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Extreme Lake-Effect Snow from a GPM Microwave Imager Perspective: Observational Analysis and Precipitation Retrieval Evaluationwaters, coastlines, and sea ice edge. These classes come from a cluster analysis, purely empirical self-grouping of emissivity characteristics ( Prigent et al. 2006 ). The TPW and T2m parameters are obtained from the Global Atmospheric Analysis (GANAL; JMA 2000 ) and the European Centre for Medium-Range Weather Forecasts ( Dee et al. 2011 ) reanalysis datasets for the operational and the climatological GPROF outputs, respectively. For this study, the 1C-R-GMI product (TBs) and the climatological 2A
waters, coastlines, and sea ice edge. These classes come from a cluster analysis, purely empirical self-grouping of emissivity characteristics ( Prigent et al. 2006 ). The TPW and T2m parameters are obtained from the Global Atmospheric Analysis (GANAL; JMA 2000 ) and the European Centre for Medium-Range Weather Forecasts ( Dee et al. 2011 ) reanalysis datasets for the operational and the climatological GPROF outputs, respectively. For this study, the 1C-R-GMI product (TBs) and the climatological 2A
, and E. J. Moyer , 2016 : Changes in spatiotemporal precipitation patterns in changing climate conditions . J. Climate , 29 , 8355 – 8376 , https://doi.org/10.1175/JCLI-D-15-0844.1 . 10.1175/JCLI-D-15-0844.1 Davis , C. , B. Brown , and R. Bullock , 2006 : Object-based verification of precipitation forecasts. Part I: Methodology and application to mesoscale rain areas . Mon. Wea. Rev. , 134 , 1772 – 1784 , https://doi.org/10.1175/MWR3145.1 . 10.1175/MWR3145.1 Demaria , E. M. C
, and E. J. Moyer , 2016 : Changes in spatiotemporal precipitation patterns in changing climate conditions . J. Climate , 29 , 8355 – 8376 , https://doi.org/10.1175/JCLI-D-15-0844.1 . 10.1175/JCLI-D-15-0844.1 Davis , C. , B. Brown , and R. Bullock , 2006 : Object-based verification of precipitation forecasts. Part I: Methodology and application to mesoscale rain areas . Mon. Wea. Rev. , 134 , 1772 – 1784 , https://doi.org/10.1175/MWR3145.1 . 10.1175/MWR3145.1 Demaria , E. M. C
modeling ( Maggioni et al. 2011 ; Nikolopoulos et al. 2010 ; White and Singham 2012 ). Though ensemble prediction models have been developed for some applications, flood forecasting in particular ( Cloke and Pappenberger 2009 ), assembling such ensembles can be nontrivial, while for complex physics-based models, the requisite multiple simulations may not be computationally feasible for real-time applications. An alternative approach is to directly ingest precipitation distributions generated by SMPP
modeling ( Maggioni et al. 2011 ; Nikolopoulos et al. 2010 ; White and Singham 2012 ). Though ensemble prediction models have been developed for some applications, flood forecasting in particular ( Cloke and Pappenberger 2009 ), assembling such ensembles can be nontrivial, while for complex physics-based models, the requisite multiple simulations may not be computationally feasible for real-time applications. An alternative approach is to directly ingest precipitation distributions generated by SMPP
observations and the thermal energy equation. More recently, Ahmed et al. (2016) built an algorithm to retrieve LH based on the sizes of convective and stratiform areas as well as their echo-top heights from a multiweek Weather Research and Forecasting (WRF) Model simulation using data from the Dynamics of the MJO (DYNAMO) field campaign in the Indian Ocean. In addition, the original TRMM-related algorithms have and will need to continue to evolve, especially with the expansion of TRMM’s successor, the
observations and the thermal energy equation. More recently, Ahmed et al. (2016) built an algorithm to retrieve LH based on the sizes of convective and stratiform areas as well as their echo-top heights from a multiweek Weather Research and Forecasting (WRF) Model simulation using data from the Dynamics of the MJO (DYNAMO) field campaign in the Indian Ocean. In addition, the original TRMM-related algorithms have and will need to continue to evolve, especially with the expansion of TRMM’s successor, the
Beyond the Pixel: Using Patterns and Multiscale Spatial Information to Improve the Retrieval of Precipitation from Spaceborne Passive Microwave Imagersassociated with medium TBs, low or medium precipitation rates are associated with high TBs, high convective precipitation rates are associated with medium TBs and extreme deep convective precipitation rates are associated with low TBs. Figure 6 shows a tropical mesoscale convective system observed by GMI at 37 and 89 GHz over the Atlantic Ocean off the coast of Brazil at 0640 UTC 9 October 2016 (orbit 14852). At 37 GHz the system appears as a 300 km by 300 km area with an average TB higher than 245 K
associated with medium TBs, low or medium precipitation rates are associated with high TBs, high convective precipitation rates are associated with medium TBs and extreme deep convective precipitation rates are associated with low TBs. Figure 6 shows a tropical mesoscale convective system observed by GMI at 37 and 89 GHz over the Atlantic Ocean off the coast of Brazil at 0640 UTC 9 October 2016 (orbit 14852). At 37 GHz the system appears as a 300 km by 300 km area with an average TB higher than 245 K
resolutions are critical for near-real-time applications such as rapid monitoring and forecasting of high-impact societal events like flash floods, debris flows, and shallow landslides. Such resolution can be obtained primarily from satellite sensors on board geostationary Earth orbit (GEO) platforms. NOAA’s Advanced Baseline Imager (ABI) sensor on board the latest generation of Geostationary Operational Environmental Satellites (GOES-R Series) provides 3 times more spectral channels, 4 times the
resolutions are critical for near-real-time applications such as rapid monitoring and forecasting of high-impact societal events like flash floods, debris flows, and shallow landslides. Such resolution can be obtained primarily from satellite sensors on board geostationary Earth orbit (GEO) platforms. NOAA’s Advanced Baseline Imager (ABI) sensor on board the latest generation of Geostationary Operational Environmental Satellites (GOES-R Series) provides 3 times more spectral channels, 4 times the
