Cross-validation of active and passive microwave snowfall products over the continental United States

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  • 1 National Centre for Earth Observation, University of Leicester, Leicester, United Kingdom
  • 2 Institute of Atmospheric Sciences and Climate (ISAC), National Research Council of Italy (CNR), Rome, Italy
  • 3 Department of Environmental, Land and Infrastructure Engineering (DIATI), Politecnico di Torino, Torino, Italy, and Earth Observation Science, Department of Physics and Astronomy, University of Leicester, Leicester, United Kingdom
  • 4 Institute of Atmospheric Sciences and Climate (ISAC), National Research Council of Italy (CNR), Rome, Italy
  • 5 School of Meteorology and School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, Oklahoma, and NOAA/National Severe Storms Laboratory, Norman, Oklahoma, and Advanced Radar Research Center, University of Oklahoma, Norman, Oklahoma
  • 6 Institute of Atmospheric Sciences and Climate (ISAC), National Research Council of Italy (CNR), Rome, Italy
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Abstract

Surface snowfall rate estimates from the Global Precipitation Measurement (GPM) mission’s Core Observatory sensors and the CloudSat radar are compared to those from the Multi-Radar Multi-Sensor (MRMS) radar composite product over the continental United States during the period from November 2014 to September 2020. The analysis includes: the Dual-Frequency Precipitation Radar (DPR) retrieval and its single frequency counterparts, the GPM Combined Radar Radiometer Algorithm (CORRA), the CloudSat Snow Profile product (2C-SNOW-PROFILE) and two passive microwave retrievals, i.e., the Goddard PROFiling algorithm (GPROF) and the Snow retrievaL ALgorithm fOr gMi (SLALOM). The 2C-SNOW retrieval has the highest Heidke Skill Score (HSS) for detecting snowfall among the products analysed. SLALOM ranks second; it outperforms GPROF and the other GPM algorithms, all detecting only 30% of the snow events. Since SLALOM is trained with 2C-SNOW, it suggests that the optimal use of the information content in the GMI observations critically depends on the precipitation training dataset. All the retrievals underestimate snowfall rates by a factor of two compared to MRMS. Large discrepancies (RMSE of 0.7 to 1.5 mm h-1) between space-borne and ground-based snowfall rate estimates are attributed to the complexity of the ice scattering properties and to the limitations of the remote sensing systems: the DPR instrument has low sensitivity, while the radiometric measurements are affected by the confounding effects of the background surface emissivity and of the emission of supercooled liquid droplet layers.

Denotes content that is immediately available upon publication as open access.

Corresponding author address: University of Leicester, University Rd, Leicester, LE1 7RH. E-mail: kamil.mroz@le.ac.uk

This article is included in the Global Precipitation Measurement (GPM) special collection.

Abstract

Surface snowfall rate estimates from the Global Precipitation Measurement (GPM) mission’s Core Observatory sensors and the CloudSat radar are compared to those from the Multi-Radar Multi-Sensor (MRMS) radar composite product over the continental United States during the period from November 2014 to September 2020. The analysis includes: the Dual-Frequency Precipitation Radar (DPR) retrieval and its single frequency counterparts, the GPM Combined Radar Radiometer Algorithm (CORRA), the CloudSat Snow Profile product (2C-SNOW-PROFILE) and two passive microwave retrievals, i.e., the Goddard PROFiling algorithm (GPROF) and the Snow retrievaL ALgorithm fOr gMi (SLALOM). The 2C-SNOW retrieval has the highest Heidke Skill Score (HSS) for detecting snowfall among the products analysed. SLALOM ranks second; it outperforms GPROF and the other GPM algorithms, all detecting only 30% of the snow events. Since SLALOM is trained with 2C-SNOW, it suggests that the optimal use of the information content in the GMI observations critically depends on the precipitation training dataset. All the retrievals underestimate snowfall rates by a factor of two compared to MRMS. Large discrepancies (RMSE of 0.7 to 1.5 mm h-1) between space-borne and ground-based snowfall rate estimates are attributed to the complexity of the ice scattering properties and to the limitations of the remote sensing systems: the DPR instrument has low sensitivity, while the radiometric measurements are affected by the confounding effects of the background surface emissivity and of the emission of supercooled liquid droplet layers.

Denotes content that is immediately available upon publication as open access.

Corresponding author address: University of Leicester, University Rd, Leicester, LE1 7RH. E-mail: kamil.mroz@le.ac.uk

This article is included in the Global Precipitation Measurement (GPM) special collection.

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