Tutorial: Estimate Wind Speed with Interpolation

In this tutorial, we will learn how to estimate wind speed using the interpolation model from the geodata library.

Warning

Performing wind speed estimation using interpolation requires a dataset with known wind speed values at multiple locations.

Currently, only the weather_data_config wind_3d_hourly from the ERA5 dataset contains the necessary wind speed data for interpolation.

Therefore, all of the information below only applies with wind_3d_hourly or cutouts derived from it. Using any other dataset will lead to a ValueError.

Step 1: Import the necessary libraries

To get started, we need to import the required libraries. We will import the WindInterpolationModel from the geodata library, as well as any other libraries needed for data handling and visualization.

import geodata
import xarray as xr

from geodata.model.wind import WindInterpolationModel

Step 2: Load the dataset

Next, we need to load the dataset that contains the wind speed data. We will use the wind_3d_hourly dataset from the ERA5 dataset.

# Load the dataset
ds = geodata.Dataset(
    module="era5",
    weather_data_config="wind_3d_hourly",
    years=slice(2006, 2006),
    months=slice(1, 1),
    bounds=[-10, 35, 10, 45] # Optional: specify the bounding box
)

if not ds.prepared:
    ds.get_data()  # Download the data if we don't have it locally

Step 3: Compute interpolation parameters

The interpolation is separated into two steps to separate the computationally-intensive step (estimating interpolation parameters) from the computationally-easy step (interpolating at desired heights). First, we compute the interpolation parameters.

# Create a model based on the above dataset. The model will be associated with
# the dataset forever. If you wish to use a different dataset, you will need to
# create a new model.

model = WindInterpolationModel(ds)
model.prepare()

If you have already prepared a cutout with wind_3d_hourly, you can also pass that into the model as well. The model treats dataset and cutouts indifferently. Simply replace ds with your cutout variable.

Note

The prepare method computes the necessary parameters for the interpolation model based on the loaded dataset. Everything will be saved under the models directory under the path GEODATA_ROOT.

Note

It is not necessary to call the prepare method every time you want to perform interpolation. You only need to call it once after loading the dataset. From that point on, you can load and use the model directly without re-preparing it.

Step 4: Estimate using the interpolation model

Now that we have prepared the model, we can perform the interpolation to estimate wind speed at the desired locations. Suppose we want to estimate the wind speed at a height of 60 m above ground during Jaunary of 2006 for the entire region covered by the original dataset, we can do this as follows:

estimated_wind_speed = model.estimate(
    height=60,
    years=slice(2006, 2006),
    months=slice(1, 1),
)

This will return an xarray DataArray containing the estimated wind speed values. Note that you can also select a subset area by passing in xs=slice(start, end) and/or ys=slice(start, end) parameters to the estimate method.