Combined Active & Passive Rain Retrieval for QuikSCAT Satellite Khalil A. Ahmad Central Florida Remote Sensing Laboratory University of Central Florida Orlando, FL, USA End of Semester Group Presentation Dec 10, 2005

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

 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

 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

SeaWinds Measurement Geometry

Passive Rain Retrieval (QRad Algorithm tuning)

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

Instantaneous Rain Rate Product By orbit, 25 km resolution QRad Rain Rate Block Diagram Calc. Polarized Excess Brightness T 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

QRad – TRMM Collocation Data Base 1 st Quarter ~ nd Quarter~ rd Quarter~ th Quarter~ 27

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

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

QRad – TMI IRR scatter

QRad – Rain Threshold TMI Oceanic Coverage

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

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

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

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

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

Rain Image Comparison QRadTMI

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

Rain Image Comparison QRadTMI

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

Active Rain Retrieval Algorithm Development

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

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:

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

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

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

Wind Induced Backscatter (σ 0 w )

Attenuation derived from PR: H-PolV-Pol

Rain Volume BackScatter derived from PR:

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

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

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