On the forecast skill of a convection-permitting ensemble

Schellander-Gorgas, Theresa; Wang, Yong; Meier, Florian; Weidle, Florian; Wittmann, Christoph; Kann, Alexander

doi:10.5194/gmd-10-35-2017

Cited by 27 publications

(29 citation statements)

References 73 publications

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“…Very recently, an hourly rapid-refresh AROME assimilation system using radar wind and reflectivity measurements was implemented at ZAMG with 2.5-and 1.2-km horizontal resolution and 90 vertical levels for nowcasting purposes. In this AROME nowcasting framework, incremental analysis updates (Bloom et al 1996) and latent heat nudging (Jones and Macpherson 1997;Meier 2015) are incorporated, based on the precipitation analysis and nowcasting of Integrated Nowcasting through Comprehensive Analysis (INCA; Haiden et al 2011;Wang et al 2017). The results are quite encouraging, particularly from a forecaster's perspective.…”

Section: Randd Highlightsmentioning

confidence: 51%

“…Recently, more effort has been put into convectionpermitting ensemble AROME EPS with 2.5-km horizontal resolution. AROME EPS was tested and evaluated in detail and was found to outperform its mesoscale ALADIN-LAEF counterpart in cases of precipitation forecasts, especially for the mountains, with a smaller impact observed for the lowlands as well (Schellander-Gorgas et al 2017).…”

Section: Regional Ensemble Prediction Most Of the Nhmss Inmentioning

confidence: 99%

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27 Years of Regional Cooperation for Limited Area Modelling in Central Europe

Wang

Belluš

Ehrlich

et al. 2018

Bulletin of the American Meteorological Society

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This paper describes 27 years of scientific and operational achievement of Regional Cooperation for Limited Area Modelling in Central Europe (RC LACE), which is supported by the national (hydro-) meteorological services of Austria, Croatia, the Czech Republic, Hungary, Romania, Slovakia, and Slovenia. The principal objectives of RC LACE are to 1) develop and operate the state-of-the-art limited-area model and data assimilation system in the member states and 2) conduct joint scientific and technical research to improve the quality of the forecasts. In the last 27 years, RC LACE has contributed to the limited-area Aire Limitée Adaptation Dynamique Développement International (ALADIN) system in the areas of preprocessing of observations, data assimilation, model dynamics, physical parameterizations, mesoscale and convection-permitting ensemble forecasting, and verification. It has developed strong collaborations with numerical weather prediction (NWP) consortia ALADIN, the High Resolution Limited Area Model (HIRLAM) group, and the European Centre for Medium-Range Weather Forecasts (ECMWF). RC LACE member states exchange their national observations in real time and operate a common system that provides member states with the preprocessed observations for data assimilation and verification. RC LACE runs operationally a common mesoscale ensemble system, ALADIN–Limited Area Ensemble Forecasting (ALADIN-LAEF), over all of Europe for early warning of severe weather. RC LACE has established an extensive regional scientific and technical collaboration in the field of operational NWP for weather research, forecasting, and applications. Its 27 years of experience have demonstrated the value of regional cooperation among small- and medium-sized countries for success in the development of a modern forecasting system, knowledge transfer, and capacity building.

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Section: Randd Highlightsmentioning

confidence: 51%

Section: Regional Ensemble Prediction Most Of the Nhmss Inmentioning

confidence: 99%

27 Years of Regional Cooperation for Limited Area Modelling in Central Europe

Wang

Belluš

Ehrlich

et al. 2018

Bulletin of the American Meteorological Society

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“…Among the studies focussing on precipitation, Schellander‐Gorgas et al . () showed the benefit of using CP‐EPSs for predicting convective precipitation over a 3‐month summer period with respect to a coarser‐resolution model. Also, Schwartz et al .…”

Section: Introductionmentioning

confidence: 99%

“…Clark et al (2016) reviewed the state-of-the-art convection-permitting models for forecasting rainfall, showing their benefit by considering a few case-studies in summer 2014 with a qualitative comparison against radar images. Among the studies focussing on precipitation, Schellander-Gorgas et al (2017) showed the benefit of using CP-EPSs for predicting convective precipitation over a 3-month summer period with respect to a coarser-resolution model. Also, Schwartz et al (2010) showed the benefit of CP-EPSs for quantitative precipitation forecasts over 35 days of spring 2007, stating that we can learn much about what type of information can be extracted from convection-allowing model output and how this information might best be used by operational weather forecasters.…”

Section: Introductionmentioning

confidence: 99%

The added value of convection‐permitting ensemble forecasts of sea breeze compared to a Bayesian forecast driven by the global ensemble

Cafaro

Frame

Methven

et al. 2019

Quart J Royal Meteoro Soc

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Dynamical downscaling of ensemble forecasts to convection‐permitting resolutions aims to improve forecast skill by explicitly resolving mesoscale dynamical features. The success of this approach is dependent on the ability of the model to spin up smaller features embedded in the larger‐scale flow and provide more local information than could be inferred from knowledge of the climatological response to the large‐scale flow alone. Here we test whether such additional information is obtained from the Met Office Global and Regional Ensemble Prediction Systems (MOGREPS) for the sea‐breeze phenomenon which is resolved properly only at convection‐permitting resolutions but is driven by large‐sale conditions that are well represented in the global driving model. The sea breeze is detected using a new automatic tracking algorithm suitable for use in convective‐scale forecast data. The skill of probabilistic forecasts of sea‐breeze occurrence from the high‐resolution ensemble is compared to that of a Bayesian forecast trained on paired high/low‐resolution ensemble members. This creates a statistical forecast of the high‐resolution ensemble member given knowledge of the global forecast ensemble alone. The aim of this paper is twofold: firstly to assess whether the information about sea‐breeze occurrence is encoded in a few large‐scale parameters and can be forecast by a statistical method, and secondly to estimate what information is gained by running the high‐resolution forecast beyond that which is contained in these large‐scale flow parameters. Comparison of the two forecasting methods using a variety of verification methods all lead to the same conclusion: although both the Bayesian forecast and convection‐permitting ensemble provide information about sea‐breeze occurrence, the convection‐permitting ensemble is significantly more able to discriminate between sea‐breeze events and non‐events for all lead times.

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“…A 12-member ensemble prediction system using AROME at 2.5 km resolution, named PEARO Raynaud and Bouttier, 2016), is also daily running on Météo-France's computing system. ZAMG also develops a comparable system based on the AROME CMC; see Schellander-Gorgas et al (2017). A detailed description of the activities regarding data assimilation, EPS and nowcasting within the ALADIN consortium is outside the scope of this paper.…”

Section: Current Status Of the Implementationsmentioning

confidence: 99%

The ALADIN System and its canonical model configurations AROME CY41T1 and ALARO CY40T1

Termonia

Fischer²,

Bazile³

et al. 2018

Geosci. Model Dev.

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161

137

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Abstract. The ALADIN System is a numerical weather prediction (NWP) system developed by the international AL-ADIN consortium for operational weather forecasting and research purposes. It is based on a code that is shared with the global model IFS of the ECMWF and the ARPEGE model of Météo-France. Today, this system can be used to provide a multitude of high-resolution limited-area model (LAM) configurations. A few configurations are thoroughly validated and prepared to be used for the operational weather forecasting in the 16 partner institutes of this consortium. These configurations are called the ALADIN canonical model configurations (CMCs). There are currently three CMCs: the AL-ADIN baseline CMC, the AROME CMC and the ALARO CMC. Other configurations are possible for research, such as process studies and climate simulations.The purpose of this paper is (i) to define the ALADIN System in relation to the global counterparts IFS and ARPEGE, (ii) to explain the notion of the CMCs, (iii) to document their most recent versions, and (iv) to illustrate the process of the validation and the porting of these configurations to the operational forecast suites of the partner institutes of the AL-ADIN consortium. This paper is restricted to the forecast model only; data assimilation techniques and postprocessing techniques are part of the ALADIN System but they are not discussed here.

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On the forecast skill of a convection-permitting ensemble

Cited by 27 publications

References 73 publications

27 Years of Regional Cooperation for Limited Area Modelling in Central Europe

27 Years of Regional Cooperation for Limited Area Modelling in Central Europe

The added value of convection‐permitting ensemble forecasts of sea breeze compared to a Bayesian forecast driven by the global ensemble

The ALADIN System and its canonical model configurations AROME CY41T1 and ALARO CY40T1

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