Spatiotemporal Interpolation for Radar Precipitation Estimates
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Solution Overview
Problem
Radar systems are unable to provide continuous measurements of rainfall, leading to incomplete or inaccurate precipitation estimates for rapidly changing or fast-moving storms, which can result in farmers making adverse decisions regarding crop management due to lack of data on rainfall distribution across large fields.
Innovation Solution
An agricultural intelligence computer system that uses spatiotemporal interpolation to generate precipitation estimates by correlating radar-based precipitation rate values across different locations and times, allowing for the computation of precipitation accumulation at unsampled locations and times, particularly for areas affected by fast-moving storms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If radar systems are used to measure rainfall, then precipitation data can be obtained over large areas, but continuous measurements cannot be provided due to discrete sampling intervals
Solution Approach 1:
The system performs preliminary tracking of storm cells by identifying and following their movement between radar samples. By predicting storm positions and using data from previous samples, the system prepares estimates for locations that will be affected by moving storms before the next radar measurement is taken.
Solution Approach 2:
The system creates copies of storm cell data and tracks them across multiple radar samples. By copying and following identified storm patterns through successive measurements, the system maintains continuous precipitation estimates even though the radar only samples discrete moments in time.
2Speed
If radar sampling interval is reduced to capture fast-moving storms, then measurement frequency increases, but the radar cannot distinguish signals from successive samples at lower intervals
Solution Approach 1:
The system introduces an intermediary tracking process that connects discrete radar samples. By using storm cell identification and trajectory prediction as intermediaries, the system bridges the gaps between radar measurements, allowing accurate tracking of fast-moving storms without requiring the radar to sample at intervals shorter than its capability.
Solution Approach 2:
The system dynamically adjusts the tracking approach based on storm characteristics. By identifying and following moving storm cells, the system adapts to varying storm speeds and patterns, maintaining measurement precision for fast-moving storms without requiring uniform high-frequency sampling across the entire radar coverage area.
3Ease of operation
If discrete radar samples are taken at fixed intervals, then device operation is simplified, but precipitation data is incomplete for locations affected by storms between samples
Solution Approach 1:
The system uses feedback from multiple radar samples to continuously refine precipitation estimates. By incorporating data from successive radar measurements and adjusting estimates based on observed storm movement and intensity changes, the system recovers information about precipitation events that occurred between samples without complicating radar operation.
Solution Approach 2:
The system adds a temporal dimension to the analysis by tracking storm cells across multiple time samples. By following storm trajectories through time and using interpolation between samples, the system recovers precipitation information for locations affected by storms between discrete measurements, maintaining simple fixed-interval radar operation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy of precipitation estimates by filling gaps in data collection, enabling farmers to make informed decisions about field management by providing more comprehensive and timely information on rainfall distribution.
Implementation Method 1
a polarized beam of energy is emitted from a radar device in a particular direction. The beam travels un-disturbed before encountering a volume of air containing hydrometeors, such as rainfall, snowfall, or hail, which causes the beam to scatter energy back to a radar receiver
Data Source
AI summary
A system and method for improving radar based precipitation estimates using spatiotemporal interpolation is provided. In an embodiment, an agricultural intelligence computer system receives a plurality of radar based precipitation rate values representing precipitation rate measurements at a plurality of locations and a plurality of times. The agricultural intelligence computer system identifies a first non-zero radar based precipitation rate value associated with a first location of the plurality of locations and a first time of the plurality of times. The agricultural intelligence computer also identifies a second non-zero radar based precipitation rate value associated with a second location of the plurality of locations and a second time of the plurality of times. The agricultural intelligence computer system determines that the first non-zero radar based precipitation rate value corresponds to the second non-zero radar based precipitation rate value. Based on the first non-zero radar based precipitation rate value and the second non-zero radar based precipitation rate value, the agricultural intelligence computer system computes a non-zero precipitation accumulation value at a third location and a third time.