The third algorithm intercomparison project （AIP-3） involved rain estimates from more than 50 satellite rainfall algorithms and ground radar measurements within the Intensive Flux Array （IFA） over the equatorial w...The third algorithm intercomparison project （AIP-3） involved rain estimates from more than 50 satellite rainfall algorithms and ground radar measurements within the Intensive Flux Array （IFA） over the equatorial western Pacific warm pool region during the Tropical Ocean Global Atmosphere coupled Ocean-Atmosphere Response Experiment （TOGA COARE）. Early results indicated that there was a sys- tematic bias between rainrates from satellite passive microwave and ground radar measurements. The mean rainrate from radar measurements is about 50% underestimated compared to that from passive microwave-based retrieval algorithms. This paper is designed to analyze rain patterns from the Florida State University rain retrieval algorithm and radar measurements to understand physically the rain discrep- ancies. Results show that there is a clear range-dependent bias associated with the radar measurements. However, this range-dependent systematical bias is almost eliminated with the corrected radar rainrates. Results suggest that the effects from radar attenuation correction, calibration and beam filling are the major sources of rain discrepancies. This study demonstrates that rain retrievals based on satellite mea- surements from passive microwave radiometers such as the Special Sensor of Microwave Imager （SSM/I） are reliable, while rain estimates from ground radar measurements are correctable.展开更多
文摘The third algorithm intercomparison project （AIP-3） involved rain estimates from more than 50 satellite rainfall algorithms and ground radar measurements within the Intensive Flux Array （IFA） over the equatorial western Pacific warm pool region during the Tropical Ocean Global Atmosphere coupled Ocean-Atmosphere Response Experiment （TOGA COARE）. Early results indicated that there was a sys- tematic bias between rainrates from satellite passive microwave and ground radar measurements. The mean rainrate from radar measurements is about 50% underestimated compared to that from passive microwave-based retrieval algorithms. This paper is designed to analyze rain patterns from the Florida State University rain retrieval algorithm and radar measurements to understand physically the rain discrep- ancies. Results show that there is a clear range-dependent bias associated with the radar measurements. However, this range-dependent systematical bias is almost eliminated with the corrected radar rainrates. Results suggest that the effects from radar attenuation correction, calibration and beam filling are the major sources of rain discrepancies. This study demonstrates that rain retrievals based on satellite mea- surements from passive microwave radiometers such as the Special Sensor of Microwave Imager （SSM/I） are reliable, while rain estimates from ground radar measurements are correctable.
