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Extreme Lake-Effect Snow from a GPM Microwave Imager Perspective: Observational Analysis and Precipitation Retrieval Evaluationrelative to the operational and PRT results. Figure 12 displays updated QPE results of the two case studies. For orbit 4140, the Lake Erie snowband (originally classified as coastline and now forced to be snow covered) is correctly detected and the core of the band shows comparable snow rates ( Fig. 12a compared to Fig. 4a ). The GPROF PRT–GV-MRMS correlation increases from 0.61 ( Table 1 ) to 0.67 ( Table 2 ), and precipitation rates are higher when forcing the GV-MRMS-only a priori database ( Fig
relative to the operational and PRT results. Figure 12 displays updated QPE results of the two case studies. For orbit 4140, the Lake Erie snowband (originally classified as coastline and now forced to be snow covered) is correctly detected and the core of the band shows comparable snow rates ( Fig. 12a compared to Fig. 4a ). The GPROF PRT–GV-MRMS correlation increases from 0.61 ( Table 1 ) to 0.67 ( Table 2 ), and precipitation rates are higher when forcing the GV-MRMS-only a priori database ( Fig
atmosphere. CESM-LENS relies on historical boundary conditions for the period 1920–2005, and the representative concentration pathway 8.5 (RCP8.5) is applied as forcing for years 2006–2100. Here we used only archives of simulation output from 1940 to 2005 to build our model covariance matrices, as the focus of our analysis is on improving prediction for the contemporary period. Note that the 40 ensemble members constitute independent but equally probable trajectories of the Earth system with historical
atmosphere. CESM-LENS relies on historical boundary conditions for the period 1920–2005, and the representative concentration pathway 8.5 (RCP8.5) is applied as forcing for years 2006–2100. Here we used only archives of simulation output from 1940 to 2005 to build our model covariance matrices, as the focus of our analysis is on improving prediction for the contemporary period. Note that the 40 ensemble members constitute independent but equally probable trajectories of the Earth system with historical
Precipitation Measurement Dual-Frequency Precipitation Radar (GPM DPR) . J. Atmos. Oceanic Technol. , 33 , 653 – 667 , https://doi.org/10.1175/JTECH-D-15-0097.1 . 10.1175/JTECH-D-15-0097.1 Heidke , P. , 1926 : Berechnung des Erfolges und der Gute der Windstarkevorhersagen im Sturmwarnungsdienst (Measures of success and goodness of wind force forecasts by the gale-warning service) . Geogr. Ann. , 8 , 301 – 349 . Hou , A. Y. , and Coauthors , 2014 : The Global Precipitation Measurement Mission
Precipitation Measurement Dual-Frequency Precipitation Radar (GPM DPR) . J. Atmos. Oceanic Technol. , 33 , 653 – 667 , https://doi.org/10.1175/JTECH-D-15-0097.1 . 10.1175/JTECH-D-15-0097.1 Heidke , P. , 1926 : Berechnung des Erfolges und der Gute der Windstarkevorhersagen im Sturmwarnungsdienst (Measures of success and goodness of wind force forecasts by the gale-warning service) . Geogr. Ann. , 8 , 301 – 349 . Hou , A. Y. , and Coauthors , 2014 : The Global Precipitation Measurement Mission
interest, exhibiting a plethora of modes caused by different physical processes (e.g., solar forcing, oceanic/atmospheric circulations, land–atmosphere interactions, etc.), and imprinting themselves at various spatial and temporal scales. The accurate identification and modeling of the modes of the climate system is necessary for many key problems in geosciences, such as weather/climate prediction, attribution of extreme events and hazards, and assessment of climate change impacts. The comprehensive
interest, exhibiting a plethora of modes caused by different physical processes (e.g., solar forcing, oceanic/atmospheric circulations, land–atmosphere interactions, etc.), and imprinting themselves at various spatial and temporal scales. The accurate identification and modeling of the modes of the climate system is necessary for many key problems in geosciences, such as weather/climate prediction, attribution of extreme events and hazards, and assessment of climate change impacts. The comprehensive
1. Introduction Global precipitation datasets, derived by combining data from various sources ranging from ground-based observations to radar and satellite data, are increasingly used by the Earth science community in applications such as land surface modeling, forcing and calibrating ecological and hydrological models, validation of climate models, trend analysis, water resources management, and extreme event characterization ( MacKellar et al. 2007 ; New et al. 2000 ). Several gridded
1. Introduction Global precipitation datasets, derived by combining data from various sources ranging from ground-based observations to radar and satellite data, are increasingly used by the Earth science community in applications such as land surface modeling, forcing and calibrating ecological and hydrological models, validation of climate models, trend analysis, water resources management, and extreme event characterization ( MacKellar et al. 2007 ; New et al. 2000 ). Several gridded
added to improve sampling. Figure 2 shows results from the new DYNAMO and GOAmazon simulations, which form the basis for the CSH algorithm’s new LUTs. On average, the model responds well to the forcing, be it fewer large events over ocean (e.g., DYNAMO) or more numerous weaker events over land (e.g., GOAmazon). Including the three new cases, a total of 10 cases (six oceanic and four continental), which are listed in Table 2 , were simulated to provide data for the new LUTs. The simulations all
added to improve sampling. Figure 2 shows results from the new DYNAMO and GOAmazon simulations, which form the basis for the CSH algorithm’s new LUTs. On average, the model responds well to the forcing, be it fewer large events over ocean (e.g., DYNAMO) or more numerous weaker events over land (e.g., GOAmazon). Including the three new cases, a total of 10 cases (six oceanic and four continental), which are listed in Table 2 , were simulated to provide data for the new LUTs. The simulations all
Enhancing PMW Satellite Precipitation Estimation: Detecting Convective Classthese projects have a valuable role in GPM mission, they do not provide a solution for accurate estimate of precipitation type from PMW observations. Apart from the fact that retrieving the precipitation type was not their primary goal, the lack of prediction skill in convective fraction likely comes from the insufficient depth of these models. With a recent increased reliance on graphical processing units (GPUs) for brute-force computations, NNs can be allowed to search for deeper, multidimensional
these projects have a valuable role in GPM mission, they do not provide a solution for accurate estimate of precipitation type from PMW observations. Apart from the fact that retrieving the precipitation type was not their primary goal, the lack of prediction skill in convective fraction likely comes from the insufficient depth of these models. With a recent increased reliance on graphical processing units (GPUs) for brute-force computations, NNs can be allowed to search for deeper, multidimensional
forcing and local conditions may exert different controls on different aspects of the water balance. From a climate perspective, there may be more correlations between precipitation and climate indices detected if climate indices were lagged in time, or if precipitation magnitudes were considered more explicitly. The presence of trends over time and correlations of information measures with climate indices indicates that the movement of precipitation in space, regardless of event size, has changed in
forcing and local conditions may exert different controls on different aspects of the water balance. From a climate perspective, there may be more correlations between precipitation and climate indices detected if climate indices were lagged in time, or if precipitation magnitudes were considered more explicitly. The presence of trends over time and correlations of information measures with climate indices indicates that the movement of precipitation in space, regardless of event size, has changed in
( Thompson et al. 2008 ) was used to provide microphysical simulation of clouds that are connected to satellite observation operators in radiance data assimilation, and Noah land surface model was used in atmospheric and land coupled simulation as well as within LIS spinup process. Boundary forcing came from the Global Forecast System ( Whitaker et al. 2008 ). Hourly accumulated rainfall fields (currently NU-WRF EDAS does not facilitate output temporal resolutions finer than hourly) are generated at 3-km
( Thompson et al. 2008 ) was used to provide microphysical simulation of clouds that are connected to satellite observation operators in radiance data assimilation, and Noah land surface model was used in atmospheric and land coupled simulation as well as within LIS spinup process. Boundary forcing came from the Global Forecast System ( Whitaker et al. 2008 ). Hourly accumulated rainfall fields (currently NU-WRF EDAS does not facilitate output temporal resolutions finer than hourly) are generated at 3-km
