New algorithms for microwave measurements of ocean winds: Applications to Seasat and the special sensor microwave imager

Abstract

Improved second generation normalized radar cross section (NRCS) and brightness temperature (TB) models and associated wind retrieval algorithms are derived for the Seasat microwave radiometer SMMR and scatterometer SASS. The derivation of the NRCS model is based on the assumption of a Rayleigh distribution of wind speeds, and no in situ anemometer measurements are used. Furthermore, the NRCS model derivation is designed to preclude, as much as possible, systematic errors in the polarization and incidence angle relationships. A constant power law NRCS model is used, except for nadir observations. The nadir NRCS for winds above 15 m/s falls off faster with increasing wind speed than is predicted by a constant power law relationship. The TB model derivation consists of finding the wind‐induced emissivity coefficients, modifying the 37‐GHz atmospheric absorption coefficients and removing biases in the TB observations. The TB biases are found to be stable except for the 18‐GHz channels, which experience large, time‐dependent biases. The NRCS and TB models are incorporated into new wind retrieval algorithms, which are used to process the SASS and SMMR 3‐month data sets. Small residual systematic errors in the SASS winds (±0.5 m/s or less) are found. A histogram of the SASS winds closely resembles a Rayleigh distribution. The SASS winds are compared with 1623 National Data Buoy Office (NDBO) buoy observations, and a 1.6‐m/s rms discrepancy, with a −0.1‐m/s bias, is found. The SASS and SMMR winds are compared on a 150‐km cell‐by‐cell basis, giving 123,000 wind comparisons for the 3‐month period. The comparisons are done using eight different combinations of three SMMR channels. Good agreement is found between the SASS and SMMR winds, except for two of the channel combinations that show little, if any, skill in retrieving wind. Over the SASS primary off‐nadir swath, the SMMR and SASS wind agreement ranges from 1.3 to 2.2 m/s, depending on the channel combination. For the SMMR versus SASS nadir wind comparisons, the agreement slightly degrades. The SMMR winds appear to be more noisy than the SASS winds for winds below 3 m/s. These results indicate that the Special Sensor Microwave Imager (SSM/I), to be launched in 1986, will have the capability to measure the near‐surface wind speed to an accuracy of about 2 m/s.

Contribuição de diversos sistemas de observação na previsão de tempo no CPTEC/INPE

“…Somente as informações de vento definidas nas camadas entre a superfície e 700 hPa e acima de 700 hPa foram usadas no GPSAS. No caso de TPW, as informações são obtidas a partir do sensor SSM/I (Ledvina e Pfaendtner, 1995), que opera a bordo do satélite de órbita polar do DMSP (Defense Meteorological Satellite Program), e foram inferidas pela aplicação do algoritmo de Wentz (Wentz et al, 1986). Para a assimilação da TPW, é realizada uma análise independente das outras observações, a fim de explorar a sua relação direta com o perfil de umidade específica do first-guess do modelo.…”

The Inclusion of IWV Estimates from AIRS/AMSU and SSM/I Sensors into the CPTEC/INPE Global Data Assimilation System

“…Other datasets were also assimilated in these experiments: these included geopotential height values from temperature profiles measured by Advanced Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder (ATOVS) satellites (Reale 2002), (Wentz et al 1986). The values obtained over continental regions, over ice, and during intense precipitation have not been used, because the results from this algorithm are significantly degraded in these circumstances.…”

On The Effect of Atmospheric Emission upon the Passive Microwave Polarimetric Response of an Azimuthally Anisotropic Sea Surface

“…The effect of the atmosphere can be considered by introducing the quantities τ (0, ∞) , T mDN and T mU P . While the vertical atmospheric opacity τ (0, ∞) must be considered unknown, the downwelling mean radiative temperature T mDN can be determined with good accuracy (within a few degrees) as function of the sea surface temperature measured by remote sensors or derived by climatology [3]; the same applies to T mU P that is just a few degree below T mDN . The total radiation at the sea surface (both emitted and scattered) is given by equations (11), (15) and (16) where, as already mentioned, the scattering contribution has a very complicated expression.…”

“…Spaceborne and airborne microwave scatterometers have been extensively used to estimate near-surface wind fields [2]. In the last decade satellite-based microwave radiometry has proved to be a potential tool for ocean wind remote sensing due to the fact that microwave thermal emission from sea surfaces is affected by surface roughness and thus indirectly by wind motion at various scales, and also by temperature, foam, salinity and the state of the atmosphere between the sensor and the target surface [3]. In particular, ocean surface wind speed estimations have been successfully performed by using the polarization diversity of the Special Sensor Microwave Imager (SSM/I) aboard the Defense Meteorological Space Program (DMSP) near-polar orbiting platforms [4].…”