Reading single grid cells from a multi-file netcdf dataset?

I have a multifile dataset made up of month-long 8-hourly netcdf datasets over nearly 30 years. The files are available from ftp://ftp.ifremer.fr/ifremer/ww3/HINDCAST/GLOBAL/, and I'm spcifically looking at e.g. 1990_CFSR/hs/ww3.199001_hs.nc for each year and month. Each file is about 45Mb, for about 15Gb total.

I want to calculate some lognormal distribution parameters of the Hs variable at each grid point (actually, only a smallish subset of points, using a mask). However, if I load the data with open_mfdataset and try to read a single lat/lon grid cell, my computer tanks, and python gets killed due to running out of memory (I have 16Gb, but even if I only try to open 1 year of data - ~500Mb, python ends up using 27% of my memory).

Is there a way in xarray/dask to force dask to only read single sub-arrays at a time? I have tried using lat/lon chunking, e.g.

mfdata_glob = '/home/nedcr/cr/data/wave/*1990*.nc'
global_ds = xr.open_mfdataset(
    mfdata_glob,
    chunks={'latitude': 1, 'longitude': 1})

but that doesn't seem to improve things.

Is there any way around this problem? I guess I could try using preprocess= to sub-select grid cells, and loop over that, but that seems like it would require opening and reading each file 317*720 times, which sounds like a recipe for a long wait.

Start with xarray.open_mfdataset using the shown latitude and longitude chunks, then inspect how single-cell Hs access behaves across the multifile dataset. Compare that with the preprocess and masked-subset approaches mentioned in the issue. Done means identifying a documented, memory-bounded way to read the selected cells without loading the full dataset.