Datasets:
The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: TypeError
Message: Couldn't cast array of type
struct<endian: string, level: int64, checksum: bool>
to
{'keepbits': Value('int64'), 'endian': Value('string'), 'typesize': Value('int64'), 'cname': Value('string'), 'clevel': Value('int64'), 'shuffle': Value('string'), 'blocksize': Value('int64')}
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
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 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 299, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2255, in cast_table_to_schema
cast_array_to_feature(
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1804, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2061, in cast_array_to_feature
casted_array_values = _c(array.values, feature.feature)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1806, in wrapper
return func(array, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2011, in cast_array_to_feature
_c(array.field(name) if name in array_fields else null_array, subfeature)
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1806, in wrapper
return func(array, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2101, 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<endian: string, level: int64, checksum: bool>
to
{'keepbits': Value('int64'), 'endian': Value('string'), 'typesize': Value('int64'), 'cname': Value('string'), 'clevel': Value('int64'), 'shuffle': Value('string'), 'blocksize': Value('int64')}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.
MERRA-2 Daily Clearsky Dataset
Dataset Description
This dataset contains daily clearsky-related atmospheric and surface parameters derived from NASA's Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). The data covers the period from 1999 to 2018 globally, at its native resolution, and is stored in a cloud-optimized format to support the worldwide evaluation of clear-sky solar irradiance with the SPARTA model (Ruiz-Arias, 2024) via sparta-solar for solar energy modeling, atmospheric research, and climatological studies.
- Repository maintained by: @josearuizarias
- Primary Source: NASA Global Modeling and Assimilation Office (GMAO)
- Temporal Range: 1999 - 2018
- Temporal Resolution: Daily
- Spatial Coverage: Global
- Spatial Resolution: 0.5° x 0.625° (Native MERRA-2 grid: 361 latitudes x 576 longitudes)
Dataset Structure
Data Fields
The dataset includes the following variables mapping key atmospheric and surface properties under clear-sky conditions:
albedo: Surface albedo (dimensionless).ozone: Total-column ozone content ($kg \cdot m^{-2}$).pwater: Total-column water vapor content (precipitable water) ($kg \cdot m^{-2}$).pressure: Surface pressure ($Pa$).altitude: Surface height above sea level ($m$).beta: Angstrom turbidity coefficient (dimensionless).alpha: Angstrom wavelength exponent (dimensionless).ssa: Aerosol single-scattering albedo (dimensionless).time: Daily timestamp dimension.lat: Latitude coordinates (-90 to 90).lon: Longitude coordinates (-180 to 175.625).
Data Origin and Methodology
The data is extracted from the official MERRA-2 products (e.g., earthaccess) and aggregated to daily time steps maintaining the native geographical grid of MERRA-2.
albedo is the ALBEDO MERRA-2 variable from collection tavg1_2d_rad_Nx (M2T1NXRAD) for radiation diagnostics, pressure, ozone and pwater are the variables PS, TO3 and TQV, respectively, from collection tavg1_2d_slv_Nx (M2T1NXSLV) for single‐level diagnostics, and beta, alpha and ssa are evaluated from the variables TOTEXTTAU, TOTSCATAU and TOTANGSTR of collection tavg1_2d_aer_Nx (M2T1NXAER) for aerosol-related diagnostics. alpha is TOTANGSTR, beta is calculated from TOTEXTTAU and TOTANGSTR using the Angstrom's formula and ssa is the ratio of TOTSCATAU to TOTEXTTAU.
File Format and Usage
The dataset is structured into individual Zarr stores per year (e.g., 1999/, 2000/, etc.) that are not chunked. The reason is that they are conceived to be accessed from local using sparta-solar in yearly blocks, which is fast for both point and grid estimates of clear-sky solar irradiance.
Citations & Acknowledgments
How to cite this dataset
If you use this dataset in your academic or professional work, please cite both this repository and the core MERRA-2 reference:
@misc{merra2_daily_clearsky,
author = {Ruiz-Arias, Jose A.},
title = {MERRA-2 Daily Clearsky Dataset},
year = {2026},
publisher = {Hugging Face},
journal = {Hugging Face Data Repository},
howpublished = {\url{https://huggingface.co}}
}
@article{merra2_official,
author = {Gelaro, Ronald and others},
title = {The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)},
journal = {Journal of Climate},
volume = {30},
number = {14},
pages = {5419-5454},
year = {2017},
doi = {10.1175/JCLI-D-16-0758.1}
}
License
This dataset is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Use of the underlying raw data must adhere to NASA's Earth Science Data and Information Policy.
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