What happened?

I updated my packages and now xarray+zarr are unable to append data to an existing Zarr store in s3.

What did you expect to happen?

That data will be appended to an existing Zarr store.

Minimal Complete Verifiable Example

```Python import s3fs import xarray import numpy as np from datetime import datetime from s3fs import S3FileSystem

append_dim = 'dt_calc' consolidated = True

ds = xarray.Dataset( {'temp': (('dt_calc', 'y', 'x'), np.array([[[1., 2., 3., 4.], [3., 4., 5., 6.]]]))}, coords={'lon': ('y', np.array([50., 51.])), 'lat': ('x', np.array([4., 5., 6., 7.])), 'dt_calc': ('dt_calc', [datetime(2022, 1, 1)])} ) ds_2 = xarray.Dataset( {'temp': (('dt_calc', 'y', 'x'), np.array([[[1., 2., 3., 4.], [3., 4., 5., 6.]]]))}, coords={'lon': ('y', np.array([50., 51.])), 'lat': ('x', np.array([4., 5., 6., 7.])), 'dt_calc': ('dt_calc', [datetime(2022, 1, 1, 1)])} )

s3_out = S3FileSystem( anon=False, s3_additional_kwargs={"StorageClass": storage_class}, ) store_out = s3fs.S3Map( root=f"s3:///{bucket_name}/{dataset_name}.zarr", s3=s3_out, check=False )

ds.to_zarr( store, mode="w-", compute=True, consolidated=consolidated )

try: ds_2.to_zarr( store, mode="w-", compute=True, consolidated=consolidated ) except zarr.errors.ContainsGroupError: ds_2.to_zarr( store, mode="a", append_dim=append_dim, compute=True, consolidated=consolidated, ) ```

MVCE confirmation

• [ ] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
• [ ] 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.
• [ ] New issue — a search of GitHub Issues suggests this is not a duplicate.

Relevant log output

```Python In [6]: xarray.open_zarr(store_out, consolidated=True) Out[6]: <xarray.Dataset> Dimensions: (dt_calc: 1, x: 4, y: 2) Coordinates: * dt_calc (dt_calc) datetime64[ns] 2022-01-01 lat (x) float64 dask.array<chunksize=(4,), meta=np.ndarray> lon (y) float64 dask.array<chunksize=(2,), meta=np.ndarray> Dimensions without coordinates: x, y Data variables: temp (dt_calc, y, x) float64 dask.array<chunksize=(1, 2, 4), meta=np.ndarray>

In [7]: dataset.to_zarr( ...: store_out, ...: mode="a", ...: append_dim=append_dim, ...: compute=True, ...: consolidated=consolidated, ...: )

ValueError Traceback (most recent call last) Cell In[7], line 1 ----> 1 dataset.to_zarr( 2 store_out, 3 mode="a", 4 append_dim=append_dim, 5 compute=True, 6 consolidated=consolidated, 7 )

File /usr/local/lib/python3.9/site-packages/xarray/core/dataset.py:2461, in Dataset.to_zarr(self, store, chunk_store, mode, synchronizer, group, encoding, compute, consolidated, append_dim, region, safe_chunks, storage_options, zarr_version, write_empty_chunks, chunkmanager_store_kwargs) 2329 """Write dataset contents to a zarr group. 2330 2331 Zarr chunks are determined in the following way: (...) 2457 The I/O user guide, with more details and examples. 2458 """ 2459 from xarray.backends.api import to_zarr -> 2461 return to_zarr( # type: ignore[call-overload,misc] 2462 self, 2463 store=store, 2464 chunk_store=chunk_store, 2465 storage_options=storage_options, 2466 mode=mode, 2467 synchronizer=synchronizer, 2468 group=group, 2469 encoding=encoding, 2470 compute=compute, 2471 consolidated=consolidated, 2472 append_dim=append_dim, 2473 region=region, 2474 safe_chunks=safe_chunks, 2475 zarr_version=zarr_version, 2476 write_empty_chunks=write_empty_chunks, 2477 chunkmanager_store_kwargs=chunkmanager_store_kwargs, 2478 )

File /usr/local/lib/python3.9/site-packages/xarray/backends/api.py:1670, in to_zarr(dataset, store, chunk_store, mode, synchronizer, group, encoding, compute, consolidated, append_dim, region, safe_chunks, storage_options, zarr_version, write_empty_chunks, chunkmanager_store_kwargs) 1668 existing_dims = zstore.get_dimensions() 1669 if append_dim not in existing_dims: -> 1670 raise ValueError( 1671 f"append_dim={append_dim!r} does not match any existing " 1672 f"dataset dimensions {existing_dims}" 1673 ) 1674 existing_var_names = set(zstore.zarr_group.array_keys()) 1675 for var_name in existing_var_names:

ValueError: append_dim='dt_calc' does not match any existing dataset dimensions {}

In [8]: dataset Out[8]: <xarray.Dataset> Dimensions: (dt_calc: 1, y: 2, x: 4) Coordinates: lon (y) float64 50.0 51.0 lat (x) float64 4.0 5.0 6.0 7.0 * dt_calc (dt_calc) datetime64[ns] 2022-01-01T01:00:00 Dimensions without coordinates: y, x Data variables: temp (dt_calc, y, x) float64 1.0 2.0 3.0 4.0 3.0 4.0 5.0 6.0

In [9]: ```

Anything else we need to know?

No response

Environment

boto3==1.26.45 aiobotocore==2.5.0 botocore==1.29.76 s3fs==2023.6.0 zarr==2.16.1 xarray==2023.8.0 dask==2023.8.1 dask[distributed]==2023.8.1 dask-cloudprovider==2022.10.0

MCVE Code Sample

```python frames_z.shape -> (25, 1100, 900) grid[:, :, 1].shape -> (1100, 900) grid[:, :, 0].shape -> (1100, 900)

xarray.DataArray(frames_z, coords={'time': timestamps, 'latitude':grid[:, :, 1],'longitude': grid[:, :, 0]}, dims=['time', 'latitude', 'longitude'])

```

Expected Output

Array with latitude and longitude as coordinates.

Problem Description

I am receiving following error message:

MissingDimensionsError: cannot set variable 'latitude' with 2-dimensional data without explicit dimension names. Pass a tuple of (dims, data) instead. And I tried several ways of defining coords and dims but it always fails.

Output of xr.show_versions()

In case of selecting 2D data, the xarray.where() does not work like numpy.where() . In the documentation you have mentioned that np.where() is the corresponding function for xr.where() but it seems that they are working totally different.

Here is my code:

```python data = xarray.open_dataset('path_to_attached_file') minLat, minLon, maxLat, maxLon = (45.08903556483102, 5.625000000000013, 48.92249926375824, 11.249999999999993) latitudes = data.lat.values longitudes = data.lon.values slice_mask = np.where( (latitudes <= maxLat) & (latitudes > minLat) & (longitudes <= maxLon) & (longitudes > minLon) ) _sliced_data = data.where( (data.lat <= maxLat) & (data.lat > minLat) & (data.lon <= maxLon) & (data.lon > minLon), drop=True, ) _sliced_data.latitude.values.max()

49.305596

latitudes[slice_mask].max()

48.922172

``` I have also tried to translate the numpy result into a boolen DataArray:

```python mask_array = data.copy() mask_array.update({'air_temperature_2m': (("y", "x"), (latitudes <= maxLat) & (latitudes > minLat) & (longitudes <= maxLon) & (longitudes > minLon))})

_sliced_data = data.where( mask_array.air_temperature_2m, drop=True, )

``` There we have the same result. It seems that the masking step does not really works correctly.

Environment:

harmonie_knmi_grid_fixture.zip

MCVE Code Sample

```python ds = xarray.tutorial.open_dataset('rasm').load() ds.sel(yc=50, xc=50, method='nearest')

```

Expected Output

An interpolated timeseries of data closest to 50, 50.

Problem Description

How do I have to define the indices that I can select on multi dimensional indices?

Output of xr.show_versions()

I guess a to_postgis feature would be helpful for many users. Similar to to_sql