Comparative Analysis of the Local Observation-Based (LOB) Method and the Nonparametric Regression-Based Method for Gridded Bias Correction in Mesoscale Weather Forecasting

Abstract

Abstract The comparative analysis of three methods for objective grid-based bias removal in mesoscale numerical weather prediction models is considered. The first technique is the local observation-based (LOB) method that extends further the approaches of several recent studies and is focused on utilizing the information obtained from meteorological stations or neighbor grid points in the proximity of a site of interest. The bias at a site of interest might then be considered as a spatiotemporal function of the weighted information on the past biases observed in the cluster of neighbors during a certain time window. The second method is an extension of model output statistics (MOS), combining several modern multiple regression techniques such as the classification and regression trees (CARTs) and the alternative conditional expectation (ACE) and, therefore, is named the CART–ACE method. The CART–ACE method allows representing possible nonlinear aspects of the bias in a parsimonious linearized statistical model. Finally, the third considered method is a natural combination of the LOB and CART–ACE methods in which the information provided by the LOB method is interpreted as an extra predictor in the regression model of the CART–ACE method. The proposed methods are illustrated by a case study of an observation-based verification and bias correction of fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) 48-h surface temperature, that is, 2-m temperature, forecasts over the Pacific Northwest.