A high resolution reference data set of German wind velocity 19512001 and comparison with regional climate model results

Keuler, Klaus; Jacob, Daniela; Knoche, R.; Block, Alexander; Kotlarski, Sven; Mueller-Westermeier, G.; Rechid, Diana; Ahrens, Wolfgang

doi:10.1127/0941-2948/2006/0162

Cited by 37 publications

(30 citation statements)

References 13 publications

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“…Wind speeds that are higher compared to the observed values are the reason for the underestimation of the UHI with REMO and ERA40 results (not shown). The wind speed overestimation was also found by Walter et al (2006) for REMO and Barstad et al (2008) for ERA40 for other regions. Figure 7.…”

Section: Urban Heat Island In the Future Climatesupporting

confidence: 56%

A statistical model for the urban heat island and its application to a climate change scenario

Hoffmann

Krueger

Schlünzen

2011

Intl Journal of Climatology

101

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A linear statistical model relating the nocturnal urban heat island (UHI) intensity of Hamburg with meteorological conditions is constructed from observations taken by the German Meteorological Service (DWD). To find the appropriate predictors the relationship between different meteorological variables and the UHI of Hamburg is analyzed. Results and physical plausibility suggest that cloud cover, wind speed and relative humidity are the relevant variables and can be used to construct a statistical model. The parameters for the statistical model are determined with the generalized least square method. With the help of the statistical model up to 42% of the UHI variance can be explained. The statistical model is then used to statistically downscale results from climate runs of the regional climate models (RCM) REMO and CLM. Both RCMs were driven with the A1B SRES emission scenario runs of the global climate model ECHAM5/MPI-OM. The resulting values for the future UHI are analyzed with respect to monthly averages and the frequency distribution. Results show that changes in the UHI are different for the different months. Significant change (decrease of UHI) in the results of both RCMs and for both realizations of the A1B scenario can be found for April in at the middle and the end of the century and in December at the end of the century. For the summer months which are most relevant to the development of adaption strategies the results differ between the RCMs. REMO results show no significant changes for the summer, while analyses of CLM suggest significant increase in July and August. The frequency distribution of the summer UHI shows no significant changes for REMO and only in one realization of CLM a significant increase in moderate and strong UHI days can be found for the end of the century.

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Section: Urban Heat Island In the Future Climatesupporting

confidence: 56%

A statistical model for the urban heat island and its application to a climate change scenario

Hoffmann

Krueger

Schlünzen

2011

Intl Journal of Climatology

101

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“…Both RCMs have been previously evaluated and their output has been compared with observational data. Walter et al (2006) provide evidence that REMO and CCLM are able to reproduce both the temporal and spatial variability of wind observations in Germany. Deviations between reanalysis-driven RCMs and observations generally do not exceed 1 m s 21 .…”

Section: Methodsmentioning

confidence: 94%

Regional Changes in Wind Energy Potential over Europe Using Regional Climate Model Ensemble Projections

Hueging¹,

Haas²,

Born³

et al. 2013

Journal of Applied Meteorology and Climatology

Self Cite

124

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The impact of climate change on wind power generation potentials over Europe is investigated by considering ensemble projections from two regional climate models (RCMs) driven by a global climate model (GCM). Wind energy density and its interannual variability are estimated based on hourly near-surface wind speeds. Additionally, the possible impact of climatic changes on the energy output of a sample 2.5-MW turbine is discussed. GCM-driven RCM simulations capture the behavior and variability of current wind energy indices, even though some differences exist when compared with reanalysis-driven RCM simulations. Toward the end of the twenty-first century, projections show significant changes of energy density on annual average across Europe that are substantially stronger in seasonal terms. The emergence time of these changes varies from region to region and season to season, but some long-term trends are already statistically significant in the middle of the twenty-first century. Over northern and central Europe, the wind energy potential is projected to increase, particularly in winter and autumn. In contrast, energy potential over southern Europe may experience a decrease in all seasons except for the Aegean Sea. Changes for wind energy output follow the same patterns but are of smaller magnitude. The GCM/RCM model chains project a significant intensification of both interannual and intra-annual variability of energy density over parts of western and central Europe, thus imposing new challenges to a reliable pan-European energy supply in future decades.

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“…The use of relative altitude (in the following the term exposure will be used as a synonym) was motivated by Walter et al (2006), who found good correlations with 10 m wind speed in Germany for altitude at a given point transformed to exposure by dividing it with the mean altitude of the surrounding area of 10 km × 10 km. Here, we calculated corresponding fields on a 1 km grid using elevation data (same sources as in Sect.…”

Section: Regressionmentioning

confidence: 99%

“…More complex interpolation methods are needed to maintain a certain quality on a relatively fine grid. As contribution to DecReg, the Deutscher Wetterdienst (DWD) aims to provide gridded observational data of daily temperature and wind speed in high resolution for the time period 1961-2010. A variety of interpolation methods can be used to derive continuous field data based on point measurements (see overview given by, for example, Li and Heap, 2008). In cases of relatively coarse grid sizes (with a high number of samples per grid cell) simple averaging techniques are sufficient.…”

Section: Introductionmentioning

confidence: 99%

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High-resolution daily gridded data sets of air temperature and wind speed for Europe

Brinckmann

Krähenmann

Bissolli

2016

Earth Syst. Sci. Data

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Abstract. New high-resolution data sets for near-surface daily air temperature (minimum, maximum and mean) and daily mean wind speed for Europe (the CORDEX domain) are provided for the period 2001-2010 for the purpose of regional model validation in the framework of DecReg, a sub-project of the German MiKlip project, which aims to develop decadal climate predictions. The main input data sources are SYNOP observations, partly supplemented by station data from the ECA&D data set (http://www.ecad.eu). These data are quality tested to eliminate erroneous data. By spatial interpolation of these station observations, grid data in a resolution of 0.044 • (≈ 5 km) on a rotated grid with virtual North Pole at 39.25 • N, 162 • W are derived. For temperature interpolation a modified version of a regression kriging method developed by Krähenmann et al. (2011) is used. At first, predictor fields of altitude, continentality and zonal mean temperature are used for a regression applied to monthly station data. The residuals of the monthly regression and the deviations of the daily data from the monthly averages are interpolated using simple kriging in a second and third step. For wind speed a new method based on the concept used for temperature was developed, involving predictor fields of exposure, roughness length, coastal distance and ERA-Interim reanalysis wind speed at 850 hPa. Interpolation uncertainty is estimated by means of the kriging variance and regression uncertainties. Furthermore, to assess the quality of the final daily grid data, cross validation is performed. Variance explained by the regression ranges from 70 to 90 % for monthly temperature and from 50 to 60 % for monthly wind speed. The resulting RMSE for the final daily grid data amounts to 1-2 K and 1-1.5 m s −1 (depending on season and parameter) for daily temperature parameters and daily mean wind speed, respectively.

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A high resolution reference data set of German wind velocity 19512001 and comparison with regional climate model results

Cited by 37 publications

References 13 publications

A statistical model for the urban heat island and its application to a climate change scenario

A statistical model for the urban heat island and its application to a climate change scenario

Regional Changes in Wind Energy Potential over Europe Using Regional Climate Model Ensemble Projections

High-resolution daily gridded data sets of air temperature and wind speed for Europe

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