Integrating physics-based WRF atmospheric variables and machine learning algorithms to predict snowfall accumulation in Northeast United States

Abstract

Accurate snowfall prediction is crucial for enhancing preparedness and resilience in the Northeast United States during winter weather events. This study introduces a novel approach that integrates Machine Learning (ML) models with atmospheric variables from the Weather Research and Forecasting (WRF) model to improve snowfall forecasts in the region. The significance lies in bridging the gap between physics-based Numerical Weather Prediction (NWP) models and the versatility of ML models, offering a promising advancement in winter storm predictions. Atmospheric variables related to 32 winter storms simulated by WRF, were used to feed Random Forest (RF) and Extreme Gradient Boosting (XGBoost) algorithms to predict 24 h accumulations of snowfall using the National Snowfall Analysis (NSA) product as reference. The comprehensive results revealed that the integrated approach provided more accurate snowfall prediction than the WRF and Air Force Weather Agency (AFWA) diagnostic, but faced challenges in the precision estimated by under-dispersion of the results. The WRF/XGBoost reduced the RMSE by 10.34 %, whereas WRF/RF reduced RMSE by 9.72 %, compared to WRF/AFWA. Similarly, WRF/XGBoost and WRF/RF increased the correlation coefficient by 12 % and 11 %. The most important variables in both ML algorithms were liquid water equivalent precipitation (LWE), snow ratio, wet-bulb temperature, temperature, and humidity at different heights, pressure tendency and wind speed, validating their ability to discern crucial factors influencing snowfall. Specific cases highlight the integrated approach’s effectiveness in addressing challenges faced by traditional diagnostics, including LWE overestimation and warm temperature bias. However, limitations emerge in capturing storms characterized by anomalous atmospheric conditions and high snowfall values, most likely attributed to underrepresentation in the training data. By highlighting strengths and acknowledging limitations, this integrated approach holds the potential for improved snowfall prediction for future storms in the region compared to the traditional approach.