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pf-grid-tiles

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Tags:
climate
precipitation
remote-sensing
earth-observation
TRMM
GPM
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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<endian: string, level: int64, checksum: bool>
to
{'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 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<endian: string, level: int64, checksum: bool>
              to
              {'endian': Value('string'), 'typesize': Value('int64'), 'cname': Value('string'), 'clevel': Value('int64'), 'shuffle': Value('string'), 'blocksize': Value('int64')}

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High Resolution Precipitation Climatologies from NASA Precipitation Measurement Missions

DOI

Author: Stephen W. Nesbitt, Department of Climate, Meteorology & Atmospheric Sciences, University of Illinois Urbana-Champaign.

Annual, 0.05° precipitation climatology from the NASA Precipitation Measurement Missions spaceborne radars: the TRMM Precipitation Radar (Ku-band, 1997–2014) and the GPM Dual-frequency Precipitation Radar (DPR, Ku band, 2014–present).

Interactive map viewer: https://huggingface.co/spaces/snesbitt/pf-grid-tiles-app

Contents

pf_tiles.zarr — a Zarr v3 store (Zstd-compressed) holding 81 variables = {GPM, TRMM, COMBINED} × 27 quantities, on a shared ±68° latitude, 0.05° grid (lat 2721 × lon 7200), CF lat/lon coordinates.

pf_tiles_ms.zarr — the same 81 variables as a GeoZarr multiscale pyramid (native 0.05° + 2×/4×/8×/16× nanmean-coarsened levels, spatial:transform per level). This is what the map app serves: xpublish-tiles selects the resolution level per zoom so low-zoom views read pre-averaged coarse levels (smooth) while zoomed-in views read native resolution.

Quantities per member ({GPM,TRMM,COMBINED}_<quantity>):

variable definition units
*_rain annual precipitation accumulation = mean unconditional rate × 8766 h/yr mm/year
*_rate unconditional mean rate = Σrain / Nviews mm/hr
*_freq precipitation frequency = Nraining / Nviews fraction
*_intensity conditional mean rate = Σrain / Nraining mm/hr
*_raining_views count of pixel-views with precipitation count
*_views count of radar pixel-views (sampling denominator) count
*_conv_rain convective annual accumulation = (Σrain_conv / Nviews) × 8766 mm/year
*_strat_rain stratiform annual accumulation = (Σrain_strat / Nviews) × 8766 mm/year
*_conv_freq convective frequency = Nraining_conv / Nviews fraction
*_strat_freq stratiform frequency = Nraining_strat / Nviews fraction
*_conv_intensity convective conditional rate = Σrain_conv / Nraining_conv mm/hr
*_strat_intensity stratiform conditional rate = Σrain_strat / Nraining_strat mm/hr
*_conv_rain_frac convective rainfall fraction = Σrain_conv / Σrain_total (accumulation) fraction
*_conv_pixel_frac convective area fraction = Nraining_conv / Nraining (occurrence) fraction
*_echotop20 convective 20 dBZ echo-top height (mean over convective pixels) m
*_echotop30 convective 30 dBZ echo-top height (mean) m
*_echotop40 convective 40 dBZ echo-top height (mean) m
*_freq_gt25 frequency of near-surface rain ≥ 25 mm/hr = N(rain≥25) / Nviews fraction
*_freq_gt50 frequency of near-surface rain ≥ 50 mm/hr fraction
*_freq_gt75 frequency of near-surface rain ≥ 75 mm/hr fraction
*_freq_gt100 frequency of near-surface rain ≥ 100 mm/hr fraction
*_eps_conv / *_eps_strat mean near-surface DSD ε (epsilon) over convective / stratiform pixels —
*_nw_conv / *_nw_strat mean near-surface log₁₀(Nw) (normalized intercept) over convective / stratiform log₁₀(mm⁻¹ m⁻³)
*_dm_conv / *_dm_strat mean near-surface Dm (mass-weighted mean diameter) over convective / stratiform mm

Convective vs stratiform follows the radar 2A typePrecip classification. Echo-tops and DSD parameters (ε, Nw, Dm) are reduced to the near-surface clutter-free gate; echo-tops use the Hirose-2023-style geometric QC and are computed for convective pixels only. Each mean is stored as Σ/N so COMBINED pools correctly. COMBINED = GPM + TRMM pooled (TRMM is zero poleward of ±38°).

Method

Every observed radar pixel is gridded directly from the orbital swaths, so views counts all sampling and the precipitation fields are consistent with that denominator. Reading the store:

import xarray as xr
ds = xr.open_zarr("pf_tiles.zarr", consolidated=True)

Source data & product versions

The gridded quantity is the near-surface precipitation rate — the precipRateNearSurface field (FS/SLV/precipRateNearSurface, the radar algorithm's near-surface rain estimate, mm hr⁻¹). The rain quantities are derived from it and the per-pixel sampling; the echo-top and DSD quantities come from the 3-D reflectivity and the FS/SLV DSD retrieval (epsilon, paramDSD Nw/Dm).

mission instrument product (short name) version
GPM Dual-frequency Precipitation Radar (DPR), Ku band GPM_2ADPR (FS swath, Ku) V07
TRMM Precipitation Radar (PR), GPM-reprocessed GPM_2APR V07

GPM is pinned to V07 for a version-uniform record: the V08/V10 DPR reprocessing is in progress and only partially covers the archive, so preferring it would mix versions across the record. TRMM PR is V07-only. (When V08 DPR covers the full archive, the record will be migrated to V08-uniform.)

Sensitivity note

TRMM's Precipitation Radar has a minimum detectable reflectivity of ≈17–18 dBZ, whereas GPM's Dual-frequency Precipitation Radar (DPR) is more sensitive (≈12 dBZ) and detects substantially more light precipitation. This can introduce discontinuities between the TRMM and GPM records — and within the COMBINED member — most pronounced where light precipitation is prevalent.

Key references

  • Nesbitt, S. W., and A. M. Anders (2009), Very high resolution precipitation climatologies from the Tropical Rainfall Measuring Mission precipitation radar, Geophys. Res. Lett., 36, L15815, doi:10.1029/2009GL038026
  • Hirose, M., and K. Nakamura (2005), Spatial and diurnal variation of precipitation systems over Asia observed by the TRMM Precipitation Radar, J. Geophys. Res., 110, D05106, doi:10.1029/2004JD004815
  • Bookhagen, B., and D. W. Burbank (2006), Topography, relief, and TRMM-derived rainfall variations along the Himalaya, Geophys. Res. Lett., 33, L08405, doi:10.1029/2006GL026037
  • Kidd, C., J. Kwiatkowski, and S. W. Nesbitt (2010), Investigations into high resolution mapping of precipitation features utilizing the TRMM precipitation radar, IGARSS 2010 (IEEE Int. Geosci. Remote Sens. Symp.), 2337–2340, doi:10.1109/IGARSS.2010.5649629
  • Biasutti, M., S. E. Yuter, C. D. Burleyson, and A. H. Sobel (2012), Very high resolution rainfall patterns measured by TRMM precipitation radar: seasonal and diurnal cycles, Clim. Dyn., 39, 239–258, doi:10.1007/s00382-011-1146-6
  • Anders, A. M., and S. W. Nesbitt (2015), Altitudinal precipitation gradients in the tropics from Tropical Rainfall Measuring Mission (TRMM) precipitation radar, J. Hydrometeorol., 16, 441–448, doi:10.1175/JHM-D-14-0178.1

Cite this dataset

Nesbitt, S. W. (2026). High Resolution Precipitation Climatologies from NASA Precipitation Measurement Missions [Data set]. Department of Climate, Meteorology & Atmospheric Sciences, University of Illinois Urbana-Champaign / Hugging Face. https://doi.org/10.57967/hf/9189

DOI: 10.57967/hf/9189

Contact & acknowledgment

© University of Illinois Board of Trustees. Contact: Steve Nesbitt. This work was supported by projects from the NASA Precipitation Measurement Missions and Weather programs to the University of Illinois.
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