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In this report, the challenges associated with long-term correction techniques for site assessment, when short-term (less than one year) measurement campaigns are used, is examined. Long-term wind speed measurements from nine sites located in Northern Europe and South Africa is used in combination with reference data from simulation results from the Weather Research and Forecasting (WRF) model. The dataset is used to evaluate the uncertainties associated with long term correction from short windows of observations, and how much the uncertainties can be reduced if the seasonality of the reference-data biases can be better predicted in the future. The main conclusions of the study are:1. Short-term measurement campaigns are currently unfeasible because the mean wind speed uncertainty grows rapidly with a shortening of the measurement time-window below one year.2. Knowing the full seasonal variations of the reference-data biases (by some future technique) allows a significant reduction of the uncertainty, upwards of 50 %, for 3 - 9-month campaigns. However, for very-short-term (1-2 months) campaigns the uncertainties remain about three times larger than a full-year campaign even when correcting for the seasonal variations of the reference data biases.

This document reports on the production of the Wind Atlas of Mexico (WAM), including the methods used to create the mesoscale component based on the Weather Research and Forecasting (WRF) model, and the microscale component based on downscaling of WRF using the Wind Atlas Analysis and Applications Program (WAsP). The report is divided into four main parts. In the first part, we document the method used to run the mesoscale simulations and to select the best suited WRF model configuration. The best model configuration is found by evaluation against the measurements from the WAM masts using various metrics. In the second part, we describe the method used to generalize and downscale the WRF model wind climate using PyWAsP, a python interface to run WAsP. We compare the results from the downscaled numerical wind atlas against the observed wind statistics from seven WAM masts in the third part to find the optimal configuration. In the last part we present the new wind resource maps for all of Mexico and their longterm climatology. In WAM, there have been many updates to the configuration DTU normally uses to perform wind atlases and that has been documented in Hahmann et al. (2018). Among the most important:1. We ran simulations for an equivalent of ten years covering the period most observed in all the WAM sites to find the WRF model configuration most suited to the simulation of the wind climatology over Mexico.2. We used a new method of generalization and downscaling of the WRFderived wind climate that uses the PyWAsP engine and was demonstrated more accurate than the previous approaches.3. We produced a high resolution (up to date) wind climatology for Mexico using the latest WRF Version 4.2.1, covering 10 years (2011{2020) of simulation for all Mexico at 3 km x 3 km spatial resolution and one hour time output.The final error statistics of the WAM wind atlas show that the WRF+PyWAsP method has a MAPE of 11.7% and 5.6% for the long-term power density and wind speed, respectively. When ignoring the mast in more complex terrain, M7, the WRF and WRF+PyWAsP downscaling significantly narrows the error distributions for both long-term wind speed and power density

A central part of the New European Wind Atlas (NEWA) is the generation of a mesoscale wind atlas which is based on 30 years of simulations with the WRF (Weather Research and Forecast) model. The atlas will cover all EU countries (plus Norway, Switzerland, the Balkans and Turkey) and associated offshore areas up to 100 km off the coast including the complete North and Baltic Seas. The first part of the presentation gives an overview of the work that was done to prepare the mesoscale wind atlas production runs. To arrive at a final model configuration for the production run, a large ensemble of nearly 50 WRF simulations with different model setups was performed. The parameters tested in the ensemble members include, among others: model version, atmospheric and sea surface temperature input, land surface model as well as surface and PBL parameterisations. Information from the ensemble runs will also be used to provide uncertainty information to the wind atlas. After presenting the final setup of the production runs, some information is given about the management of the production runs, which were performed on the Tier-0 supercomputer MareNostrum. Finally, results from the production runs are shown.

This report explores the quantification of uncertainty in the NEWA production run and probabilistic ensemble. Uncertainty quantification relies on the availability of model simulations and observations. In areas where observations are not available, uncertainty addresses the quantification of the variability resulting from the sensitivity of the model chain tool resulting from the sampling of all the available model configurations tested. Uncertainty can be here regarded as the quantification of model sensitivity or model spread. In areas with availability of observations, the realism of the model ensemble is evaluated by establishing metrics of model-data comparison. Therefore, uncertainty is understood here as the result of the contributions of model sensitivity to different model setups, and of model errors in a model-data comparison framework. The first part of this report explores the uncertainty derived from model sensitivity subjected to the decisions taken regarding the use of different models setups and how these produce variability in model output. The range of this variability has been regarded as spread in model output and has been quantified in various manners. The second part of this report addresses how model performance can be characterised with the data at hand and whether decisions regarding selection of a given model setup for a production run can be taken on the basis of model performance in a variety of situations, using different variables and datasets as observational targets: wind data from tall masts in the Vestas database; wind speed profiles from tall masts and offshore lidars; surface (10-meter) wind data; satellite data and reanalysis outputs.

En vigtig del af planlægning af vindfarme er præcise vindbetingelsesestimater. I komplekstterræn kræves ikke-linære mikroskalamodeller som tager højde for stabilitetseffekter for atopnå præcise estimater, men disse modeller kan i sig selv ikke tage højde for storskala vejrvariationer, hvilket kræver kopling til mesoskalamodeller.Med udgangspunkt i mesoskalamodellen Weather Research and Forecasting (WRF) ogURANS mikroskalamodellen EllipSys3D, præsenterer denne afhandling en meso- til miroskalakoblingsmetode til vind nedskalering, ved at drive mikroskalamodellen med momentum ogtemperatur kildeled udtrukket fra mesoskalamodellen.Metoden bliver i første omgang studeret i terræn med simpelt kompleksitet. Her vises det,at en Single-Column model (SCM) version af mikroskalamodellen drevet af tendenser fraWRF resulterer i vindstatistik som er sammenlignelig med WRFs præcision. Resultaterneviser også, at når forskellige Planetary Boundary Layer og Surface Layer skemaer anvendesi WRF, følger SCM resultatererne WRF resultater, mens en statistisk ens respons ses i dennederste del af grænselaget.Dernæst bliver metoden brugt til at modellere Perdigão, en dobbelt-bakke, i komplekst terræn.Ved en simulering med 80 m celleafstande og et diskretiseringsskema med lav numerisknøjagtighed, viste studiet at metoden resulterede i store vindstatistike forbedringer sammenlignetmed nedskalering med WRF ned til 333 m celleafstande. På toppen af bakkerne var dengennemsnitlige afvigelse for vindhastigheder mindre end 3% ved toppen af masterne, mensde var henholdsvis 8-12% og 17-25% for WRF domæner med 1 km og 333 m celleafstande.Selvom resultaterne er lovende, ses en klar afhængighed af celleafstande og numeriskemetoder. Der er fortsat usikkerheder omkring mulig double-counting af turbulente fluxe, oghviken indflydelse det har når RANS closures anvendes til transient modelering af atmosfæreni høj opløsning. Accurate estimation of local site conditions, i.e. wind power resources and the turbulence impacting wind turbines turbines, is an important part of wind farm planning. In complex terrain, non-linear microscale models that account for atmospheric stability effects are needed for accurate site assessment, but by themselves they are unable to account for the changing large-scale weather conditions. Therefore, coupling to a mesoscale model, which can provide these variations to the microscale models, is required. This thesis describes, implements and validates with observed tall mast measurements a novel coupling strategy using the Weather Research and Forecasting (WRF) mesoscale model and the EllipSys3D URANS microscale model for wind downscaling. The coupling strategy is based on forcing the microscale model with momentum and temperature source terms extracted from the time-evolving mesoscale model simulation. These terms are included as source terms instead of the usual lateral boundary conditions. Two cases are presented for sites in simple and complex terrain. First, results from simple terrain cases are presented. It is shown that a Single-Column Model (SCM) version of microscale model forced by tendencies from WRF results in long-term wind statistics of comparable statistical accuracy to results using the WRF model itself. Using different Planetary Boundary Layer and Surface Layer schemes in the WRF model simulations shows that the SCM results tend to follow the WRF model results, while maintaining a statistically similar response near the surface. Second, the coupling method was used at the complex double hill site Perdigão. It was shown that for a simulation using 80 m grid spacing and a first-order accurate discretisation scheme, the coupled approach results in large improvements in wind statistics compared to downscaling with the WRF model with an innermost domain of 333 m grid spacing. At four masts situated on top of the two ridge tops, the mean biases in wind speed for a 32-day period were less than 3% at the top most anemometer on each mast, compared to mean errors of 8–12% and 17–25% for WRF domains of 1 km and 333 m grid spacing, respectively. Despite the encouraging results, a clear dependence on grid spacing and numerical methods was seen in the coupled model results. Uncertainties remain about possible double-counting of turbulent fluxes, and its impact on the simulated wind speed, when RANS turbulence closures are used for transient atmospheric modeling at fine resolution.

At the Danish Meteorological Institute (DMI) the NWP nowcasting system utilizes a newly developed nudging based technique to assimilate radar-derived precipitation observations in the NWP model. In this method the observed rain rates are used to estimate a vertical profile of horizontal divergence changes corresponding to the rain deficit in the model. Verification of precipitation forecasts by the nowcasting system using this technique is presented for 17 days of august 2010. This period was particularly wet with several heavy precipitation events with observed precipitation above 36 mm of rain during one hour. Three of these events are studied in detail. The verification is based on the fractions skill score (FSS), which give scale selective scores of forecast skill relative to observations (radar-derived precipitation fields). The study shows that the nudging technique have a strong impact on the spatial skill oftheforecast in the first 1-2 hours of the free forecast, and a smaller but still clear impact 3-4 hours in to the free forecast.

A central part of the New European Wind Atlas (NEWA) is the generation of a mesoscale wind atlas which is based on 30 years of simulations with the WRF (Weather Research and Forecast) model. The atlas will cover all EU countries (plus Norway, Switzerland, the Balkans and Turkey) and associated offshore areas up to 100 km off the coast including the complete North and Baltic Seas. The first part of the presentation gives an overview of the work that was done to prepare the mesoscale wind atlas production runs. To arrive at a final model configuration for the production run, a large ensemble of nearly 50 WRF simulations with different model setups was performed. The parameters tested in the ensemble members include, among others: model version, atmospheric and sea surface temperature input, land surface model as well as surface and PBL parameterisations. Information from the ensemble runs will also be used to provide uncertainty information to the wind atlas. After presenting the final setup of the production runs, some information is given about the management of the production runs, which were performed on the Tier-0 supercomputer MareNostrum. Finally, results from the production runs are shown.