Center for Hydrometeorology and Remote Sensing - University of California, Irvine Precipitation Detection and Estimation Using Multi-Spectral Remotely Sensed Data 1 Center For Hydrometeorology and Remote Sensing (CHRS), University of California, Irvine 2 NASA/GSFC Code 613.1 3 NOAA/NESDIS Center for Satellite Applications and Research (STAR), Camp Springs, MD Ali Behrangi 1 Kuo-lin Hsu 1 Bisher Imam 1 Soroosh Sorooshian 1 George Huffman 2 Robert J. Kuligowski 3
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Center for Hydrometeorology and Remote Sensing - University of California, Irvine Precipitation Detection and Estimation Using Multi-Spectral Remotely.
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Center for Hydrometeorology and Remote Sensing - University of California, Irvine
Precipitation Detection and Estimation Using Multi-Spectral Remotely Sensed Data
1 Center For Hydrometeorology and Remote Sensing (CHRS), University of California, Irvine2 NASA/GSFC Code 613.1
3 NOAA/NESDIS Center for Satellite Applications and Research (STAR), Camp Springs, MD
Ali Behrangi1
Kuo-lin Hsu1
Bisher Imam1
Soroosh Sorooshian1
George Huffman2
Robert J. Kuligowski3
Center for Hydrometeorology and Remote Sensing - University of California, Irvine
LEO (PMW): LEO (PMW): More accurate estimateMore accurate estimateEven after 3 hour accumulation still we have gapsEven after 3 hour accumulation still we have gaps
GEO (VIS/IR):GEO (VIS/IR):Less accurate estimateLess accurate estimateGlobal coverage is available frequentlyGlobal coverage is available frequently
Introduction: Problem Statement
Center for Hydrometeorology and Remote Sensing - University of California, Irvine
Introduction: Solution
1) Interpolating the precipitation intensity obtained from LEO (PMW) Satellites
2) GEO (VIS/IR) satellites provide high-resolution (time and space) images
(Joyce et al., 2004)
Center for Hydrometeorology and Remote Sensing - University of California, Irvine
Question:
Can Multi–spectral images help us to improve GEO-based precipitation estimation ?
Center for Hydrometeorology and Remote Sensing - University of California, Irvine
MULTI-SPECTRAL imagesSpinning Enhanced Visible and Infra-red Imager (SEVIRI). 12 different wavelengths once every 15 minutes,
Center for Hydrometeorology and Remote Sensing - University of California, Irvine
The ABI (Advanced Baseline Imager) on
Future GOES-R
Figure courtesy of ITT Industries
(Advanced Baseline Imager )
Multi- Spectral Precipitating Estimation
Center for Hydrometeorology and Remote Sensing - University of California, Irvine
Multi- Spectral Precipitating Estimation
Kurino (1997) 6.7, 11 and 12 µm
Rosenfeld and Gutman (1997) 0.65, 3.7, 10.8 and 12 µm
Inoue and Aonashi ( 2000) 0.6, 1.6, 3.8, 11 and 12 µm
Ba and Gruber (2001) 0.65, 3.9, 6.7, 11 and 12 µm
Capacci and Conway (2005)9 MODIS and corresponding SEVIRI
channels
- IR 11µm & 12 µm: => removal of thin cirrus cloud
- IR 11µm & WV 6.7 µm: => sign of deep convective
- NIR 3.7 µm : => sensitive to cloud drop size distribution - VIS : => cloud optical thickness.
Center for Hydrometeorology and Remote Sensing - University of California, Irvine
Relative-frequency distributions of different channels under rain and no-rain conditions
No RainRain
(0.65 μm) (3.9 μm) (6.7 μm)
(10.8 μm) (13.3 μm)
Center for Hydrometeorology and Remote Sensing - University of California, Irvine
Multi- Spectral Precipitating Estimation
Algorithm Development:
1- Grid-box based :
2- Cloud Patch based :
Center for Hydrometeorology and Remote Sensing - University of California, Irvine
Grid-Box Based Approach
Center for Hydrometeorology and Remote Sensing - University of California, Irvine