Terrestrial Precipitation:
1900-2006 Gridded Monthly Time Series

(Version 1.01)

interpolated and documented by

Kenji Matsuura and Cort J. Willmott
(with support from IGES and NASA)


For additional information concerning this archive,
please contact us at:

Center for Climatic Research
Department of Geography
University
of Delaware
Newark, DE 19716
(302) 831-2294

or

kenjisan@udel.edu


Archive (Version 1.01) created June, 2007


STATION DATA SOURCES:

Station data, monthly total precipitation (P, mm), were compiled from several updated sources including a recent version of the Global Historical Climatology Network (GHCN2); the Atmospheric Environment Service/Environment Canada; the Hydrometeorological Institute in St. Petersburg, Russia (courtesy of Nikolay Shiklomanov); GC-Net data (Steffen et al., 1996); Greenland station records from the Automatic Weather Station Project (courtesy of Charles R. Stearns at the University of Wisconsin-Madison); the National Center for Atmospheric Research (NCAR) daily India data1; Sharon Nicholson’s archive of African precipitation data (2001)2; Webber and Willmott’s (1998) South American monthly precipitation station records3 (for notes 1, 2, and 3, see the README file); and the Global Surface Summary of Day (GSOD). Station climatologies from Legates and Willmott’s (1990) unadjusted (for raingage undercatch) archive also were used as a part of the background climatology (see Spatial Interpolation below).  Station P values were not adjusted to reduce raingage undercatch bias.

For stations and months with GHCN2 observations, the GHCN2 observations were used as “our” P values, because of GHCN2 quality-control measures.  When and where GHCN2 observations were unavailable, other station records often were merged to create a composite monthly station-record series. During this process, station records that had the same geographical coordinates were interleaved or blended to create a single, station time series for that location. For some data sets, monthly values were derived from daily values; that is, when at least 90 percent of the daily values during a month were available. If there were two or more station observations for a given month, the median of these observations was taken as P for that month. When there was only one station observation for a month, it was taken as P for that month. This was done to make use of all available data. Observations from stations which had different geographical coordinates were assumed to belong to different station records, although sometimes parts of nearby station records were extremely similar. The resultant number of stations used for estimating monthly total precipitation ranges from about 4,100 to 23,300 globally.

A number of monthly values derived from the GSOD archive, primarily within the period from 1951 to 1971, were observed to deviate systematically from (overestimate) corresponding values (same station, same month) within GHCN2.  Therefore, during this period, each monthly value obtained from the GSOD archive was compared with an estimated value obtained/interpolated from nearby GHCN2 observations.  Our GHCN2 estimate was derived by interpolating spatially from nearby GHCN2 station values to the location of the GSOD station of interest. Then, if the absolute difference between the GSOD and estimated GHCN2 values was greater than 80mm/month, the GSOD value was replaced with the estimated GHCN2 value.

SPATIAL INTERPOLATION:

Station values of monthly total precipitation (P) were interpolated to a 0.5 degree by 0.5 degree of latitude/longitude grid, where the grid nodes are centered on 0.25 degree.  Climatologically aided interpolation (CAI) (Willmott and Robeson, 1995) was used to estimate our monthly total precipitation fields. By using a background climatology based on a relatively dense network of stations, CAI can increase the accuracy of spatially interpolated time series of monthly climate variables. For the background climatology used here, two station climatologies were merged. The first was calculated at those of our precipitation time-series stations which had at least ten years of observations for each month (within the period 1960-1990). The second was the monthly station P (raw raingage) climatology of Legates and Willmott (1990). Only those Legates and Willmott stations which were not collocated with our own 1960-1990 station climatology were included in the background climatology for CAI. A monthly P value at each time-series station was differenced from our climatologically averaged P for that month which was available at or was interpolated spatially to the time-series station location. Traditional interpolation then was performed on the monthly station differences to obtain a gridded difference field. Finally, each gridded monthly difference field was added to interpolated estimates of the month’s climatology at the same set of grid points. 

Traditional interpolation was accomplished with the spherical version of Shepard’s algorithm, which employs an enhanced distance-weighting method (Shepard, 1968; Willmott et al., 1985). The number of nearby stations that influenced a grid-node estimate was increased to an average of 20, from an average of 7 in earlier applications. This resulted in smaller cross-validation errors (see below) and visually more realistic precipitation fields. A more robust neighbor finding algorithm, based on spherical distance, also was used.
 
SPATIAL CROSS VALIDATION:

To indicate (roughly) the spatial interpolation errors, station-by-station cross validation was employed (Willmott and Matsuura, 1995). One station was removed at a time, and the precipitation value was then interpolated to the removed station location from the surrounding nearby stations. The difference between the real station value and the interpolated value is a local estimate of interpolation error. After each station cross validation was made, the removed station was put back into the network. To reduce network biases on cross-validation results, absolute values of the errors at the stations were interpolated to the same spatial resolution as the precipitation field.

ARCHIVE STRUCTURE:

global_p_ts.tar.gz:

Monthly total precipitation for the years 1900-2006 interpolated to a 0.5 by 0.5 degree grid resolution (centered on 0.25 degree). The format of each record is:

 

Field

Columns

Variable

Fortran Format

1

1 - 8

Longitude (decimal degrees)

F8.3

2

9 - 16

Latitude (decimal degrees)

F8.3

3-14

17 - 112

Monthly Total Precipitation (mm)

12F8.1

 

global_p_cv_ts.tar.gz:

Cross-validation errors (absolute values) associated with precipitation for the years 1900-2006 interpolated to a 0.5 by 0.5 degree grid resolution. The format of each record is:

 

Field

Columns

Variable

Fortran Format

1

1 - 8

Longitude (decimal degrees)

F8.3

2

9 - 16

Latitude (decimal degrees)

F8.3

3-14

17 - 112

Cross-validation errors (absolute values) of Monthly Total Precipitation (mm)

12F8.1

SELECTED REFERENCES:

Legates, D. R. and C. J. Willmott (1990).  Mean seasonal and spatial variability in gauge-corrected, global precipitation.  International Journal of Climatology, 10, 111-127.

Peterson, T. C. and R. S. Vose (1997). An overview of the Global Historical Climatology Network temperature database. Bulletin of the American Meteorological Society, 78, 2837-2849.

Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. Proceedings, 1968 ACM National Conference, 517-523.

Steffen, K., J. E. Box, and W. Abdalati (1996).
Greenland Climate Network: GC-Net. Colbeck, S. C. Ed. CRREL 96-27 Special Report on Glaciers, Ice Sheets and Volcanoes, trib. to M. Meier, 98-103.

 

Willmott, C. J. and K. Matsuura (1995). Smart interpolation of annually averaged air temperature in the United States. Journal of Applied Meteorology, 34, 2577-2586.

 

Willmott, C.J. and S.M. Robeson (1995).  Climatologically aided interpolation (CAI) of terrestrial air temperature. International Journal of Climatology, 15(2), 221-229.


Willmott, C. J., C. M. Rowe and W. D. Philpot (1985). Small-scale climate maps: a sensitivity analysis of some common assumptions associated with grid-point interpolation and contouring. American Cartographer, 12, 5-16.