Use of Radar-Derived Extinction to Diagnose Snow Intensity

Details

• Journal Title:
  Weather and Forecasting
• Personal Author:
  Rosenow, Andrew A. ; Bukovčić, Petar ; Reeves, Heather D. ; Baldwin, Michael
• NOAA Program & Office:
  NWS (National Weather Service) ; OAR (Oceanic and Atmospheric Research) ; CIWRO (Cooperative Institute for Severe and High-Impact Weather Research and Operations) ; NSSL (National Severe Storms Laboratory) ; SPC (Storm Prediction Center)
• Description:
  Winter storms create significant impacts on life and property. These impacts depend on, among other things, how quickly snow falls. The current surface observation network does not directly measure snowfall rate with meaningful spatial and temporal resolution. Surface meteorological stations, such as the Automated Surface Observing System, typically report a visibility-derived snow intensity. While this parameter is not a true stand-in for snowfall rate due to its dependence on other factors (humidity, snow morphology, presence of blowing snow, etc.), snow intensity often forms the basis for stakeholder decisions in the absence of other observations to fill the decision support hole. This study evaluates a radar-based visibility algorithm as the basis for a two-dimensional, gridded snow intensity analysis. The algorithm requires only Multi-Radar Multi-Sensor precipitation rate and surface pressure to calculate extinction—the loss of light energy over a distance—which is then used to derive visibility and finally snow intensity. The algorithm’s snow intensities outperform a reflectivity-derived baseline. Changing the characteristics of the particle size distribution in the algorithm impacted the type of snow intensity it verified best against. Verification was performed using both a traditional two-category framework and a three-category framework. An example of the decision support capability is shown using a case study, illustrating the potential of this product to fill in spatial and temporal gaps in coverage within the surface observation network.
• Source:
  Weather and Forecasting, 40(11), 2495-2505
• DOI:
  https://doi.org/10.1175/WAF-D-25-0070.1
• ISSN:
  0882-8156 ; 1520-0434
• Format:
  pdf
• Publisher:
  American Meteorological Society
• Document Type:
  Journal Article
• Rights Information:
  Other
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:97dabe637103e043c859bea89a2f9f778c690e56a6a5a04d5d08570fcfd0428d7308843e88bd46bb4512eee60417fceeb31281f97fde0bd6e9f66b2063f8ad1d
• File Type: