#PRISM SCALE SERIES#

#PRISM SCALE SERIES#

PRISM produces three types of datasets: 30-year normals (averages), and time series designed for short-term and long-term use. PRISM simulates how weather and climate vary spatially with physiographic features on the earth’s surface, such as terrain and coastlines, and uses topographic indices to identify areas that are subject to temperature inversions and rain shadows. In the years since its inception, PRISM has undergone nearly constant development, and has been operationalized to produce monthly and daily time series grids of an expanding list of meteorological variables, including precipitation, temperature (min, max, mean), dew point, and vapor pressure deficit (min, max). The original algorithm was written to mimic the decisions an expert climatologist makes while developing a map showing long-term averages of temperature and precipitation. PRISM (Parameter-elevation Regressions on Independent Slopes Model) was first developed by Dr. The PRISM Climate Group is part of the Northwest Alliance for Computational Science and Engineering (NACSE), within the College of Engineering.

In the United States, high-resolution spatial weather and climate data sets are developed on an ongoing basis by Oregon State University’s PRISM Climate Group. These grids typically describe conditions over a monthly or daily time step, and offer estimates where weather stations do not exist. Spatial weather and climate data, usually in the form of continuous grids of pixels, are often key inputs to decision support systems and tools that require environmental data. The following was contributed by Christopher Daly in February, 2019:

Prism scale