A Reanalysis System for the Generation of Mesoscale Climatographies

Hahmann, Andrea N.; Rostkier‐Edelstein, Dorita; Warner, Thomas T.; Vandenberghe, François; Liu, Yubao; Babarsky, Richard; Swerdlin, Scott

doi:10.1175/2009jamc2351.1

Cited by 60 publications

(42 citation statements)

References 48 publications

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“…In contrast, WRF overestimated mean wind speeds by about 13% at 10 m AGL and 10% at 50 m AGL in July and by about 11% at 10 m AGL and 10% at 50 m AGL in December. The underestimation of MM5 and its larger bias magnitude in winter was noted in some recent high-resolution dynamical downscaling studies also over the sea surface where the model underestimated the near-surface winds for nearly À15% in July and À20% in January [Hahmann et al, 2010]. On the other hand, WRF results for wind speed at 10 m AGL differ from recent results achieved in the complex terrain of Spain, where the WRF model at 2 km grid spacing with somewhat different choice of parameterizations generally underestimated the daily mean wind speeds [Jiménez et al, 2010].…”

Section: Resultsmentioning

confidence: 99%

Sub‐kilometer dynamical downscaling of near‐surface winds in complex terrain using WRF and MM5 mesoscale models

et al. 2012

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[1] Sub-kilometer dynamical downscaling was performed using the Weather Research and Forecasting (WRF) and Mesoscale Model Version 5 (MM5) models. The models were configured with horizontal grid spacing ranging from 27 km in the outermost telescoping to 333 m in the innermost domains and verified with observations collected at four 50-m towers in west-central Nevada during July and December 2007. Moment-based and spectral verification metrics showed that the performance of WRF was superior to MM5. The modeling results were more accurate at 50 m than at 10 m AGL. Both models accurately simulated the mean near-surface wind shear; however, WRF (MM5) generally overestimated (underestimated) mean wind speeds at these levels. The dispersion errors were the dominant component of the root-mean square errors. The major weakness of WRF was the overestimation of the intensity and frequency of strong nocturnal thermally driven flows and their sub-diurnal scale variability, while the main weaknesses of MM5 were larger biases, underestimation of the frequency of stronger daytime winds in the mixed layer and underestimation of the observed spectral kinetic energy of the major energy-containing motions. Neither of the verification metrics showed systematic improvement in the models' accuracy with increasing the horizontal resolution and the share of dispersion errors increased with increased resolution. However, a profound improvement in the moment-based accuracy was found for the mean vertical wind shear and the temporal variability of wind speed, in particular for summer daytime simulations of the thermally driven flows. The most prominent spectral accuracy improvement among the primary energy-containing frequency bands was found for both models in the summertime diurnal periods. Also, the improvement for WRF (MM5) was more (less) apparent for longer-than-diurnal than for sub-diurnal periods. Finally, the study shows that at least near-kilometer horizontal grid spacing is necessary for dynamical downscaling of near-surface wind speed climate over complex terrain; however, some of the physics options might be less appropriate for grid spacing nearing the scales of the energy-containing turbulent eddies, i.e., resolutions of several hundred meters. In addition to the effects of the lower boundary, the accuracy of the lateral boundary conditions of the parent domains also controls the onset and evolution of the thermally driven flows.Citation: Horvath, K., D. Koracin, R. Vellore, J. Jiang, and R. Belu (2012), Sub-kilometer dynamical downscaling of near-surface winds in complex terrain using WRF and MM5 mesoscale models,

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Section: Resultsmentioning

confidence: 99%

Sub‐kilometer dynamical downscaling of near‐surface winds in complex terrain using WRF and MM5 mesoscale models

et al. 2012

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“…In this manner we assume that the reanalysis fields provide an appropriate description of the regions large-scale and synoptic flows, whereas the mesoscale model is used to resolve smaller scales and processes not properly simulated in the reanalysis due to its limited spatial resolution (e.g. Miguez-Macho et al, 2005;Barstad et al, 2009;Hahmann et al, 2010).…”

Section: Model Setup and Methodsmentioning

confidence: 99%

“…The control simulation covered the period 2006-2011, and was run in a series of 11-day long overlapping simulations, with the output from the first day being discarded. This method is based on the assumptions described in Hahmann et al (2010). The simulation used grid nudging that continuously relaxes the model solution towards the gridded reanalysis (every 6 h) but this was done only on the outer domain and above the boundary layer (level 10, located ∼800 m AGL) to allow for the mesoscale processes near the surface to develop freely.…”

Section: Model Setupmentioning

confidence: 99%

Wind climate estimation using WRF model output: method and model sensitivities over the sea

et al. 2014

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High-quality tall mast and wind lidar measurements over the North and Baltic Seas are used to validate the wind climatology produced from winds simulated by the Weather, Research and Forecasting (WRF) model in analysis mode. Biases in annual mean wind speed between model and observations at heights around 100 m are smaller than 3.2% at offshore sites, except for those that are affected by the wake of a wind farm or the coastline. These biases are smaller than those obtained by using winds directly from the reanalysis. We study the sensitivity of the WRF-simulated wind climatology to various model setup parameters. The results of the year-long sensitivity simulations show that the long-term mean wind speed simulated by the WRF model offshore in the region studied is quite insensitive to the global reanalysis, the number of vertical levels, and the horizontal resolution of the sea surface temperature used as lower boundary conditions. Also, the strength and form (grid vs spectral) of the nudging is quite irrelevant for the mean wind speed at 100 m. Large sensitivity is found to the choice of boundary layer parametrization, and to the length of the period that is discarded as spin-up to produce a wind climatology. It is found that the spin-up period for the boundary layer winds is likely larger than 12 h over land and could affect the wind climatology for points offshore for quite a distance downstream from the coast.

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“…The simulation used spectral nudging which continuously relaxes the model solution towards the gridded reanalysis, but this is done only on the outer domain and above level 10 to allow for the mesoscale processes near the surface to develop freely. This method is based on the assumptions described in Hahmann et al (2010) and validated in Hahmann et al (2015) for wind * (USA) National Oceanic and Atmospheric Administration/ National Centers for Environmental Prediction. energy applications offshore in the Baltic and North Seas.…”

Section: Mesoscale Simulationsmentioning

confidence: 99%

Correlation and coherence of mesoscale wind speeds over the sea

Mehrens

Hahmann

Larsén

et al. 2016

Quart J Royal Meteoro Soc

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A large offshore observational dataset from stations across the North and Baltic Seas is used to investigate the planetary boundary-layer wind characteristics and their coherence, correlation and power spectra. The data from thirteen sites, with pairs of sites at horizontal distances of 4 to 848 km, are analyzed for typical wind turbine nacelle heights. Mean wind characteristics, correlation and coherence are also calculated for analogous wind data from simulations with the Weather Research and Forecasting (WRF) model.Results indicate a general good agreement for the coherence calculated based on measurements and the WRF-derived time series. By normalizing the frequency axes with the distance and mean wind speed, it can be demonstrated that, even for data with a wide range of distances, the coherence is a function of the frequency, mean wind and distance, which is consistent with earlier studies. However the correlation coefficient as a function of distance calculated from WRF is higher than observed in the measurements. For the power spectra, wind speed and wind speed step change distribution, the results for all sites are quite similar. The land masses strongly influence the individual wind direction distribution at each site. The ability of the WRF model to reproduce the coherence of the measurements demonstrates that its output can be used to estimate the coherence of fluctuations for the integration of offshore energy. The power spectra of WRF time series underestimates the high-frequency fluctuations. Due to the large number of measurement sites, the results can be used for further plausibility validation for mesoscale model runs over the sea.

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A Reanalysis System for the Generation of Mesoscale Climatographies

Cited by 60 publications

References 48 publications

Sub‐kilometer dynamical downscaling of near‐surface winds in complex terrain using WRF and MM5 mesoscale models

Sub‐kilometer dynamical downscaling of near‐surface winds in complex terrain using WRF and MM5 mesoscale models

Wind climate estimation using WRF model output: method and model sensitivities over the sea

Correlation and coherence of mesoscale wind speeds over the sea

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