Successful development of precipitation retrieval algorithms requires highly accurate physical models that relate satellite-based observations to atmospheric state and topographic features. Such systems have been developed at the MIT Lincoln Laboratory for simulating data for both the cross-track ATMS (Advanced Technology Microwave Sounder) and the conically scanning MIS (Microwave Imager/Sounder). This system incorporates the MM5 cloud-scale numerical weather prediction model, the Rosenkranz radiative transfer model (IEEE TGRS, 40(8), 2002), the Surussavadee and Staelin hydrometeor electromagnetic modeling technique (IEEE TGRS, 44(10), 2006), and a satellite geometry toolbox for MATLAB developed at MIT Lincoln Laboratory for computing quantities relevant to satellite scans and geographical positions.
This simulation system predicts brightness temperatures with promising agreement with observations from the Advanced Microwave Sounding Unit instruments AMSU-A/B aboard the NOAA-16 satellite over Typhoon Pongsona in the western Pacific in Dec. 2002. This simulation system is also being validated with ~2-km resolution observations from the aircraft-based NPOESS Aircraft Sounder Testbed-Microwave (NAST-M) instrument. This simulation system is flexible and could be adapted for other current or future operational microwave or infrared instruments.
Precipitation algorithms are currently being developed for ATMS, MIS, and NAST-M. ATMS, like AMSU, will have channels in the opaque 54-GHz oxygen and 183-GHz water vapor resonance bands plus some window channels. It is an improvement over AMSU-A/B and similar NOAA and METOP satellite instruments. ATMS will have additional channels in the 183-GHz water vapor resonance band, Nyquist sampling in the 54-GHz oxygen resonance band, and ~33-km spatial resolution for channels in the 54-GHz band. The AMSU-A/B precipitation algorithm developed by Chen and Staelin (IEEE TGRS, 41(2), 2003) will be adapted for ATMS, and the 33-km resolution of ATMS and increased number of channels suggests that a superior precipitation retrieval algorithm can be developed. The new algorithm will be fast, statistics-based, and involve extensive signal processing and use of neural networks, in contrast to purely variational physics-based approaches which involve repeated radiative transfer computations. The signal processing methods will include regional Laplacian interpolation for cloud clearing, principal component analysis for temperature profile characterization, and image sharpening for enabling retrievals at the finest resolution possible. A major component of this study will involve understanding how various signal processing methods can be used or improved.
Detailed characteristics of the MIS system are currently being formulated. The flexibility of the simulation system facilitates selection of optimal channel sets, sampling rates, and resolutions. The MIS precipitation algorithm will leverage lessons learned from both AMSU and previously launched conical scanning spectrometers such as TMI, AMSR-E, and SSMIS.
Precipitation products on NPP and the NPOESS satellite series will maintain the important continuous observations provided by currently operational instruments. NPP will not only help provide continuity but will also facilitate the development of improved algorithms.
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* This work was supported by the National Oceanic and Atmospheric Administration under Air Force contract FA8721-05-C-0002. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the United States Government.
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