C‐LAEF: Convection‐permitting Limited‐Area Ensemble Forecasting system

Abstract

C‐LAEF (Convection‐permitting Limited‐Area Ensemble Forecasting) has been developed at the Austrian national weather service ZAMG (Zentralanstalt für Meteorologie und Geodynamik) and has been running operationally at the European Centre for Medium Range Weather Forecasts (ECMWF) supercomputer since November 2019. It includes (a) an ensemble 3D variational blending technique to deal with atmospheric initial uncertainties, (b) an ensemble of land surface data assimilation to account for uncertainties in the initial land surface conditions, (c) a hybrid stochastic physics perturbation scheme to treat model uncertainties in the different physics parametrization schemes, and (d) a coupling with the global ensemble system IFS‐ENS (Integrated Forecasting System‐ENSemble) to consider uncertainties in the lateral boundary conditions. C‐LAEF has a horizontal resolution of 2.5 km and consists of 16 perturbed members plus one unperturbed control run. It runs four times a day and provides probabilistic forecasts up to 60 hr on a domain covering the whole Alpine area. This article describes the C‐LAEF system in detail and evaluates the relative contributions of the different perturbation techniques. The ensemble variational blending technique and the ensemble surface data assimilation provide additional spread in the first forecast hours, while the hybrid stochastically perturbed parametrization scheme improves the performance of C‐LAEF during the whole forecast range. The performance of C‐LAEF is evaluated extensively over one summer and one winter period and compared with its mesoscale counterpart ALADIN‐LAEF (Aire Limitee Adaptation dynamique Developpement InterNational – Limited‐Area Ensemble Forecasting) and the IFS‐ENS. State‐of‐the‐art probabilistic measures indicate that C‐LAEF is able to outperform ALADIN‐LAEF for all considered upper‐air variables. At the surface, C‐LAEF outperforms ALADIN‐LAEF and IFS‐ENS according to most conventional measures, particularly for wind and precipitation. C‐LAEF benefits from the higher resolution and the explicit treatment of deep convection and can provide more accurate probabilistic information for weather warnings.

Elucidating the causes of errors in 2.2 km Met Office Unified Model simulations of a convective case over the US Great Plains

“…Convective‐scale ensemble systems allow these errors to be quantified. Such systems have been adopted by several operational forecast centres including Deutscher Wetterdienst (Bouallégue et al ., 2013), Météo‐France (Nuissier et al ., 2016; Schellander‐Gorgas et al ., 2017) and Zentralanstalt für Meteorologie und Geodynamik (Wastl et al ., 2021). Since 2012, the Met Office have been running a 12‐member 2.2 km grid‐length ensemble operationally over the United Kingdom (MOGREPS‐UK).…”

Quantifying the analysis uncertainty for nowcasting application

“…Nowcasting is essential for severe weather warnings and protecting life and property, as it accurately predicts high-impact weather events in near-real time (Wang et al, 2017a, b;Wastl et al, 2018;Schmid et al, 2019). A very high-resolution weather analysis is the base for a skilful nowcasting since the analysis describes the real-time atmospheric conditions accurately at the initial time (Wastl et al, 2021). However, due to the chaotic nature of the atmosphere, errors in data and the imperfect nowcasting models, nowcasting is with uncertainties (…”

Optimizing convection‐permitting ensemble via selection of the coarse ensemble driving members

“…SPPT randomly perturbs the tendencies from the physical parameterization schemes (Buizza et al, 1999; Palmer et al, 2009), while PP randomly perturbs the key tunable parameters within the parameterization schemes. Many studies have been performed to analyse and compare these methods in their different variations (e.g., Berner et al, 2011; Christensen et al, 2017; Frogner et al, 2022; Hermoso et al, 2021; Jankov et al, 2017, 2019; Lang et al, 2021; Lock et al, 2019; McCabe et al, 2016; Wang et al, 2020; Wastl et al, 2019, 2021; Xu et al, 2020; Zhang, 2019). Although most of the studies show the advantages of stochastic physics for RCPEs, the optimal strategy to represent parameterization uncertainties remains unclear.…”