Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
shape: list<item: int64>
child 0, item: int64
data_type: string
chunk_grid: struct<name: string, configuration: struct<chunk_shape: list<item: int64>>>
child 0, name: string
child 1, configuration: struct<chunk_shape: list<item: int64>>
child 0, chunk_shape: list<item: int64>
child 0, item: int64
chunk_key_encoding: struct<name: string, configuration: struct<separator: string>>
child 0, name: string
child 1, configuration: struct<separator: string>
child 0, separator: string
fill_value: string
codecs: list<item: struct<name: string, configuration: struct<endian: string, level: int64, checksum: bool>> (... 1 chars omitted)
child 0, item: struct<name: string, configuration: struct<endian: string, level: int64, checksum: bool>>
child 0, name: string
child 1, configuration: struct<endian: string, level: int64, checksum: bool>
child 0, endian: string
child 1, level: int64
child 2, checksum: bool
attributes: struct<long_name: string, standard_name: string, units: string, point_spacing: string, axis: string, (... 38 chars omitted)
child 0, long_name: string
child 1, standard_name: string
child 2, units: string
child 3, point_spacing: string
child 4, axis: string
child 5, NAVO_code: int64
child 6, _FillValue: string
dimension_names: list<item: string>
child 0, item: string
zarr_format: int64
node_type: string
storage_transformers: list<item: null>
child 0, item: null
to
{'shape': List(Value('null')), 'data_type': Value('string'), 'chunk_grid': {'name': Value('string'), 'configuration': {'chunk_shape': List(Value('null'))}}, 'chunk_key_encoding': {'name': Value('string'), 'configuration': {'separator': Value('string')}}, 'fill_value': Value('string'), 'codecs': List({'name': Value('string'), 'configuration': {'endian': Value('string'), 'level': Value('int64'), 'checksum': Value('bool')}}), 'attributes': {'long_name': Value('string'), 'standard_name': Value('string'), 'units': Value('string'), 'positive': Value('string'), 'axis': Value('string'), 'NAVO_code': Value('int64'), '_FillValue': Value('string')}, 'zarr_format': Value('int64'), 'node_type': Value('string'), 'storage_transformers': List(Value('null'))}
because column names don't match
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 483, 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 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
shape: list<item: int64>
child 0, item: int64
data_type: string
chunk_grid: struct<name: string, configuration: struct<chunk_shape: list<item: int64>>>
child 0, name: string
child 1, configuration: struct<chunk_shape: list<item: int64>>
child 0, chunk_shape: list<item: int64>
child 0, item: int64
chunk_key_encoding: struct<name: string, configuration: struct<separator: string>>
child 0, name: string
child 1, configuration: struct<separator: string>
child 0, separator: string
fill_value: string
codecs: list<item: struct<name: string, configuration: struct<endian: string, level: int64, checksum: bool>> (... 1 chars omitted)
child 0, item: struct<name: string, configuration: struct<endian: string, level: int64, checksum: bool>>
child 0, name: string
child 1, configuration: struct<endian: string, level: int64, checksum: bool>
child 0, endian: string
child 1, level: int64
child 2, checksum: bool
attributes: struct<long_name: string, standard_name: string, units: string, point_spacing: string, axis: string, (... 38 chars omitted)
child 0, long_name: string
child 1, standard_name: string
child 2, units: string
child 3, point_spacing: string
child 4, axis: string
child 5, NAVO_code: int64
child 6, _FillValue: string
dimension_names: list<item: string>
child 0, item: string
zarr_format: int64
node_type: string
storage_transformers: list<item: null>
child 0, item: null
to
{'shape': List(Value('null')), 'data_type': Value('string'), 'chunk_grid': {'name': Value('string'), 'configuration': {'chunk_shape': List(Value('null'))}}, 'chunk_key_encoding': {'name': Value('string'), 'configuration': {'separator': Value('string')}}, 'fill_value': Value('string'), 'codecs': List({'name': Value('string'), 'configuration': {'endian': Value('string'), 'level': Value('int64'), 'checksum': Value('bool')}}), 'attributes': {'long_name': Value('string'), 'standard_name': Value('string'), 'units': Value('string'), 'positive': Value('string'), 'axis': Value('string'), 'NAVO_code': Value('int64'), '_FillValue': Value('string')}, 'zarr_format': Value('int64'), 'node_type': Value('string'), 'storage_transformers': List(Value('null'))}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Maritime SAR operations -- forcing archive
Gridded wind and surface-current fields over the western North Atlantic, published as multi-resolution Zarr so a web map can scrub through five years without downloading five years.
Supporting data for an undergraduate investigation project at the University of the Witwatersrand on stochastic and machine-learning-driven maritime search patterns for strong-current environments. The code that produces it is at https://github.com/Aditya-Raghunandan/MaritimeSAROptimisation.
What is here
| Domain | 17-36 N, 82-63 W -- the Gulf Stream and the western Bermuda Triangle |
| Period | 2019-01-01 to 2024-01-01 |
| Wind | ERA5 10 m u10, v10, 0.25 deg, hourly at source |
| Current | HYCOM GLBy0.08/expt_93.0 water_u, water_v at depth level 0, 0.08 deg lon x 0.04 deg lat, 3-hourly at source |
Each product is published at several time resolutions, because nobody can perceive hourly detail while scrubbing across a year and nobody should download it: the hourly wind tier is 1.27 GB against 0.07 GB for the daily one.
<product>_archive.json the manifest: grid, bbox, tiers, provenance
<product>_hourly.zarr full cadence
<product>_6-hourly.zarr every sixth step
<product>_daily.zarr every twenty-fourth step
Coarser tiers are strided, not averaged. A 24 h mean of a rotating wind vector is close to zero, so an averaged daily tier would show five years of calm. A stride shows a real hour, just fewer of them.
Conventions
- Longitude is -180..180 here. The project stores 0-360 internally and converts once, at the presentation boundary, which is this export.
- Latitude is ascending, which ERA5 is not at source and HYCOM is.
- Arrays are
(time, lat, lon),float32,m/s. - Coordinates are regular, so the manifest carries
lat0/dlat/nlatand the time axis asstart+step_seconds+frames. A tier whose time axis has gaps is marked"regular": falseand its timestamps must be read from the store's owntimearray rather than reconstructed. - Chunks are 48 timesteps x the whole spatial box, about 1.11 MB, which is the 48 h scenario window at hourly cadence.
- Compression is zstd level 19. Blosc compresses no better here and
zarrita.jscannot decode it in a browser.
Reading it
import xarray as xr
url = "https://huggingface.co/datasets/AdityaRugs/MaritimeSARoperations/resolve/main/wind_daily.zarr"
ds = xr.open_zarr(url, consolidated=False)
import * as zarr from "zarrita";
const store = new zarr.FetchStore(
"https://huggingface.co/datasets/AdityaRugs/MaritimeSARoperations/resolve/main/wind_daily.zarr");
const u = await zarr.open(zarr.root(store).resolve("u10"), {{ kind: "array" }});
Provenance and licence
Derived from two public sources, reprojected and downsampled but not otherwise altered:
- ERA5 (Hersbach et al., Copernicus Climate Change Service) via ARCO-ERA5 on Google Cloud. Contains modified Copernicus Climate Change Service information; neither the European Commission nor ECMWF is responsible for any use of it.
- HYCOM + NCODA Global 1/12 deg Analysis,
GLBy0.08/expt_93.0, via the HYCOM THREDDS server.
Code and this derived packaging are MIT. The underlying data carry their originators' terms.
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