Palmer drought severity forecast October-December, 2010 (based on COLA 7-mo weather forecast)

Nov 8, 2010 (Last modified Sep 7, 2012)
The Palmer Drought Severity Index (PDSI) is a measure of drought derived from both precipitation and temperature. Negative (i.e., dry) values of PDSI are closely associated with a high potential for wildland fire.

PDSI is based on a supply-and-demand model of soil moisture originally developed by Wayne Palmer, who published his method in the 1965 paper Meteorological Drought for the Office of Climatology of the U.S. Weather Bureau.The index has proven to be most effective in indicating long-term drought (or wetness) over a matter of several months.

PDSI calculations are standardized for an individual station (or grid cell) based on the long-term variability of precipitation and temperature at that location. PDSI values can thus be compared across regions with different climates.

This dataset shows PDSI (in classes ranging from extremely wet to extremely dry) for the three-month forecast period identified in the dataset title.

PDSI was calculated from the long-term precipitation and temperature record up to the last observed month and from forecasted values provided by the weather model indicated in the dataset title. 

Future climate forecasts are available through cooperation with the International Research Institute for Climate Prediction (IRI) of Columbia University which provides monthly updates of 7-month future climate forecasts from five different general circulation models (GCMs) of the global atmosphere. GCM results come from the University of Maryland (COLA), the University of Hamburg (ECHAM4.5), the National Weather Service’s Climate Prediction Center (NCEP), NASA’s Goddard Institute of Space Studies (NSIPP), and the Scripps Oceanographic Institute (ECPC).

Data Provided By:
United States Forest Service, MAPSS team--Raymond Drapek
Content date:
10/2010 - 12/2010
Spatial Resolution:
4km x 4km
Contact Organization:
United States Forest Service
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Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 3.0 License.
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