Precipitation Estimate Quality Control Using Forecast Probability
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Solution Overview
Problem
Radar-based precipitation monitoring systems in areas with less advanced networks or lower gauge density often produce errors such as false identification or overidentification of precipitation, leading to inaccurate agricultural decisions and potential crop damage due to inadequate water application.
Innovation Solution
An agricultural intelligence computer system that combines radar-based precipitation estimates with forecast data to generate a probability of precipitation and correct high values at persistent clutter locations, ensuring accurate measurement by identifying locations with low probability of precipitation and reducing erroneous high readings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If radar-based precipitation monitoring is used in areas with less advanced networks or lower gauge density, then coverage area is expanded, but measurement precision deteriorates due to false identification or overidentification of precipitation
Solution Approach 1:
The system merges radar-based precipitation estimates with forecast data from multiple sources (including NOAA and private forecasters) to create a composite precipitation product. This combination allows the system to maintain expanded geographic coverage while improving measurement precision by cross-validating radar data with independent forecast models, thereby reducing false identification and overidentification errors in areas with lower gauge density.
2Device complexity
If radar systems with lower gauge density are used, then device complexity is reduced, but reliability deteriorates due to increased measurement errors
Solution Approach 1:
The system introduces forecast data as an intermediary layer between radar observations and final precipitation estimates. By using forecast models from multiple sources as mediators, the system can maintain simpler radar infrastructure with lower gauge density while improving reliability through the validating and correcting influence of independent forecast products that help distinguish true precipitation signals from radar errors.
3Productivity
If precipitation measurements are used for agricultural decisions, then productivity is improved through optimized water application, but loss of information occurs due to measurement errors leading to incorrect decisions
Solution Approach 1:
The system implements feedback mechanisms by continuously comparing radar-based precipitation estimates against forecast data from multiple independent sources. This feedback loop identifies and corrects measurement errors, false positives, and overidentification issues before the data is used for agricultural decision-making, ensuring that productivity improvements from optimized water application are not compromised by inaccurate precipitation information.
Data Source
AI summary
Systems and methods for improving the use of precipitation sensors, such as radar and rain gauges, are described herein. In an embodiment, an agricultural intelligence computer system receives one or more digital precipitation records comprising a plurality of digital data values representing precipitation amount at a plurality of locations. The system additionally receives one or more digital forecast records comprising a plurality of digital data values representing precipitation forecasts, each of which comprising predictions of precipitation at a plurality of lead times. The system identifies a plurality of forecast values for a plurality of locations at a particular time, each of the plurality of forecast values corresponding to a different lead time. The system uses the plurality of forecast values to generate a probability of precipitation at each of the plurality of locations. The system determines that the probability of precipitation at a particular location is lower than a stored threshold value and, in response, store data identifying the particular location as having received no precipitation.