Ensemble Postprocessing of Daily Precipitation Sums over Complex Terrain Using Censored High-Resolution Standardized Anomalies

Reto Stauffer,Georg J Mayr,Achim Zeileis,Nikolaus Umlauf,Jakob W Messner

doi:10.1175/mwr-d-16-0260.1

Abstract

Probabilistic forecasts provided by numerical ensemble prediction systems have systematic errors and are typically underdispersive. This is especially true over complex topography with extensive terrain-induced small-scale effects, which cannot be resolved by the ensemble system. To alleviate these errors, statistical postprocessing methods are often applied to calibrate the forecasts. This article presents a new full-distributional spatial postprocessing method for daily precipitation sums based on the standardized anomaly model output statistics (SAMOS) approach. Observations and forecasts are transformed into standardized anomalies by subtracting the long-term climatological mean and dividing by the climatological standard deviation. This removes all site-specific characteristics from the data and makes it possible to fit one single regression model for all stations at once. As the model does not depend on the station locations, it directly allows the creation of probabilistic forecasts for any arbitrary location. SAMOS uses a left-censored power-transformed logistic response distribution to account for the large fraction of zero observations (dry days), the limitation to nonnegative values, and the positive skewness of the data. ECMWF reforecasts are used for model training and to correct the ECMWF ensemble forecasts with the big advantage that SAMOS does not require an extensive archive of past ensemble forecasts as only the most recent four reforecasts are needed, and it automatically adapts to changes in the ECMWF ensemble model. The application of the new method to the central Alps shows that the new method is able to depict the small-scale properties and returns accurate fully probabilistic spatial forecasts.

Highlights

In mountainous regions, large amounts of precipitation can lead to severe floods and land slides during spring and summer and to dangerous avalanche conditions during winter
As the ENS is only able to represent the topography as one smooth ridge (Fig. 1), the only feature which can be identified in the ENS prediction is a gradual decrease of precipitation from north to south over the main alpine ridge
Larger amounts of precipitation are typically observed in Southern Germany north of Tyrol, while the wellmarked alpine valleys in Tyrol typically receive less precipitation

Summary

Introduction

Large amounts of precipitation can lead to severe floods and land slides during spring and summer and to dangerous avalanche conditions during winter. An accurate and reliable knowledge about the expected precipitation can be crucial for strategic planning and to raise awareness among the public. Precipitation forecasts, or weather forecasts in general, are typically provided by numerical. SAMOS Ensemble Post-Processing for Precipitation weather prediction models. Nowadays most forecast centers compute probabilistic forecasts based on numerical ensemble prediction systems (EPS; Epstein 1969; Buizza et al 2005) as a probabilistic information can be crucial for e.g., strategic planning, or decision makers. An ensemble consists of several (independent) forecast runs with slightly di↵erent initial conditions, model physics, and/or parametrizations. EPS are undergoing constant improvements, they are not able to provide fully reliable forecasts and are typically underdispersive (Mullen and Buizza 2001; Hagedorn et al 2012)

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