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Beyond the Pixel: Using Patterns and Multiscale Spatial Information to Improve the Retrieval of Precipitation from
Clément Guilloteau1, Efi Foufoula-Georgiou2
1Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California.
Journal of Atmospheric and Oceanic Technology
|June 23, 2021
Summary
This study introduces a new method for estimating precipitation using satellite microwave data. By analyzing spatial patterns in brightness temperatures, retrieval accuracy is improved, reducing errors by up to 11%.
Area of Science:
- Earth Science
- Atmospheric Science
- Remote Sensing
Background:
- Passive microwave imagers have estimated precipitation for over 30 years.
- Current methods link pixel-level brightness temperatures (TBs) to precipitation, ignoring spatial organization.
- Precipitation is a dynamic variable with complex spatial and temporal structures.
Purpose of the Study:
- To develop a novel approach for precipitation retrieval using spatial neighborhood information.
- To improve the handling of complex observation geometries in passive microwave remote sensing.
- To reduce uncertainty in quantitative precipitation estimation (QPE).
Main Methods:
- Utilized variations in brightness temperatures (TBs) within a spatial neighborhood.
- Computed "nonlocal" radiometric parameters using spatial convolution filters.
- Integrated nonlocal parameters with spectral information in a k-nearest neighbors retrieval scheme.
Main Results:
- Nonlocal radiometric parameters capture spatial patterns and scale-dependent structures of TB fields.
- These parameters act as "geometric signatures" for precipitation structures like convective cells.
- Enriching spectral data with nonlocal parameters reduced retrieval uncertainty by 6%-11%.
Conclusions:
- Analyzing spatial variations in TBs offers valuable information for precipitation retrieval.
- The proposed method enhances the understanding of complex satellite observation geometries.
- This approach significantly reduces uncertainty in satellite-based quantitative precipitation estimation.
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