Integrating NWP Forecasts and Observation Data to Improve Nowcasting Accuracy

Abstract

Abstract This study addresses the issue of improving nowcasting accuracy by integrating several numerical weather prediction (NWP) model forecasts with observation data. To derive the best algorithms for generating integrated forecasts, different integration methods were applied starting with integrating the NWP models using equal weighting. Various refinements are then successively applied including dynamic weighting, variational bias correction, adjusted dynamic weighting, and constraints using current observation data. Three NWP models—the Canadian Global Environmental Multiscale (GEM) regional model, the GEM Limited Area Model (LAM), and the American Rapid Update Cycle (RUC) model—are used to generate the integrated forecasts. Verification is performed at two Canadian airport locations [Toronto International Airport (CYYZ), in Ontario, and Vancouver International Airport (CYVR), in British Columbia] over the winter and summer seasons. The results from the verification for four weather variables (temperature, relative humidity, and wind speed and gust) clearly show that the integrated models with new refinements almost always perform better than each of the NWP models individually and collectively. When the integrated model with innovative dynamic weighting and variational bias correction is further updated with the most current observation data, its performance is the best among all models, for all the selected variables regardless of location and season. The results of this study justify the use of integrated NWP forecasts for nowcasting provided they are properly integrated using appropriate and specifically designed rules and algorithms.