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

Supporting Files


Details

  • Journal Title:
    Journal of Hydrometeorology
  • Personal Author:
  • NOAA Program & Office:
  • Description:
    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.
  • Keywords:
  • Source:
    Journal of Hydrometeorology (2021)
  • DOI:
  • ISSN:
    1525-755X ; 1525-7541
  • Format:
    PDF
  • Publisher:
    American Meteorological Society
  • Document Type:
    Journal Article
  • Rights Information:
    Other
  • Compliance:
    Library
  • Main Document Checksum:
    urn:sha-512:93f2993e5e9cb6ff9a48a04ab0eccae26b70684b0dd1a05fb1526f16cf9f9db5148d099db11a8ec6821baf40dd5baff97c6ee594280d42116e0e785a6e77eac4
  • Download URL:
  • File Type:
    Filetype[PDF - 3.80 MB ]

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