MÉRA-based Wind Atlas for Irish Continental Shelf region

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MÉRA-based Wind Atlas for Irish Continental Shelf region

Wind Atlas Prototype

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 areas of interest (AOIs). One example that focuses on Irish offshore AOIs is the Sustainable Energy Authority of Ireland (SEAI) Wind Atlas, which is described by UK Met Office, 2015 - Remodelling the Irish national onshore and offshore wind atlas.

This notebook demonstrates the utility of the Pangeo software ecosystem in the development of an Irish offshore wind atlas prototype, covering offshore renewable energy assessment areas in the Irish Continental Shelf (ICS) region. It uses the EOOffshore catalog MÉRA data set created for this region, which is an analysis-ready, cloud-optimized (ARCO) dataset featuring 16 years of reanalysis wind data products. Scalable processing and visualization of this ARCO catalog is demonstrated with analysis of provided data variables and computation of new variables as required for AOIs, avoiding redundant storage and processing requirements for areas not under assessment. The MÉRA data set is described in the MÉRA Wind Data for Irish Continental Shelf region notebook.

The prototype implementation provides:

How to cite: O’Callaghan, D. and McBreen, S.: Scalable Offshore Wind Analysis With Pangeo, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2746, https://doi.org/10.5194/egusphere-egu22-2746, 2022.

Note:

  • Full interactive dashboard functionality is only available when this notebook is executed.

  • If viewing this notebook on the EOOffshore website:

    • Click the “Fullscreen Mode” button at the top of the page to ensure that the wind atlas is correctly displayed.

    • The dashboard will be displayed with limited functionality.

    • Code cells have been hidden, but may be viewed via the corresponding “> Click to show” buttons.

%matplotlib inline
from dataclasses import dataclass, field
from datetime import datetime
import geopandas as gpd
from intake import Catalog, open_catalog
import json
from matplotlib.colors import LinearSegmentedColormap
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import regionmask
import seaborn as sns
import shapely.geometry as sgeom
from sklearn.neighbors import NearestNeighbors
from typing import Dict, List, Tuple
from windrose import WindroseAxes
import xarray as xr

import holoviews as hv
hv.extension("bokeh")
import datashader as dsh
import geoviews as gv
import geoviews.feature as gf
from holoviews.operation.datashader import rasterize
import hvplot.pandas
import hvplot.xarray
import panel as pn

import warnings
warnings.filterwarnings('ignore')