| Paper: | TH-A1.12 |
| Session: | Applications of Radiometry I |
| Time: | Thursday, March 29, 09:00 - 10:20 |
| Presentation: |
Poster
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| Topic: |
Theory, physical principles and electromagnetic models: |
| Title: |
An Active/Passive Microwave Retrieval Algorithm for Inferring Ocean Vector Winds from TRMM |
| Authors: |
Alamgir Hossan; University of Central Florida | | |
| | Maria Jacob; Facultad de Matematica, Astronomia y Fisica, Universidad Nacional de Cordoba | | |
| | W. Linwood Jones; University of Central Florida | | |
| | Harriet Medrozo; University of Central Florida | | |
| Abstract: |
This paper describes a novel ocean vector wind (OVW) retrieval algorithm that uses Ku-band Precipitation Radar (PR) and the multi-frequency TRMM Microwave Imager (TMI) on the Tropical Rainfall Measuring Mission (TRMM) satellite. The basis of this algorithm is the anisotropic nature of ocean backscatter (sig-0) and brightness temperature (Tb), which are used in a maximum likelihood estimation procedure to infer wind speed and wind direction.
After F. Wentz in 1992 published his paper on SSMI wind retrievals comparison with buoys, it was apparent that the linearly polarized Tb measurements contained wind direction information; however, because the overlying atmospheric influence obscured this weak signal, the use of linearly polarized V- and H-pol brightness temperatures (Tb’s) for wind direction retrievals has not been achieved. However, in this paper, we use the modified 2nd Stokes parameter (weighted difference of V & H-pol Tb’s) to largely cancel the atmosphere, which enhances the WD signal. When combined with the anisotropic backscatter signature of the Ku-band scatterometer, it is possible to retrieve both the corresponding WS and WD of the ocean surface wind.
The Geophysical Model Function (GMF), was empirically derived using spatial/temporal collocated PR and TMI observations with near-simultaneous “surface truth” consisting of environmental parameters from NOAA’s numerical weather model reanalysis (Global Data Assimilation System, GDAS) and TMI environmental retrievals: sea surface temperature (SST), water vapor (WV), and cloud liquid water (CLW). Also, ocean surface wind speed (WS) and wind direction (WD) were derived from the collocations with QuikSCAT and WindSat.
Examples of retrieved ocean winds are presented, and the TRMM OVW retrieval algorithm is validated by comparing retrievals with collocated QuikSCAT wind vectors. Finally, the statistics of WS and WD differences are presented.
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