TITLE:
An Independent Assessment of the Monthly PRISM Gridded Precipitation Product in Central Oklahoma
AUTHORS:
Jeanne M. Schneider, Donald L. Ford
KEYWORDS:
Precipitation; PRISM; Gridded Estimate; Rain Gauge; Spatiotemporal Variability
JOURNAL NAME:
Atmospheric and Climate Sciences,
Vol.3 No.2,
April
30,
2013
ABSTRACT: Accurate, long-term records of precipitation are required for the
development of climate-informed decision support tools for agriculture. But
rain gauges are too sparsely located to meet this need, and radar-derived
precipitation measurements are too recent in duration. Using all readily
available station records, spatiotemporally continuous estimates of
precipitation were created by the PRISM Climate Group to address this problem.
As with all interpolated data, the validity of the gridded PRISM product
requires validation, and given the extreme spatiotemporal variability of precipitation,
such validation is essential. Previous work comparing the monthly precipitation
product against contributing rain gauge data revealed inconsistencies that
prompted the analysis reported herein. As a basis for checking the accuracy of
the PRISM product, independent precipitation data gathered at a USDA research
laboratory in central Oklahoma
were quality controlled, including comparison to a co-located automated rain
gauge operated by the Oklahoma Mesonet. Results indicate that the independent
USDA gauge data are of sufficient quality to use in the evaluation of the PRISM
product. The area average of the independent USDA data over a matching size
area was then used to validate colocated gridded PRISM estimates. The
validation results indicate that the monthly gridded PRISM precipitation
estimates are close to the independent observed data in terms of means (smaller
by 3% to 4.5%) and cumulative probability distributions (within ~4%), but with
variances too small by 7% to 11%. From the point of view of agricultural
decision support, these results indicate that PRISM estimates might be useful
for probabilistic applications, such as downscaling climate forecasts or
driving weather generators, assuming appropriate corrections to the
higher-order statistics were applied. However, the number of months with
potentially significant differences precludes the use of PRISM estimates for
any retrospective month-by-month analyses of possible interactions between
climate, crop management, and productivity.