Integrating nowcasting with crisis management and risk prevention in a transnational and interdisciplinary framework

Yong, Wang; Meirold-Mautner, Ingo; Kann, Alexander; Slak, Alenka Šajn; Simon, André Louis; Vivoda, Jozef; Bica, Benedikt; Böcskör, Ernst; Březková, Lucie; Dantinger, Johann; Giszterowicz, Mateusz; Heizler, György; Iwanski, Rafal; Jachs, Siegfried; Bernard, Thomas; Kršmanc, Rok; Merše, Janko; Micheletti, Stefano; Schmid, Franziska; Steininger, Michael; Haiden, Thomas; Regec, Adam; Buzzi, Matteo; Derková, Mariá; Kozarić, Tomislav; Qiu, Xuexing; Reyniers, Maarten; Yang, Jia; Yong, Huang; Vadislavsky, Elyakom

doi:10.1127/metz/2017/0843

Cited by 14 publications

(12 citation statements)

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“…The C-LAEF (Convection-permitting -Limited Area Ensemble Forecasting) system has been developed at the Austrian national meteorological service ZAMG (Zentralanstalt für Meteorologie und Geodynamik) and is based on the convection-permitting AROME model (Seity et al, 2011). AROME is under active development within the international NWP (Numerical Weather Prediction) consortia ALADIN (Aire Limitée Adaptation dynamique Développement Inter-National; Termonia et al, 2018), HIRLAM (High Resolution Limited Area Model;Bengtsson et al, 2017) and RC LACE (Regional Cooperation for Limited Area Modelling in Central Europe; Wang et al, 2018). AROME has been operationally used at ZAMG since 2014.…”

Section: The C-laef Systemmentioning

confidence: 99%

Independent perturbations for physics parametrization tendencies in a convection-permitting ensemble (pSPPT)

et al. 2019

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Abstract. A modification of the widely used SPPT (Stochastically Perturbed Parametrisation Tendencies) scheme is proposed and tested in a Convection-permitting – Limited Area Ensemble Forecasting system (C-LAEF) developed at ZAMG (Zentralanstalt für Meteorologie und Geodynamik). The tendencies from four physical parametrization schemes are perturbed: radiation, shallow convection, turbulence, and microphysics. Whereas in SPPT the total model tendencies are perturbed, in the present approach (pSPPT hereinafter) the partial tendencies of the physics parametrization schemes are sequentially perturbed. Thus, in pSPPT an interaction between the uncertainties of the different physics parametrization schemes is sustained and a more physically consistent relationship between the processes is kept. Two configurations of pSPPT are evaluated over two separate months (one in summer and another in winter). Both schemes increase the stability of the model and lead to statistically significant improvements in the probabilistic performance compared to a reference run without stochastic physics. An evaluation of selected test cases shows that the positive effect of stochastic physics is much more pronounced on days with high convective activity. Small discrepancies in the humidity analysis can be dedicated to the use of a very simple supersaturation adjustment. This and other adjustments are discussed to provide some suggestions for future investigations.

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Section: The C-laef Systemmentioning

confidence: 99%

Independent perturbations for physics parametrization tendencies in a convection-permitting ensemble (pSPPT)

et al. 2019

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“…The comprehensive analysis method used here is the analysis part of the INCA method (hereafter, referred to as INCA). The method starts with a numerical weather prediction (NWP) forecast (or reanalysis) temperature field as a first guess, which is then corrected based on observation-forecast differences (Haiden et al, 2011;Kann et al, 2012;Wang et al, 2017). The specific steps of the method are as follows.…”

Section: Gridding Methods 211 | Comprehensive Analysis Methodsmentioning

confidence: 99%

“…As presented in the introduction, the INCA method has several advantages, such as integration of the NWP model forecast and station observations, considering the physical influences on the temperature profile, reconstructing missing data and convenience for business operations. The INCA method has the superior ability to analyse temperature in complex terrain areas (Haiden et al, 2011;Kann et al, 2012;Wang et al, 2017).…”

Section: Estimation Of the 3d Part Of The Temperature Correctionmentioning

confidence: 99%

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Establishment and assessment of hourly high‐resolution gridded air temperature data sets in Zhejiang, China

Chen

Yang

et al. 2019

Meteorological Applications

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A high temporal–spatial resolution (1 hr/1 km) gridded temperature data set with a long time‐series (2006–2015) is established for Zhejiang province, China, which has a complex terrain, including plains, hills, basins, mountains, islands and oceans. A comprehensive analysis method is used to combine regional surface station data with reanalysis data from the European Centre for Medium‐Range Weather Forecasts (ECMWF) such that the observations at the stations are reproduced, and the reanalysis data provide the spatial structure for the interpolation. The elevation correction method and inverse distance‐weighted interpolation method are selected for comparison. The quality of the data set is evaluated and analysed by comparison with the cross‐validation results, third‐party data validation and other gridded data sets. The results show that the comprehensive analysis method provides the best outcome, followed by the inverse distance‐weighted method, and the reanalysis data elevation correction method has the worst result. The temperature error obtained from the gridding methods increases with altitude, decreases with increasing station density and varies with different synoptic conditions. The obtained data set can describe the temperature feature of peaks and valleys more precisely, which produces lower errors and higher spatial correlation co‐efficients compared with observation than the other gridded data sets. Therefore, the established data set can meet the needs of fine meteorological businesses and has potential applications in many fields.

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“…In this work, we use Fractions Skill Score (FSS: Roberts and Lean, ), which is a form of a neighbourhood approach (Gilleland et al, ), and it is computed following the ensemble formulation proposed by Duc et al (). Integrated Nowcasting through Comprehensive Analyses (INCA: Haiden et al, ; Wang et al, ) analyses are used as a reference for precipitation verification. The INCA system is currently being developed at ZAMG, and it uses data drawn from automatic weather stations, remote‐sensing data (radar, satellite), forecast fields of NWP models, and high‐resolution topographic data (Haiden et al, ) to generate a high‐resolution precipitation analysis field for a domain covering Austria and surroundings (Figure ).…”

Section: Experimental Designmentioning

confidence: 99%

Improving initial condition perturbations in a convection‐permitting ensemble prediction system

Keresturi

Wang

Meier

et al. 2019

Quart J Royal Meteoro Soc

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One of the main challenges presented by a limited‐area model ensemble prediction system (LAMEPS) concerns the limited capacity for its initial condition perturbations to correctly represent large‐scale flow uncertainties due to its limited‐size domain and deficiencies in formulating lateral boundary conditions. In addition, a mismatch between LAMEPS (initial condition) and host EPS lateral boundary perturbations can form spurious gravity waves at the boundaries. In the present work, an ensemble Jk blending method is proposed for improving representation of large‐scale uncertainties and for addressing consistent initial conditions and lateral boundary perturbations. Our approach involves employing Jk blending within a framework of three‐dimensional variation (3D‐Var) ensemble data assimilation (EDA). In such a system, small‐scale perturbations are generated from 3D‐Var EDA, while large‐scale perturbations are generated from the host ensemble via Jk blending. The ensemble Jk method is implemented in the C‐LAEF (Convection‐permitting Limited‐Area Ensemble Forecasting) system and is compared to the standard perturbed‐observation EDA approach, i.e. perturbed‐observation EDA without large‐scale constraint. The comparison shows that the ensemble Jk method gives a more skilful and reliable EPS, especially for upper‐air variables. In addition, positive effects on the surface pressure and precipitation of large‐scale perturbations are shown. Finally, the ensemble Jk method's capacity to alleviate perturbation mismatches is demonstrated.

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Integrating nowcasting with crisis management and risk prevention in a transnational and interdisciplinary framework

Cited by 14 publications

References 0 publications

Independent perturbations for physics parametrization tendencies in a convection-permitting ensemble (pSPPT)

Independent perturbations for physics parametrization tendencies in a convection-permitting ensemble (pSPPT)

Establishment and assessment of hourly high‐resolution gridded air temperature data sets in Zhejiang, China

Improving initial condition perturbations in a convection‐permitting ensemble prediction system

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