You need to analyze wind data for a critical project. What tools will you use?

MERRA2 from NASA Power and ERA5 from ECMWF reanalysis are good references for consitent worlwide long term data if no station is available.

NASA's MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, Version 2): Provides high-quality global atmospheric reanalysis data, including wind speed and direction, at various altitudes and time intervals. NASA Earth Observing System Data and Information System (EOSDIS): Offers access to a wide range of satellite-based data products, such as scatterometer measurements, which can be used to derive wind speed and direction over oceans and other remote regions. NASA Global Wind Climatology Data: Provides long-term climatological data on wind patterns derived from satellite observations, useful for understanding regional wind characteristics and trends.

Wind Resource Assessment Tools: Utilize software like WAsP, WindPRO, or OpenWind for detailed wind resource mapping and analysis. GIS Software: Use Geographic Information Systems (GIS) for spatial analysis and site selection. Data Analysis Software: Employ MATLAB or Python for custom data analysis and modeling. CFD Software: Apply Computational Fluid Dynamics (CFD) tools like ANSYS Fluent for detailed airflow and performance simulations. SCADA Systems: Leverage Supervisory Control and Data Acquisition (SCADA) data for real-time wind farm monitoring and optimization. Statistical Software: Utilize R or Excel for statistical analysis of wind data trends and variability.

Matlab: A powerful computational tool commonly used for data processing and analysis, including interpolation, filtering, and statistical analysis of wind data. Python: With libraries like NumPy, Pandas, and SciPy, Python offers extensive capabilities for data manipulation, visualization, and analysis, making it well-suited for processing large datasets of wind data efficiently.

Python is very for analysis wind data. It is possible to automate some data QC/QA. However, in some case, expert manual validation is still needed, specialy when there's some icing on the anemometers.

Wind Atlas Analysis and Application Program (WAsP): A software tool specifically designed for wind resource assessment and wind energy planning, including wind flow modeling, turbulence analysis, and wind farm layout optimization. Weather Research and Forecasting Model (WRF): A widely-used numerical weather prediction model that can simulate atmospheric processes and generate high-resolution wind field forecasts for specific locations, useful for short to medium-term wind data analysis and forecasting.

Quality Control: Ensure that the wind data undergoes rigorous quality control measures to identify and correct any errors or anomalies before analysis, especially for critical projects where accuracy is paramount. Spatial and Temporal Resolution: Consider the spatial and temporal resolution of the wind data sources and analysis tools to ensure they meet the project's requirements, particularly if detailed site-specific information is needed. Integration with Other Data Sources: Integrate wind data analysis with other relevant data sources, such as terrain data, land use data, or power grid infrastructure data, to provide comprehensive insights for the project.

It is also important to properly include and calculate the uncertainty of each step. Uncertainty is key in wind project since if affects the P90/P99 energy yield values. Lenders will look at theses values to understant the level of risk associated with the financing of the project.