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The overarching goal of this proposal is to develop, improve, and deliver physically-based at-launch falling snow detection and estimation algorithms for radiometers of the Global Precipitation Measurement (GPM) mission. Historically, retrievals of falling snow have been difficult due to the relative insensitivity of satellite rain-based channels as used in the past and yet falling snow is an important part of the water cycle. We will emphasize the use of high frequency passive microwave channels (85-200 GHz) since these are most sensitive to the ice in clouds. Three of the major challenges addressed in the proposed work include: (1) assessing the effects of land surface signatures that contaminate falling snow signatures in brightness temperature (TB) observations; (2) adequately modeling the physical characteristics of precipitating snow in radiative transfer calculations for database construction; and (3) ensuring that the retrieval database is representative of falling snow events. The first specific task of the proposed research is to determine the sensitivity of snowfall detection and estimation to input variables such as surface emission, snowflake particle shape, and vertical temperature (T) and humidity (RH) profiles. These input variables will be provided by collaborators (or the GPM project) and our proposed work will use theoretical end-to-end simulations to link the uncertainty of these inputs to the resultant accuracy (and errors) of the retrievals. The second specific task is to generate a falling snow database for the Bayesian retrievals. Here we propose to develop a radar-enhanced high frequency (REHF) database in conjunction with our previously developed Bayesian falling snow retrieval algorithm to perform snow retrievals in high frequency passive microwave swaths. The key benefit of being "radar-enhanced" is that the inherent uncertainties in the vertical structure and microphysical properties are necessarily reduced due to the additional information inferred, pre-launch, from radar derived vertical profiles in the database. Following launch, the database will be augmented with DPR-derived observations. The third specific task is to develop falling snow detection and estimation algorithms, implement into code, test/validate, improve and deliver to PPS. We plan to have the following deliverables as part of this proposal: [1] A REHF database containing vertical profiles of the physical properties of falling snow, associated radar reflectivities, and high frequency passive microwave TBs, [2] Detection and estimation code with affiliated Algorithm Theoretical Basis Document (ATBD), [3] Thresholds of detection and estimation rates for falling snow, [4] Publications conveying our methodology, validation, and results. The significance of our proposed work is that it will provide falling snow detection and estimation algorithms for the GPM core satellite and for use on other constellation radiometers with millimeter-wave channel sets. This work will also allow for an improved understanding of the relationships between radiative properties associated with radar reflectivities, TB, and the physical properties of frozen precipitation within a cloud. The PI, Co-I, and collaborators assembled for this study have demonstrated capabilities for snowfall retrievals, active passive retrievals, measuring the physical properties of snow, and modeling the radiative properties of non-spherical ice and mixed-phase precipitation particles. This proposal directly addresses the Precipitation Science (PMM) ROSES NRA Research Category of Algorithm Development and Validation and subcategory 1.1.4 "Development, testing, and validation of radiometer retrieval algorithms for improved detection and estimation of light rain and falling snow over land and ocean in the middle and high latitudes."

Project PI: Gail Skofronick Jackson/NASA Goddard Space Flight Center

NASA/GSFC Mail Code 613.1 Greenbelt, MD 20771

Phone: 301.614.5720

Fax: 301.614.5492

Email: gail.s.jackson@nasa.gov

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Started: Sep 29, 2010

Last Activity: Mar 16, 2011

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