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PERSIANN Dynamic Infrared-Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global Satellite Precipitation Dataset
Phu Nguyen1, Mohammed Ombadi1, Vesta Afzali Gorooh1
1Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California.
Journal of Hydrometeorology
|June 23, 2021
Summary
The new Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Dynamic Infrared Rain Rate (PDIR-Now) dataset offers improved near-real-time precipitation estimates. This infrared-based dataset shows enhanced accuracy over the previous PERSIANN-CCS, particularly for extreme weather events.
Area of Science:
- Hydrology and Remote Sensing
- Artificial Intelligence in Earth Observation
- Meteorological Data Analysis
Background:
- Near-real-time precipitation data is crucial for various applications, including flood forecasting and drought monitoring.
- Existing datasets like PERSIANN-Cloud Classification System (PERSIANN-CCS) have limitations in accuracy and latency.
- Advancements in artificial neural networks and remote sensing offer opportunities for improved precipitation estimation.
Purpose of the Study:
- Introduce and evaluate the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Dynamic Infrared Rain Rate (PDIR-Now) dataset.
- Compare the performance of PDIR-Now with the PERSIANN-CCS dataset.
- Assess the utility of PDIR-Now for near-real-time precipitation monitoring and extreme event analysis.
Main Methods:
- Utilized infrared satellite data as input for the Artificial Neural Networks (ANN) model.
- Developed the PDIR-Now algorithm for hourly, quasi-global precipitation estimation at 0.04° × 0.04° resolution.
- Conducted extensive dataset evaluation over 2017-18, assessing performance at annual, monthly, daily, and subdaily scales, including extreme events.
Main Results:
- PDIR-Now demonstrates significant improvements over PERSIANN-CCS across all temporal scales.
- PDIR-Now achieved a Critical Success Index (CSI) of 0.53 for rain/no-rain days, outperforming PERSIANN-CCS (0.47).
- The dataset accurately captured seasonal/diurnal precipitation cycles, regional patterns, and extreme events like Hurricane Harvey (CORR=0.64) and Netherlands thunderstorms (CORR=0.76).
Conclusions:
- PDIR-Now is a valuable near-real-time precipitation dataset with enhanced accuracy and reduced latency compared to PERSIANN-CCS.
- The dataset effectively captures precipitation patterns, including those during extreme weather events.
- PDIR-Now is disseminated through the Center for Hydrometeorology and Remote Sensing (CHRS) web interfaces, facilitating its use in operational applications.
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