RETRIEVAL OF ICE CLOUD PARAMETERS USING DMSP SPECIAL SENSOR MICROWAVE IMAGER/SOUNDER
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Clouds exert a profound influence on both the water balance of the atmosphere and the earth's radiation budget (Stephens 2005; Stephens and Kummerow 2007). Among the global distribution, 30% of them are ice clouds (Riedi et al. 2000). It is important to improve our knowledge of the ice cloud properties in order to determine their influence to the global ecosystem. For ice clouds with millimeter-size ice particles, which are generally found in anvil cirrus and deep convections, microwave and millimeter wave length satellite measurements are suitable for the ice cloud microphysical property retrieval because of its strong ability to penetrate deeper into dense ice clouds. For these types of ice clouds, brightness temperatures at the top of the atmosphere are analytically derived as a function of vertically integrated ice water content (i.e. ice water path), effective particle diameter, and bulk volume density. In general, three brightness temperature measurements are needed to retrieve the three ice cloud microphysical parameters. A two-stream radiative transfer theory was applied to data from the Advanced Microwave Sounding Unit (AMSU) and the Moisture Humidity Sensor (MHS) in order to generate global ice water paths operationally. This research further applied the model and theory to derive ice water path (IWP) from the Special Sensor Microwave Imager/Sounder (SSMIS) onboard the Defense Meteorological Satellite Program (DMSP) F-16 satellite. Compared to AMSU/MHS, which have field of views (FOV) varying with scan position, SSMIS scans the Earth's atmosphere at a constant viewing angle of 53o and therefore offers a uniform FOV within each scan. This unique feature allows for improved global mapping and monitoring of ice clouds so that a more accurate and realistic IWP and ice particle effective diameter distribution is expected. A direct application of SSMIS-derived ice water path is its relationship with surface rain rate as derived previously for AMSU and MHS instruments. Here, SSMIS-derived rain rate was compared to the AMSU and MHS rainfall products and hourly synthetic precipitation observations from rain gauges and surface radar. Results show that SSMIS surface precipitation distribution is spatially consistent and does not have apparent artificial boundary near coastal zones as previously seen in other algorithms. Also, the ice water path associated with a severe storm reasonably delineates the strong convective precipitation areas and has a spatial variation consistent with surface precipitation. From retrieved instantaneous surface precipitation, a tropical and subtropical oceanic precipitation anomaly time series is constructed from 5 year's worth (2005-2009) of SSMIS data. This data record is also linked to the previous constructed SSM/I 15-year (1992-2006) data record to provide a longer term climate study by satellite observations. In future studies, refined algorithms for the estimate of ice cloud base temperature and ice particle bulk volume density are going to be developed to improve the accuracy of IWP retrieval under various cloud vertical distributions. Meanwhile, a better inter-sensor cross calibration scheme is the key to make satellite measurements more useful in climate change study.