Improving high-resolution IR satellite-based precipitation estimation: A procedure for cloud-top relief displacement adjustment

摘要

An efficient and simple method has been developed to improve quality and accuracy of satellite-based VIS/IR images through cloud-top relief spatial displacements adjustment. The products of this algorithm, including cloud-top temperatures and heights, atmospheric temperature profiles for cloudy sky, and displacement-adjusted cloud images, can be useful for weather/climate and atmospheric studies, particularly for high-resolution hydrologic applications such as developing IR satellite-based rainfall estimates, which are urgently needed by mesoscale atmospheric modeling and studies, severe weather monitoring, and heavy precipitation and flash flood forecasting. Cloud-top height and displacement are estimated by applying stereoscopic analysis to a pair of corresponding scan-synchronous infrared images from geostationary satellites (GOES-east and GOES-west). A piecewise linear approximation relationship between cloud-top height and temperature, with a few (6 and 8) parameters is developed to simplify and speed-up the retrieval process. Optimal parameters are estimated using the Shuffled Complex Evolution (SCE-UA) algorithm to minimize the discrepancies between the brightness temperatures of the same location as registered by two satellites. The combination of the linear approximation and the fast optimization algorithm simplifies stereoscopic analysis and allows for its implementation on standard desktop computers. When compared to the standard isotherm matching approaches the proposed method yields higher correlation between simultaneous GOES-8 and GOES-9 images after parallax adjustment. The validity of the linear approximation was also tested against temperature profiles obtained from ground sounding measurements of the TRMM-TEFLUN experiments. This comparison demonstrated good fit between the optimized relationship and atmospheric sounding profile. The accuracy of cloud pixel geo-location was demonstrated through a spatial comparison between correlation of ground-based radar rainfall rate and corresponding both adjusted and original satellite IR images. Higher correlation was represented using displacement-adjusted IR images from both geostationary satellites (GOES) with high altitudes and low altitude satellite (TRMM). Higher correlation and lower RMSE between ground-based NEXRAD observations and estimated rainfall rates from spatial adjusted IR images, using an artificial neural networks algorithm (PERSIANN), present the rainfall retrieval improvement. The ability to differentiate ground surface particularly snow-covered areas from clouds in near-real-time is another useful application of estimated cloud-top height.