Digital Rainfall Regression Model for Spatial Coverage Bias Correction
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Solution Overview
Problem
Current rainfall prediction methods, such as weather radars and rain gauges, face limitations in accuracy and spatial coverage, with radars being biased by latent variables and rain gauges providing localized but incomplete data, making it challenging to estimate rainfall across large areas effectively.
Innovation Solution
A computer-implemented method using a digital rainfall regression model that aggregates and transforms radar and rain-gauge data into covariate matrices to estimate adjusted rainfall values for new geo-locations, accounting for spatial variations and biases, thereby improving rainfall estimation accuracy and coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If weather radar is used for rainfall estimation, then spatial coverage is improved, but measurement precision deteriorates due to bias from latent variables
Solution Approach 1:
The patent uses rain gauge data as an intermediary to correct radar rainfall estimates. The rain gauge measurements serve as a mediator that provides ground truth data to adjust and calibrate the radar-derived rainfall values, thereby reducing the bias inherent in radar measurements while preserving the broad spatial coverage capability of radar systems.
Solution Approach 2:
The patent transforms the rainfall estimation by applying correction factors derived from rain gauge data to the radar measurements. This parameter change approach adjusts the radar rainfall estimates by modifying them with empirically derived correction coefficients, converting biased radar data into more accurate rainfall estimates without sacrificing spatial coverage.
2Measurement precision
If rain gauge is used for rainfall estimation, then measurement precision is improved, but spatial coverage deteriorates
Solution Approach 1:
The patent merges rain gauge data with radar data to create a hybrid rainfall estimation system. By combining the high-precision point measurements from rain gauges with the broad-area coverage of radar, the system achieves both accurate local measurements and comprehensive spatial coverage that neither instrument could provide alone.
Solution Approach 2:
The patent creates a multi-functional rainfall estimation system that can operate in multiple modes: using rain gauges for high-precision local measurements, using radar for broad spatial coverage, and combining both for optimal performance. This universal system adapts to different spatial and temporal requirements, making it versatile for various agricultural and meteorological applications.
3Measurement precision
If rain gauge data is collected from multiple locations, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent uses radar data as a copy or surrogate for ground truth rainfall measurements across areas where rain gauges are not present. The radar provides a spatially distributed copy of rainfall information that can be calibrated against rain gauge data, effectively extending the precision benefits of rain gauges to broader areas without requiring physical rain gauge installation at every location.
Data Source
AI summary
A method for estimating 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 transformed into one or more distribution sets. The distribution sets are then used to determine regression parameters for a digital rainfall regression model. The digital rainfall regression model then is used to estimate adjusted rainfall values for a new set of geo-locations. The server computer system then generates a digital image that includes the geo-locations and the adjusted rainfall values.