CMORPH CDR¶

NOAA Climate Data Record (CDR) of CPC Morphing Technique (CMORPH) High Resolution Global Precipitation Estimates, Version 1

Available

At a glance¶

Note on period: 1 January 1998 to the present (about 3–4 months behind). When checked on 2026-10-13, the daily, hourly and 30-minute data in the AWS bucket ran through 2026-05-31, and the NCAR GDEX copy through 2026-01-31. The older real-time version produced since December 2002 (V0.x, with an evolving algorithm) has been superseded by V1.0 (the NCAR archive of it ends on 2017-10-02).

Overview¶

CMORPH is NOAA CPC's method for producing global (60°S–60°N) precipitation at 30-minute, 8-km resolution: passive microwave (PMW) precipitation estimates from low-Earth-orbit satellites are transported with cloud motion vectors derived from geostationary IR imagery, and the features are "morphed" between overpasses by time-weighted interpolation. CDR v1.0 is a reprocessing from 1998 with a fixed algorithm and fixed inputs, bias-corrected against the CPC daily gauge analysis over land and the GPCP pentad analysis over ocean. The morphing technique in GPM IMERG also descends from CMORPH.

What changes with the successor¶

CMORPH V0.x (CPC real-time version) → CMORPH CDR V1.0¶

Transition: 2015-10

V0.x, produced since 2002 with an evolving algorithm and inputs, was superseded by V1.0, reprocessed from 1998 with a fixed algorithm and fixed inputs. Unlike the satellite-only V0.x, V1.0 provides versions bias-corrected against gauges and GPCP, and was first released as an NCEI CDR in October 2015.

Sources: CMORPH V1.0 README (NCAR GDEX d502002) · CMORPH CDR Climate Algorithm Theoretical Basis Document (C-ATBD, 2018) · Xie et al. (2017)

Available variables¶

The NCEI metadata and the algorithm document list a single variable, surface precipitation. According to the NCEI ISO metadata (C00948), the precipitation variable in all three netCDF4 file types is cmorph (stored as short, lwe_precipitation_rate), with time, lat and lon coordinates and their _bounds variables.

instantaneous mean accumulated maximum minimum static — instantaneous: value at the time stamp · mean/accumulated/maximum/minimum: over the indicated interval · static: does not change with time

30-minute (8 km, CMORPH_V1.0_ADJ_8km-30min)¶

Hourly (0.25°, CMORPH_V1.0_ADJ_0.25deg-HLY)¶

Daily (0.25°, CMORPH_V1.0_ADJ_0.25deg-DLY_00Z)¶

The full variable list is in the official documentation.

How to get the data¶

• NOAA NCEI (direct HTTPS download) — HTTPS download (no account)
  • Organized into 30min/8km, hourly/0.25deg and daily/0.25deg with year/month(/day) folders. NCEI warns of possible access delays during its October 2026 cloud migration.
• AWS S3 public bucket (NOAA Open Data Dissemination) — S3 (us-east-1, anonymous access) / HTTPS
  • Laid out as s3://noaa-cdr-precip-cmorph-pds/data/30min/8km/YYYY/MM/DD/, data/hourly/0.25deg/YYYY/MM/DD/ and data/daily/0.25deg/YYYY/MM/, with the algorithm document and technical note in the documentation/ folder.
• Google Cloud Storage — Public GCS bucket
• NCAR GDEX (d502002) — HTTPS / THREDDS (NCAR copy, DOI 10.5065/0EFN-KZ90)
  • When checked on 2026-10-13 it ran through 2026-01-31, behind NCEI and AWS.
• NOAA CPC preliminary data (CMORPH_RT) — HTTPS/FTP
  • The CPC folder that the NCEI product page links as "Preliminary Data". It is not the formal CDR, so do not use it for long-term analysis.

Example code¶

Downloads 0.25° daily data for July 2023 from the public AWS bucket without an account and computes the Korean Peninsula area mean (requires s3fs, xarray and netCDF4/h5netcdf). Because the variable name inside the files could not be confirmed from documentation, the code picks the three-dimensional (time, lat, lon) variable. Run print(ds) first to check the structure.

import s3fs
import xarray as xr

fs = s3fs.S3FileSystem(anon=True)
paths = sorted(fs.glob("noaa-cdr-precip-cmorph-pds/data/daily/0.25deg/2023/07/*.nc"))
files = [fs.open(p) for p in paths]
ds = xr.open_mfdataset(files, engine="h5netcdf", combine="by_coords")
print(ds)                                    # check variable names, units and coordinate ranges

name = [v for v in ds.data_vars if ds[v].ndim == 3][0]   # daily precipitation (mm/day)
la = [c for c in ds[name].dims if c.lower().startswith("lat")][0]
lo = [c for c in ds[name].dims if c.lower().startswith("lon")][0]
pr = ds[name]
box = pr.where((ds[la] >= 33) & (ds[la] <= 39) & (ds[lo] >= 124) & (ds[lo] <= 132), drop=True)
print(box.mean([la, lo]).values)             # 00–24 UTC days

Practical tips¶

• The 30-minute data are rates in mm/hr. Adding two 30-minute slots as they are doubles the hourly amount.
• Coverage is 60°S–60°N only, and there are no retrievals over snow- or ice-covered surfaces. The product is not suited to winter snowfall or high-latitude precipitation.
• The algorithm document notes that heavy rain tends to be underestimated and light rain can be missed, and that annual totals in the tropics are still too low.
• After TRMM TMI ended in April 2015, inter-calibration across sensors relies on historical tables, and from January 2021 the input microwave precipitation switched from NESDIS MSPPS to NASA GPROF. Watch for small discontinuities around these dates.
• Bias correction relies on the CPC daily gauge analysis (land) and the GPCP pentad analysis (ocean), so magnitudes are uncertain where gauges are sparse.
• The old CPC 'global_CMORPH' real-time data (V0.x, from 2002-12) were built with an evolving algorithm and inputs and are notably inhomogeneous in 2003–2006. Use CDR v1.0 for research.
• IMERG (30-minute, 0.1°) inherits CMORPH's morphing approach but has different inputs and calibration. Do not splice the two into one time series.

Notices and change history¶

References for your paper¶

Dataset citation¶

CMORPH CDR Version 1 (NOAA NCEI) — DOI 10.25921/w9va-q159

@misc{cmorph_cdr_v1,
  author    = {Xie, Pingping and Joyce, Robert and Wu, Shaorong and Yoo, S.-H. and Yarosh, Yelena and Sun, Fengying and Lin, Roger and {NOAA CDR Program}},
  title     = {{NOAA} Climate Data Record ({CDR}) of {CPC} Morphing Technique ({CMORPH}) High Resolution Global Precipitation Estimates, Version 1},
  year      = {2019},
  publisher = {NOAA National Centers for Environmental Information},
  doi       = {10.25921/w9va-q159}
}

Reference papers¶

CMORPH reprocessing and bias correction (Xie et al., 2017) — DOI 10.1175/JHM-D-16-0168.1

@article{xie2017cmorph,
  author  = {Xie, Pingping and Joyce, Robert and Wu, Shaorong and Yoo, Soo-Hyun and Yarosh, Yelena and Sun, Fengying and Lin, Roger},
  title   = {Reprocessed, Bias-Corrected {CMORPH} Global High-Resolution Precipitation Estimates from 1998},
  journal = {Journal of Hydrometeorology},
  volume  = {18},
  number  = {6},
  pages   = {1617--1641},
  year    = {2017},
  doi     = {10.1175/JHM-D-16-0168.1}
}

The CMORPH method (Joyce et al., 2004) — DOI 10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2

@article{joyce2004cmorph,
  author  = {Joyce, Robert J. and Janowiak, John E. and Arkin, Phillip A. and Xie, Pingping},
  title   = {{CMORPH}: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution},
  journal = {Journal of Hydrometeorology},
  volume  = {5},
  number  = {3},
  pages   = {487--503},
  year    = {2004},
  doi     = {10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2}
}

Acknowledgements¶

NCEI asks users to cite the dataset (DOI 10.25921/w9va-q159), indicating the subset used (30-minute, hourly or daily) and the access date. For the algorithm, cite Xie et al. (2017) together with Joyce et al. (2004). If you obtained the data from NCAR GDEX, its DOI (10.5065/0EFN-KZ90) can also be cited.

Information on the datasets used in this study was compiled with the help of the Korean Climate Data Navigator (KCDN) (Sim, 2026; https://doi.org/10.5281/zenodo.23300923).

Links¶

• NCEI CMORPH CDR product page
• NCEI metadata and citation (DOI 10.25921/w9va-q159)
• CMORPH CDR algorithm document (C-ATBD)
• Technical note — switch to GPROF input (2022)
• CPC CMORPH description page (old real-time version and QMORPH)
• AWS Open Data — NOAA atmospheric CDRs
• NCAR GDEX d502002