South American Air Temperature:
1960-1990 Gridded Monthly Time Series
(Version 1.01)
interpolated and documented by
Scott R. Webber and Cort J. Willmott
(with support from NASA's Seasonal to Interannual ESIP)
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
webber@udel.edu
Archive (Version 1.01) created November 12, 1998
STATION DATA SOURCE:
Monthly-mean air temperature records for 350 stations from
version 2 of the Global Historical Climatology Network
(Peterson and Vose, 1998) were used to produce this archive.
SPATIAL INTERPOLATION:
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). Station averages of air temperature 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. The number of nearby stations that
influence 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 air-temperature fields. A more
robust neighbor finding algorithm, based on spherical
distance, also was developed and used.
Incorporating elevational influences, through an average
air-temperature lapse rate, can further increase the
accuracy of spatially interpolating average air temperature
(Willmott and Matsuura, 1995). Digital-elevation-model- or
DEM-assisted interpolation of air temperature, therefore,
was employed. Briefly, station air temperature is first
"brought down" to sea level at the average environmental
lapse rate (6.5 deg C/km). Traditional interpolation is
performed on the adjusted-to-sea-level station air
temperatures. Then, the gridded sea-level air temperatures
are brought up to the DEM-grid height, again, at the average
environmental lapse rate.
SPATIAL CROSS VALIDATION:
To indicate (roughly) the spatial interpolation errors,
station-by-station cross validation was employed (Willmott
and Matsuura, 1995). One station is removed at a time, and
the air temperature is 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 is made, the
removed station is 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 air temperature field.
ARCHIVE STRUCTURE:
air_temp.trad.ts.tar:
Monthly-mean air temperatures for the years 1960-90 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 17-100 Monthly Air Temperature 12F7.1
(deg C, Jan-Dec)
air_temp.trad.cve.ts.tar:
Cross-validation errors associated with air temperatures for
the years 1960-90 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 17-100 Cross-validation errors for 12F7.1
Monthly Temperature (deg C, Jan-Dec)
air_temp.dai.ts.tar:
Monthly-mean air temperatures for the years 1960-90 interpolated
with Willmott and Matsuura's (1995) DEM-assisted algorithm to
a 0.5 by 0.5 degree grid resolution. The format for each
record is the same as for the air_temp.trad.ts.tar files.
air_temp.dai.cve.ts.tar:
DEM-assisted air-temperature cross-validation errors for
the year 1960-90 interpolated to a 0.5 by 0.5 degree grid
resolution. The format of each record is the same as for
the air_temp.trad.cve.ts.tar files.
SELECTED REFERENCES:
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.
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.
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.