id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
801672790,MDU6SXNzdWU4MDE2NzI3OTA=,4862,Obtaining fresh data from the disk when reopening a NetCDF file a second time,488992,closed,0,,,2,2021-02-04T22:09:09Z,2023-03-30T20:01:06Z,2023-03-30T20:01:06Z,CONTRIBUTOR,,,,"I have a program where I open a `.nc` file, do something with it, and want to reopen it later, after an external program has been modifying it, and my issue is that the caching mechanism will give me the already opened version of the file, and not the refreshed version on the disk. To demonstrate this behavior, let's say you have two files: `bla.nc` and `bla_mod.nc`, with different content:

```python
import shutil
import xarray as xr

a = xr.open_dataset(""bla.nc"")

# Simulate external process modifying bla.nc while this script is running
shutil.copy(""bla_mod.nc"", ""bla.nc"")

# a.close()  # this is the only thing that WOULD make it work!

b = xr.open_dataset(""bla.nc"") 

# Here I would expect b to be different than a, but it is not
```

I understand that the file SHOULD be `close`d (or that I should use a context manager) in an ideal world, and that if so it would work but let's say it is not (perhaps we forgot, or we're simply being lazy).

At first I thought that I could use the `cache` parameter to `open_dataset` for that purpose, but after studying the code, I discovered that it is connected to a different caching mechanism than the one that is at play here. 

After some experiments to better understand the code, I came to the conclusion that the only way my particular use case could be supported (that is, _without_ using an explicit `close` or a context manager, which is, in itself, debatable, I admit) is that if the underlying `netCDF4._netCDF4.Dataset` file object is explicitly closed, like it is when flushed out of the cache:

https://github.com/pydata/xarray/blob/5735e163bea43ec9bc3c2e640fbf25a1d4a9d0c0/xarray/backends/file_manager.py#L222

Given that I cannot really see how, in the particular case where the user calls `open_dataset` for a _second_ time, she wouldn't want the fresh version on disk, it made me think that a fix for that behavior would be to simply explicitly flush the cache immediately after the `CachingFileManager` for a particular dataset has been created, as I do here:

https://github.com/pydata/xarray/compare/master...cjauvin:netcdf-caching-bug

Because I admit that this looks weird at first sight (why close an object immediately after having created it?), I imagine that a better option would probably be to add a boolean option to the `CachingFileManager`, in order to make it optional (something like `flush_and_close_file_if_already_present`).

I think this subtle change would result in a more coherent experience with the exact use case that I present, but admittedly, I didn't study the overall code deeply enough to be certain that it couldn't result in unwanted side effects for some other backends.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4862/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue