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transitions to closed-cell stratocumulus ( McCoy et al. 2017 ; Bodas-Salcedo et al. 2016 ; Naud et al. 2016 ). In the warm sector of cyclones, water vapor is drawn into the system from lower latitudes where it converges along the warm conveyor belt ( Carlson 1998 ; Ralph et al. 2004 ) to form snow that provides energy to deepening cyclones ( Browning and Pardoe 1973 ; Shapiro and Keiser 1990 ). In the cold air sectors that are dominated by transitions between open- and closed-cellular convection, the
transitions to closed-cell stratocumulus ( McCoy et al. 2017 ; Bodas-Salcedo et al. 2016 ; Naud et al. 2016 ). In the warm sector of cyclones, water vapor is drawn into the system from lower latitudes where it converges along the warm conveyor belt ( Carlson 1998 ; Ralph et al. 2004 ) to form snow that provides energy to deepening cyclones ( Browning and Pardoe 1973 ; Shapiro and Keiser 1990 ). In the cold air sectors that are dominated by transitions between open- and closed-cellular convection, the
, the reflectivity–snow rate ( Z–S ) relationships employed in radar-based algorithms, either explicitly or implicitly, depend on the particular assumptions about microphysical properties and their uncertainties that are made within the algorithms. For passive microwave retrievals as from GMI, snowfall estimates can also be affected by variable surface emissivity, especially over snow-covered surfaces. The land surface variable emissivity hinders falling snow detection compared to oceanic
, the reflectivity–snow rate ( Z–S ) relationships employed in radar-based algorithms, either explicitly or implicitly, depend on the particular assumptions about microphysical properties and their uncertainties that are made within the algorithms. For passive microwave retrievals as from GMI, snowfall estimates can also be affected by variable surface emissivity, especially over snow-covered surfaces. The land surface variable emissivity hinders falling snow detection compared to oceanic
limitations, hail retrievals trained in one location (often the United States) may not translate to other locations around the globe. Cecil and Blankenship (2012) attempt to mitigate this issue by applying regional scaling factors to different regional boxes throughout the AMSR-E domain. Those scaling factors are based on empirical relationships between T b and radar profiles in each region. Surface snow or ice cover can be a major problem for retrievals at high latitudes, or over mountains. Icy or
limitations, hail retrievals trained in one location (often the United States) may not translate to other locations around the globe. Cecil and Blankenship (2012) attempt to mitigate this issue by applying regional scaling factors to different regional boxes throughout the AMSR-E domain. Those scaling factors are based on empirical relationships between T b and radar profiles in each region. Surface snow or ice cover can be a major problem for retrievals at high latitudes, or over mountains. Icy or
1. Introduction Solid precipitation and its deposition as snow are of great importance for Earth’s energy budget and its hydrological cycle. Especially in the Arctic and already at latitudes higher than 60°N, snowfall is the predominant precipitation type ( Levizzani et al. 2011 ). We know today that temperatures are rising about 2 times faster in the Arctic than anywhere else on Earth due to global warming ( IPCC 2007 ; Serreze and Barry 2011 ), known as Arctic amplification. This has a great
1. Introduction Solid precipitation and its deposition as snow are of great importance for Earth’s energy budget and its hydrological cycle. Especially in the Arctic and already at latitudes higher than 60°N, snowfall is the predominant precipitation type ( Levizzani et al. 2011 ). We know today that temperatures are rising about 2 times faster in the Arctic than anywhere else on Earth due to global warming ( IPCC 2007 ; Serreze and Barry 2011 ), known as Arctic amplification. This has a great
found that channels near 85 and 150 GHz appear useful for the detection of snow, and the response to snow and/or graupel increases with increasing frequency. Hong et al. (2005) found that these high-frequency channels are most sensitive to the presence of graupel, followed by cloud ice and snow. The assumption of spherical particles greatly simplifies radiative transfer simulations but is not realistic for many frozen particles. Olson et al. (2016) were able to match 165-GHz BT measurements
found that channels near 85 and 150 GHz appear useful for the detection of snow, and the response to snow and/or graupel increases with increasing frequency. Hong et al. (2005) found that these high-frequency channels are most sensitive to the presence of graupel, followed by cloud ice and snow. The assumption of spherical particles greatly simplifies radiative transfer simulations but is not realistic for many frozen particles. Olson et al. (2016) were able to match 165-GHz BT measurements
(GPROF surface types 3–5, corresponding to “maximum vegetation,” “high vegetation,” and “moderate vegetation”) or ocean (GPROF surface type 1). The “ocean” classification can include large water bodies, for example, the Great Lakes. Sea ice, arid regions, surface snow cover, rivers, coasts, and precipitation scenes are excluded. Each orbit is divided into 5° latitude bins. Statistics are derived separately for each of these bins that has at least 10 land and 10 water pixels without precipitation
(GPROF surface types 3–5, corresponding to “maximum vegetation,” “high vegetation,” and “moderate vegetation”) or ocean (GPROF surface type 1). The “ocean” classification can include large water bodies, for example, the Great Lakes. Sea ice, arid regions, surface snow cover, rivers, coasts, and precipitation scenes are excluded. Each orbit is divided into 5° latitude bins. Statistics are derived separately for each of these bins that has at least 10 land and 10 water pixels without precipitation
the three datasets, we need to ensure that these categories are not dependent on each dataset’s resolution. This is because the frequency of occurrence of precipitation changes with resolution ( Stephens et al. 2010 ). Matching CloudSat ’s one-dimensional sampling with IMERG two-dimensional product is not a trivial exercise. We need to define the length of a segment along the CloudSat track that would best represent the area covered by IMERG. This length should be between the actual size of an
the three datasets, we need to ensure that these categories are not dependent on each dataset’s resolution. This is because the frequency of occurrence of precipitation changes with resolution ( Stephens et al. 2010 ). Matching CloudSat ’s one-dimensional sampling with IMERG two-dimensional product is not a trivial exercise. We need to define the length of a segment along the CloudSat track that would best represent the area covered by IMERG. This length should be between the actual size of an
CRM (i.e., GCE) simulations for their LUTs. The National Aeronautics and Space Administration–Japan Aerospace Exploration Agency (NASA–JAXA) joint science team initially recommended using the current TRMM CSH and SLH algorithms to produce GPM LH products over the same domain as TRMM at the beginning of July 2015. However, to cover the entire GPM domain the CSH and SLH algorithms will need to be able to retrieve LH structures (profiles) associated with high-latitude weather events (i.e., frontal
CRM (i.e., GCE) simulations for their LUTs. The National Aeronautics and Space Administration–Japan Aerospace Exploration Agency (NASA–JAXA) joint science team initially recommended using the current TRMM CSH and SLH algorithms to produce GPM LH products over the same domain as TRMM at the beginning of July 2015. However, to cover the entire GPM domain the CSH and SLH algorithms will need to be able to retrieve LH structures (profiles) associated with high-latitude weather events (i.e., frontal
events satisfying these criteria from GPM, color coded by season. The requirement of surface air temperature > 10°C reduces artifacts from surface snow and ice cover. The seasonality of PFs satisfying these criteria ( Fig. 4c ) generally conforms to expectations, with mid- and high-latitude events occurring almost exclusively during summer and subtropical events mostly mixed among the spring and summer months. Note that a few hail PFs are found in local winter in midlatitude regions. For example
events satisfying these criteria from GPM, color coded by season. The requirement of surface air temperature > 10°C reduces artifacts from surface snow and ice cover. The seasonality of PFs satisfying these criteria ( Fig. 4c ) generally conforms to expectations, with mid- and high-latitude events occurring almost exclusively during summer and subtropical events mostly mixed among the spring and summer months. Note that a few hail PFs are found in local winter in midlatitude regions. For example
