Xarray supports weighted reductions.
For demonstration, we will create a “weights” array proportional to cosine of latitude. Modulo a normalization, this is the correct area-weighting factor for data on a regular lat-lon grid.
import numpy as np import xarray as xr import matplotlib.pyplot as plt %config InlineBackend.figure_format='retina'
ds = xr.tutorial.load_dataset("ersstv5") ds
<xarray.Dataset> Dimensions: (lat: 89, lon: 180, time: 624, nbnds: 2) Coordinates: * lat (lat) float32 88.0 86.0 84.0 82.0 ... -82.0 -84.0 -86.0 -88.0 * lon (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 352.0 354.0 356.0 358.0 * time (time) datetime64[ns] 1970-01-01 1970-02-01 ... 2021-12-01 Dimensions without coordinates: nbnds Data variables: time_bnds (time, nbnds) float64 9.969e+36 9.969e+36 ... 9.969e+36 9.969e+36 sst (time, lat, lon) float32 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan Attributes: (12/37) climatology: Climatology is based on 1971-2000 SST, Xue, Y.... description: In situ data: ICOADS2.5 before 2007 and NCEP i... keywords_vocabulary: NASA Global Change Master Directory (GCMD) Sci... keywords: Earth Science > Oceans > Ocean Temperature > S... instrument: Conventional thermometers source_comment: SSTs were observed by conventional thermometer... ... ... creator_url_original: https://www.ncei.noaa.gov license: No constraints on data access or use comment: SSTs were observed by conventional thermometer... summary: ERSST.v5 is developed based on v4 after revisi... dataset_title: NOAA Extended Reconstructed SST V5 data_modified: 2022-06-07
weights = np.cos(np.deg2rad(ds.lat)) display(weights.dims) weights.plot()
[<matplotlib.lines.Line2D at 0x7f15fcdff850>]
Thanks to the automatic broadcasting and alignment discussed earlier, if we multiply this by SST, it “just works,” and the arrays are broadcasted properly:
(ds.sst * weights).dims
('time', 'lat', 'lon')
We could imagine computing the weighted spatial mean of SST manually.
sst_mean = (ds.sst * weights).sum(dim=("lon", "lat")) / weights.sum(dim="lat") sst_mean.plot() plt.title("This is wrong!")
Text(0.5, 1.0, 'This is wrong!')
That would be wrong, however, because the denominator (
needs to be expanded to include the
lon dimension and modified to account for
the missing values (land points).
In general, weighted reductions on multidimensional arrays are complicated. To make it a bit easier, Xarray provides a mechanism for weighted reductions.
It does this by creating a special intermediate
DataArrayWeighted object, to
which different reduction operations can applied.
sst_weighted = ds.sst.weighted(weights) sst_weighted
DataArrayWeighted with weights along dimensions: lat
sst_weighted.mean(dim=("lon", "lat")).plot() plt.title("Correct Global Mean SST");
A handful of reductions have been implemented: mean, sum, std, var.