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Intense mesoscale convective systems (MCS) pose challenges for precipitation estimation and contribute a substantial portion of the normal precipitation in certain regions, particularly mid-latitude continental locations. These systems have well-developed mixed phase regions with abundant large graupel and often hail, much of which melts while falling to the surface as rain. The hypothesis of this proposal is that precipitation retrieval algorithms that are well suited for more common rainfall situations may have larger errors due to the radar and radiometric characteristics of these intense MCS. This proposed study will examine the structure and precipitation properties of intense MCS, with an emphasis on how they are treated by precipitation retrieval algorithms. It will also examine the spatial / temporal variability of intense MCS occurrence, and implications for error characteristics of precipitation climatologies. A large sample of intense MCS observed by the TRMM satellite (TMI, PR, LIS, and VIRS measurements, retrievals from the TRMM algorithms, and retrievals from future algorithms as they become available through the science team) will be used to evaluate the variability and relative error characteristics among precipitation retrieval algorithms, and to characterize the types of systems and environments that pose particular challenges for retrievals. The full 1997-present TRMM dataset will also be used to quantify the contribution to regional / seasonal rainfall from these intense MCS in the regions where they occur. Passive microwave measurements from AMSR-E and SSM/I will be used for extending the study through middle and high latitudes. Field campaign measurements (e.g., Mid-latitude Continental Convective Clouds Experiments (MC3E) in the North American plains; campaigns led by South American GPM investigators) will be used for more detailed study of individual intense MCS. These field campaigns and additional ground-based validation measurements (e.g., rain gauge networks in the U.S., Argentina, and Brazil) will contribute to quantifying the error characteristics of retrieval algorithms for intense MCS. This proposal addresses multiple topics in the NASA Precipitation Science program. It is submitted under research category 2.1 (algorithm development and validation), topics 1.1.2, 1.1.3, and 1.3 (testing and validation of radar and radiometer retrieval algorithms, and error characterization of satellite rainfall retrievals and/or ground-based measurements...). It will examine the performance and error characteristics of current satellite rainfall products in intense MCS, and future products as they become available from GPM team members. Although not contributing development of a new algorithm, it will contribute to testing / validation of radar and radiometer retrieval algorithms (primarily over land, where the intense storms most often occur). Results will be routinely conveyed to algorithm developers, so that test versions of algorithms can be improved before they are finalized. The proposal also applies to research category 2, topics 2.1 (use of satellite and field campaign data to study precipitation and microphysical processes, particularly for mixed phase and frozen precipitation...) and 2.3 (analysis of TRMM and other current satellite-based precipitation information for studies of climate and weather...).
Project PI: Daniel Cecil/University of Alabama in Huntsville
Earth System Science Center University of Alabama in Huntsville 320 Sparkman Dr. NW Huntsville, AL 35805
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