1206 Characterization of ATMS Bias in Cloudy Conditions Using GMI and Different Scattering Databases

Wednesday, 10 January 2018
Exhibit Hall 3 (ACC) (Austin, Texas)
Lin Lin, CICS–Univ. Maryland Earth System Science Interdisciplinary Center, College Park, MD; and F. Weng

Direct satellite radiance assimilation has resulted in a steady increase of forecast precision of global medium range forecast models at all major Numerical Weather Prediction (NWP) centers. However, the amount of satellite data utilized in NWP models is still only a small fraction of the available satellite data. Large amount of satellite data are excluded by quality control procedure that usually consists of cloud detection algorithm to remove cloud- or rain- affected data. The cloud- and rain-affected data render their quantitative interpretations (e.g., retrievals of atmospheric temperature profiles) much more prone to error than those from clear-sky conditions. In fact, cloudy radiances provided by satellite instruments naturally contain some useful information about clouds and precipitation presented in the filed-of-vies (FOVs) of the instruments, and should be desired by NWP models. In order to benefit from the assimilation of cloud- or rain-affected microwave radiance data, it requires having a better characterization of the observation errors for satellite radiances under cloudy conditions than that used in the current gridpoint statistical interpolation (GSI). To achieve that, it is necessary to first understand the biases from the satellite observations and corresponding simulations.

In this study, the simulation is provided by the Community Radiative Transfer Model (CRTM) developed by the US Joint Center of Satellite Data Assimilation (JCSDA), which has been widely used in NWP community for years. In cloud- or rain-affected regions, cloud scattering is one of the key properties of clouds and precipitation that affect the atmospheric radiative transfer. The current CRTM version employs the Mie scattering theory for cloud scattering, which assumes spherical liquid and ice water cloud particles for all cases in microwave frequencies. Obviously, this is unrealistic, especially in deep-convective clouds. Therefore, a more advanced model, the Discrete Dipole Approximation (DDA), which represents a particle as a three-dimensional array of polarizable points and provides a better model of the optical properties of non-spherical particles, is considered and tested for US Suomi National Polar-Orbiting Partnership (S-NPP) the Advanced Technology of Microwave Sounder (ATMS) brightness temperature simulation by using the atmospheric state profiles from European Centre for Medium-Range Weather Forecasts (ECMWF) and the retrieved hydrometeor profiles from collocated JAXA Global Precipitation Measurement/Dual-frequency Precipitation Radar (GPM/DPR) as inputs to CRTM. It’s found that at high frequencies, where scattering from upper-level ice and snow is expected to depress the brightness temperatures, the Mie simulations do not provide enough scattering, and the DDA simulations can correct the insufficient scattering and improve the accuracy of brightness temperature simulations. In addition to the particle shape effect, there are many differences between the Mie and DDA database, and the discrepancy in simulated brightness temperatures from uses of Mie and DDA scattering database is under further investigation. In addition, the scattering database generated using T-matrix method by Texas A&M University and another scattering database provided by NASA are also evaluated.

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