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Quality Assessment of Space-Borne Active and Passive Microwave Snowfall Products Over the Continental United States
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  • Mario Montopoli,
  • Kamil Mroz,
  • Giulia Panegrossi,
  • Luca Baldini,
  • Alessandro Battaglia,
  • Pierre Kirstetter
Mario Montopoli
CNR Institute of Atmospheric Sciences and Climate

Corresponding Author:m.montopoli@isac.cnr.it

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Kamil Mroz
National Centre for Earth Observation, University of Leicester
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Giulia Panegrossi
CNR Institute of Atmospheric Sciences and Climate
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Luca Baldini
CNR Institute of Atmospheric Sciences and Climate
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Alessandro Battaglia
Department of Physics and Astronomy, University of Leicester, Leicester, United Kingdom
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Pierre Kirstetter
NOAA/National Severe Storms Laboratory
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Surface snowfall rate estimates from the Global Precipitation Measurement (GPM) mission’s core satellite sensors and CloudSat radar are compared to those from the Multi-Radar Multi-Sensor (MRMS) radar composite product generated over the continental United States (CONUS). The considered algorithms include: Dual-Frequency Precipitation Radar (DPR) product and its single frequency counterparts (Ka- and Ku-only); the combined DPR and multifrequency microwave imager (CORRA) product; the CloudSat SnowProfile product (2C-SNOW-PROFILE); two passive microwave products i.e. the Goddard PROFiling algorithm (GPROF) and the Snow retrievaL ALgorithm fOr gMi (SLALOM). The spaceborne and ground-based snowfall products are collocated spatially and temporally and compared at the spatial resolution of spaceborne instruments over the period spanning from January 2016 to March 2020 (4 winters). Detection capabilities of the sensors is assessed in terms of the most commonly used forecast metrices (Probability of Detection, False Alarm Ratio, etc.) whereas precision of the products is quantified by the mean error (ME) and root-mean-square-error (RMSE). 2C-SNOW product agrees with MRMS by far better than any other product. Passive microwave algorithms tend to detect more precipitation events than the DPR and CORRA retrievals, but they also trigger more false alarms. Due to limited sensitivity, DPR detects only approx. 30% of the snow events. All the retrievals underestimate snowfall rates, for the detected snowstorms they produce approximately only a half of the precipitation reported by MRMS. Large discrepancies (RMSE from 0.7 to 2.5 mm/h) between spaceborne and ground-based snowfall rate estimates is the result of limitations of both systems and complex ice scattering properties. The MRMS product is based on a power law relation and it has difficulties in detecting precipitation at far ranges; the DPR system is affected by low sensitivity while the GPM Microwave Imager (GMI) measurements are affected by the confounding effect of the background surface emissivity for snow-covered surfaces and of the emission of supercooled liquid droplet layers.