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1. Introduction Snow plays an important role in the Earth energy exchange processes and is a fundamental element of the water cycle. Higher-latitude regions are experiencing significant modifications related to climate change. While the effect on temperatures is relatively well known, the impacts on precipitation, snow/ice extent, and snow/ice properties are less documented and less understood. The use of satellites for snowfall monitoring and quantification and for retrieving snow-cover
1. Introduction Snow plays an important role in the Earth energy exchange processes and is a fundamental element of the water cycle. Higher-latitude regions are experiencing significant modifications related to climate change. While the effect on temperatures is relatively well known, the impacts on precipitation, snow/ice extent, and snow/ice properties are less documented and less understood. The use of satellites for snowfall monitoring and quantification and for retrieving snow-cover
Johnson 2011 ). These characteristics are difficult to accurately parameterize as of today. Second, the already weak snowfall scattering signal tends to be masked by the increased atmospheric emissivity and liquid water content in precipitating conditions ( Liu and Seo 2013 ; Wang et al. 2013 ; Panegrossi et al. 2017 ). Third, changes in surface emissivity due to snow accumulation on the ground can significantly alter the snowfall microwave signal. Dry snow cover scatters the upwelling surface
Johnson 2011 ). These characteristics are difficult to accurately parameterize as of today. Second, the already weak snowfall scattering signal tends to be masked by the increased atmospheric emissivity and liquid water content in precipitating conditions ( Liu and Seo 2013 ; Wang et al. 2013 ; Panegrossi et al. 2017 ). Third, changes in surface emissivity due to snow accumulation on the ground can significantly alter the snowfall microwave signal. Dry snow cover scatters the upwelling surface
/ocean boundaries), respectively (see Grecu et al. 2004 , 2016 ). Alternatively, MRMS is used over snow-covered-land surfaces. One year of Ku-V04, CORRA-V04 retrievals (from September 2014 to August 2015) and two years of matched MRMS data (from April 2014 to August 2016) populate the a priori dataset. Out of the three different precipitation estimates provided by GPROF (surfacePrecipitation, mostLikelyPrecipitation, frozenPrecipitation), only the surfacePrecipitation product is assessed for the agreement
/ocean boundaries), respectively (see Grecu et al. 2004 , 2016 ). Alternatively, MRMS is used over snow-covered-land surfaces. One year of Ku-V04, CORRA-V04 retrievals (from September 2014 to August 2015) and two years of matched MRMS data (from April 2014 to August 2016) populate the a priori dataset. Out of the three different precipitation estimates provided by GPROF (surfacePrecipitation, mostLikelyPrecipitation, frozenPrecipitation), only the surfacePrecipitation product is assessed for the agreement
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
Precipitation Measurement (GPM) Core Observatory , was launched in February 2014 and extends observations of both falling snow and heavy to light rain past 65°N/S ( Hou et al. 2014 ). To provide nearly global coverage with short revisit times, the TRMM and GPM missions rely on a constellation of partner satellites. The TRMM Multisatellite Precipitation Analysis (TMPA) covers the area from 50°N/S from 2000 to present ( Table 1 ), while the Integrated Multisatellite Retrievals for GPM (IMERG) covers 60°N
Precipitation Measurement (GPM) Core Observatory , was launched in February 2014 and extends observations of both falling snow and heavy to light rain past 65°N/S ( Hou et al. 2014 ). To provide nearly global coverage with short revisit times, the TRMM and GPM missions rely on a constellation of partner satellites. The TRMM Multisatellite Precipitation Analysis (TMPA) covers the area from 50°N/S from 2000 to present ( Table 1 ), while the Integrated Multisatellite Retrievals for GPM (IMERG) covers 60°N
alternative to study and potentially nowcast landslide hazard globally ( Hong et al. 2007 ; Kirschbaum et al. 2009 ; Farahmand and AghaKouchak 2013 ). This approach has recently culminated into an automatic hazard awareness system, based on daily and antecedent (7 days) rainfall derived from satellite multisensor precipitation products (SMPP) and susceptibility maps derived from global maps of land cover, slope and distance to roads and faults ( Kirschbaum et al. 2015 ; Kirschbaum and Stanley 2018
alternative to study and potentially nowcast landslide hazard globally ( Hong et al. 2007 ; Kirschbaum et al. 2009 ; Farahmand and AghaKouchak 2013 ). This approach has recently culminated into an automatic hazard awareness system, based on daily and antecedent (7 days) rainfall derived from satellite multisensor precipitation products (SMPP) and susceptibility maps derived from global maps of land cover, slope and distance to roads and faults ( Kirschbaum et al. 2015 ; Kirschbaum and Stanley 2018
. With its onboard Dual-Frequency Precipitation Radar (DPR) and 13-channel GPM Microwave Imager (GMI), the GPM satellite extends into future decades the global surveillance of precipitation provided until 2014 by the Tropical Rainfall Measuring Mission (TRMM) satellite and broadens coverage to higher latitudes, where many of Earth’s snow-covered mountain ranges are located. GPM also serves as a reference for other satellites carrying a variety of microwave imaging or sounding radiometers [see Hou et
. With its onboard Dual-Frequency Precipitation Radar (DPR) and 13-channel GPM Microwave Imager (GMI), the GPM satellite extends into future decades the global surveillance of precipitation provided until 2014 by the Tropical Rainfall Measuring Mission (TRMM) satellite and broadens coverage to higher latitudes, where many of Earth’s snow-covered mountain ranges are located. GPM also serves as a reference for other satellites carrying a variety of microwave imaging or sounding radiometers [see Hou et
, 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
Ground Validation of Surface Snowfall Algorithm in GPM Dual-Frequency Precipitation Radar. The solid black line is the left boundary of the DPR outer swath. Black dashed lines are the boundaries of the DPR inner swath. In Fig. 4b , hydrometeor type from ground radar shows most of the scan is covered by crystal and dry snow, with a small area of scattered rain identified to the west of the radar. DPR reflectivity at 2-km height is in Fig. 4c and the corresponding snow flag is illustrated in Fig. 4d . The region between KCLE 100-km range (big circle) and the DPR inner swath is pretty
. The solid black line is the left boundary of the DPR outer swath. Black dashed lines are the boundaries of the DPR inner swath. In Fig. 4b , hydrometeor type from ground radar shows most of the scan is covered by crystal and dry snow, with a small area of scattered rain identified to the west of the radar. DPR reflectivity at 2-km height is in Fig. 4c and the corresponding snow flag is illustrated in Fig. 4d . The region between KCLE 100-km range (big circle) and the DPR inner swath is pretty
Retrieval of Snow Water Equivalent by the Precipitation Imaging Package (PIP) in the Northern Great Lakeset al. 2018 , 2020 ). The Multi-Angle Snowflake Camera (MASC) is another dedicated instrument designed to measure the properties of falling snowflakes. It captures high-resolution photographs of hydrometeors from three angles from which the habit can be identified ( Garrett et al. 2012 ). The Precipitation Imaging Package (PIP), formerly called the Snow Video Imager (SVI) was developed to measure the size and fall velocity of snowflakes. It has been widely used since its introduction ( Newman
et al. 2018 , 2020 ). The Multi-Angle Snowflake Camera (MASC) is another dedicated instrument designed to measure the properties of falling snowflakes. It captures high-resolution photographs of hydrometeors from three angles from which the habit can be identified ( Garrett et al. 2012 ). The Precipitation Imaging Package (PIP), formerly called the Snow Video Imager (SVI) was developed to measure the size and fall velocity of snowflakes. It has been widely used since its introduction ( Newman
