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Datasets:
markmclaren
/
climate-webmap-zarr

Modalities:
Geospatial
Tags:
climate
zarr
geospatial
multiscale
web-mercator
License:
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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:    TypeError
Message:      Couldn't cast array of type
struct<chunk_shape: list<item: int64>, codecs: list<item: struct<name: string, configuration: struct<endian: string, level: int64, checksum: bool>>>, index_codecs: list<item: struct<name: string, configuration: struct<endian: string>>>, index_location: string>
to
{'endian': Value('string'), 'level': Value('int64'), 'checksum': Value('bool')}
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 2109, in cast_array_to_feature
                  casted_array_values = _c(array.values, feature.feature)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1854, in wrapper
                  return func(array, *args, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2059, in cast_array_to_feature
                  _c(array.field(name) if name in array_fields else null_array, subfeature)
                  ~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1854, in wrapper
                  return func(array, *args, **kwargs)
                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<chunk_shape: list<item: int64>, codecs: list<item: struct<name: string, configuration: struct<endian: string, level: int64, checksum: bool>>>, index_codecs: list<item: struct<name: string, configuration: struct<endian: string>>>, index_location: string>
              to
              {'endian': Value('string'), 'level': Value('int64'), 'checksum': Value('bool')}

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Climate Webmap Zarr

This repository hosts multiscale climate data pyramids (average temperature + precipitation, 12 months of climatology) derived from WorldClim v2.1. It contains three versions of the dataset:

  1. High-Resolution Modern Version (Zarr v3 / topozarr 30s): Built using topozarr at 30-arc-second resolution, kept in WGS84 (EPSG:4326), and optimized with sharding (~2.8 GB).
  2. Standard Modern Version (Zarr v3 / topozarr): Built using topozarr at 10-minute resolution, kept in WGS84 (EPSG:4326), and optimized with sharding (~179 MB).
  3. Original Version (Zarr v2): Built using ndpyramid at 10-minute resolution, reprojected to Web Mercator (EPSG:3857) (~541 MB).

Webmap Application

This dataset is used by the Climate Webmap Zarr application:

Datasets Overview

1. High-Resolution Modern Version (Zarr v3 / topozarr 30s)

  • Path in Repo: /tavg-prec-month-topozarr-30s.zarr/
  • Size: ~2.8 GB
  • Format: Zarr v3 with sharding (sharding_indexed codec) for optimal range-request performance.
  • CRS: EPSG:4326 (WGS84 lat/lon, native grid, no reprojection).
  • Structure: Separated variables (tavg and prec as distinct arrays).
  • Pyramid Direction: 0 = finest/native resolution ($21600 \times 43200$), 9 = coarsest.
  • Metadata Spec: GeoZarr (multiscales + proj + spatial metadata conventions).

2. Standard Modern Version (Zarr v3 / topozarr)

  • Path in Repo: /tavg-prec-month-topozarr.zarr/
  • Size: ~179 MB
  • Format: Zarr v3 with sharding (sharding_indexed codec) for optimal range-request performance.
  • CRS: EPSG:4326 (WGS84 lat/lon, native grid, no reprojection).
  • Structure: Separated variables (tavg and prec as distinct arrays).
  • Pyramid Direction: 0 = finest/native resolution ($1080 \times 2160$), 5 = coarsest.
  • Metadata Spec: GeoZarr (multiscales + proj + spatial metadata conventions).

3. Original Version (Zarr v2 / ndpyramid)

  • Path in Repo: /tavg-prec-month/
  • Size: ~541 MB
  • Format: Zarr v2 with consolidated .zmetadata.
  • CRS: EPSG:3857 (Web Mercator, reprojected at build time).
  • Structure: Single climate array with stacked variables along a band dimension (band=["tavg", "prec"]).
  • Pyramid Direction: 0 = coarsest, 5 = finest/native ($4096 \times 4096$).

Usage in Python (Streaming)

Opening the topozarr Zarr v3 dataset

You can open the DataTree from Hugging Face directly without downloading the whole repository. Make sure you have xarray, zarr>=3.0.0, and fsspec installed.

import xarray as xr

# Open the root group. Consolidated is set to False as topozarr writes Zarr v3 groups.
url = "https://huggingface.co/datasets/markmclaren/climate-webmap-zarr/resolve/main/tavg-prec-month-topozarr.zarr"
dt = xr.open_datatree(url, engine="zarr", consolidated=False)
print(dt)

Opening the original Zarr v2 dataset

For the older v2 dataset, you can stream using a v2-compatible client:

import zarr
import fsspec

url = "https://huggingface.co/datasets/markmclaren/climate-webmap-zarr/resolve/main/tavg-prec-month"
mapper = fsspec.get_mapper(url)
store = zarr.open(mapper, mode="r")
print(store.tree())

License

Licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). Free for academic research and non-commercial purposes.
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