Area-covering postprocessing of ensemble precipitation forecasts using topographical and seasonal conditions

TL;DR

This paper introduces an area-covering postprocessing method for ensemble precipitation forecasts that leverages topographical and seasonal information to improve forecast accuracy across Switzerland, reducing errors by up to 4.5%.

Contribution

It develops a topographical covariate-based EMOS approach that achieves local forecast performance without requiring local historical data, and introduces a decision mechanism to prevent performance degradation in summer.

Findings

Postprocessing improves forecast accuracy by 4.5% CRPS on average.

Topographical covariates enable local performance without local data.

A decision rule reduces degradation during summer, further improving CRPS by 1.7%.

Abstract

Probabilistic weather forecasts from ensemble systems require statistical postprocessing to yield calibrated and sharp predictive distributions. This paper presents an area-covering postprocessing method for ensemble precipitation predictions. We rely on the ensemble model output statistics (EMOS) approach, which generates probabilistic forecasts with a parametric distribution whose parameters depend on (statistics of) the ensemble prediction. A case study with daily precipitation predictions across Switzerland highlights that postprocessing at observation locations indeed improves high-resolution ensemble forecasts, with 4.5% CRPS reduction on average in the case of a lead time of 1 day. Our main aim is to achieve such an improvement without binding the model to stations, by leveraging topographical covariates. Specifically, regression coefficients are estimated by weighting the training data in relation to the topographical similarity between their station of origin and the prediction location. In our case study, this approach is found to reproduce the performance of the local model without using local historical data for calibration. We further identify that one key difficulty is that postprocessing often degrades the performance of the ensemble forecast during summer and early autumn. To mitigate, we additionally estimate on the training set whether postprocessing at a specific location is expected to improve the prediction. If not, the direct model output is used. This extension reduces the CRPS of the topographical model by up to another 1.7% on average at the price of a slight degradation in calibration. In this case, the highest improvement is achieved for a lead time of 4 days.