Thursday, 26 January 2017: 1:45 PM
Conference Center: Yakima 2 (Washington State Convention Center )
An ATMS snowfall rate (SFR) product has been developed at NOAA/NESDIS with the support of the JPSS Proving Ground and Risk Reduction program. The algorithm includes two main components: snowfall detection and snowfall rate estimation. Both components rely on the high frequencies at 88 GHz and above due to their sensitivity to solid precipitation. The former takes a statistical approach and employs principal component analysis and logistic regression model. In addition, a set of NWP model based filters is also applied to improve the accuracy of snowfall detection. The snowfall rate component is a physical algorithm that first retrieves cloud properties following a variational approach. Snowfall rate is further derived from these properties. The product has been validated extensively against gauge observations and radar snowfall rate estimates with satisfactory results (e.g. comparable correlation as passive microwave rain rate products). The ATMS SFR product, together with the operational SFR product derived from AMSU/MHS, can provide about ten snowfall rate estimates daily in mid-latitudes. As mandated by NOAA, the SFR product has operational users in such fields like weather forecasting and hydrology. Product assessment at the National Weather Service has demonstrated the value of this satellite product in filling radar gaps in observation-deprived regions. The SFR product also enables global blended precipitation analyses (such as NOAA CMORPH) to include satellite-based winter precipitation estimates. Traditionally, such data sets either lack snowfall estimates or rely on other data sources (gauge, model, etc.).
This presentation will include a description of the SFR algorithm and some recent development, product validation against ground observations and radar product, and its applications in weather forecasting and hydrology.
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