A novel convective-scale regional reanalysis COSMO-REA2: Improving the representation of precipitation

Wahl, Sabrina; Bollmeyer, Christoph; Crewell, Susanne; Figura, Clarissa; Friederichs, Petra; Hense, Andreas; Keller, Jan D.; Ohlwein, Christian

doi:10.1127/metz/2017/0824

Cited by 71 publications

(73 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Both COSMO reanalyses are resolved at the meso-γ scale (2-20 km). However, only COSMO-REA2 has an effective resolution which could also capture meso-γ scale phenomena (14 km) (Wahl et al 2016). Indeed we see evidence that meso-γ scale phenomena are reproduced by COSMO-REA2 in the complex terrain of Switzerland.…”

Section: Discussionmentioning

confidence: 80%

“…Effective resolution is the resolution of meteorological phenomena that a given model can accurately depict, and is larger than the size of the model's grid size. Effective resolution for COSMO reanalyses from (Wahl et al 2016). We have no source for the effective resolution of MERRA-2, but a working assumption in meteorology is that effective resolution is normally 2-4 times higher than the model resolution.…”

Section: Validation Datasetsmentioning

confidence: 99%

“…The Consortium for Small-scale Modeling (COSMO) regional reanalyses may offer a solution by accurately modelling local variability of wind while still providing consistent long-term time series across wider geographic areas (Kaiser-Weiss et al 2019). Two versions exist: one with 2 km (COSMO-REA2) (Wahl et al 2016) and another with 6 km (COSMO-REA6) (Bollmeyer et al 2015) horizontal resolution. On flat terrain across Europe, COSMO-REA6 has been shown to accurately simulate wind speed (Borsche et al 2016, Frank et al 2019, Ramirez Camargo et al 2019, often with improved performance over COSMO-REA2 (Frank et al 2020, Ramirez Camargo et al 2019.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sub-national variability of wind power generation in complex terrain and its correlation with large-scale meteorology

Pickering

Grams

Pfenninger

2020

Environ. Res. Lett.

View full text Add to dashboard Cite

The future European electricity system will depend heavily on variable renewable generation, including wind power. To plan and operate reliable electricity supply systems, an understanding of wind power variability over a range of spatio-temporal scales is critical. In complex terrain, such as that found in mountainous Switzerland, wind speeds are influenced by a multitude of meteorological phenomena, many of which occur on scales too fine to capture with commonly used meteorological reanalysis datasets. Past work has shown that anticorrelation at a continental scale is an important way to help balance variable generation. Here, we investigate systematically for the first time the possibility of balancing wind variability by exploiting anticorrelation between weather patterns in complex terrain. We assess the capability for the Consortium for Small-scale Modeling (COSMO)-REA2 and COSMO-REA6 reanalyses (with a 2 and 6 km horizontal resolution, respectively) to reproduce historical measured data from weather stations, hub height anemometers, and wind turbine electricity generation across Switzerland. Both reanalyses are insufficient to reproduce site-specific wind speeds in Switzerland's complex terrain. We find however that mountain-valley breezes, orographic channelling, and variability imposed by European-scale weather regimes are represented by COSMO-REA2. We discover multi-day periods of wind electricity generation in regions of Switzerland which are anticorrelated with neighbouring European countries. Our results suggest that significantly more work is needed to understand the impact of fine scale wind power variability on national and continental electricity systems, and that higher-resolution reanalyses are necessary to accurately understand the local variability of renewable generation in complex terrain.

show abstract

Section: Discussionmentioning

confidence: 80%

Section: Validation Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sub-national variability of wind power generation in complex terrain and its correlation with large-scale meteorology

Pickering

Grams

Pfenninger

2020

Environ. Res. Lett.

View full text Add to dashboard Cite

show abstract

“…In order to gain further insights into the spatial differences in fog genesis, it is planned to include further meteorological and geophysical variables in the analysis, e.g. land cover information available through the CORINE (COoRdination of InformatioN on the Environment) Land Cover project as well as air temperature and pressure fields from the ERA-5 assimilation dataset produced by the Copernicus Climate Change Service or the regional COSMO-REA2 reanalysis product from the Hans Ertel Center for Weather Research (Wahl et al, 2017). For a better delineation of fog patches and thus more precise information on fog frequency distribution, it is also planned to increase the resolution of the fog dataset through the integration of the high-resolution visible (HRV) band of MSG and by exploiting data from low Earth orbiting satellite systems like the Moderate Resolution Imaging Spectroradiometer (MODIS) or the Advanced Very High Resolution Radiometer (AVHRR).…”

Section: Resultsmentioning

confidence: 99%

A spatially explicit and temporally highly resolved analysis of variations in fog occurrence over Europe

Egli

Thies

Bendix

2019

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

Fog plays a major role in many ecological aspects and it influences human life in various ways. In this study, a temporally highly resolved and spatially explicit analysis of variations in fog occurrence was conducted for Europe and links to general weather conditions were investigated. To this end, a high‐resolution fog product based on Meteosat Second Generation data was developed. Characteristic fog distributions were identified by applying a Self Organizing Map approach to the dataset. It was found that the resulting fog patterns are primarily determined by terrain characteristics. Simultaneous occurrences between these patterns and the predominant general weather situations were analyzed. The results show that the general weather situations can be categorized into three main groups, each responsible for the formation of a different group of fog patterns. Additionally, distinct regional differences could be identified in the diurnal and annual fog frequency cycles and the derived region‐specific frequency variations were used to draw conclusions about the fog types prevailing in these regions.

show abstract

“…Eckley's texture analysis is applied to an ensemble of forecasts with the high-resolution NWP model COSMO-DE 1 , operated by the German Meteorological Service (COSMO-DE-EPS, Baldauf et al 2011;Gebhardt et al 2011;Peralta et al 2012). As observations we use the regional reanalysis COSMO-REA2 (Wahl et al 2017), where latent heat nudging is used to assimilate precipitation estimates from radar observations. Our investigation concentrates on 14 days in 2011 with considerable precipitation over Germany due to convective, stratiform and mixed convective-stratiform precipitation.…”

Section: Introductionmentioning

confidence: 99%

Spatial verification of high-resolution ensemble precipitation forecasts using local wavelet spectra

Kapp¹,

Friederichs²,

Brune³

et al. 2018

metz

Self Cite

View full text Add to dashboard Cite

The purpose of this study is to perform verification of the structural characteristics of highresolution spatial forecasts without relying on an object identification algorithm. To this end, a wavelet approach developed for image texture analysis is applied to an ensemble of high-resolution quantitative precipitation forecasts. The forecasts are verified against estimates from a high-resolution regional reanalysis with a similar model version. The wavelet approach estimates an averaged wavelet spectrum for each spatial field of the ensemble forecasts and the reanalysis, thereby removing all information on the localization of precipitation and investigating solely the overall structure of forecasts and reanalysis. In order to assess skill using a multivariate score, an additional reduction of dimensionality is needed. This is performed using singular vectors from a linear discriminant analysis as it favors data compression in the direction where the ensemble is most discriminating. We discuss implications of this strategy, show that the averaged wavelet spectra give valuable information on forecast performance. The skill difference between a so-called perfect forecast using for verification a member of the ensemble, and the non-perfect forecast using the reanalysis points to significant deficiencies of the forecast ensemble. Overall, the discriminating power solely based on global spectral information is remarkable, and the COSMO-DE-EPS is a quite good forecast ensemble with respect to the reanalysis.

show abstract

A novel convective-scale regional reanalysis COSMO-REA2: Improving the representation of precipitation

Cited by 71 publications

References 0 publications

Sub-national variability of wind power generation in complex terrain and its correlation with large-scale meteorology

Sub-national variability of wind power generation in complex terrain and its correlation with large-scale meteorology

A spatially explicit and temporally highly resolved analysis of variations in fog occurrence over Europe

Spatial verification of high-resolution ensemble precipitation forecasts using local wavelet spectra

Contact Info

Product

Resources

About