2003年10月18日，美国国防气象卫星计划成功发射搭载了专用传感器微波成像仪/探测仪的F-16卫星。然而由于天线自身热辐射和校准暖黑体的不稳定，第一个专用传感器微波成像仪/探测仪出现了一些重要的异常观测。美国海军实验室与美国国家海洋和大气管理局(NOAA)分别开发了两种算法来订正这些异常值。剔除了定标异常值后，专用传感器微波成像仪/探测仪资料目前在探空数据产品反演和资料同化中发挥了更加显著的作用。NOAA利用专用传感器微波成像仪已有算法生成了专用传感器微波成像仪/探测仪成像仪产品。此外，一些新开发的算法可以从专用传感器微波成像仪/探测仪资料中提取出云和降水的信息。在云冰云水的反演算法中，亮温是与云冰云水路径和粒子的平均直径相关的。利用一维变分反演系统，同时反演出了多数大气和地表条件下的大气温、湿度以及水凝物的垂直廓线。在各种天气形势下，由专用传感器微波成像仪/探测仪资料反演得到的温度和湿度廓线的均方根误差通常分别小于2K和15%。为了同化专用传感器微波成像仪/探测仪资料，还发展了新的质量控制和偏差订正方法。在NOAA的全球预报系统中同化了专用传感器微波成像仪/探测仪资料后，对提高全球中期数值预报水平产生了中性和较小的正效果。

On October 18, 2003, the Defense Meteorological Satellite Program (DMSP) successfully launched the F-16 satellite with the Special Sensor Microwave Imager/Sounder (SSMIS) on board. However, this first SSMIS instrument exhibited several major measurement anomalies due to instabilities in its antenna emission and calibration target. Two algorithms have been developed at Naval Research Laboratory (NRL) and the National Oceanic and Atmospheric Administration (NOAA), respectively, for correcting for these anomalies. After removal of the calibration anomalies, SSMIS data are now much more useful for sounding product retrievals and data assimilation. NOAA generates SSMIS imager products from its legacy SSM/I algorithms. Several new algorithms have been developed to extract from SSMIS the information on clouds and precipitation. In the cloud ice water retrieval algorithm, a parametric relationship relates brightness temperatures to cloud ice water path and particle mean diameter. Atmospheric temperature and water vapor profiles are simultaneously retrieved along with cloud hydrometeor profiles through a one-dimensional variational (1D-Var) retrieval system, which works well under most atmospheric and surface conditions. Rootmean-square (RMS) errors of temperature and water vapor profiles from SSMIS are typically 2K and 15%, respectively, under all weather conditions. A new quality control algorithm and a bias correction algorithm have also been developed for SSMIS data assimilation. Assimilation of SSMIS data in the NOAA Global Forecast System (GFS) results in neutral and small positive impacts on global medium range forecast scores.