A method for estimating confidence bounds for adjusted rainfall values for a set of geo-locations using agricultural data comprises using a server computer system that receives, via a network, agricultural data records that are used to estimate rainfall values for the set of geo-locations. Within the server computer system, rainfall calculation instructions receive digital data including observed radar and rain-gauge agricultural data records. The computer system then aggregates the agricultural data records and creates and stores the agricultural data sets. The agricultural data records are then used to estimate adjusted rainfall values for a set of geo-locations. Rainfall confidence bounds instructions estimate a set of confidence bounds for each of the adjusted rainfall values for the set of geo-locations. The set of confidence bounds provide a range for each of the adjusted rainfall values that represents a particular level of confidence associated with each of the adjusted rainfall values.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: using rainfall calculation instructions in a server computer system, receiving one or more agricultural data records that represent observed agricultural data points for specific geo-locations at a specific time, wherein the observed agricultural data points include at least one of observed radar data or rain gauge data that comprise a plurality of precipitation data values and that have been obtained from radar computers coupled to radars or a weather station computers coupled to rain gauges; using the rainfall calculation instructions, aggregating the one or more agricultural data records into one or more agricultural data sets, where each agricultural data set from the one or more agricultural data sets represents a single type of observed agricultural data; using rainfall estimation instructions in the server computer system, estimating a set of adjusted rainfall values for a set of geo-locations using the one or more agricultural data sets, wherein the set of adjusted rainfall values represent calculated rainfall value estimations based upon calculated adjust factors; using rainfall confidence bounds instructions, estimating a set of confidence bounds for the set of adjusted rainfall values, wherein the set of confidence bounds provides a range for each adjusted rainfall value in the set of adjusted rainfall values that represents a particular level of confidence for the set of adjusted rainfall values. 2. The method of claim 1, wherein estimating a set of adjusted rainfall values for a set of geo-locations using the one or more agricultural data sets further comprises: transforming the one or more agricultural data sets into one or more agricultural distribution sets, where the one or more agricultural distribution sets represent a normalized distribution of the one or more agricultural data sets; generating a covariate matrix from the one or more agricultural distribution sets and storing the covariate matrix in digital memory, by deriving at least some values within the covariate matrix from the one or more agricultural distribution sets; estimating the set of adjusted rainfall values for the set of geo-locations that correspond to the values within the covariate matrix using defined regression parameters from a digital rainfall regression model and previously stored adjusted rainfall data sets. 3. The method of claim 2, wherein estimating the set of adjusted rainfall values for the set of geo-locations that correspond to the values within the covariate matrix using defined regression parameters from a digital rainfall regression model and previously stored adjusted rainfall data sets comprises generating a joint distribution between the one or more agricultural distribution sets and the previously stored adjusted rainfall data sets. 4. The method of claim 3, wherein estimating a set of confidence bounds for the set of adjusted rainfall values further comprises: generating an inverse cumulative distribution based upon mean and variance values for the set of adjusted rainfall values from the joint distribution; determining a lower confidence bound as a 10th percentile of the inverse cumulative distribution; determining an upper confidence bound as a 90th percentile of the inverse cumulative distribution; compiling the set of confidence bounds for the set of adjusted rainfall values, wherein each confidence bounds in the set of confidence bounds includes a pair of bounds comprising the lower confidence bound and the upper confidence bound. 5. The method of claim 1, wherein estimating a set of adjusted rainfall values for a set of geo-locations using the one or more agricultural data sets further comprises: calculating a correction factor that describes correlation of values between agricultural data points that represent observed radar data and agricultural data points that represent rain gauge data; wherein the correction factor is an aggregate ratio calculated as a sum of the agricultural data points that represent rain gauge data from the one or more agricultural data sets divided by a sum of the agricultural data points that represent observed radar data from the one or more agricultural data sets; calculating the set of adjusted rainfall values for the set of geo-locations by multiplying agricultural data points that represent observed radar data for the set of geo-locations by the correction factor. 6. The method of claim 5, wherein estimating a set of confidence bounds for the set of adjusted rainfall values further comprises: calculating a set of subsampled correction factors from the one or more agricultural data sets, wherein each subsampled correction factor of the set of subsampled correction factors is calculated from a subset of the one or more agricultural data sets that represent agricultural data records received at over a specific time period; generating a correction factor distribution of the set of subsampled correction factors; generating an inverse cumulative distribution function based upon the correction factor distribution of the set of subsampled correction factors; determining a lower confidence bound as a 10th percentile of the inverse cumulative distribution function; determining an upper confidence bound as a 90th percentile of the inverse cumulative distribution function; compiling the set of confidence bounds for the set of adjusted rainfall values, wherein each confidence bounds in the set of confidence bounds includes a pair of bounds comprising the lower confidence bound and the upper confidence bound. 7. The method of claim 5, wherein estimating a set of confidence bounds for the set of adjusted rainfall values further comprises: calculating a set of correction factors based upon the one or more agricultural data sets, wherein each correction factor in the set of correction factors represents a specific geo-location within the one or more agricultural data sets and is a ratio calculated as rain gauge data at the specific geo-location divided by observed radar data at the specific geo-location; generating a correction factor distribution set based upon the set of correction factors; determining a lower confidence bound as a 10th percentile of the correction factor distribution set; determining an upper confidence bound as a 90th percentile of the correction factor distribution set; compiling the set of confidence bounds for the set of adjusted rainfall values, wherein each confidence bounds in the set of confidence bounds includes a pair of bounds comprising the lower confidence bound and the upper confidence bound. 8. The method of claim 1, wherein estimating a set of adjusted rainfall values for a set of geo-locations using the one or more agricultural data sets further comprises: generating a gamma distribution set based upon the one or more agricultural data sets, wherein the gamma distribution set comprises rain gauge-to-radar ratio values that are each calculated as rain gauge data divided by corresponding observed radar data for a specific geo-location at a specific time; calculating a set of gamma distribution parameters based upon the gamma distribution set; calculating the set of adjusted rainfall values for the set of geo-locations based upon the gamma distribution set and the set of gamma distribution parameters. 9. The method of claim 8, wherein estimating a set of confidence bounds for the set of adjusted rainfall values further comprises: generating a cumulative distribution based upon shape and scale parameters from the set of gamma distribution parameters; determining a set of marginal distributions for the set of geo-locations based upon the cumulative distribution; determining a lower confidence bound as a 10th percentile of the set of marginal distributions; determining an upper confidence bound as a 90th percentile of the set of marginal distributions; compiling the set of confidence bounds for the set of adjusted rainfall values, wherein each confidence bounds in the set of confidence bounds includes a pair of bounds comprising the lower confidence bound and the upper confidence bound. 10. The method of claim 1, further comprising: using confidence bound aggregation instructions in a server computer system, determining an aggregated confidence bounds for a rainfall estimate over a specific period of time by: calculating lower precipitation estimates by aggregating lower confidence bounds from the set of confidence bounds for each geo-location over the specific period of time; calculating upper precipitation estimates by aggregating upper confidence bounds from the set of confidence bounds for each geo-location over the specific period of time; using the confidence bound aggregation instructions, generating a set of aggregated confidence bounds comprising aggregated lower and aggregated upper confidence bounds for each geo-location in the set of geo-locations. 11. The method of claim 1, further comprising: using confidence bound aggregation instructions in a server computer system, determining a set of aggregated confidence bounds for a rainfall estimate over a specific period of time by determining a quadrature for specific sub-time intervals that make up the specific period of time for a specific geo-location in the set of geo-locations; wherein the quadrature is calculated as a square root of a sum of differences squared between a lower confidence bound and an upper confidence bound within the set of confidence bounds for each of the specific sub-time intervals. using the confidence bound aggregation instructions, generating a set of aggregated confidence bounds comprising aggregated confidence bounds for each geo-location in the set of geo-locations. 12. The method of claim 1, further comprising: causing displaying of the set of confidence bounds in a graphical display of the set of geo-locations, wherein the set of confidence bounds are displayed as rainfall data for the set of geo-locations. 13. The method of claim 1, further comprising: causing exporting of confidence bounds data to one or more agricultural equipment, wherein the confidence bounds data comprises formatted data of at least the set of confidence bounds for the set of geo-locations or instructions for updating agricultural tasks for the set of geo-locations based upon the set of confidence bounds. 14. A data processing system comprising: a memory; one or more processors coupled to the memory; rainfall calculation instructions stored in memory, executed by the one or more processors, and configured to cause the one or more processors to receive one or more agricultural data records that represent observed agricultural data points for specific geo-locations at a specific time, wherein the observed agricultural data points include at least one of observed radar data or rain gauge data that comprise a plurality of precipitation data values and that have been obtained from radar computers coupled to radars or a weather station computers coupled to rain gauges; the rainfall calculation instructions stored in memory, executed by the one or more processors, and configured to cause the one or more processors to aggregate the one or more agricultural data records into one or more agricultural data sets, where each agricultural data set from the one or more agricultural data sets represents a single type of observed agricultural data; rainfall estimation instructions stored in memory, executed by the one or more processors, and configured to cause the one or more processors to estimate a set of adjusted rainfall values for a set of geo-locations using the one or more agricultural data sets, wherein the set of adjusted rainfall values represent calculated rainfall value estimations based upon calculated adjust factors; rainfall confidence bounds instructions stored in memory, executed by the one or more processors, and configured to cause the one or more processors to estimate a set of confidence bounds for the set of adjusted rainfall values, wherein the set of confidence bounds provides a range for each adjusted rainfall value in the set of adjusted rainfall values that represents a particular level of confidence for the set of adjusted rainfall values. 15. The data processing system of claim 14, wherein the rainfall estimation instructions stored in memory, executed by the one or more processors, and configured to further cause the one or more processors to: transform the one or more agricultural data sets into one or more agricultural distribution sets, where the one or more agricultural distribution sets represent a normalized distribution of the one or more agricultural data sets; generate a covariate matrix from the one or more agricultural distribution sets and storing the covariate matrix in digital memory, by deriving at least some values within the covariate matrix from the one or more agricultural distribution sets; estimate the set of adjusted rainfall values for the set of geo-locations that correspond to the values within the covariate matrix using defined regression parameters from a digital rainfall regression model and previously stored adjusted rainfall data sets. 16. The data processing system of claim 15, wherein the rainfall estimation instructions stored in memory and configured to further cause the one or more processors to estimate the set of adjusted rainfall values for the set of geo-locations that correspond to the values within the covariate matrix using defined regression parameters from a digital rainfall regression model and previously stored adjusted rainfall data sets comprises instructions to cause the one or more processors to generate a joint distribution between the one or more agricultural distribution sets and the previously stored adjusted rainfall data sets. 17. The data processing system of claim 16, wherein the rainfall confidence bounds instructions stored in memory, executed by the one or more processors, and configured to further cause the one or more processors to: generate an inverse cumulative distribution based upon mean and variance values for the set of adjusted rainfall values from the joint distribution; determine a lower confidence bound as a 10th percentile of the inverse cumulative distribution; determine an upper confidence bound as a 90th percentile of the inverse cumulative distribution; compile the set of confidence bounds for the set of adjusted rainfall values, wherein each confidence bounds in the set of confidence bounds includes a pair of bounds comprising the lower confidence bound and the upper confidence bound. 18. The data processing system of claim 14, wherein the rainfall estimation instructions stored in memory, executed by the one or more processors, and configured to further cause the one or more processors to: calculate a correction factor that describes correlation of values between agricultural data points that represent observed radar data and agricultural data points that represent rain gauge data; wherein the correction factor is an aggregate ratio calculated as a sum of the agricultural data points that represent rain gauge data from the one or more agricultural data sets divided by a sum of the agricultural data points that represent observed radar data from the one or more agricultural data sets; calculate the set of adjusted rainfall values for the set of geo-locations by multiplying agricultural data points that represent observed radar data for the set of geo-locations by the correction factor. 19. The data processing system of claim 18, wherein the rainfall confidence bounds instructions stored in memory, executed by the one or more processors, and configured to further cause the one or more processors to: calculate a set of sub sampled correction factors from the one or more agricultural data sets, wherein each subsampled correction factor of the set of subsampled correction factors is calculated from a subset of the one or more agricultural data sets that represent agricultural data records received at over a specific time period; generate a correction factor distribution of the set of subsampled correction factors; generate an inverse cumulative distribution function based upon the correction factor distribution of the set of subsampled correction factors; determine a lower confidence bound as a 10th percentile of the inverse cumulative distribution function; determine an upper confidence bound as a 90th percentile of the inverse cumulative distribution function; compile the set of confidence bounds for the set of adjusted rainfall values, wherein each confidence bounds in the set of confidence bounds includes a pair of bounds comprising the lower confidence bound and the upper confidence bound. 20. The data processing system of claim 18, wherein the rainfall confidence bounds instructions stored in memory, executed by the one or more processors, and configured to further cause the one or more processors to: calculate a set of correction factors based upon the one or more agricultural data sets, wherein each correction factor in the set of correction factors represents a specific geo-location within the one or more agricultural data sets and is a ratio calculated as rain gauge data at the specific geo-location divided by observed radar data at the specific geo-location; generate a correction factor distribution set based upon the set of correction factors; determine a lower confidence bound as a 10th percentile of the correction factor distribution set; determine an upper confidence bound as a 90th percentile of the correction factor distribution set; compile the set of confidence bounds for the set of adjusted rainfall values, wherein each confidence bounds in the set of confidence bounds includes a pair of bounds comprising the lower confidence bound and the upper confidence bound. 21. The data processing system of claim 14, wherein the rainfall estimation instructions stored in memory, executed by the one or more processors, and configured to further cause the one or more processors to: generate a gamma distribution set based upon the one or more agricultural data sets, wherein the gamma distribution set comprises rain gauge-to-radar ratio values that are each calculated as rain gauge data divided by corresponding observed radar data for a specific geo-location at a specific time; calculate a set of gamma distribution parameters based upon the gamma distribution set; calculate the set of adjusted rainfall values for the set of geo-locations based upon the gamma distribution set and the set of gamma distribution parameters. 22. The data processing system of claim 21, wherein the rainfall confidence bounds instructions stored in memory, executed by the one or more processors, and configured to further cause the one or more processors to: generate a cumulative distribution based upon shape and scale parameters from the set of gamma distribution parameters; determine a set of marginal distributions for the set of geo-locations based upon the cumulative distribution; determine a lower confidence bound as a 10th percentile of the set of marginal distributions; determine an upper confidence bound as a 90th percentile of the set of marginal distributions; compile the set of confidence bounds for the set of adjusted rainfall values, wherein each confidence bounds in the set of confidence bounds includes a pair of bounds comprising the lower confidence bound and the upper confidence bound. 23. The data processing system of claim 14, further comprising confidence bounds aggregation instructions stored in memory, executed by the one or more processors, and configured to cause the one or more processors to: determine an aggregated confidence bounds for a rainfall estimate over a specific period of time by: calculating lower precipitation estimates by aggregating lower confidence bounds from the set of confidence bounds for each geo-location over the specific period of time; calculating upper precipitation estimates by aggregating upper confidence bounds from the set of confidence bounds for each geo-location over the specific period of time; generate a set of aggregated confidence bounds comprising aggregated lower and aggregated upper confidence bounds for each geo-location in the set of geo-locations. 24. The data processing system of claim 14, further comprising confidence bounds aggregation instructions stored in memory, executed by the one or more processors, and configured to cause the one or more processors to: determine a set of aggregated confidence bounds for a rainfall estimate over a specific period of time by determining a quadrature for specific sub-time intervals that make up the specific period of time for a specific geo-location in the set of geo-locations; wherein the quadrature is calculated as a square root of a sum of differences squared between a lower confidence bound and an upper confidence bound within the set of confidence bounds for each of the specific sub-time intervals. generate a set of aggregated confidence bounds comprising aggregated confidence bounds for each geo-location in the set of geo-locations. 25. The data processing system of claim 14, further comprising instructions stored in memory, executed by the one or more processors, and configured to cause the one or more processors to cause display of the set of confidence bounds in a graphical display of the set of geo-locations, wherein the set of confidence bounds are displayed as rainfall data for the set of geo-locations. 26. The data processing system of claim 14, further comprising instructions stored in memory, executed by the one or more processors, and configured to cause the one or more processors to cause export of confidence bounds data to one or more agricultural equipment, wherein the confidence bounds data comprises formatted data of at least the set of confidence bounds for the set of geo-locations or instructions for updating agricultural tasks for the set of geo-locations based upon the set of confidence bounds.