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-ocean regions that experience significant amounts of shallow cumuliform snow over vast geographical expanses. A common trait shared among the peak oceanic shallow snowfall regions is reduced or negligible winter sea ice coverage. Other oceanic Northern Hemispheric areas are associated with somewhat elevated shallow snow fractions exceeding ~40% [e.g., Hudson Bay (60°N, 85°W), Baffin Bay (73°N, 67°W), the Arctic Ocean near Alaska and eastern Siberia, and Kara Sea (77°N, 77°E)], but increased ice cover most
-ocean regions that experience significant amounts of shallow cumuliform snow over vast geographical expanses. A common trait shared among the peak oceanic shallow snowfall regions is reduced or negligible winter sea ice coverage. Other oceanic Northern Hemispheric areas are associated with somewhat elevated shallow snow fractions exceeding ~40% [e.g., Hudson Bay (60°N, 85°W), Baffin Bay (73°N, 67°W), the Arctic Ocean near Alaska and eastern Siberia, and Kara Sea (77°N, 77°E)], but increased ice cover most
( Ferraro et al. 2000 ; Weng et al. 2003 ; Vila et al. 2007 ) employs a technique ( Kongoli et al. 2003 ; H. Meng et al. 2012, meeting presentation) through which a combination of MW sounding channels is used to distinguish between the scattering features over land surfaces (especially snow cover) and that of the atmosphere (precipitation-sized ice particles). However, a long-standing difficulty remains in dry atmospheres (e.g., total water vapor column of less than 10–15 mm), where even the 183-GHz
( Ferraro et al. 2000 ; Weng et al. 2003 ; Vila et al. 2007 ) employs a technique ( Kongoli et al. 2003 ; H. Meng et al. 2012, meeting presentation) through which a combination of MW sounding channels is used to distinguish between the scattering features over land surfaces (especially snow cover) and that of the atmosphere (precipitation-sized ice particles). However, a long-standing difficulty remains in dry atmospheres (e.g., total water vapor column of less than 10–15 mm), where even the 183-GHz
constellation PMW-only satellites [e.g., the Advanced Microwave Scanning Radiometer-2 (AMSR-2) onboard the Global Change Observation Mission–Water (GCOM-W) spacecraft, operated by JAXA; Hou et al. 2014 ]. With its 65° inclination, GPM observes more land surface area relative to the predecessor Tropical Rainfall Measuring Mission (TRMM) and associated extremes in seasonality, cold and snow-covered surfaces, inland water, and forest and vegetation. From space, the associated radar surface backscatter and the
constellation PMW-only satellites [e.g., the Advanced Microwave Scanning Radiometer-2 (AMSR-2) onboard the Global Change Observation Mission–Water (GCOM-W) spacecraft, operated by JAXA; Hou et al. 2014 ]. With its 65° inclination, GPM observes more land surface area relative to the predecessor Tropical Rainfall Measuring Mission (TRMM) and associated extremes in seasonality, cold and snow-covered surfaces, inland water, and forest and vegetation. From space, the associated radar surface backscatter and the
study period. Figure 2 highlights the temporal variability of percentage of area contribution for MW and IR sensors over the CONUS. In general, the IR contribution over the United States is relatively high during the winter season of 2013/14 (reaches up to 60% in February), whereas the MW coverage is high from April to November (with nearly 90% area coverage from June to September). We speculate that the lesser MW contribution during the winter is related to the presence of snow and ice cover and
study period. Figure 2 highlights the temporal variability of percentage of area contribution for MW and IR sensors over the CONUS. In general, the IR contribution over the United States is relatively high during the winter season of 2013/14 (reaches up to 60% in February), whereas the MW coverage is high from April to November (with nearly 90% area coverage from June to September). We speculate that the lesser MW contribution during the winter is related to the presence of snow and ice cover and
. Section 4 presents the categorization method for the time series and the error metrics applied in the following analysis. Results are reviewed in section 5 , and conclusions are drawn in section 6 . 2. Study area and data a. Study area The study area is the upper Adige River basin closed at Bronzolo (~7000 km 2 ), a mountainous region covered by broadleaf and conifer forests located in the eastern Italian Alps ( Fig. 1 ). This region is characterized by steep topographic gradients with elevation
. Section 4 presents the categorization method for the time series and the error metrics applied in the following analysis. Results are reviewed in section 5 , and conclusions are drawn in section 6 . 2. Study area and data a. Study area The study area is the upper Adige River basin closed at Bronzolo (~7000 km 2 ), a mountainous region covered by broadleaf and conifer forests located in the eastern Italian Alps ( Fig. 1 ). This region is characterized by steep topographic gradients with elevation
scale. While rain (and snow) gauges remain the de facto source of conventional information on precipitation, their uneven distribution, and therefore their representativeness, limits their usefulness for measuring global precipitation. Similarly, surface radar datasets have limited spatial extent. The ability of satellite instrumentation to provide regular global observations is therefore a key component of any global precipitation measurement system. Satellite observations using visible (Vis) and
scale. While rain (and snow) gauges remain the de facto source of conventional information on precipitation, their uneven distribution, and therefore their representativeness, limits their usefulness for measuring global precipitation. Similarly, surface radar datasets have limited spatial extent. The ability of satellite instrumentation to provide regular global observations is therefore a key component of any global precipitation measurement system. Satellite observations using visible (Vis) and
from a single convective episode (e.g., Li et al. 2010 ). Tao and Matsui (2015) decomposed a reflectivity CFAD from a cloud simulation with a bulk microphysics scheme (see their Fig. 8). Their study suggests that the majority of the mode reflectivities in the solid-phase zone are dominated by snow aggregates from the deep stratiform portion of their simulated MCS, while the infrequent occurrence of intense echoes is predominantly contributed by hail within the convective cores. In this way
from a single convective episode (e.g., Li et al. 2010 ). Tao and Matsui (2015) decomposed a reflectivity CFAD from a cloud simulation with a bulk microphysics scheme (see their Fig. 8). Their study suggests that the majority of the mode reflectivities in the solid-phase zone are dominated by snow aggregates from the deep stratiform portion of their simulated MCS, while the infrequent occurrence of intense echoes is predominantly contributed by hail within the convective cores. In this way
