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Object-Based Comparison of Data-Driven and Physics-Driven Satellite Estimates of Extreme Rainfall
Zhe Li1, Daniel B Wright1, Sara Q Zhang2,3
1Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, Wisconsin.
Journal of Hydrometeorology
|June 24, 2021
Summary
Comparing satellite precipitation estimates, the NASA-Unified Weather Research and Forecasting (NU-WRF) model showed more accurate rain rates, while Integrated Multisatellite Retrievals for GPM (IMERG) better captured storm location and shape.
Area of Science:
- Earth and Space Science
- Atmospheric Science
- Remote Sensing
Background:
- The Global Precipitation Measurement (GPM) constellation offers vital precipitation data.
- Satellite observations are crucial for deriving gridded precipitation estimates.
- These estimates are generated using data-driven algorithms or assimilation into numerical weather models.
Purpose of the Study:
- To compare data-driven (IMERG) and assimilation-enabled (NU-WRF) precipitation estimates.
- To evaluate performance against Stage IV reference precipitation for extreme rainfall events.
- To utilize an object-based analysis framework for diagnosing storm properties and accuracy.
Main Methods:
- Comparison of Integrated Multisatellite Retrievals for GPM (IMERG) and NASA-Unified Weather Research and Forecasting (NU-WRF) models.
- Utilized Stage IV reference precipitation data for validation.
- Employed an object-based analysis framework to decompose gridded precipitation into storm objects.
Main Results:
- Both IMERG and NU-WRF captured tropical cyclone evolution but struggled with less organized mesoscale convective systems.
- NU-WRF provided more accurate rain rates, while IMERG excelled in storm location and shape accuracy.
- Both models demonstrated higher skill for large, intense storms compared to smaller, weaker ones.
Conclusions:
- Object-based analysis offers deeper insights into satellite precipitation performance.
- NU-WRF's accuracy is independent of input data sources, unlike IMERG.
- Hybrid data-driven and physics-driven estimates should be explored for optimal satellite data utilization.
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