Mesoscale and microscale downscaling for the Wind Atlas of Mexico (WAM) project

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