SOLA
Online ISSN : 1349-6476
ISSN-L : 1349-6476
Ground- and Satellite-based Evaluation of WRF Snowfall Prediction
Jae-yong LeeSeung-Min LeeSeung-Jae Lee
Author information
JOURNAL OPEN ACCESS Advance online publication

Article ID: 2022-028

Details
Abstract

This study performed 4-day numerical integration in 1-hour intervals using the Weather Research and Forecasting (WRF) model for four major cases of heavy snowfall that occurred from 2020 to 2021. The model-predicted snow depth data were compared with the ground-observed snow depth and the satellite-observed snow cover data and then were statistically verified. The scalar verification results for ground data from the four cases showed a root–mean–square error of 2.55-16.67 cm and a correlation coefficient of 0.48-0.80, whereas the verification results with satellite data showed the correlation coefficients of 0.38-0.60. For categorical verification, using a threshold value of a snow depth exceeding 5 cm, the proportion correct was 90% or higher for ground observations of each case. In addition, in the satellite categorical verification, when the threshold value of the Snow Cover Fraction (SCF) exceeds 0.5, the proportion correct was 50% or more. These results are meaningful because the model snow depth verification methods were devised strategically for the first time using both the snow depth data of the mesoscale ground observation networks and ultra-high-resolution Sentinel-2 satellite data currently available in Korea. The findings of this study will contribute to the development of a high-resolution numerical prediction model and its verification methodology for snowfalls in the Korean Peninsula, eventually leading to increased prediction accuracy and reduced snow damage.

Content from these authors
© The Author(s) 2022. This is an open access article published by the Meteorological Society of Japan under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.

This article is licensed under a Creative Commons [Attribution 4.0 International] license.
https://creativecommons.org/licenses/by/4.0/
feedback
Top