P2.1 AN OCEAN SURFACE WIND VECTOR MODEL FOR WINDSAT MICROWAVE RADIOMETER USING DUAL-POLARIZATION Seubson Soisuvarn* and Zorana Jelenak UCAR NOAA/NESDIS/STAR Paul S. Chang NOAA/NESDIS/STAR channels to retrieve relevant surface and atmospheric 1. INTRODUCTION parameters that complement the wind vector retrieval requirements. Ocean surface wind field information is vital for NOAA/NESDIS WindSat’s wind vector retrieval scientists and forecasters for their understanding the algorithm utilizes an empirical model function that Earth’s global weather and climate. As the demand for establishes relationship between the third and fourth global wind vector information was increasing in the last Stokes parameters and the surface wind truth (Jelenak three decades, number of satellite missions that carried et at. 2004). Although the principal polarizations (vertical instruments for surface wind measurement were and horizontal) are also functions of wind direction, this launched. However, there is still not sufficient number of dependence is very weak and often masked by other instruments flying simultaneously in orbit to fulfill the surface or atmospheric parameters. However it has operational, meteorological and non-real-time scientific been shown (Meissner and Wentz, 2002) that certain wind vector requirements (Chang and Jelenak, 2006). linear combination between the vertical and horizontal brightness temperatures are proved to approximately Starting from the late 1970’s up to the recent years, cancel the atmospheric effect and are predominantly all ocean surface wind vector missions from space were dependent on the sea surface temperature, wind speed utilizing scatteromery measurements technique (Moore and direction. We extended this work and shown that and Jones, 2004). The scatterometers are microwave the brightness temperature combinations can be radar instruments that measure the ocean surface expressed as AT -T where A is a ratio of the surface normalized radar cross-section (NRSC) and infer the BV BH reflectivities. wind vector information by comparing NRCS with the Geophysical Model Function (GMF) specifically defined For low to moderate wind speeds the wind direction for backscatter measurements. The currently used sensitivity of ATBV-TBH is weak and lies within the model function QSCAT-1 was developed empirically measurement noise level. However, the more robust using known surface truth and observed NRSC from wind direction signal in ATBV-TBH reveals the opportunity QuikSCAT scatterometry measurements (Freilich, to use it in combination with the third and forth Stokes 1993). Due to biharmonic nature of the GMF, the wind measurements for possible wind vector retrieval vectors retrieved from the NRSC measurements are not improvement for fully polarimetric radiometers. In this unique. There are up to four possible solutions for each paper, we investigate the ATBV-TBH characteristic for the measurement. In order to select correct vector from WindSat’s 10.7, 18.7 and 37.0 GHz channels and retrieved wind vector ambiguities NRCS measurements present preliminary model functions for these three are acquired from multiple looks. (Schroeder et al., WindSat frequencies. 1985; Shaffer et al., 1991) The paper is organized as the following: In section 2, In January 2003, the Naval Research Laboratory dataset that was used for model function development is launched the first fully polarimetric passive microwave described. The dataset was separted into two groups radiometer - WindSat on board of Coriolis satellite with two different sea surface temperature sources as (Gaiser and Bettenhausen, 2005). WindSat is the first we will describe in more details below. In section 3, the passive microwave instrument flown in space capable of approximation of the linear combination between the measuring ocean surface wind speed and direction. vertical and horizontal brightness temperatures is WindSat operates at five frequency bands: 6.8, 10.7, presented.The A parameter has been derived. Next the 18.7, 23.8 and 37.0 GHz. The 10.7, 18.7 and 37.0 GHz empirical relationship has been developed for ATBV-TBH channel are fully polarimetric establishing all four Stokes and surface parameters and is presented in section 4. parameters and are primary channels used for wind Finally the conclusions and discussion is presented in vector retrieval. The 6.8 and 23.8 GHz channels are section 5. dual-polarized and are used in conjunction with other 2. DATASET *Corresponding author address: Seubson Soisuvarn, NOAA Several data sources were combined in order to Science Center, room 102, 5200 Auth Road, Camp Springs, MD develop new model functions for WindSat 20746; e-mail: [email protected] measurements. From WindSat, linearly polarized (V and H) brighness temperatures are available from all five atmospheric physical parameters contribute to the total frequency channels. To avoid predominent atmospheric apparent brightness temperatures measured over the sensitivity in the 23.8 GHz and SST sensitivity in 6.8 top of the atmosphere by a microwave radiometer. For GHz channel and also to match the frequency use for higher microwave frequencies, the dominant brightness wind retrieval in WindSat’s third and forth Stokes temperature signal contribution comes from the parameters, only 10.7, 18.7 and 37.0 GHz channels atmosphere. For lower microwave frequencies (36 GHz were used in the model development together with and lower) the surface signal becomes strong enough to WindSat’s own geophysical retrieval parameters such provide for wind speed retrievals using the V or H as: cloud liquid water, rain rate, water vapor, wind polarization microwave observation; however, the wind speed, and sea surface temperature (SST) (Jelenak et direction signal is very weak. The knowledge of the al., 2004). atmospheric variables is critical if the single polarization is to be used for full wind vector retrieval. Furthermore a Collocated wind vector data from SeaWinds small error in the atmospheric corrections will lead to a scatterometer on QuikSCAT (from now on refer to significant error in the wind direction retrievals. QuikSCAT wind vectors) were used as a surface wind vector truth. The spacial collocation requirement was set However with the certain linear combination between to 25-km and time collocation was within ±1 hr time. the V and H brightness temperatures, the atmospheric correction may not be required. This linear combination Another external data source was needed for better was previously found to be mostly independent of the sea surface temperature (SST) estimates. This atmospheric parameters and is predominantly a function parameter was obtained from the National Centers for of the surface wind speed, wind direction and sea Environmental Prediction’s (NCEP) Global Data surface temperature (SST) (Wentz, 1992; Meissner and Assimilation System (GDAS). The GDAS is a global Wentz, 2002). The linear brightness temperature map model of the Earth’s atmosphere and ocean combination is expressed as ATBV-TBH or simply AV-H, surface generated every 6 hr. for 00Z, 06Z, 12Z and 18Z where A is a constant dependent on microwave daily. The current NOAA/NESDIS version of GDAS used frequency used (Jelanak, 2005), and will be discussed provides a 1° × 1° global latitude/longitude grid below. resolution for a limited selection of parameters significant for satellite geophysical retrievals validation. From the radiative transfer theory, the total apparent Since GDAS produce four global maps per day, the brightness temperature collected at the radiometer closest maps timeframe within ±3 hr of WindSat antenna may be expressed as (Meissner and Wentz, overpass were selected. Next the closest four 2002) surronding points of GDAS were then interpolated to the (1) WindSat point location (Connor, 2004). TB = TBU + τETs + τR(1 +Ω)(TBD + τTc ) All the data from WindSat, QuikSCAT and GDAS The TBU and TBD are upwelling and downwelling sets were combined together in a common data atmospheric brightness temperature respectively and structure to simplify data processing. To make the are given by Meissner and Wentz (2002) in (2) dataset suitable for model function development, the ∞ dataset was binned with respect to different surface TBU = ∫ α(z)T(z)τ(z,S)dz parameter as obtained from QuikSCAT or GDAS model 0 (2) field. The dataset was first sorted by QuikSCAT’s wind ∞ speeds into 1 m/s wind speed bins. TBD = ∫ α(z)T(z)τ(0,z)dz From the wind speed bins, the data were further 0 subdivided into SST bins with 2 °C bin size. It would be The α(z) and T(z) are the atmospheric absorption desirable to use the derived SST from WindSat alone and physical temperature profile at altitude z without using other external SST source like GDAS. To respectively. The atmospheric transmittance between assess the requirement of an external SST source, both two points in the atmosphere are defined in term of α(z) SST from WindSat retrievals (Mavor et al, 2004) and as GDAS SST was used to bin the data. Here we refer to z the two version of dataset as version-1 (ver-1) and ⎛ 2 ⎞ ⎜ ⎟ (3) version-2 (ver-2) corresponding to SST bins used based τ(z1,z2 ) = exp⎜ − ∫ α(z)dz⎟ on GDAS’s and WindSat’s respectively. ⎝ z1 ⎠ Finally, these two dimensional wind speed and SST The τ, defined as τ(0,S) from (3), in (1) is the total bins, were further divided in to relative wind direction atmospheric transmittance between the sea level and bins of 10° size. The term “relative” refers to the the top of the atmosphere S. The emissivity E and the difference between true wind direction and WindSat’s reflectivity R in related by Kirchhoff’s Law as R=1-E. The measurement azimuth. Ω is the relative surface scattering reflection factor due to wind roughen surface. The Tc is the cosmic brightness temperature equals to 2.7 K and Ts is the 3. BRIGHTNESS TEMPERATURE COMBINATIONS SST in degree Kelvin. If the atmosphere is assumed to be homogeneous, The microwave radiometers measure naturally that is if the atmospheric absorption is considered occurring microwave emission from the Earth’s surface constant, and the atmospheric temperature profile T(z) and its overlaying atmosphere.