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This document reports on the methods used to create and the results of the WRF-based numerical wind atlases developed for the Wind Atlas for South Africa Phase 3 (WASA3) project.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 using the measurements from the WASA masts. In the second part, we describe the method used to generalise and downscale the WRF model wind climate. We compare the results from the downscaled numerical wind atlas against the observed wind statistics from the 19 WASA masts in the third part. In the last part, we present the new wind resource maps and their long-term climatology. In WASA3, there have been many updates to the configuration of the 2018 WASA2 simulations documented inHahmann et al (2018). Among the most important:• We ran thirteen two-year simulations covering the period most observed in all the WASA sites to find the WRF model configuration most suited to the simulation of the wind climatology over South Africa.• We used a new method of generalisation and downscaling of the WRF-derived wind climate that uses the PyWAsP engine and was demonstrated more accurate than the previous approach.• We produced the most extensive to date wind climatology for South Africa, 30 years (1990–2019) simulation covering all South Africa at 3.33 km x 3.33 km spatial resolution and 30 minutes time output.The final error statistics of the WASA3 wind atlas show that the WRF+PyWAsP method has a MAPE of 14.2 % and 4.3 % for the long-term power density and wind speed, respectively. This is improved from the same validation in WASA2. When ignoring the two more complex masts, WM09 and WM11, the WRF and WRF+PyWAsP downscaling significantly narrows the error distributions for both long-term wind speed and power density.

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

This is the first of two papers that document the creation of the New European Wind Atlas (NEWA). It describes the sensitivity analysis and evaluation procedures that formed the basis for choosing the final setup of the mesoscale model simulations of the wind atlas. The suitable combination of model setup and parameterizations, bound by practical constraints, was found for simulating the climatology of the wind field at turbine-relevant heights with the Weather Research and Forecasting (WRF) model. Initial WRF model sensitivity experiments compared the wind climate generated by using two commonly used planetary boundary layer schemes and were carried out over several regions in Europe. They confirmed that the most significant differences in annual mean wind speed at 100 m a.g.l. (above ground level) mostly coincide with areas of high surface roughness length and not with the location of the domains or maximum wind speed. Then an ensemble of more than 50 simulations with different setups for a single year was carried out for one domain covering northern Europe for which tall mast observations were available. We varied many different parameters across the simulations, e.g. model version, forcing data, various physical parameterizations, and the size of the model domain. These simulations showed that although virtually every parameter change affects the results in some way, significant changes in the wind climate in the boundary layer are mostly due to using different physical parameterizations, especially the planetary boundary layer scheme, the representation of the land surface, and the prescribed surface roughness length. Also, the setup of the simulations, such as the integration length and the domain size, can considerably influence the results. We assessed the degree of similarity between winds simulated by the WRF ensemble members and the observations using a suite of metrics, including the Earth Mover's Distance (EMD), a statistic that measures the distance between two probability distributions. The EMD was used to diagnose the performance of each ensemble member using the full wind speed and direction distribution, which is essential for wind resource assessment. We identified the most realistic ensemble members to determine the most suitable configuration to be used in the final production run, which is fully described and evaluated in the second part of this study (Dörenkämper et al., 2020).

Background Synoptic circulation patterns (large-scale tropospheric motion systems) affect air pollution and, potentially, air-pollution-morbidity associations. We evaluated the effect of synoptic circulation patterns (air masses) on the association between ozone and hospital admissions for asthma and myocardial infarction (MI) among adults in North Carolina. Methods Daily surface meteorology data (including precipitation, wind speed, and dew point) for five selected cities in North Carolina were obtained from the U.S. EPA Air Quality System (AQS), which were in turn based on data from the National Climatic Data Center of the National Oceanic and Atmospheric Administration. We used the Spatial Synoptic Classification system to classify each day of the 9-year period from 1996 through 2004 into one of seven different air mass types: dry polar, dry moderate, dry tropical, moist polar, moist moderate, moist tropical, or transitional. Daily 24-hour maximum 1-hour ambient concentrations of ozone were obtained from the AQS. Asthma and MI hospital admissions data for the 9-year period were obtained from the North Carolina Department of Health and Human Services. Generalized linear models were used to assess the association of the hospitalizations with ozone concentrations and specific air mass types, using pollutant lags of 0 to 5 days. We examined the effect across cities on days with the same air mass type. In all models we adjusted for dew point and day-of-the-week effects related to hospital admissions. Results Ozone was associated with asthma under dry tropical (1- to 5-day lags), transitional (3- and 4-day lags), and extreme moist tropical (0-day lag) air masses. Ozone was associated with MI only under the extreme moist tropical (5-day lag) air masses. Conclusions Elevated ozone levels are associated with dry tropical, dry moderate, and moist tropical air masses, with the highest ozone levels being associated with the dry tropical air mass. Certain synoptic circulation patterns/air masses in conjunction with ambient ozone levels were associated with increased asthma and MI hospitalizations.