Enhancing Icing Detection and Characterization using the New York State Mesonet

Abstract

AbstractThe accurate detection and monitoring of freezing rain and icing conditions at the surface is a notoriously challenging but important problem. This work attempts to enhance icing detection and characterization utilizing data from the New York State Mesonet (NYSM). NYSM is the first operational network measuring winds at 10 meters from two independent sensors: propeller and sonic anemometers. During and after freezing rain events, large wind speed differences are frequently reported between the two anemometers because the propeller develops a coating of ice, thus either stopping or slowing its rotation. Such errors of propeller data provide a signal for identifying icing conditions. An automated method for identifying “active freezing rain” (AFR) and a continuation of “frozen surface” (FS) conditions is developed. Hourly maps of AFR and FS sites are generated using four criteria: (1) a wind speed difference (sonic – propeller) of > 1 m s-1 or 0 m s-1 propeller wind speed for at least half hour, (2) a temperature threshold of -5°C to 2°C for AFR and less than 2°C for FS, (3) insignificant hourly snow accumulation, and (4) with (or without) significant hourly precipitation accumulation for AFR (or FS). The AFR events detected by the automated method for last four winters (2017-2021) show very good agreements in starting and ending times with that from ASOS data. A case study of the ice storm during 14-16 April 2018 further demonstrates the validity of the methodology and highlights the benefit of NYSM profiler and camera data.