We can apply rollingmean along multiple dimensions as a 2D smoother in (lat, lon). Here is an example of a 5-point running mean applied along both the lat and lon dimensions. Note the addition of NaNs at the data boundaries and near continental boundaries.
RollingRolling is also similar to pandas. It does not change the length of the arrays. Instead, it allows a moving window to be applied to the data at each point.
Perform basic geospatial operations, such as clipping, reprojection, and masking, using rioxarray. Use rioxarray to manage CRS and spatial dimensions in raster datasets.
Rollingmean/running mean/moving average is a technique to smooth short-term fluctuations to enhance the signal-to-noise ratio. You use the [.rolling()](https://xarray.pydata.org/en/stable/generated/xarray.Dataset.rolling.html) method and supply it the dimension to apply the mean over and the window length.
xarray is licensed under the Apache License, Version 2.0. The xarray license is included as LICENSE_xarray. This package was originally templated with with Cookiecutter.
xarray.DataArray.rolling # DataArray.rolling(dim=None, min_periods=None, center=False, **window_kwargs) [source] # Rolling window object for DataArrays. Parameters: dim (dict, optional) – Mapping from the dimension name to create the rolling iterator along (e.g. time) to its moving window size.
By default and when a lock instance is provided, a xarray.backends.CachingFileManager is used to cache File objects. Since rasterio also caches some data, this will make repeated reads from the same object fast. When lock=False, no lock is used, allowing for completely parallel reads from multiple threads or processes.
xarray is licensed under the Apache License, Version 2.0. The xarray license is included as LICENSE_xarray. This package was originally templated with with Cookiecutter.