1. Combined Active & Passive Rain Retrieval for QuikSCAT Satellite Khalil A. Ahmad Central Florida Remote Sensing Laboratory University of Central Florida Orlando, FL, USA http://www.engr.ucf.edu/centers/cfrsl/ End of Semester Group Presentation Dec 10, 2005

2. Presentation Outline:  Description of QuikSCAT Rain Algorithm  Passive Rain Rate retrievals (QRad Algorithm)  Physical Basis  QRad Algo. Tuning  Validation of QRad retrievals (JPL L2B Data Product)  Active rain rate algorithm development  Physical Basis  Sigma-0 Forward Model  Summary & Concluding Remarks

3.  Oceanic instantaneous integrated rain rate, 0.25 deg grid resolution.  Uses SeaWinds remote sensor on the QuikSCAT satellite Polarized Microwave brightness temperatures. Polarized Microwave brightness temperatures. Polarized normalized radar cross section (Sigma-0s) Polarized normalized radar cross section (Sigma-0s) Retrieved wind speeds Retrieved wind speeds   Based upon near-simultaneous collocations with TRMM Microwave Imager (TMI) oceanic rain rates (TRMM 2A12 Data product) QuikSCAT Rain Algorithm Description

4.  Passive rain retrieval component (QRad):  Statistical retrieval algorithm (Tex – IRR relationship)  Improved  T by averaging / spatial filtering  Provides simultaneous, collocated precipitation measurements with QuikSCAT ocean surface wind vectors for rain-flagging contaminated wind vector retrievals  Increase Oceanic rain sampling by ~ 10% QuikSCAT Rain Algorithm Description

5. SeaWinds Measurement Geometry

6. Passive Rain Retrieval (QRad Algorithm tuning)

7. Excess Brightness Temperature  Rain absorbs and re-emits radiation, thus increases the observed microwave brightness temperature  The polarized microwave “excess brightness” (Tex p ) is proportional to the integrated rain rate –T b ocean = ocean background (includes atmospheric Emissions without rain) based upon 7 year SSMI climatology –T b w.speed = wind speed brightness bias

8. Instantaneous Rain Rate Product By orbit, 25 km resolution QRad Rain Rate Block Diagram Calc. Polarized Excess Brightness T ex @ 25 km Combine using a weighted average Using ( T ex - IRR ) Calc. Polarized Instantaneous Rain Rate QRad Tb (L2A) Ocean Tb background QuikSCAT wind Speed (L2B) Spatial Filtering 3x3 Window Apply threshold

9. QRad – TRMM Collocation Data Base 1 st Quarter ~ 106 2 nd Quarter~ 121 3 rd Quarter~ 167 4 th Quarter~ 27

10. Remove Tex Biases H-pol eToh= 1 k V-pol eTov= - 0.8 k ~ 300 Revs ~ 15,000,000 points Tex

11. QRad Tex – TMI IRR Transfer Functions (421 Collocated Rain events) 3 rd order polynomial Odd symmetry

12. QRad – TMI IRR scatter

13. QRad – Rain Threshold TMI Oceanic Coverage

14. Comparisons of QRad Retrievals with TMI 2A12 Rain Rates (JPL L2B Validation)

15. Validation Data Set  JPL Data: 173 Revs, sampled from April ~ Oct ’03  Rain Collocation Data: 70 Collocated Rain events < 30 min

16. Tex Biases / Rain (173 Revs Apr’03~ Oct’03) ± 1 K ± 1 km mm/hr

17. Comparison of ~ 70 Instantaneous QRad – TRMM 2A12 Collocated Rain Events

18. Rain Statistics – ( 70 Collocated events)  Rain Pattern:  Agreement percentage ~ 83.43 %  Mis-Rain ~ 7.42%  False Alarm ~ 9.14 %  Rain Magnitude :  Within 3dB ~ 80.54 %  Within 1dB ~ 58.99 %  Within 0.5 dB ~ 52.52%

19. Rain Image Comparison QRadTMI

20. TMI >0, QRad >0 TMI =0, QRad >0 TMI >0, QRad =0 TMI =0, QRad=0 Agree = 89.52 % False alram = 6.08% Mis-rain = 3.35% QRad / TMI Rain Pattern Classification

21. Rain Image Comparison QRadTMI

22. QRad / TMI Rain Pattern Classification TMI >0, QRad >0 TMI =0, QRad >0 TMI >0, QRad =0 TMI =0, QRad=0 Agree = 89.31 % False alram =3.94% Mis-rain = 6.76%

23. Active Rain Retrieval Algorithm Development

24. SeaWinds Scatterometer: Ocean Surface   o : Normalized Radar Cross Section (NRCS) of the ocean surface

25. Ocean Backscattering:  is a function of incidence angle, frequency, polarization and ocean wind vector (speed and direction)   o is a function of incidence angle, frequency, polarization and ocean wind vector (speed and direction)  The geophysical model function (GMF): An empirical relationship between and the ocean near surface wind velocity:  The geophysical model function (GMF): An empirical relationship between  o and the ocean near surface wind velocity:

26. Rain Effects on Ocean Rain Effects on Ocean  o  In the presence of Rain, three major factors affect the measured ocean surface :  In the presence of Rain, three major factors affect the measured ocean surface  o : – Two way path attenuation Reduces received power Reduces received power – Volume backscatter Enhances received power Enhances received power – Surface perturbation “Splash Effect” Alters ocean surface roughness structure Alters ocean surface roughness structure

27. SeaWinds Backscatter Forward Model SeaWinds Backscatter Forward Model σ 0 m : Measured SeaWinds backscatter σ 0 w ind : Wind induced backscatter σ 0 rain-vol : Volume-backscatter due to rain σ 0 surf : Surface perturbation due to rain σ 0 Ex-rain : Excess-backscatter due to rain α : Two-way path attenuation

28. Wind Induced Backscatter (σ 0 wind ) Model H/V Polarized Wind induced Sigma-0’s By orbit, 25 km resolution Combine FWD/AFT & Earth Locate L2A Data Product L2B Data Product Model Wind speed Model Wind Dir WVC Geolocation Cell Azimuth Cell Incidence Co-register On L2B Grid QuikSCAT GMF QSCAT-1 Calc. Relative Azimuth L2B Cell Incidence 4-flavour σ 0 w L2B Cell Azimuth

29. Wind Induced Backscatter (σ 0 w )

30. Attenuation derived from PR: H-PolV-Pol

31. Rain Volume BackScatter derived from PR:

32. Rain Backscatter (σ 0 EX-rain ) H-Pol V-Pol

33. Future Work  Combine/Validate Sigma-0 Model  Develop a complementary active rain retrieval  Combine Active/Passive Rain retrievals Minimize:

34. Summary:  JPL rain processing is in excellent agreement with CFRSL processing  QRad provides quantitative estimates of instantaneous rain rates over oceans  QRad rain measurements are in good agreement with TRMM 2A12