What happened?

Hi all, as the title already summarizes, I'm running into performance issues when aggregating over the time-dimension of a 3D DataArray using the quantiles method with skipna=True. See the section below for some dummy data that represents what I'm working with (e.g., similar to this). Aggregating over the time-dimension of this dummy data I'm getting the following wall times:

| | | | | --------------- | --------------- | --------------- | | 1 | da.median(dim='time', skipna=True) | 1.35 s | | 2 | da.quantile(0.95, dim='time', skipna=False) | 5.95 s | | 3 | da.quantile(0.95, dim='time', skipna=True) | 6 min 6s |

I'm currently using a compute node with 40 CPUs and 180 GB RAM. Here is what the resource utilization looks like. First small bump are 1 and 2. Second longer peak is 3.

In this small example, the process at least finishes after a few seconds. With my actual dataset the quantile calculation takes hours...

I guess the following issue is relevant and should be revived: https://github.com/numpy/numpy/issues/16575

Are there any possible work-arounds?

What did you expect to happen?

No response

Minimal Complete Verifiable Example

```Python import pandas as pd import numpy as np import xarray as xr

Create dummy data with 20% random NaNs

size_spatial = 2000 size_temporal = 20 n_nan = int(size_spatial*20.2)

time = pd.date_range("2000-01-01", periods=size_temporal) lat = np.random.uniform(low=-90, high=90, size=size_spatial) lon = np.random.uniform(low=-180, high=180, size=size_spatial) data = np.random.rand(size_temporal, size_spatial, size_spatial) index_nan = np.random.choice(data.size, n_nan, replace=False) data.ravel()[index_nan] = np.nan

Create DataArray

da = xr.DataArray(data=data, dims=['time', 'x', 'y'], coords={'time': time, 'x': lon, 'y': lat}, attrs={'nodata': np.nan})

Calculate 95th quantile over time-dimension

da.quantile(0.95, dim='time', skipna=True) ```

MVCE confirmation

• [x] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
• [x] Complete example — the example is self-contained, including all data and the text of any traceback.
• [ ] Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
• [x] New issue — a search of GitHub Issues suggests this is not a duplicate.

Relevant log output

No response

Anything else we need to know?

No response

Environment

INSTALLED VERSIONS ------------------ commit: None python: 3.10.6 | packaged by conda-forge | (main, Aug 22 2022, 20:36:39) [GCC 10.4.0] python-bits: 64 OS: Linux OS-release: 5.4.0-125-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: en_US.UTF-8 LANG: en_US.UTF-8 LOCALE: ('en_US', 'UTF-8') libhdf5: 1.12.2 libnetcdf: 4.9.0 xarray: 2022.12.0 pandas: 1.5.0 numpy: 1.23.3 scipy: 1.9.1 netCDF4: 1.6.1 pydap: None h5netcdf: None h5py: 3.7.0 Nio: None zarr: None cftime: 1.6.2 nc_time_axis: None PseudoNetCDF: None rasterio: 1.3.3 cfgrib: None iris: None bottleneck: 1.3.5 dask: 2022.10.0 distributed: 2022.10.0 matplotlib: 3.6.1 cartopy: 0.21.0 seaborn: 0.12.0 numbagg: None fsspec: 2022.8.2 cupy: None pint: None sparse: None flox: None numpy_groupies: None setuptools: 65.5.0 pip: 22.3 conda: 4.12.0 pytest: None mypy: None IPython: 8.5.0 sphinx: None