Overview

This document provides an overview of the precipitation data Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) Enhanced Version. There are monthly and pentad global (nearly spatially complete) gridded precipitation rate values derived from five types of satellite estimates and gauge measurements, as well as additional filling of missing data using the NCEP reanalysis.

Provider’s contact information

GPCP is produced the NOAA Climate Prediction Center

Dataset Point of Contact:

Physical Sciences Laboratory: Data Management
NOAA/ESRL/PSL
325 Broadway
Boulder, CO 80305-3328
psl.data@noaa.gov

Licensing and citation

Cite as:

Xie, P., and P.A. Arkin, 1997: Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc., 78, 2539 - 2558.

Variable name and units

Spatial coverage and resolution

This dataset combines observations, satellite precipitation, and reanalysis precipitation data into a 2.5°x2.5° global grid.

Temporal coverage and resolution

The monthly dataset covers the period 1979/01-present. The pentad dataset covers 1979/01 to 2016/12/27.

Information about observations (number, homogeneity)

Poor coverage at high latitudes, so uses reanalysis precipitation to fill the gaps.

Methodology

CMAP/A (enhanced) record is assembled by maximum-likelihood method which determines weighting coefficients to the input data:

- IR-based GPI

- OLR-based OPI

- MSU-based Spencer data set

- SSM/I-scattering-based NOAA/NESDIS dataset

- SSM/I-emission-based Chang data set

Next, variational blending method combines this remote sensed hybrid product with the gauge-based analyses to improve the bias.

Finally, missing and existing data are adjusted with a weighted contribution from the NCAR reanalysis precipitation fields.

More detail, from NOAA source (cpc.ncep.noaa.gov/products/global_precip/html/wpage.cmap.html):

First, the random error is reduced by linearly combining the satellite estimates using the maximum likelihood method, in which case the linear combination coefficients are inversely proportional to the square of the local random error of the individual data sources. Over global land areas the random error is defined for each time period and grid location by comparing the data source with the rain gauge analysis over the surrounding area. Over oceans, the random error is defined by comparing the data sources with the rain gauge observations over the Pacific atolls.

Information about the technical and scientific quality

CMAP has been shown to represent a suppressed annual cycle amplitude and low precipitation over land compared to other products.

Limitations and strengths for application in North Canada

Quality of the analysis is highly dependent on the amount and type of input data, improving where there are high gauge densities. There is no inclusion of AIRS/TOVS satellite observations (increase high latitude observations) nor is there adjustment for undercatch in gauge measurements.

Generally, greater uncertainty with increasing latitude; poorest quality in polar regions.

While CMAP and GPCP are valuable because of their long record, it is likely that more recently developed datasets are more accurate for the common periods of record, due to greater uniformity of input data sources and more advanced satellite-derived products.

References to documents describing the methodology and/or the dataset

Xie, P., Arkin P.A., Janowiak J.E. (2007) CMAP: The CPC Merged Analysis of Precipitation. In: Levizzani V., Bauer P., Turk F.J. (eds) Measuring Precipitation From Space. Advances In Global Change Research, vol 28. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-5835-6_25

Xie, P., and P. A. Arkin, 1997: Global Precipitation: A 17-Year Monthly Analysis Based on Gauge Observations, Satellite Estimates, and Numerical Model Outputs. Bulletin of the American Meteorological Society, 78(11), 2539–2558. https://doi.org/10.1175/1520-0477(1997)078<2539:gpayma>2.0.co;2

Yin, X. G., A. Gruber, and P. Arkin, 2004: Comparison of the GPCP and CMAP merged gauge-satellite monthly precipitation products for the period 1979–2001. Journal of Hydrometeorology, 5(6), 1207– 1222..

Link to download the data and format of data

Link to download available here: https://psl.noaa.gov/data/gridded/data.cmap.html

Data are in netCDF-4 file format.

Publications including dataset evaluation or comparison with other data in Canada

Anderson, B. T., N. Feldl, and B. R. Lintner, 2018: Emergent Behavior of Arctic Precipitation in Response to Enhanced Arctic Warming. Journal of Geophysical Research: Atmospheres, 123(5), 2704–2717. https://doi.org/10.1002/2017jd026799

Behrangi, A., et al. (2016), Status of high-latitude precipitation estimates from observations and reanalyses, J. Geophys. Res. Atmos., 121, 4468– 4486, doi:10.1002/2015JD024546.