Abstract
Abstract. Non-homogeneous regression is a frequently used post-processing method for increasing the predictive skill of probabilistic ensemble weather forecasts. To adjust for seasonally varying error characteristics between ensemble forecasts and corresponding observations, different time-adaptive training schemes, including the classical sliding training window, have been developed for non-homogeneous regression. This study compares three such training approaches with the sliding-window approach for the application of post-processing near-surface air temperature forecasts across central Europe. The predictive performance is evaluated conditional on three different groups of stations located in plains, in mountain foreland, and within mountainous terrain, as well as on a specific change in the ensemble forecast system of the European Centre for Medium-Range Weather Forecasts (ECMWF) used as input for the post-processing. The results show that time-adaptive training schemes using data over multiple years stabilize the temporal evolution of the coefficient estimates, yielding an increased predictive performance for all station types tested compared to the classical sliding-window approach based on the most recent days only. While this may not be surprising under fully stable model conditions, it is shown that “remembering the past” from multiple years of training data is typically also superior to the classical sliding-window approach when the ensemble prediction system is affected by certain model changes. Thus, reducing the variance of the non-homogeneous regression estimates due to increased training data appears to be more important than reducing its bias by adapting rapidly to the most current training data only.
Highlights
The need for accurate probabilistic weather forecasts is steadily increasing, because reliable information about the expected uncertainty is crucial for optimal risk assessment in agriculture and industry or for personal planning of outdoor activities
Afterwards, the predictive performance of the training schemes is evaluated in terms of the CRPS conditional on the three data sets with and without the change in the horizontal resolution of the ensemble prediction systems (EPSs) (Fig. 2) and grouped for stations classified as topographically plain, mountain foreland, and alpine sites (Fig. 1)
In its original version it was used for temperature forecasts employing a Gaussian response distribution, but over the last decade various statistical model extensions have been proposed for other quantities employing different response distributions or to enhance its predictive performance
Summary
The need for accurate probabilistic weather forecasts is steadily increasing, because reliable information about the expected uncertainty is crucial for optimal risk assessment in agriculture and industry or for personal planning of outdoor activities. To quantify the uncertainty of a specific forecast, an EPS provides a set of numerical weather predictions using slightly perturbed initial conditions and different model parameterizations (Palmer, 2002). Due to various constraints and required simplifications in the EPS, these forecasts often show systematic biases and capture only parts of the expected uncertainty, especially when EPS forecasts are directly compared to point measurements (Gneiting and Katzfuss, 2014). In order to increase the predictive skill of the forecasts for specific locations, statistical post-processing is often applied to correct for these systematic errors in the forecasts’ expectation and uncertainty. This type of model is known as distributional regression (Klein et al, 2014) since all parameters of a specific response distribution are optimized simultaneously conditional on respective sets of covariates
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