Statistical Bias Correction Methods for Numerical Weather Prediction Model (NWP) Forecasts of Maximum and Minimum Temperatures

Abstract

We have investigated the performance of four bias correction methods for improving both maximum and minimum temperature forecasts produced by the NWP model. The objective of bias correction is to minimize the systematic error of the next forecast using bias from past errors. The need for bias corrections arises from the many sources of systematic errors in NWP modeling systems. NWP models have shortcomings in the physical parameterization of weather events and have the inability to handle sub-grid phenomena successfully. The statistical algorithms used for minimizing the bias of the next forecast are Running-Mean (RM) bias correction, Best Easy Systematic (BES) estimator, simple Linear Regression (LR) and the Nearest Neighborhood Weighted (NNW) mean, as they are suitable for small samples. Bias correction is done for four global NWP model maximum and minimum temperature forecasts. The magnitude of the bias at a grid point depends upon geographical location and season. Validation of the bias correction methodology is carried out using daily observed and bias corrected model maximum and minimum temperature forecast over India during July-September 2011. The bias corrected NWP model forecast generally outperforms direct model output (DMO). The spatial distribution of Mean Absolute Error (MAE) and Root mean squared Error (RMSE) for bias corrected forecast over India indicate that both the RM and NNW methods produces the best skill among other bias correction methods. The inter-comparison reveals that statistical bias correction methods improves the DMO forecast in terms of accuracy in forecast and has the potential for operational applications.