable.

An alternative way of handling the perishability of the fine scale components in advected precipitation fields is to model them stochastically. This approach will be discussed in section 4.3.

#### **4.2.3 Performance of nowcasting algorithms**

The Critical Success Index is often used to report the accuracy of nowcasting algorithms presented in the literature. Ruzanski et al. (2011) found a CSI of approximately 0.5 after 10 minutes at a spatial resolution of 0.5 km. Liang et al. (2010) calculated a CSI of approximately 0.35 after 60 minutes for echoes in the 15-45 dBZ range at 2 km resolution. Berenguer et al. (2011) report a CSI for 60 minute forecasts of reflectivity (dBZ) at 1 km resolution of approximately 0.5 for widespread rainfall, and in the range of 0.1 to 0.3 for isolated convection. Poli et al. (2008) discovered that the CSI was generally low at the start and end of a storm, reaching a peak of around 0.4 for 1 km resolution T+60 minute forecasts of reflectivity greater than 30 dBZ.

Nine nowcasting systems were implemented for the Sydney 2000 Forecast Demonstration Project (Ebert et al., 2004) and eight nowcasting systems participated in the Beijing 2008 Olympics' Forecast Demonstration Project (Wang et al., 2009; Wilson et al., 2010). Wang et al. (2009) demonstrated that the overall performance of the nowcasting systems had improved during the years from 2000 to 2008. They showed that the maximum CSI for forecasts of hourly precipitation accumulation greater than 1 mm/h increased from 0.2 in 2000 to 0.45 in 2008, although the maximum CSI for rain greater than 10 mm/h was still only 0.15.

Lee et al. (2009) found that the CSI decreased with increasing rain rate and forecast lead time: the CSI for 60 minute rainfall forecasts decreased from 0.60 for 0.1 mm/h to 0.2 for 10 mm/h rain rates. Ebert et al. (2004) reported that the CSI for rain greater than 20 mm/h is essentially zero. This implies that the use of nowcasting techniques to predict the precise location of extreme rain for flash flood warning may not be viable.

Doppler Radar Observations – 108 Weather Radar, Wind Profiler, Ionospheric Radar, and Other Advanced Applications

errors in the location of rainfall. Fabry and Seed (2009) showed that forecasts of high rain rates were generally over-predictions and that the performance of advection forecasts in the recent past is not a good predictor of future performance. The best predictors were found to be raining fraction and the rate of change in mean areal precipitation over the

Germann and Zawadzki (2002) demonstrated that filtering the rainfall analysis field with a 64 km, low-pass filter increased Lagrangian life times by between 40 and 60 minutes, depending on the extent to which small scale features are embedded in larger-scale rain areas. Germann et al. (2006) state that the upper bound for an advection-based nowcasting system that does not include growth and dissipation of rainfall is about six hours. The typical lifetime of a storm is closely related to the scale of the storm, and is often represented as a power law of the scale (e.g. Marsan et al., 1996; Schertzer et al., 1997; Seed et al., 1999). Therefore, some nowcasting systems improve the accuracy (in the RMS error sense) of their predictions by progressively smoothing out the small scale features present in the analysis field (e.g. Seed, 2003; Turner et al., 2004). This removes features from the nowcast that are

An alternative way of handling the perishability of the fine scale components in advected precipitation fields is to model them stochastically. This approach will be discussed in

The Critical Success Index is often used to report the accuracy of nowcasting algorithms presented in the literature. Ruzanski et al. (2011) found a CSI of approximately 0.5 after 10 minutes at a spatial resolution of 0.5 km. Liang et al. (2010) calculated a CSI of approximately 0.35 after 60 minutes for echoes in the 15-45 dBZ range at 2 km resolution. Berenguer et al. (2011) report a CSI for 60 minute forecasts of reflectivity (dBZ) at 1 km resolution of approximately 0.5 for widespread rainfall, and in the range of 0.1 to 0.3 for isolated convection. Poli et al. (2008) discovered that the CSI was generally low at the start and end of a storm, reaching a peak of around 0.4 for 1 km resolution T+60 minute forecasts

Nine nowcasting systems were implemented for the Sydney 2000 Forecast Demonstration Project (Ebert et al., 2004) and eight nowcasting systems participated in the Beijing 2008 Olympics' Forecast Demonstration Project (Wang et al., 2009; Wilson et al., 2010). Wang et al. (2009) demonstrated that the overall performance of the nowcasting systems had improved during the years from 2000 to 2008. They showed that the maximum CSI for forecasts of hourly precipitation accumulation greater than 1 mm/h increased from 0.2 in 2000 to 0.45 in 2008, although the maximum CSI for rain greater than 10 mm/h was still

Lee et al. (2009) found that the CSI decreased with increasing rain rate and forecast lead time: the CSI for 60 minute rainfall forecasts decreased from 0.60 for 0.1 mm/h to 0.2 for 10 mm/h rain rates. Ebert et al. (2004) reported that the CSI for rain greater than 20 mm/h is essentially zero. This implies that the use of nowcasting techniques to predict the precise

location of extreme rain for flash flood warning may not be viable.

forecast domain.

essentially unpredictable.

**4.2.3 Performance of nowcasting algorithms**

of reflectivity greater than 30 dBZ.

section 4.3.

only 0.15.

#### **4.3 Probabilistic techniques**

#### **4.3.1 Justification**

Given the magnitude of the errors in a 30 minute precipitation nowcast, it is reasonable to adopt a probabilistic approach to nowcasting and attempt to convey to the users the uncertainty that is associated with a particular weather situation. As explained earlier, within the extrapolation nowcast framework, errors can be categorized into those attributable to the radar observations and processing, inaccuracies in the field of motion used to advect the observations, and errors arising from assumptions made about the Lagrangian evolution of the advected precipitation field.

#### **4.3.2 Methods of handling uncertainties**

A number of techniques have been developed for modelling nowcast errors with a view to producing probabilistic precipitation nowcast products. One of the simplest entails timelagging a consecutive series of deterministic nowcasts using techniques similar to those demonstrated in a NWP post-processing context (Mittermaier, 2007). Each member of the time-lagged ensemble is assigned a weight which is a function of lead time. SWIRLS generates probabilistic nowcasts using this approach (Wang et al., 2009).

Another approach relies on the assumption that errors in the diagnosed advection velocity field predominate. Consequently, the probability of exceeding a chosen precipitation threshold at a given location can be derived from the distribution of precipitation in a neighbourhood surrounding the forecast location. The neighbourhood size increases with lead time to reflect to the growth in advection errors (Andersson & Ivarsson, 1991; Schmid et al., 2000).

Germann and Zawadzki (2004) compared four methods of generating probabilistic precipitation nowcasts based upon radar extrapolation. They concluded that the most skilful method was one based upon the local Lagrangian technique. Essentially, this produces an advection forecast using a semi-Lagrangian backward advection scheme and then uses the probability distribution of forecast rain rates in some search area centred on a pixel to calculate the probability of exceeding a threshold at that location. The size of the search area increases with lead time to reflect the increasing forecast uncertainty. This approach has since been exploited by others, for example Megenhardt et al*.* (2004), and more recently, Kober et al. (2011).

Other authors have focused their attentions on modelling errors using stochastic space-time models. Pegram and Clothier (2001) used a power law model to filter Gaussian distributed random numbers to generate stochastic realizations of radar precipitation fields in their String of Beads model (SBM). Noise generation techniques similar to these were combined with a stochastic model of extrapolation velocity errors in the Short Term Ensemble Prediction System (STEPS; Bowler et al., 2006) to produce operational precipitation nowcasts

Nowcasting 111

orographic rainfall and predictors describing mesoscale flow and air mass stability, to identify past events with predictors similar to those derived from real time observations. The authors present verification results showing that NORA performs better than Eulerian

In the past few years, increasing availability of high powered computers and the implementation of non-hydrostatic models have made NWP at the convective scales (1 km– 4 km horizontal grid length) a reality for national weather services. Many centres are already running these models operationally with update cycles of between 3 and 6 hours to generate short-range forecasts up to about T+36 hours. Traditionally, these forecasts have been deployed in combination with nowcasting techniques to deliver optimal guidance. However, recently, centres have begun to explore the use of NWP-based systems for

For nowcasting purposes, the key component of NWP is the data assimilation of high resolution observations in space and time, especially radar and geostationary satellite data. Traditional nowcasting techniques use these observations to produce forecasts of rain, cloud and associated weather with observation derived advection velocities, or NWP forecast wind fields, or a combination of both. Nowcasts are also produced from analyses of other weather elements including screen temperature, visibility, 10 m wind and wind gusts. However, these systems do not use the observations in an optimal manner and may not use

Data assimilation into NWP models potentially offers the ability to use all observations in a consistent and synergistic manner to provide the best estimate of the state of the atmosphere from which to produce a nowcast. At this time, nudging, variational data assimilation (3D-Var and 4D-Var) and ensemble Kalman filters (EnKF; Sun, 2005b) for high resolution data assimilation are being used in weather services or are under development in research centres around the world. Indeed, some national weather services are already running operational NWP models with data assimilation at grid lengths in the range 1 km-10 km. Most of this work relies heavily on the exploitation of Doppler radar measured radial winds

One challenge for NWP-based nowcasting is to match the skill of traditional methods in the first two hours. Traditional nowcasts closely fit the observations because they employ extrapolation techniques and so use the observations themselves (i.e. radar derived surface rain rate) at analysis time. This is challenging for NWP because unresolved scales are excluded from the model state, data assimilation systems are designed, not to match observations, but to achieve a good and balanced forecast over a longer period of time, and the T+0 fields from the NWP system are essentially a weighted fit to both the NWP forecast

persistence for nowcasts with lead times of more than an hour.

**5. NWP-based nowcasting**

**5.1 Introduction**

nowcasting.

**5.2 The challenges**

all available observation types.

and the observations.

and reflectivity data or derived surface rain rates.

quantifying uncertainties in phase as well as amplitude. In STEPS, the noise serves several purposes: it enables ensembles of equally likely nowcast solutions to be generated by perturbing predicted features as they lose skill; it also downscales an NWP forecast, injecting variance at scales lacking power (variance) relative to the radar.

#### **4.3.3 Treatment of observation errors**

Uncertainties in nowcasts of precipitation also derive from errors in the radar observations and processing. Austin (1987) categorized radar errors into physical biases, measurement biases and random sampling errors. Historically, much effort has been invested in improving deterministic estimates of precipitation accumulation at the surface by correcting physical (e.g. ground clutter and beam blockage) and measurement (e.g. Z-R conversion) biases. However, more recently, a growing number of researchers have focused their attentions on the treatment of random sampling errors and how these can be utilized within stochastic, integrated system frameworks to improve hydro-meteorological nowcasting.

Two main approaches to the modelling of random sampling errors in QPE have been described in the literature: one entails a statistical description of the difference between the radar estimates and a reference (e.g. Ciach et al., 2007; Llort et al., 2008; Germann et al., 2009); a second involves modelling the characteristics of individual sources of error (e.g. Jordan et al., 2003; Lee & Zawadzki, 2005a, 2005b, 2006; Lee, 2006; Lee et al., 2007). The challenge with the first approach is the need for a reference field: this is usually derived from a dense network of rain gauges. The difficulty with the second approach is that the true error structure of QPEs can vary significantly depending on the meteorological conditions and is therefore largely unknowable.

Germann et al. (2009) describe a radar ensemble generator using LU decomposition (factorization) of the radar-gauge error covariance matrix to derive an ensemble of precipitation fields. Each ensemble member is the sum of the bias corrected, deterministically derived radar precipitation field and a stochastic perturbation representing the random error. The stochastic term is generated such that it preserves the correct space– time error covariances. The authors present the results of the coupling of a real-time implementation of the radar ensemble generator with a semi-distributed hydrological model.

Norman et *al*. (2010) implemented several radar ensemble generators and compared their performance on a selection of case study events using rain gauges. An implementation of the Germann et al. (2009) scheme was found to be marginally superior to one comprising separate models of Z-R (Lee et al., 2007) and VPR (Jordan et al., 2003) errors. Pierce et al. (2011) integrated these two ensemble generators to produce ensembles of radar-based analyses of surface precipitation rate for input to STEPS. They evaluated the impact of these ensembles on the performance of STEPS ensemble precipitation nowcasts. Verification results demonstrated that accounting for QPE errors improved the ensemble spread-skill relationship in the first hour of the nowcasts.

One alternative to the stochastic QPE and QPN schemes described above is the use of historical analogues. Panziera et al. (2011) describe an analogue-based heuristic tool for nowcasting orographically forced precipitation. The system known as Nowcasting of Orographic Rainfall by means of Analogues, exploits the strong correlation between Doppler Radar Observations – 110 Weather Radar, Wind Profiler, Ionospheric Radar, and Other Advanced Applications

quantifying uncertainties in phase as well as amplitude. In STEPS, the noise serves several purposes: it enables ensembles of equally likely nowcast solutions to be generated by perturbing predicted features as they lose skill; it also downscales an NWP forecast,

Uncertainties in nowcasts of precipitation also derive from errors in the radar observations and processing. Austin (1987) categorized radar errors into physical biases, measurement biases and random sampling errors. Historically, much effort has been invested in improving deterministic estimates of precipitation accumulation at the surface by correcting physical (e.g. ground clutter and beam blockage) and measurement (e.g. Z-R conversion) biases. However, more recently, a growing number of researchers have focused their attentions on the treatment of random sampling errors and how these can be utilized within stochastic, integrated system frameworks to improve hydro-meteorological nowcasting.

Two main approaches to the modelling of random sampling errors in QPE have been described in the literature: one entails a statistical description of the difference between the radar estimates and a reference (e.g. Ciach et al., 2007; Llort et al., 2008; Germann et al., 2009); a second involves modelling the characteristics of individual sources of error (e.g. Jordan et al., 2003; Lee & Zawadzki, 2005a, 2005b, 2006; Lee, 2006; Lee et al., 2007). The challenge with the first approach is the need for a reference field: this is usually derived from a dense network of rain gauges. The difficulty with the second approach is that the true error structure of QPEs can vary significantly depending on the meteorological

Germann et al. (2009) describe a radar ensemble generator using LU decomposition (factorization) of the radar-gauge error covariance matrix to derive an ensemble of precipitation fields. Each ensemble member is the sum of the bias corrected, deterministically derived radar precipitation field and a stochastic perturbation representing the random error. The stochastic term is generated such that it preserves the correct space– time error covariances. The authors present the results of the coupling of a real-time implementation of the radar ensemble generator with a semi-distributed hydrological

Norman et *al*. (2010) implemented several radar ensemble generators and compared their performance on a selection of case study events using rain gauges. An implementation of the Germann et al. (2009) scheme was found to be marginally superior to one comprising separate models of Z-R (Lee et al., 2007) and VPR (Jordan et al., 2003) errors. Pierce et al. (2011) integrated these two ensemble generators to produce ensembles of radar-based analyses of surface precipitation rate for input to STEPS. They evaluated the impact of these ensembles on the performance of STEPS ensemble precipitation nowcasts. Verification results demonstrated that accounting for QPE errors improved the ensemble spread-skill

One alternative to the stochastic QPE and QPN schemes described above is the use of historical analogues. Panziera et al. (2011) describe an analogue-based heuristic tool for nowcasting orographically forced precipitation. The system known as Nowcasting of Orographic Rainfall by means of Analogues, exploits the strong correlation between

injecting variance at scales lacking power (variance) relative to the radar.

**4.3.3 Treatment of observation errors**

conditions and is therefore largely unknowable.

relationship in the first hour of the nowcasts.

model.

orographic rainfall and predictors describing mesoscale flow and air mass stability, to identify past events with predictors similar to those derived from real time observations. The authors present verification results showing that NORA performs better than Eulerian persistence for nowcasts with lead times of more than an hour.

#### **5. NWP-based nowcasting**

#### **5.1 Introduction**

In the past few years, increasing availability of high powered computers and the implementation of non-hydrostatic models have made NWP at the convective scales (1 km– 4 km horizontal grid length) a reality for national weather services. Many centres are already running these models operationally with update cycles of between 3 and 6 hours to generate short-range forecasts up to about T+36 hours. Traditionally, these forecasts have been deployed in combination with nowcasting techniques to deliver optimal guidance. However, recently, centres have begun to explore the use of NWP-based systems for nowcasting.

#### **5.2 The challenges**

For nowcasting purposes, the key component of NWP is the data assimilation of high resolution observations in space and time, especially radar and geostationary satellite data. Traditional nowcasting techniques use these observations to produce forecasts of rain, cloud and associated weather with observation derived advection velocities, or NWP forecast wind fields, or a combination of both. Nowcasts are also produced from analyses of other weather elements including screen temperature, visibility, 10 m wind and wind gusts. However, these systems do not use the observations in an optimal manner and may not use all available observation types.

Data assimilation into NWP models potentially offers the ability to use all observations in a consistent and synergistic manner to provide the best estimate of the state of the atmosphere from which to produce a nowcast. At this time, nudging, variational data assimilation (3D-Var and 4D-Var) and ensemble Kalman filters (EnKF; Sun, 2005b) for high resolution data assimilation are being used in weather services or are under development in research centres around the world. Indeed, some national weather services are already running operational NWP models with data assimilation at grid lengths in the range 1 km-10 km. Most of this work relies heavily on the exploitation of Doppler radar measured radial winds and reflectivity data or derived surface rain rates.

One challenge for NWP-based nowcasting is to match the skill of traditional methods in the first two hours. Traditional nowcasts closely fit the observations because they employ extrapolation techniques and so use the observations themselves (i.e. radar derived surface rain rate) at analysis time. This is challenging for NWP because unresolved scales are excluded from the model state, data assimilation systems are designed, not to match observations, but to achieve a good and balanced forecast over a longer period of time, and the T+0 fields from the NWP system are essentially a weighted fit to both the NWP forecast and the observations.

Nowcasting 113

dedicated observations, and to run a 500 m grid length model in a configuration comparable with conventional nowcasts (Ludovic Auger, MeteoFrance, personal communication

DWD has a 2.8 km forecast model with a nudging assimilation scheme (Consortium for Small-scale Modeling, COSMO) covering Germany (COSMO-DE, Stephan et al., 2008) and is developing PP KENDA ( Priority Project "KENDA" – Km-scale Ensemble-based Data Assimilation) for a 1 km-3 km scale Ensemble Prediction System known as LETKF (Local Ensemble Transform Kalman Filter; Ott et al., 2004). MeteoSwiss is running a 2 km version of COSMO. Various collaborating meteorological services are running, or are planning to

The HIRLAM (HIgh Resolution Limited Area Model) European community run their 3D-Var system (Gustafsson et al., 2001) with the HIRLAM model at grid lengths down to about 3.3 km and are developing a new HARMONIE system to run at about 2.5 km. They have also run experiments comparing three hourly and hourly cycling at 11 km and are exploring the impact of GPS and Doppler radar radial wind data (Magnus Lindskog, HIRLAM personal communication WMO/WWRP Workshop on Use of NWP for Nowcasting,

In the USA, NCEP (National Center for Enviromental Prediction) has an operational RUC (Rapid Update Cycle) system with hourly data assimilation (Benjamin et al., 2004). As of September 2011, this was due to be replaced by the Rapid Refresh. The RR uses a version of the WRF model (currently v3.2+) and the Grid-point Statistical Interpolation (GSI) analysis largely developed at NCEP/EMC (Environmental Modelling Center, NOAA), using hourly cycling and a 13 km grid length. NCEP also run a 3 km model nested in the RR, but this has no separate data assimilation (Steve Weygandt et al., Earth System Research Lab, Boulder, personal communication WMO/WWRP Workshop on Use of NWP for Nowcasting,

In Japan, JMA (Japan Meteorological Agency) runs a Mesoscale Model (MSM) for Japan and its surrounding areas using a 5 km grid length and 4D-VAR with forecasts every 3 hours to 15 or 33 hours (Honda et al., 2005; Saito et al., 2006). This is a non-hydrostatic model (JMA-NHM). Development of NWP at a higher resolution (Local Forecast Model, LFM) is also in progress to help produce sophisticated disaster-prevention and aviation information

A trial operation of a 9-hour LFM forecast run on a 2 km grid length was performed in 2010 and 2011, and operational implementation is scheduled to start in 2012. LFM also uses JMA-NHM as a forecast model, and its initial condition is generated from a 3D-Var rapid update cycle. The cycle uses the MSM forecast as the first guess, and runs a JMA non-hydrostatic model-based variational data assimilation system (JNoVA) – a 3DVar (a degenerate version of JNoVA-4DVar) analysis and 1-hour JMA-NHM forecast in turn – over 3 hours using a

The Korean Meteorological Agency (KMA) is currently running the WRF 3D-Var (Barker et al., 2004, Xiao et al., 2008) at 10 km but is planning to use the 1.5 km, variable resolution Met Office Unified Model (UM) system with 3D-Var in the near future. In the past they have

tested a 3.3 km version of WRF 4D-Var (Huang et al., 2009).

WMO/WWRP Workshop on Use of NWP for Nowcasting, Boulder 2011).

Boulder 2011 and HIRLAM Newsletter No. 58, November 2011).

Boulder 2011, Stensrud et al., 2009; Smith et al., 2008; Weygandt et al., 2008).

run versions of these systems.

services.

5 km grid length.

Also, traditional nowcasts can produce forecasts within a few minutes of data time, but complex data assimilation methods and numerical integration of the governing atmospheric equations are more costly and therefore take longer. However, if these techniques produce improved forecasts at longer lead times, the benefits outweigh the timeliness issue and reduced accuracy in the first 2 hours.

Another performance issue with NWP-based nowcasts relates to the latency of the boundary conditions. This arises because domain sizes are usually small and are nested in coarser resolution forecasts or larger domain forecasts with less frequent analysis cycling and later data cut-off times. The consequences are that the boundary conditions and synoptic scale forcing cannot be refreshed as frequently or as recently on the larger domain(s) as they are on the nowcast inner domain. This limits the skill at longer forecast ranges and possibly close to the boundaries.

Nonetheless, the advantage of NWP-based nowcasting lies in the fact that model formulation, dynamical equations and physical parameterizations can predict the non-linear evolution of weather elements and, in particular, the generation and decay of precipitating weather systems.

#### **5.3 A status report**

To investigate the direct use of NWP for nowcasting, the Met Office in the UK is developing an hourly cycling 4D-Var high resolution (1.5 km) NWP system to run on a domain covering southern England (see section 5.4). This is nested within the most recent forecasts for the whole of the UK (1.5 km resolution forecasts produced every 6 hours from 3 hourly 3D-Var data assimilation cycles at 3 km resolution) to obtain boundary conditions. The latter may be up to 6 hours old. Although 4D-Var is more expensive than 3D-Var, the aim is to evaluate the benefit of assimilating high time-frequency sub-hourly data (Ballard et al., 2011): see section 5.4 for more details.

Over the past 20 years, NCAR has undertaken many studies to explore the assimilation of radar data into high resolution cloud and NWP forecast models. These have included using the Variational Doppler Radar Assimilation System (VDRAS – Sun, 2005a, 2005b; Sun & Crook, 1994, 1997, 1998, 2001; Sun & Zhang, 2008) with 4D-VAR (Sun et al., 1991, 2012). These tend to use very short time-windows and have exploited the mesoscale model, MM5 3D-Var (Xiao et al., 2005) and the Weather Research & Forecasting Model (WRF) 3D-Var and 4D-VAR, or ensemble Kalman filter (Caya et al., 2005). These were run using VDRAS as part of the forecast demonstration project during the Beijing Olympics (Sun et al., 2010).

Meteo-France has a 2.5 km, 3-hourly cycling 3D-Var scheme covering France (the Application of Research to Operations at Mesoscale – AROME-France). This has been operational since December 2008 (Seity et al., 2011; Brousseau et al., 2011). Radial Doppler winds (Montmerle & Faccani, 2009) and humidity profiles derived from radar reflectivity (Caumont et al., 2010) are assimilated. Meteo-France is also undertaking a project entitled, "AROME-Nowcasting", to adapt their 2.5 km grid length model, AROME, to meet the requirements of nowcasting. The main difference to AROME-France is the production of an analysis every hour, but without cycling. The potential benefits of a system called AROMEairport, based at Charles de Gaulle airport near Paris, are also being explored. This model will provide an input to a Wake-Vortex forecast model. The main goal is to add new, Doppler Radar Observations – 112 Weather Radar, Wind Profiler, Ionospheric Radar, and Other Advanced Applications

Also, traditional nowcasts can produce forecasts within a few minutes of data time, but complex data assimilation methods and numerical integration of the governing atmospheric equations are more costly and therefore take longer. However, if these techniques produce improved forecasts at longer lead times, the benefits outweigh the timeliness issue and

Another performance issue with NWP-based nowcasts relates to the latency of the boundary conditions. This arises because domain sizes are usually small and are nested in coarser resolution forecasts or larger domain forecasts with less frequent analysis cycling and later data cut-off times. The consequences are that the boundary conditions and synoptic scale forcing cannot be refreshed as frequently or as recently on the larger domain(s) as they are on the nowcast inner domain. This limits the skill at longer forecast ranges and possibly

Nonetheless, the advantage of NWP-based nowcasting lies in the fact that model formulation, dynamical equations and physical parameterizations can predict the non-linear evolution of weather elements and, in particular, the generation and decay of precipitating

To investigate the direct use of NWP for nowcasting, the Met Office in the UK is developing an hourly cycling 4D-Var high resolution (1.5 km) NWP system to run on a domain covering southern England (see section 5.4). This is nested within the most recent forecasts for the whole of the UK (1.5 km resolution forecasts produced every 6 hours from 3 hourly 3D-Var data assimilation cycles at 3 km resolution) to obtain boundary conditions. The latter may be up to 6 hours old. Although 4D-Var is more expensive than 3D-Var, the aim is to evaluate the benefit of assimilating high time-frequency sub-hourly data (Ballard et al., 2011): see

Over the past 20 years, NCAR has undertaken many studies to explore the assimilation of radar data into high resolution cloud and NWP forecast models. These have included using the Variational Doppler Radar Assimilation System (VDRAS – Sun, 2005a, 2005b; Sun & Crook, 1994, 1997, 1998, 2001; Sun & Zhang, 2008) with 4D-VAR (Sun et al., 1991, 2012). These tend to use very short time-windows and have exploited the mesoscale model, MM5 3D-Var (Xiao et al., 2005) and the Weather Research & Forecasting Model (WRF) 3D-Var and 4D-VAR, or ensemble Kalman filter (Caya et al., 2005). These were run using VDRAS as part

Meteo-France has a 2.5 km, 3-hourly cycling 3D-Var scheme covering France (the Application of Research to Operations at Mesoscale – AROME-France). This has been operational since December 2008 (Seity et al., 2011; Brousseau et al., 2011). Radial Doppler winds (Montmerle & Faccani, 2009) and humidity profiles derived from radar reflectivity (Caumont et al., 2010) are assimilated. Meteo-France is also undertaking a project entitled, "AROME-Nowcasting", to adapt their 2.5 km grid length model, AROME, to meet the requirements of nowcasting. The main difference to AROME-France is the production of an analysis every hour, but without cycling. The potential benefits of a system called AROMEairport, based at Charles de Gaulle airport near Paris, are also being explored. This model will provide an input to a Wake-Vortex forecast model. The main goal is to add new,

of the forecast demonstration project during the Beijing Olympics (Sun et al., 2010).

reduced accuracy in the first 2 hours.

close to the boundaries.

weather systems.

**5.3 A status report**

section 5.4 for more details.

dedicated observations, and to run a 500 m grid length model in a configuration comparable with conventional nowcasts (Ludovic Auger, MeteoFrance, personal communication WMO/WWRP Workshop on Use of NWP for Nowcasting, Boulder 2011).

DWD has a 2.8 km forecast model with a nudging assimilation scheme (Consortium for Small-scale Modeling, COSMO) covering Germany (COSMO-DE, Stephan et al., 2008) and is developing PP KENDA ( Priority Project "KENDA" – Km-scale Ensemble-based Data Assimilation) for a 1 km-3 km scale Ensemble Prediction System known as LETKF (Local Ensemble Transform Kalman Filter; Ott et al., 2004). MeteoSwiss is running a 2 km version of COSMO. Various collaborating meteorological services are running, or are planning to run versions of these systems.

The HIRLAM (HIgh Resolution Limited Area Model) European community run their 3D-Var system (Gustafsson et al., 2001) with the HIRLAM model at grid lengths down to about 3.3 km and are developing a new HARMONIE system to run at about 2.5 km. They have also run experiments comparing three hourly and hourly cycling at 11 km and are exploring the impact of GPS and Doppler radar radial wind data (Magnus Lindskog, HIRLAM personal communication WMO/WWRP Workshop on Use of NWP for Nowcasting, Boulder 2011 and HIRLAM Newsletter No. 58, November 2011).

In the USA, NCEP (National Center for Enviromental Prediction) has an operational RUC (Rapid Update Cycle) system with hourly data assimilation (Benjamin et al., 2004). As of September 2011, this was due to be replaced by the Rapid Refresh. The RR uses a version of the WRF model (currently v3.2+) and the Grid-point Statistical Interpolation (GSI) analysis largely developed at NCEP/EMC (Environmental Modelling Center, NOAA), using hourly cycling and a 13 km grid length. NCEP also run a 3 km model nested in the RR, but this has no separate data assimilation (Steve Weygandt et al., Earth System Research Lab, Boulder, personal communication WMO/WWRP Workshop on Use of NWP for Nowcasting, Boulder 2011, Stensrud et al., 2009; Smith et al., 2008; Weygandt et al., 2008).

In Japan, JMA (Japan Meteorological Agency) runs a Mesoscale Model (MSM) for Japan and its surrounding areas using a 5 km grid length and 4D-VAR with forecasts every 3 hours to 15 or 33 hours (Honda et al., 2005; Saito et al., 2006). This is a non-hydrostatic model (JMA-NHM). Development of NWP at a higher resolution (Local Forecast Model, LFM) is also in progress to help produce sophisticated disaster-prevention and aviation information services.

A trial operation of a 9-hour LFM forecast run on a 2 km grid length was performed in 2010 and 2011, and operational implementation is scheduled to start in 2012. LFM also uses JMA-NHM as a forecast model, and its initial condition is generated from a 3D-Var rapid update cycle. The cycle uses the MSM forecast as the first guess, and runs a JMA non-hydrostatic model-based variational data assimilation system (JNoVA) – a 3DVar (a degenerate version of JNoVA-4DVar) analysis and 1-hour JMA-NHM forecast in turn – over 3 hours using a 5 km grid length.

The Korean Meteorological Agency (KMA) is currently running the WRF 3D-Var (Barker et al., 2004, Xiao et al., 2008) at 10 km but is planning to use the 1.5 km, variable resolution Met Office Unified Model (UM) system with 3D-Var in the near future. In the past they have tested a 3.3 km version of WRF 4D-Var (Huang et al., 2009).

Nowcasting 115

with UK4 or UKV precipitation forecasts. An 8 member ensemble and control member (unperturbed) nowcast to T+7 h are produced every 15 minutes. Recent Root Mean Squared Factor error statistics for STEPS control member advection nowcasts and UK4 and UKV forecasts of precipitation have shown that STEPS nowcasts are superior in the first 2.5 hours

Fig. 3. Root Mean Squared Factor errors for November 2009 based on hourly accumulations

The implementation of an NWP-based nowcast system in the Met Office is focused on improving the prediction of convective storms for flood forecasting. The ultimate aim is to replace the existing extrapolation-based precipitation nowcasts and site specific forecasting techniques. Boundary conditions will be provided by the 6 hourly 1.5 km UKV system.

An hourly analysis and forecast system for southern England has been run experimentally for a limited number of cases of summer rain and convection, using conventional data and 3D-Var or 4D-Var, plus latent heat nudging of radar derived rain rates and humidity nudging based on analysed 3D cloud cover nudging (Macpherson et al*.,* 1996; Jones & Macpherson, 1997; Dixon et al., 2009). The direct variational assimilation of cloud cover has not yet been tested in the hourly cycling system. This has used a fixed 1.5 km resolution configuration of the Unified Model and a 3 km resolution 4D-Var grid or 1.5 km and 3 km

greater than 1mm, smoothed to a scale of 6km, and measured using radar derived accumulations as the reference observation. The performance of 1.5 km (green) and 4 km (red) grid length, UK configurations of the Met Office's Unified Model are compared with control member STEPS nowcasts blending radar extrapolation with UM: 1.5 km (purple) and UM: 4km (blue) model forecasts. The performance of the STEPS nowcast blending extrapolation with the UM: 1.5 km forecast does not asymptote to that of UM: 1.5 km model

because this was an experimental configuration run without prior calibration.

(see Figure 3).

resolution 3D-Var grid.

Over recent years, CAPS (Center for Analysis and Prediction of Storms, Oklahoma, USA) has been carrying out experimental, real time forecasting including the generation of 1 km grid length forecasts on a continental U.S. domain once a day, and the production of rapidly updated NWP-model-based nowcasts producing two hour, 1 km forecasts every 10 minutes (Xue et al., 2011; Kong et al., 2011; Clark et al., 2011; Brewster et al., 2010). These forecasts assimilate US operational WSR-88D radar data and/or high-resolution experimental X-band radar data, with and without assimilation cycles.

Comparison forecasts show systematically positive impacts of assimilating radar data on short-range precipitation forecasting, lasting up to 12 hours on average. To address forecast uncertainty and to provide probabilistic forecast information, storm-scale ensemble predictions have also been carried out and the products have been evaluated at an experimental forecasting facility. Extensive research has also been undertaken using ensemble-based data assimilation methods for initializing storm-scale NWP models, with very promising results.

CAPS has investigated a Mesoscale Convective System/vortex case study exploiting nested 400m /2 km grids and assimilating radar data at 5 min intervals using their Advanced Regional Prediction System (ARPS) 3DVAR+cloud analysis (Schenkman et al., 2011a) as well as EnKF (Snook et al., 2011). These show data impact on Collaborative Adaptive Sensing of the Atmosphere (CASA; Schenkman et al., 2011b) and probabilistic forecast skill with EnKF analyses (Snook et al., 2011; 2012).

Environment Canada has begun developing a convective-scale EnKF in order to examine the assimilation of radar data (e.g. over the Montreal region; Luc Fillion, personal communication WMO/WWRP Workshop on Use of NWP for Nowcasting, Boulder 2011). This is based on adaptation of the Global EnKF code available at Environment Canada (Houtekamer & Mitchell scheme) to a limited-area domain. The analysis step and the forecast model configuration (1 km horizontal grid length) are being validated.

#### **5.4 Development of an NWP-based nowcasting system in the UK**

#### **5.4.1 Progress to date**

The Met Office has run an operational, 4 km grid length NWP model for the UK (UK4) since December 2005. It has also run a 1.5 km UK configuration (UKV) routinely since summer 2010. Both models use three-hourly cycling 3D-Var and produce forecasts to 36 hours ahead with 70 levels. These are based on the Met Office's Unified Model (Davies et al*.,* 2005) and variational data assimilation system (Lorenc et al., 2000; Rawlins et al*.,* 2007), plus latent heat nudging (Macpherson et al*.,* 1996; Jones & Macpherson, 1997; Dixon et al., 2009). They also include direct variational assimilation of analysed 3D cloud cover via associated relative humidity. The UKV has a 1.5 km grid length over the UK and a 4km stretched boundary nested in the 12 km NAE (North Atlantic and European) model. The UKV uses 3 km 3D-VAR over the whole domain. Collaborations with KMA and CAWCR (Centre for Australian Weather and Climate Research, Australian Government Bureau of Meteorology) are aiming to implement 1.5 km versions of the UM with 3D-Var or 4D-Var.

The Met Office's UK Post-Processing system (UKPP) incorporates a STEPS precipitation nowcast (Bowler et al., 2006). This combines a stochastic, radar-based extrapolation nowcast Doppler Radar Observations – 114 Weather Radar, Wind Profiler, Ionospheric Radar, and Other Advanced Applications

Over recent years, CAPS (Center for Analysis and Prediction of Storms, Oklahoma, USA) has been carrying out experimental, real time forecasting including the generation of 1 km grid length forecasts on a continental U.S. domain once a day, and the production of rapidly updated NWP-model-based nowcasts producing two hour, 1 km forecasts every 10 minutes (Xue et al., 2011; Kong et al., 2011; Clark et al., 2011; Brewster et al., 2010). These forecasts assimilate US operational WSR-88D radar data and/or high-resolution experimental X-band

Comparison forecasts show systematically positive impacts of assimilating radar data on short-range precipitation forecasting, lasting up to 12 hours on average. To address forecast uncertainty and to provide probabilistic forecast information, storm-scale ensemble predictions have also been carried out and the products have been evaluated at an experimental forecasting facility. Extensive research has also been undertaken using ensemble-based data assimilation methods for initializing storm-scale NWP models, with

CAPS has investigated a Mesoscale Convective System/vortex case study exploiting nested 400m /2 km grids and assimilating radar data at 5 min intervals using their Advanced Regional Prediction System (ARPS) 3DVAR+cloud analysis (Schenkman et al., 2011a) as well as EnKF (Snook et al., 2011). These show data impact on Collaborative Adaptive Sensing of the Atmosphere (CASA; Schenkman et al., 2011b) and probabilistic forecast skill

Environment Canada has begun developing a convective-scale EnKF in order to examine the assimilation of radar data (e.g. over the Montreal region; Luc Fillion, personal communication WMO/WWRP Workshop on Use of NWP for Nowcasting, Boulder 2011). This is based on adaptation of the Global EnKF code available at Environment Canada (Houtekamer & Mitchell scheme) to a limited-area domain. The analysis step and the

The Met Office has run an operational, 4 km grid length NWP model for the UK (UK4) since December 2005. It has also run a 1.5 km UK configuration (UKV) routinely since summer 2010. Both models use three-hourly cycling 3D-Var and produce forecasts to 36 hours ahead with 70 levels. These are based on the Met Office's Unified Model (Davies et al*.,* 2005) and variational data assimilation system (Lorenc et al., 2000; Rawlins et al*.,* 2007), plus latent heat nudging (Macpherson et al*.,* 1996; Jones & Macpherson, 1997; Dixon et al., 2009). They also include direct variational assimilation of analysed 3D cloud cover via associated relative humidity. The UKV has a 1.5 km grid length over the UK and a 4km stretched boundary nested in the 12 km NAE (North Atlantic and European) model. The UKV uses 3 km 3D-VAR over the whole domain. Collaborations with KMA and CAWCR (Centre for Australian Weather and Climate Research, Australian Government Bureau of Meteorology)

The Met Office's UK Post-Processing system (UKPP) incorporates a STEPS precipitation nowcast (Bowler et al., 2006). This combines a stochastic, radar-based extrapolation nowcast

forecast model configuration (1 km horizontal grid length) are being validated.

are aiming to implement 1.5 km versions of the UM with 3D-Var or 4D-Var.

**5.4 Development of an NWP-based nowcasting system in the UK**

radar data, with and without assimilation cycles.

with EnKF analyses (Snook et al., 2011; 2012).

very promising results.

**5.4.1 Progress to date**

with UK4 or UKV precipitation forecasts. An 8 member ensemble and control member (unperturbed) nowcast to T+7 h are produced every 15 minutes. Recent Root Mean Squared Factor error statistics for STEPS control member advection nowcasts and UK4 and UKV forecasts of precipitation have shown that STEPS nowcasts are superior in the first 2.5 hours (see Figure 3).

Fig. 3. Root Mean Squared Factor errors for November 2009 based on hourly accumulations greater than 1mm, smoothed to a scale of 6km, and measured using radar derived accumulations as the reference observation. The performance of 1.5 km (green) and 4 km (red) grid length, UK configurations of the Met Office's Unified Model are compared with control member STEPS nowcasts blending radar extrapolation with UM: 1.5 km (purple) and UM: 4km (blue) model forecasts. The performance of the STEPS nowcast blending extrapolation with the UM: 1.5 km forecast does not asymptote to that of UM: 1.5 km model because this was an experimental configuration run without prior calibration.

The implementation of an NWP-based nowcast system in the Met Office is focused on improving the prediction of convective storms for flood forecasting. The ultimate aim is to replace the existing extrapolation-based precipitation nowcasts and site specific forecasting techniques. Boundary conditions will be provided by the 6 hourly 1.5 km UKV system.

An hourly analysis and forecast system for southern England has been run experimentally for a limited number of cases of summer rain and convection, using conventional data and 3D-Var or 4D-Var, plus latent heat nudging of radar derived rain rates and humidity nudging based on analysed 3D cloud cover nudging (Macpherson et al*.,* 1996; Jones & Macpherson, 1997; Dixon et al., 2009). The direct variational assimilation of cloud cover has not yet been tested in the hourly cycling system. This has used a fixed 1.5 km resolution configuration of the Unified Model and a 3 km resolution 4D-Var grid or 1.5 km and 3 km resolution 3D-Var grid.

Nowcasting 117

Figure 5 shows the evolution of radar derived surface rain rate between 1200 UTC and 2100 UTC on 3 June 2007. It is apparent that a rain band in the west over Ireland at 1200 UTC reduces in intensity and moves only slightly eastward during the following 9 hours. However, bands to the east develop from about 1700 UTC onwards and intensify. The STEPS nowcast from 1800 UTC has not been able to reproduce the development of the eastern-most rain band seen in Figure 5. Nowcasts starting from later analysis times contain

more precipitation but tend, incorrectly, to maintain the shape of individual features.

Fig. 5. Radar derived surface rain rates valid between 1200 UTC and 2100 UTC on 3 June 2007. The data for 1800 UTC, 1900 UTC, 2000 UTC and 2100 UTC were used to derive the T+3 h, T+2 h, T+1 h and T+0 h STEPS nowcasts shown in Fig. 4. The key shown below represents precipitation rate in units of mm/h. Note that dry areas are represented by the

Figure 6 compares T+1 hour, T+2 hour and T+3 hour 1.5 km NWP-based nowcasts of surface precipitation all valid at 2100 UTC on 3 June 2007 with radar derived precipitation rates for the same time. This model has used latent heat nudging of radar derived rain rates available every 15 minutes, and nudging of hourly humidity derived from 3-D cloud cover analyses in conjunction with hourly cycles of 4D-Var assimilation of conventional observations over 1 hour time windows. The NWP nowcasts improve at shorter lead times due to the benefit of data assimilation. In particular, they benefit from the latent heat nudging of surface precipitation rates derived from the sub-hourly radar data. In comparison with the STEPS nowcasts, the 1.5 km NWP nowcast has a better representation of the rain band in the east at both T+3 hours and T+2 hours. However, the representation of

colour white.

the rain in the south-west of England is inferior.

The ultimate aim is to use 4D-VAR, if affordable and beneficial, in a real-time, routinely running NWP-based nowcast system. This will exploit high resolution (in time and space) Doppler radar measured radial winds and reflectivity or derived surface rain rates directly within the variational analysis scheme. Direct use in 4D-VAR should allow optimum extraction of information through interaction with other data sources, and the potential to modify the dynamical and physical forcing of precipitation and convective storms.

Research is also proceeding to investigate the background errors, balances and control variables required for use in convective scale data assimilation. The aim is to have a realtime system running continuously from summer 2012 for southern England.

#### **5.4.2 A case study comparison of conventional and NWP-based nowcasts**

Figure 4 compares T+1 hour, T+2 hour and T+3 hour STEPS control member nowcasts of surface precipitation rate, all valid at 2100 UTC on 3 June 2007 with a radar-based analysis of surface precipitation rate for the same time. At T+3 h, the STEPS nowcast is a combination of an extrapolation nowcast and UK4 forecast precipitation. The UK4 forecast tends to produce individual convective precipitation elements that are too large. It also fails to predict the full extent of the bands of convective precipitation to the east of the precipitation area lying through south-west England and west Wales. At T+2 hours, the STEPS scheme has re-produced the line of convection in the east but this is too narrow, possibly due to convergence in the diagnosed advection velocity field. By T+1 h, a reasonable nowcast has been produced.

Fig. 4. STEPS T+0 h, T+1 h, T+2 h and T+3 h nowcasts of surface rain rate all valid at 2100 UTC on 3 June 2007. The key shown on the right-hand side represents precipitation rate in units of mm/h. Dry areas are shown in white. Note that dark blue areas in the STEPS nowcasts are not included in the colour key. These represent light drizzle.

Doppler Radar Observations – 116 Weather Radar, Wind Profiler, Ionospheric Radar, and Other Advanced Applications

The ultimate aim is to use 4D-VAR, if affordable and beneficial, in a real-time, routinely running NWP-based nowcast system. This will exploit high resolution (in time and space) Doppler radar measured radial winds and reflectivity or derived surface rain rates directly within the variational analysis scheme. Direct use in 4D-VAR should allow optimum extraction of information through interaction with other data sources, and the potential to

Research is also proceeding to investigate the background errors, balances and control variables required for use in convective scale data assimilation. The aim is to have a real-

Figure 4 compares T+1 hour, T+2 hour and T+3 hour STEPS control member nowcasts of surface precipitation rate, all valid at 2100 UTC on 3 June 2007 with a radar-based analysis of surface precipitation rate for the same time. At T+3 h, the STEPS nowcast is a combination of an extrapolation nowcast and UK4 forecast precipitation. The UK4 forecast tends to produce individual convective precipitation elements that are too large. It also fails to predict the full extent of the bands of convective precipitation to the east of the precipitation area lying through south-west England and west Wales. At T+2 hours, the STEPS scheme has re-produced the line of convection in the east but this is too narrow, possibly due to convergence in the diagnosed advection velocity field. By T+1 h, a reasonable nowcast has

Fig. 4. STEPS T+0 h, T+1 h, T+2 h and T+3 h nowcasts of surface rain rate all valid at 2100 UTC on 3 June 2007. The key shown on the right-hand side represents precipitation rate in units of mm/h. Dry areas are shown in white. Note that dark blue areas in the STEPS

nowcasts are not included in the colour key. These represent light drizzle.

modify the dynamical and physical forcing of precipitation and convective storms.

time system running continuously from summer 2012 for southern England.

been produced.

**5.4.2 A case study comparison of conventional and NWP-based nowcasts**

Figure 5 shows the evolution of radar derived surface rain rate between 1200 UTC and 2100 UTC on 3 June 2007. It is apparent that a rain band in the west over Ireland at 1200 UTC reduces in intensity and moves only slightly eastward during the following 9 hours. However, bands to the east develop from about 1700 UTC onwards and intensify. The STEPS nowcast from 1800 UTC has not been able to reproduce the development of the eastern-most rain band seen in Figure 5. Nowcasts starting from later analysis times contain more precipitation but tend, incorrectly, to maintain the shape of individual features.

Fig. 5. Radar derived surface rain rates valid between 1200 UTC and 2100 UTC on 3 June 2007. The data for 1800 UTC, 1900 UTC, 2000 UTC and 2100 UTC were used to derive the T+3 h, T+2 h, T+1 h and T+0 h STEPS nowcasts shown in Fig. 4. The key shown below represents precipitation rate in units of mm/h. Note that dry areas are represented by the colour white.

Figure 6 compares T+1 hour, T+2 hour and T+3 hour 1.5 km NWP-based nowcasts of surface precipitation all valid at 2100 UTC on 3 June 2007 with radar derived precipitation rates for the same time. This model has used latent heat nudging of radar derived rain rates available every 15 minutes, and nudging of hourly humidity derived from 3-D cloud cover analyses in conjunction with hourly cycles of 4D-Var assimilation of conventional observations over 1 hour time windows. The NWP nowcasts improve at shorter lead times due to the benefit of data assimilation. In particular, they benefit from the latent heat nudging of surface precipitation rates derived from the sub-hourly radar data. In comparison with the STEPS nowcasts, the 1.5 km NWP nowcast has a better representation of the rain band in the east at both T+3 hours and T+2 hours. However, the representation of the rain in the south-west of England is inferior.

Nowcasting 119

Potentially, weather radar provides a high resolution source of wind observations via the Doppler returns from hydro-meteors and insects. Currently, four weather radars in the south of England produce Doppler radial winds operationally every 5 minutes when there is precipitation (see Figure 7). The radars each perform scans at 5 elevations. The majority are at 1, 2, 4, 6 and 9 degrees, although one radar near London scans at 1, 2, 4, 5 and 5.5 degree elevations. Doppler winds are available to a range of about 100 km. This provides a small amount of dual or triple Doppler overlap in southern England as can be seen in

Fig. 7. A comparison of UK weather radar network coverage (left) and Doppler radar radial