Integrating NWP Forecasts and Observation Data to Improve Nowcasting Accuracy

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.

Using Smart Persistence and Random Forests to Predict Photovoltaic Energy Production

“…NWP are commonly used [21][22][23] for daily forecasts. These studies show the powerful prediction capabilities of forecasts as machine learning inputs.…”

The Canadian Airport Nowcasting System (CAN‐Now)

“…The Weighting, Evaluation, Bias Correction and Integrated System for Nowcasting (WEBIS), or Integrated Weighting System (INTW; Huang, ; Huang et al , ) for its short title, examines several different models, dynamically weighs those models based on recent performance (6 h), and applies dynamic and variational bias corrections to produce a short term forecast out to 6 h. The INTW system uses the REG, RUC, and LAM models, the 1 min data from the observation sites at CYYZ and CYVR, and the hourly observations from other sites.…”

“…A similar scheme has been implemented in the Weather Support to Deicing Decision Making (WSDDM; Rasmussen et al, 2001) system. A lightning extrapolation scheme moves lightning stroke point locations forward in time for 2 h based on the NWP Huang, 2011;Huang et al, 2012) for its short title, examines several different models, dynamically weighs those models based on recent performance (6 h), and applies dynamic and variational bias corrections to produce a short term forecast out to 6 h. The INTW system uses the REG, RUC, and LAM models, the 1 min data from the observation sites at CYYZ and CYVR, and the hourly observations from other sites.…”

“…A Weighting, Evaluation, Bias Correction and Integrated System (WEBIS) has been developed for generating integrated weighted forecasts (INTW) from observations and NWP models for nowcasting (Huang, ; Huang et al , ). The WEBIS adopts a new approach in blending the underlying model forecasts to improve overall forecast accuracy.…”

Verification and bias correction of ECMWF forecasts for Irish weather stations to evaluate their potential usefulness in grass growth modelling

“…Harrison et al ., ; Boi, ; Sweeney et al ., ; Vannitsem and Hagedorn, ). A variety of approaches, including the moving window technique and model output statistics, have been found to improve the accuracy of rainfall and 2 m air temperature forecasts (see Yussouf and Stensrud, ; Huang et al ., , for example). Many studies attempt to correct forecasts spatially over a domain of interest (Louka et al ., ; Vrac and Friederichs, , for example), and applying bias correction techniques at individual station locations can yield improvements in forecast accuracy (Taylor and Leslie, ).…”