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Enhancing GEO-based Precipitation Estimation Using Multi-Spectral Data

This study explores using multi-spectral data to improve precipitation estimation from geostationary satellites. The research involves analyzing different wavelengths, such as infrared and visible, to enhance the accuracy of precipitation estimates. The algorithm development includes grid-box and cloud-patch approaches, utilizing textural information and unsupervised classification. The study showcases case studies in Florida, New York, and Texas and demonstrates the potential of multi-spectral data in precipitation retrieval. The research concludes that multi-spectral data shows promise for improving precipitation estimation accuracy and suggests future work in developing a combined algorithm. Precise statistics will be provided in the near future.

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Enhancing GEO-based Precipitation Estimation Using Multi-Spectral Data

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  1. Precipitation Detection and Estimation Using Multi-Spectral Remotely Sensed Data Ali Behrangi1 Kuo-lin Hsu1 Bisher Imam1 Soroosh Sorooshian1 George Huffman2 Robert J. Kuligowski3 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

  2. Introduction: Problem Statement LEO (PMW): More accurate estimate Even after 3 hour accumulation still we have gaps GEO (VIS/IR): Less accurate estimate Global coverage is available frequently

  3. Introduction: Solution 1) Interpolating the precipitation intensity obtained from LEO (PMW) Satellites (Joyce et al., 2004) 2) GEO (VIS/IR) satellites provide high-resolution (time and space) images

  4. Question: Can Multi–spectral images help us to improve GEO-based precipitation estimation ?

  5. MULTI-SPECTRAL images Spinning Enhanced Visible and Infra-red Imager (SEVIRI). 12 different wavelengths once every 15 minutes,

  6. Figure courtesy of ITT Industries Multi- Spectral Precipitating Estimation The ABI (Advanced Baseline Imager) on Future GOES-R (Advanced Baseline Imager )

  7. Multi- Spectral Precipitating Estimation - 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.

  8. (0.65 μm) (3.9 μm) (6.7 μm) No Rain Rain (10.8 μm) (13.3 μm) Relative-frequency distributions of different channels under rain and no-rain conditions

  9. Multi- Spectral Precipitating Estimation Algorithm Development: 1- Grid-box based : 2- Cloud Patch based :

  10. Grid-Box Based Approach

  11. Algorithm Development: Multi-spectral Images Textural information Grid-box Approach PCA Unsupervised Classification Rain Probability/Intensity A : Thick-Cold cloud (i.e., Convective) B : Thin-Cold cloud (i.e., Cirrus) C : Clear Sky Clusters (MRR)

  12. IR (10.8 µm) d) Ch5 ETS=25 POD=74 FAR=45 ETS=29 POD=77 FAR=42 ETS=27 POD=78 FAR=44 g) Ch4+Ch5 f) Ch3+Ch5 f) Ch3+Ch5 VIS ETS=36 POD=76 FAR=35 ETS=30 POD=80 FAR=42 ETS=30 POD=72 FAR=39 ETS=35 POD=79 FAR=37 i) Ch3+Ch4+Ch5 ETS=37 POD=78 FAR=35 ETS=37 POD=80 FAR=36 ETS=48 POD=75 FAR=22 ETS=49 POD=79 FAR=24 Under Estimation Over Estimation Hit Ch 1 : 0.6 µm Ch2 : 3.9 µm Ch3 : 6.5 µm Ch4:10.7µm Ch5 : 13.3µm Case Study 1: Florida : August 30 2006

  13. Case Study 2: d) Ch5 f) Ch3+Ch5 g) Ch4+Ch5 i) Ch3+Ch4+Ch5 Over Estimation Ch 1 : 0.6 µm Ch2 : 3.9 µm Ch3 : 6.5 µm Ch4:10.7µm Ch5 : 13.3µm

  14. Case Study 3: Precipitation Estimation (using SEVIRI) IR (10.8 µm) VIS (0.65 µm) SEVIRI (MSG)

  15. Over Estimate Under Estimate

  16. Overall Results Rain/No-rain Detection Rain Rate Estimation CC ETS POD/ FAR RMSE BIAS (volume) BIAS (area) Scenario Scenario

  17. New York Florida Texas Multi-spectral & Diurnal Cycle of Precipitation

  18. Diurnal Cycle over Florida, USA (Summer 2006) BT 10.8 µm Day: BT (0.65 &10.8) µm Night: BT (6.7 & 10.8 )µm Day & Night: BT (6.7 & 10.8 )µm NEXRAD

  19. Patch Based Approach

  20. Multi-spectral - Patch based IR-Based Patching VIS - Based Patching

  21. Multi-spectral - Patch based IR-Based Patching Cold Thin cloud VIS - Based Patching Warm thick cloud

  22. Results of Multi-spectral Cloud Classification experiment: In general results are encouraging ! Detail Statistics will be provided in near future

  23. Conclusions: • Multi-spectral data are promising for precipitation retrieval, Particularly for delineation of areal extent of precipitation. • In addition to 10.8 μm band, VIS channel for day time and WV channel for night time seems to be good candidates. • Future Work • Developing a combined algorithm using multi-spectral data and PMW estimate, …. is ongoing.

  24. Thank You !

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