Precipitation Detection and Estimation Using Multi-Spectral Remotely Sensed Data

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)

13. 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

14. 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

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

18. Over Estimate Under Estimate

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

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

21. 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

22. Patch Based Approach

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

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

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

27. 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.

28. Thank You !