The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: TypeError
Message: Couldn't cast array of type
struct<long_name: string, units: string, comment1: string, comment2: string, _FillValue: string>
to
{'long_name': Value('string'), 'units': Value('string'), 'comment': Value('string'), '_FillValue': Value('string')}
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2303, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1852, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2149, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<long_name: string, units: string, comment1: string, comment2: string, _FillValue: string>
to
{'long_name': Value('string'), 'units': Value('string'), 'comment': Value('string'), '_FillValue': Value('string')}Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
MACDA — ARCO Format
Analysis-Ready and Cloud Optimized Zarr conversion of the MACDA v2.0. reanalysis (Valeanu et al. 2026), covering Mars Years 24–35.
Variables
| Variable | Description | Units |
|---|---|---|
| temp | Atmospheric temperature | K |
| uwind | Zonal wind | m s-1 |
| vwind | Meridional wind | m s-1 |
| psurf | Surface pressure | Pa |
| tsurf | Surface temperature | K |
| coldust | Column dust optical depth | 1 |
| dustmmr | Dust mass mixing ratio (3D) | kg kg-1 |
| geop | Geopotential | m2 s-2 |
| omega | Vertical velocity | Pa s-1 |
| co2ice | Surface CO2 ice | kg m-2 |
| swflux | Shortwave surface flux | W m-2 |
| lwflux | Longwave surface flux | W m-2 |
Citation
Bhattacharya, A. (2026). ARCO-MACDA: Analysis-Ready Cloud-Optimized MACDA Reanalysis. https://doi.org/10.57967/hf/8771
Valeanu, A.; Rajendran, K.; Montabone, L.; Read, P.L. (2026): Mars Analysis Correction Data Assimilation (MACDA): Atmospheric and surface fields produced from assimilated MGS/TES, ODY/THEMIS, and MRO/MCS observations, v2.0. NERC EDS Centre for Environmental Data Analysis, 30 January 2026. doi:10.5285/cd037a9ea387438fabf4d674dbe53088. https://dx.doi.org/10.5285/cd037a9ea387438fabf4d674dbe53088
Read, P. L., A. Valeanu, Tao Ruan, L. Montabone, S. R. Lewis, and R. M. B. Young. "MACDA II: A New Reanalysis for the Martian Atmosphere Using Vertically-Resolved Dust Opacity Observations." (2022).
Battalio, Michael, Istvan Szunyogh, and Mark Lemmon. "Energetics of the martian atmosphere using the Mars Analysis Correction Data Assimilation (MACDA) dataset." Icarus 276 (2016): 1-20.
BibTeX
@misc{bhattacharya2026arco_macda, author = {Bhattacharya, Ananyo}, title = {{ARCO-MACDA}: Analysis-Ready Cloud-Optimized {MACDA} Reanalysis}, year = {2026}, publisher = {Hugging Face}, doi = {https://doi.org/10.57967/hf/8771}, url = {https://huggingface.co/datasets/ananyo01/ARCO-MACDA} }
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