atlite Unable to generate SARAH cutouts

Version Checks (indicate both or one)

[X] I have confirmed this bug exists on the lastest release of Atlite.
[ ] I have confirmed this bug exists on the current master branch of Atlite.

Issue Description

Hey everyone, I'm trying to create SARAH cutouts for later use in producing PyPSA-Eur networks for different climate years, but keep getting the same error message after running cutout.prepare():

"ValueError: dimension lat on 0th function argument to apply_ufunc with dask='parallelized' consists of multiple chunks, but is also a core dimension. To fix, either rechunk into a single array chunk along this dimension, i.e., .chunk(dict(lat=-1)), or pass allow_rechunk=True in dask_gufunc_kwargs but beware that this may significantly increase memory usage."

I've tried the rechunking method suggested, but keep getting the same error regardless. I'm stuck now and not sure what to do, and ChatGPT is sending me in circles. Anyone experienced this issue and managed to find a solution?

Reproducible Example

import atlite
import logging

logging.basicConfig(level=logging.INFO)

cutout_2015_sarah = atlite.Cutout(
    path="europe-2015-sarah.nc",
    module=["sarah", "era5"],
    sarah_dir="/Users/robertcarr/Documents/HZB/Modelling/SARAH data/2014_2016",
    x=slice(-16.5, 40.5),
    y=slice(32.70, 75),
    time=slice("2015-01-01", "2015-02-01")
)


# Rechunk along the 'y' dimension
cutout_2015_sarah.data = cutout_2015_sarah.data.chunk(dict(y=-1))
cutout_2015_sarah.prepare()

Expected Behavior

Installed Versions

name: pypsa-eur-RDC channels:

bioconda
defaults
conda-forge dependencies:
affine=2.4.0=pyhd8ed1ab_0
ampl-mp=3.1.0=hbec66e7_1006
amply=0.1.6=pyhd8ed1ab_0
appdirs=1.4.4=pyh9f0ad1d_0
appnope=0.1.4=pyhd8ed1ab_0
argparse-dataclass=2.0.0=pyhd8ed1ab_0
asttokens=2.4.1=pyhd8ed1ab_0
atk-1.0=2.38.0=hd03087b_2
atlite=0.2.13=pyhd8ed1ab_0
attrs=23.2.0=pyh71513ae_0
aws-c-auth=0.7.22=h8a62e84_10
aws-c-cal=0.7.1=h94d0942_1
aws-c-common=0.9.23=h99b78c6_0
aws-c-compression=0.2.18=h94d0942_7
aws-c-event-stream=0.4.2=hb74cd8f_15
aws-c-http=0.8.2=had1507a_6
aws-c-io=0.14.10=hcdb10ff_1
aws-c-mqtt=0.10.4=h856d8ab_8
aws-c-s3=0.6.0=ha9fd6de_2
aws-c-sdkutils=0.1.16=h94d0942_3
aws-checksums=0.1.18=h94d0942_7
aws-crt-cpp=0.27.3=h9d3339c_2
aws-sdk-cpp=1.11.329=he6360a2_9
azure-core-cpp=1.12.0=hd01fc5c_0
azure-identity-cpp=1.8.0=h0a11218_1
azure-storage-blobs-cpp=12.11.0=h77cc766_1
azure-storage-common-cpp=12.6.0=h7024f69_1
azure-storage-files-datalake-cpp=12.10.0=h64d02d0_1
beautifulsoup4=4.12.3=pyha770c72_0
blosc=1.21.6=h5499902_0
bokeh=3.5.0=pyhd8ed1ab_0
bottleneck=1.4.0=py311h5d790af_1
branca=0.7.2=pyhd8ed1ab_0
brotli=1.1.0=hb547adb_1
brotli-bin=1.1.0=hb547adb_1
brotli-python=1.1.0=py311ha891d26_1
bzip2=1.0.8=h99b78c6_7
c-ares=1.32.3=h99b78c6_0
c-blosc2=2.15.0=h5063078_1
ca-certificates=2024.7.4=hf0a4a13_0
cads-api-client=1.1.0=pyhd8ed1ab_0
cairo=1.18.0=hc6c324b_2
cartopy=0.23.0=py311h4b4568b_1
cdsapi=0.7.0=pyhd8ed1ab_0
certifi=2024.7.4=pyhd8ed1ab_0
cffi=1.16.0=py311h4a08483_0
cfgv=3.3.1=pyhd8ed1ab_0
cfitsio=4.4.1=h793ed5c_0
cftime=1.6.4=py311h5d790af_0
charset-normalizer=3.3.2=pyhd8ed1ab_0
click=8.1.7=unix_pyh707e725_0
click-plugins=1.1.1=py_0
cligj=0.7.2=pyhd8ed1ab_1
cloudpickle=3.0.0=pyhd8ed1ab_0
coin-or-cbc=2.10.11=h700c273_0
coin-or-cgl=0.60.7=hf050ae7_0
coin-or-clp=1.17.8=h65c2c7c_0
coin-or-osi=0.108.10=h0dc0bf9_0
coin-or-utils=2.11.11=hafd1a81_1
coincbc=2.10.11=0_metapackage
colorama=0.4.6=pyhd8ed1ab_0
comm=0.2.2=pyhd8ed1ab_0
conda-inject=1.3.2=pyhd8ed1ab_0
configargparse=1.7=pyhd8ed1ab_0
connection_pool=0.0.3=pyhd3deb0d_0
contourpy=1.2.1=py311hcc98501_0
country_converter=1.2=pyhd8ed1ab_0
cppad=20240000.5=h00cdb27_0
cycler=0.12.1=pyhd8ed1ab_0
cytoolz=0.12.3=py311h05b510d_0
dask=2024.7.1=pyhd8ed1ab_0
dask-core=2024.7.1=pyhd8ed1ab_0
dask-expr=1.1.9=pyhd8ed1ab_0
datrie=0.8.2=py311heffc1b2_7
debugpy=1.8.2=py311hb9542d7_0
decorator=5.1.1=pyhd8ed1ab_0
deprecation=2.1.0=pyh9f0ad1d_0
descartes=1.1.0=py_4
distlib=0.3.8=pyhd8ed1ab_0
distributed=2024.7.1=pyhd8ed1ab_0
docutils=0.21.2=pyhd8ed1ab_0
dpath=2.2.0=pyha770c72_0
entsoe-py=0.6.8=pyhd8ed1ab_0
et_xmlfile=1.1.0=pyhd8ed1ab_0
exceptiongroup=1.2.2=pyhd8ed1ab_0
executing=2.0.1=pyhd8ed1ab_0
expat=2.6.2=hebf3989_0
filelock=3.15.4=pyhd8ed1ab_0
fiona=1.9.6=py311hf75b9fa_3
fmt=10.2.1=h2ffa867_0
folium=0.17.0=pyhd8ed1ab_0
font-ttf-dejavu-sans-mono=2.37=hab24e00_0
font-ttf-inconsolata=3.000=h77eed37_0
font-ttf-source-code-pro=2.038=h77eed37_0
font-ttf-ubuntu=0.83=h77eed37_2
fontconfig=2.14.2=h82840c6_0
fonts-conda-ecosystem=1=0
fonts-conda-forge=1=0
fonttools=4.53.1=py311hd3f4193_0
freetype=2.12.1=hadb7bae_2
freexl=2.0.0=hfbad9fb_0
fribidi=1.0.10=h27ca646_0
fsspec=2024.6.1=pyhff2d567_0
gdal=3.9.1=py311h08b0975_8
gdk-pixbuf=2.42.12=h7ddc832_0
geographiclib=2.0=pyhd8ed1ab_0
geojson-rewind=1.1.0=pyhd8ed1ab_0
geopandas=1.0.1=pyhd8ed1ab_0
geopandas-base=1.0.1=pyha770c72_0
geopy=2.4.1=pyhd8ed1ab_1
geos=3.12.2=h00cdb27_1
geotiff=1.7.3=h7e5fb84_1
gflags=2.2.2=hc88da5d_1004
giflib=5.2.2=h93a5062_0
gitdb=4.0.11=pyhd8ed1ab_0
gitpython=3.1.43=pyhd8ed1ab_0
glog=0.7.1=heb240a5_0
glpk=5.0=h6d7a090_0
gmp=6.3.0=h7bae524_2
graphite2=1.3.13=hebf3989_1003
graphviz=11.0.0=h9bb9bc9_0
gtk2=2.24.33=h91d5085_5
gts=0.7.6=he42f4ea_4
h2=4.1.0=pyhd8ed1ab_0
harfbuzz=9.0.0=h1836168_0
hdf4=4.2.15=h2ee6834_7
hdf5=1.14.3=nompi_hec07895_105
hpack=4.0.0=pyh9f0ad1d_0
humanfriendly=10.0=pyhd8ed1ab_6
hyperframe=6.0.1=pyhd8ed1ab_0
icu=73.2=hc8870d7_0
identify=2.6.0=pyhd8ed1ab_0
idna=3.7=pyhd8ed1ab_0
immutables=0.20=py311heffc1b2_1
importlib-metadata=8.1.0=pyha770c72_0
importlib_metadata=8.1.0=hd8ed1ab_0
importlib_resources=6.4.0=pyhd8ed1ab_0
iniconfig=2.0.0=pyhd8ed1ab_0
ipopt=3.14.16=h387674d_4
ipykernel=6.29.5=pyh57ce528_0
ipython=8.26.0=pyh707e725_0
jedi=0.19.1=pyhd8ed1ab_0
jinja2=3.1.4=pyhd8ed1ab_0
joblib=1.4.2=pyhd8ed1ab_0
json-c=0.17=he54c16a_1
jsonschema=4.23.0=pyhd8ed1ab_0
jsonschema-specifications=2023.12.1=pyhd8ed1ab_0
jupyter_client=8.6.2=pyhd8ed1ab_0
jupyter_core=5.7.2=py311h267d04e_0
kealib=1.5.3=h848a2d4_1
kiwisolver=1.4.5=py311he4fd1f5_1
krb5=1.21.3=h237132a_0
lcms2=2.16=ha0e7c42_0
lerc=4.0.0=h9a09cb3_0
libabseil=20240116.2=cxx17_h00cdb27_1
libaec=1.1.3=hebf3989_0
libarchive=3.7.4=h83d404f_0
libarrow=17.0.0=h2a00445_0_cpu
libarrow-acero=17.0.0=h00cdb27_0_cpu
libarrow-dataset=17.0.0=h00cdb27_0_cpu
libarrow-substrait=17.0.0=hc68f6b8_0_cpu
libblas=3.9.0=23_osxarm64_openblas
libbrotlicommon=1.1.0=hb547adb_1
libbrotlidec=1.1.0=hb547adb_1
libbrotlienc=1.1.0=hb547adb_1
libcblas=3.9.0=23_osxarm64_openblas
libcrc32c=1.1.2=hbdafb3b_0
libcurl=8.9.0=hfd8ffcc_0
libcxx=18.1.8=h167917d_0
libdeflate=1.20=h93a5062_0
libedit=3.1.20191231=hc8eb9b7_2
libev=4.33=h93a5062_2
libevent=2.1.12=h2757513_1
libexpat=2.6.2=hebf3989_0
libffi=3.4.2=h3422bc3_5
libgd=2.3.3=hfdf3952_9
libgdal=3.9.1=hce30654_8
libgdal-core=3.9.1=hf00468f_8
libgdal-fits=3.9.1=h7a7a030_8
libgdal-grib=3.9.1=hdd4b840_8
libgdal-hdf4=3.9.1=h94124bd_8
libgdal-hdf5=3.9.1=hf90b89a_8
libgdal-jp2openjpeg=3.9.1=h54bcb16_8
libgdal-kea=3.9.1=hacb1b3e_8
libgdal-netcdf=3.9.1=h1723b65_8
libgdal-pdf=3.9.1=h4cf08c4_8
libgdal-pg=3.9.1=h7d28298_8
libgdal-postgisraster=3.9.1=h7d28298_8
libgdal-tiledb=3.9.1=hbb20944_8
libgdal-xls=3.9.1=hb39617b_8
libgfortran=5.0.0=13_2_0_hd922786_3
libgfortran5=13.2.0=hf226fd6_3
libglib=2.80.3=h59d46d9_1
libgoogle-cloud=2.26.0=hfe08963_0
libgoogle-cloud-storage=2.26.0=h1466eeb_0
libgrpc=1.62.2=h9c18a4f_0
libhwloc=2.11.1=default_h7685b71_1000
libiconv=1.17=h0d3ecfb_2
libintl=0.22.5=h8fbad5d_2
libjpeg-turbo=3.0.0=hb547adb_1
libkml=1.3.0=h00ed6cc_1020
liblapack=3.9.0=23_osxarm64_openblas
liblapacke=3.9.0=23_osxarm64_openblas
libnetcdf=4.9.2=nompi_he469be0_114
libnghttp2=1.58.0=ha4dd798_1
libopenblas=0.3.27=openmp_h517c56d_1
libparquet=17.0.0=hcf52c46_0_cpu
libpng=1.6.43=h091b4b1_0
libpq=16.3=h7afe498_0
libprotobuf=4.25.3=hbfab5d5_0
libre2-11=2023.09.01=h7b2c953_2
librsvg=2.58.2=h1db61d3_1
librttopo=1.1.0=h31fb324_16
libscotch=7.0.4=h7c38b86_5
libsodium=1.0.18=h27ca646_1
libspatialite=5.1.0=hf7a34df_8
libsqlite=3.46.0=hfb93653_0
libssh2=1.11.0=h7a5bd25_0
libthrift=0.19.0=h026a170_1
libtiff=4.6.0=h07db509_3
libutf8proc=2.8.0=h1a8c8d9_0
libwebp=1.4.0=h54798ee_0
libwebp-base=1.4.0=h93a5062_0
libxcb=1.16=hf2054a2_0
libxml2=2.12.7=h9a80f22_3
libxslt=1.1.39=h223e5b9_0
libzip=1.10.1=ha0bc3c6_3
libzlib=1.3.1=hfb2fe0b_1
linopy=0.3.13=pyhd8ed1ab_0
llvm-openmp=18.1.8=hde57baf_0
locket=1.0.0=pyhd8ed1ab_0
lxml=5.2.2=py311hf9a6a72_0
lz4=4.3.3=py311hd44b8e9_0
lz4-c=1.9.4=hb7217d7_0
lzo=2.10=h93a5062_1001
mapclassify=2.6.1=pyhd8ed1ab_0
markupsafe=2.1.5=py311h05b510d_0
matplotlib=3.9.1=py311ha1ab1f8_0
matplotlib-base=3.9.1=py311hba6b155_0
matplotlib-inline=0.1.7=pyhd8ed1ab_0
memory_profiler=0.61.0=pyhd8ed1ab_0
metis=5.1.0=h13dd4ca_1007
minizip=4.0.7=h27ee973_0
mpfr=4.2.1=h41d338b_1
msgpack-python=1.0.8=py311h6bde47b_0
multiurl=0.3.1=pyhd8ed1ab_0
mumps-include=5.7.2=hce30654_0
mumps-seq=5.7.2=hab9b160_0
munkres=1.1.4=pyh9f0ad1d_0
nbformat=5.10.4=pyhd8ed1ab_0
ncurses=6.5=hb89a1cb_0
nest-asyncio=1.6.0=pyhd8ed1ab_0
netcdf4=1.7.1=nompi_py311h42682c7_101
networkx=3.3=pyhd8ed1ab_1
nodeenv=1.9.1=pyhd8ed1ab_0
nspr=4.35=hb7217d7_0
nss=3.102=hc42bcbf_0
numexpr=2.10.0=py311h4b4568b_0
numpy=1.26.4=py311h7125741_0
openjdk=22.0.1=h363fedd_1
openjpeg=2.5.2=h9f1df11_0
openpyxl=3.1.4=py311h1fc4b72_0
openssl=3.3.1=hfb2fe0b_2
orc=2.0.1=h47ade37_1
packaging=24.1=pyhd8ed1ab_0
pandas=2.2.2=py311h4b4568b_1
pango=1.54.0=h9ee27a3_1
parso=0.8.4=pyhd8ed1ab_0
partd=1.4.2=pyhd8ed1ab_0
patsy=0.5.6=pyhd8ed1ab_0
pcre2=10.44=h297a79d_0
pexpect=4.9.0=pyhd8ed1ab_0
pickleshare=0.7.5=py_1003
pillow=10.4.0=py311hd7951ec_0
pip=24.0=pyhd8ed1ab_0
pixman=0.43.4=hebf3989_0
pkgutil-resolve-name=1.3.10=pyhd8ed1ab_1
plac=1.4.3=pyhd8ed1ab_0
platformdirs=4.2.2=pyhd8ed1ab_0
pluggy=1.5.0=pyhd8ed1ab_0
ply=3.11=pyhd8ed1ab_2
polars=1.2.1=py311h9e175c1_0
poppler=24.07.0=h9787579_0
poppler-data=0.4.12=hd8ed1ab_0
postgresql=16.3=hdfa2ec6_0
powerplantmatching=0.5.15=pyhd8ed1ab_0
pre-commit=3.7.1=pyha770c72_0
progressbar2=4.4.2=pyhd8ed1ab_0
proj=9.4.1=hfb94cee_0
prompt-toolkit=3.0.47=pyha770c72_0
psutil=6.0.0=py311hd3f4193_0
pthread-stubs=0.4=h27ca646_1001
ptyprocess=0.7.0=pyhd3deb0d_0
pulp=2.8.0=py311h267d04e_0
pure_eval=0.2.3=pyhd8ed1ab_0
py-cpuinfo=9.0.0=pyhd8ed1ab_0
pyarrow=17.0.0=py311h35c05fe_0
pyarrow-core=17.0.0=py311hf5072a7_0_cpu
pyarrow-hotfix=0.6=pyhd8ed1ab_0
pycountry=24.6.1=pyhd8ed1ab_0
pycparser=2.22=pyhd8ed1ab_0
pygments=2.18.0=pyhd8ed1ab_0
pyogrio=0.8.0=py311he661659_2
pyomo=6.6.1=py311ha891d26_0
pyparsing=3.1.2=pyhd8ed1ab_0
pyproj=3.6.1=py311h5e0e26b_7
pypsa=0.28.0=pyhd8ed1ab_0
pyscipopt=5.1.1=py311hb9542d7_0
pyshp=2.3.1=pyhd8ed1ab_0
pysocks=1.7.1=pyha2e5f31_6
pytables=3.9.2=py311h5d6d252_3
pytest=8.3.1=pyhd8ed1ab_0
python=3.11.9=h932a869_0_cpython
python-dateutil=2.9.0=pyhd8ed1ab_0
python-fastjsonschema=2.20.0=pyhd8ed1ab_0
python-tzdata=2024.1=pyhd8ed1ab_0
python-utils=3.8.2=pyhd8ed1ab_0
python_abi=3.11=4_cp311
pytz=2024.1=pyhd8ed1ab_0
pyxlsb=1.0.10=pyhd8ed1ab_0
pyyaml=6.0.1=py311heffc1b2_1
pyzmq=26.0.3=py311h9bed540_0
qhull=2020.2=h420ef59_5
rasterio=1.3.10=py311he66545a_4
re2=2023.09.01=h4cba328_2
readline=8.2=h92ec313_1
referencing=0.35.1=pyhd8ed1ab_0
requests=2.32.3=pyhd8ed1ab_0
reretry=0.11.8=pyhd8ed1ab_0
rioxarray=0.17.0=pyhd8ed1ab_0
rpds-py=0.19.0=py311h98c6a39_0
scikit-learn=1.5.1=py311hbfb48bc_0
scip=9.1.0=h55df89c_0
scipy=1.14.0=py311hceeca8c_1
seaborn=0.13.2=hd8ed1ab_2
seaborn-base=0.13.2=pyhd8ed1ab_2
setuptools=71.0.4=pyhd8ed1ab_0
setuptools-scm=8.1.0=pyhd8ed1ab_0
setuptools_scm=8.1.0=hd8ed1ab_0
shapely=2.0.5=py311h0f19114_0
six=1.16.0=pyh6c4a22f_0
smart_open=7.0.4=pyhd8ed1ab_0
smmap=5.0.0=pyhd8ed1ab_0
snakemake-interface-common=1.17.2=pyhdfd78af_0
snakemake-interface-executor-plugins=9.2.0=pyhdfd78af_0
snakemake-interface-report-plugins=1.0.0=pyhdfd78af_0
snakemake-interface-storage-plugins=3.2.3=pyhdfd78af_0
snakemake-minimal=8.16.0=pyhdfd78af_0
snappy=1.2.1=hd02b534_0
snuggs=1.4.7=py_0
sortedcontainers=2.4.0=pyhd8ed1ab_0
soupsieve=2.5=pyhd8ed1ab_1
spdlog=1.13.0=h5fcca99_0
sqlite=3.46.0=h5838104_0
stack_data=0.6.2=pyhd8ed1ab_0
statsmodels=0.14.2=py311h5d790af_0
stopit=1.1.2=py_0
tabula-py=2.7.0=py311h267d04e_1
tabulate=0.9.0=pyhd8ed1ab_1
tbb=2021.12.0=h420ef59_3
tblib=3.0.0=pyhd8ed1ab_0
threadpoolctl=3.5.0=pyhc1e730c_0
throttler=1.2.2=pyhd8ed1ab_0
tiledb=2.24.2=h5def871_2
tk=8.6.13=h5083fa2_1
tomli=2.0.1=pyhd8ed1ab_0
toolz=0.12.1=pyhd8ed1ab_0
toposort=1.10=pyhd8ed1ab_0
tornado=6.4.1=py311hd3f4193_0
tqdm=4.66.4=pyhd8ed1ab_0
traitlets=5.14.3=pyhd8ed1ab_0
typing-extensions=4.12.2=hd8ed1ab_0
typing_extensions=4.12.2=pyha770c72_0
tzcode=2024a=h93a5062_0
tzdata=2024a=h0c530f3_0
ukkonen=1.0.1=py311he4fd1f5_4
unidecode=1.3.8=pyhd8ed1ab_0
unixodbc=2.3.12=h0e2417a_0
uriparser=0.9.8=h00cdb27_0
urllib3=2.2.2=pyhd8ed1ab_1
validators=0.33.0=pyhd8ed1ab_0
virtualenv=20.26.3=pyhd8ed1ab_0
wcwidth=0.2.13=pyhd8ed1ab_0
wheel=0.43.0=pyhd8ed1ab_1
wrapt=1.16.0=py311h05b510d_0
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xyzservices=2024.6.0=pyhd8ed1ab_0
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- tsam==2.3.1 prefix: /Users/robertcarr/miniforge3/envs/pypsa-eur-RDC

Aug 09 '24 14:08 rdcarr2

Hey there!

I haven't seen the issue before. I think the underlying rechunk would need to happen on the SARAH data, not on the cutout. A wild guess would be that you could you try to pass the chunk argument to the prepare(...) function similarly as shown in the example here:

https://atlite.readthedocs.io/en/latest/examples/create_cutout_SARAH.html#Specifying-the-cutout

Aug 22 '24 20:08 euronion

Hi, I am having the same issue as you are, @rdcarr2, and I have not been able to solve it yet. Did you find a way around it? Thanks.

Mar 10 '25 08:03 martabresco

Hey Marta,

I never managed to figure it out no matter what I tried, including reinstalling everything in a new environment from scratch. Luckily, the SARAH cutouts are no longer required for building capacity factors in PyPSA, everything that is needed can be extracted from the ERA5 cutout instead.

Mar 12 '25 12:03 rdcarr2

Hi both,

Have you tried my comment above? I don't see why you would want to rechunk the data at all, but doing the chunking after the cutout is prepared should still work.

@martabresco Do you use the identical code, or a different one?

Mar 12 '25 13:03 euronion

Hey,

Thanks for helping out! Worth noting I am quite new to atlite still.

I didn't want to rechunk anything at first (I don't even know what rechunking is lol), but I kept getting this error:

"ValueError: dimension lat on 0th function argument to apply_ufunc with dask='parallelized' consists of multiple chunks, but is also a core dimension. To fix, either rechunk into a single array chunk along this dimension, i.e., .chunk(dict(lat=-1)), or pass allow_rechunk=True in dask_gufunc_kwargs but beware that this may significantly increase memory usage."

then i started with the rechunking stuff which you see on the screenshot above, which didn't work.

It's been a while and I got a new computer, so I'll download the SARAH data overnight and try building a cutout again tomorrow. Will update this thread if it works!

Mar 12 '25 14:03 rdcarr2

Hi,

In my case, rechunking helps because I am working with an HPC and with large files. However, even without the chunk command, I am getting the same error as @rdcarr2, see below. I saw your previous comment @euronion, but when trying to pass the chunk argument to prepare( ) I get that this is an unexpected argument. This issue is weird, since I have tried to do a cutout with two days of SARAH-3 V004 data from 2025, exactly with the same cutout definition, and I did not have issues. But when trying to do it for 2 days of SARAH-3 V004 data from 2014, I get this error which I have not managed to fix. And I am using the same environment for both.

Mar 12 '25 14:03 martabresco

Hi @martabresco , I unfortunately don't have the files for the SARAH dataset so i can't test anything easily atm.

Please try adding chunks={"time": 100, "lat": -1, "lon": -1} to the atlite.Cutout(..., chunks=...) definition call. Those are passed through to the place where the sarah files are opened.
If you are still receiving an error please get the full stacktrace (ie. after clicking on "open in a text editor") and copy it here as text (ideally within code fences). Screenshots are not searchable.

Thanks,

Mar 12 '25 19:03 coroa

Hi @coroa, The trick worked. Thanks for your time :)

Mar 17 '25 09:03 martabresco