An updated analysis of the ocean surface wind direction signal in passive microwave brightness temperatures

Abstract

Abstract-We analyze the wind direction signal for vertically ( ) and horizontally ( ) polarized microwave radiation at 37 GHz, 19 GHz, and 11 GHz and an Earth incidence angle of 53 . We use brightness temperatures from SSM/I and TMI and wind vectors from buoys and the QUIKSCAT scatterometer. The wind vectors are space and time collocated with the radiometer measurements. Water vapor, cloud water and sea surface temperature are obtained from independent measurements and are uncorrelated with the wind direction. We find a wind direction signal that is noticeably smaller at low and moderate wind speeds than a previous analysis had indicated. We attribute the discrepancy to errors in the atmospheric parameters that were present in data set of the earlier study. We show that the polarization combination 2 Index Terms-Microwave radiometers, ocean wind vector retrieval.

Effects of foam on ocean surface microwave emission inferred from radiometric observations of reproducible breaking waves

“…The ocean surface wind vector is one of the key environmental data records for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Conical Microwave Imager/Sounder (CMIS) instruments, first planned for launch in 2010. To date, aircraft and satellite measurements, as well as modeling results, indicate that brightness temperature variations with wind direction are small, on the order of 1-3 K peak-to-peak at 19 and 37 GHz [2]- [5]. Therefore, quantitative knowledge of the dependence of the ocean surface emissivity on properties such as surface roughness and wave breaking is critical for wind vector retrieval.…”

Insights on the OAFlux ocean surface vector wind analysis merged from scatterometers and passive microwave radiometers (1987 onward)

“…The footprint resolution varies from 75 km 3 43 km at 6.9 GHz to 6 km 3 4 km at 89 GHz. The lowfrequency channels (6.9 and 10.6 GHz) penetrate deeper and are more sensitive to sea surface temperature and wind but less sensitive to the atmosphere [Meissner and Wentz, 2002]. The SST and wind speed algorithms are essentially the same, except that the SST algorithm uses all five AMSR-E lower-frequency …”

“…Albeit a wind speed only sensor, the six-sensor series of the Special Sensor Microwave/Imager (SSM/I) on the Defense Meteorological Satellite Program (DMSP) that were launched subsequently on different platforms starting from July 1987 [Hollinger et al, 1990;Wentz, 1997], together with the follow-on Special Sensor Microwave Imager/Sounder (SSMIS) sensors [Kunkee et al, 2008] that have been in operation since 2005, constitute a continuous and reliable data record of global wind speed for 26 years and continuing. In addition to the SSM/I and SSMIS series, the database of satellite wind speed data records is further augmented by the launch of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) in November 1997, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) in May 2002 [Meissner and Wentz, 2002], and the WindSat Polarimetric Radiometer in January 2003. WindSat is a new type of passive microwave sensor that is equipped with an ability of retrieving both ocean wind speed and vector (above 8 m s 21 ) through measuring the complex correlation between vertically and horizontally polarized microwave radiation [Gaiser et al, 2004].…”

A Statistical Approach to WindSat Ocean Surface Wind Vector Retrieval

“…This occurs because the wind speed signal in is large [5], while the directional signals are relatively small [6], [15]. The through regressions must extract parts of the small directional signals, while compensating for atmospheric and SST variations.…”

“…Our initial wind direction performance is within 20 of QuikSCAT for wind speeds above 9.9, 8.8, 7.6, and 7.2 m/s for the FR, MF, MFNG, and CL ambiguities, respectively. Below 5 m/s, the wind direction signals are very small [6], [15], and the wind direction retrieval performance degrades accordingly. Fig.…”