Scalable Offshore Wind Analysis With Pangeo

The expansion of renewable energy portfolios to utilise offshore wind resources is a key objective of energy policies focused on the generation of low carbon electricity. Wind atlases have been developed to provide energy resources maps, containing information on wind speeds and related variables at multiple heights above sea level for offshore regions of interest (ROIs). However, these atlases are often associated with legacy projects, where access to corresponding data products may be restricted preventing further development by third parties. Reliable, long-term observations are crucial inputs to the offshore wind farm area assessment process, with observations typically measured close to the ocean surface using in situ meteorological masts. Remote sensing techniques have been proposed to address resolution and coverage issues associated with in situ measurements, in particular, the use of space-borne Earth Observation (EO) instruments for ocean and sea surface wind estimations. In recent years, a variety of initiatives have emerged that provide public access to wind speed data products, which have potential for application in wind atlas development and offshore wind farm assessment. Combining products from multiple data providers is challenging due to differences in spatial and temporal resolution, product access, and product formats. In particular, the associated large dataset sizes are significant obstacles to data retrieval, storage, and subsequent computation. The traditional process of retrieval and local analysis of a relatively small number of ROI products is not readily scalable to accommodate longitudinal studies of multiple ROIs. 

This work presents a case study that demonstrates the utility of the Pangeo software ecosystem to address these issues in the development of offshore wind speed and power density estimations, increasing wind measurement coverage of offshore renewable energy assessment areas in the Irish Continental Shelf region. The Intake library is used to manage a new data catalog created for this region, consisting of a collection of analysis-ready, cloud-optimized (ARCO) datasets generated using the Zarr format. This ARCO catalog features up to 21 years of available in situ, reanalysis, and satellite observation data products. The xarray and Dask libraries enable scalable catalog processing, including analysis of provided data variables and derivation of new variables as required for candidate wind farm ROIs, avoiding redundant storage and processing requirements for regions not under assessment. Individual catalog datasets have been regridded to relevant spatial grids, or appropriately chunked in time and space, by means of the xESMF and Rechunker libraries respectively. A set of Jupyter notebooks has been created to demonstrate catalog visualization and processing, following the conventions of notebooks in the current Pangeo Gallery. These notebooks provide detailed descriptions of each ARCO dataset, along with an evaluation of wind speed extrapolation and power density estimation methods. The employment of new approaches such as Pangeo Forge for future catalog and dataset creation is also explored. This case study has determined that the Pangeo ecosystem approach is extremely beneficial in the development of open architectures operating on large volumes of disparate data, while also contributing to the objectives of scientific code sharing and reproducibility.