Latest developments around the ALADIN operational short-range ensemble prediction system in Hungary

Abstract

A B S T R A C T Ensemble prediction systems (EPSs) are an essential part of numerical weather prediction for the provision of probabilistic forecast guidance. The Hungarian Meteorological Service has implemented a limited area EPS (called ALADIN HUNEPS) based on the ALADIN mesoscale limited area model coupled to the French global ARPEGE EPS (PEARP). The dynamical downscaling method is assessed in terms of ensemble verification scores taking also into account the recent upgrade of the PEARP global system. The verification results point towards some weaknesses of the ALADIN HUNEPS, mainly with respect to the near-surface parameters. Therefore, some improvements are needed so as to provide better and more reliable ensemble predictions for the Carpathian Basin. The application of near-surface perturbations into the surface data assimilation scheme is implemented and tested. The first results show that the surface perturbation method slightly improves the ALADIN HUNEPS, however, further experiments should be made to find out the optimal settings of the method for definite robust improvements of ALADIN HUNEPS.

Joint probabilistic forecasting of wind speed and temperature using Bayesian model averaging

“…This idea is justified by the method of generating their initial conditions. To obtain the initial conditions for the ALADIN-HUNEPS forecasts, only five perturbations are calculated and then they are added to (odd numbered members) and subtracted from (even numbered members) the unperturbed initial conditions (Horányi et al, 2011;Baran et al, 2013Baran et al, , 2014. In this way we obtain the following PDFs for the forecasted vector of wind speed and temperature corresponding to models (4) and (5)…”

“…Using a forecast ensemble one can estimate the probability distribution of future weather variables, which allows probabilistic weather forecasting , where not only the future atmospheric states are predicted but also the related uncertainty information such as variance, probabilities of various events, and such. The ensemble prediction method was proposed by Leith (1974), and since its first operational implementation (Buizza et al, 1993;Toth and Kalnay, 1997) it became a widely used technique all over the world (see, e.g., Eckel and Mass, 2005;Leutbecher and Palmer, 2008;Gebhardt et al, 2011;Horányi et al, 2011). However, although, for example, the ensemble mean on average yields better forecasts of a meteorological quantity than any of the individual ensemble members, it is often the case that the ensemble is under-dispersive and in this way, uncalibrated (Buizza et al, 2005), therefore calibration is absolutely needed to account for this deficiency.…”

PEARP, the Météo‐France short‐range ensemble prediction system

“…These systems use limited‐area models (LAMs) with higher resolution than typically used for medium‐range but covering only a limited part of the globe. The Meteorological Office (Bowler et al ., ), Spanish Met Service (AEMET: Garcia‐Moya et al, ), Norwegian Meteorological Institute (Frogner et al ., ), Consortium for Small‐scale Modelling Limited‐Area Ensemble Prediction System (COSMO–LEPS: Marsigli et al, ) and the Limited Area Model for Central Europe (LACE) consortium (Horànyi et al ., ) have developed such systems.…”

Application of European numerical weather prediction models for hydrological purposes

“…In the first decade of the 21st century, a non-hydrostatic model was developed in the framework of an international cooperation. The AROME model became operational at OMSZ in 2010 (Horányi et al, 2011). This non-hydrostatic model could provide very useful information, especially in summer extreme precipitation events.…”