PRISM Spatial Climate Layers

An Overview of the USDA-NRCS Spatial Climate Mapping

Project, Including an Introduction to the PRISM (Parameterelevation Regressions on Independent Slopes Model) System

Developed by Oregon State University and used to Produce

Spatial Climate Products.

This also Serves as a Tutorial for Anyone Interested in how

these Products are Derived, what Products are Available, and

how to Access them
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Table of Contents

I............Development of the PRISM Climate Layers ..............................................3

I.A.........Background .....................................................................................3

I.B.........PRISM Overview .............................................................................4

1.B.1 ......Governing Equation - The General Elevation Regression Function...................4

1.B.2 ......Station Weighting ..............................................................................5

I.C.........Climate Mapping Issues.......................................................................5

I.D.........Construction of the PRISM Climate Grids .............................................. 15

I.D.1 ......Collect Station Data ......................................................................... 15

I.D.2 ......Develop PRISM Climate Grids............................................................ 16

I.D.3. .....Subject Grids to Peer Review and Make Improvements .............................. 18

I.E.........Bibliography .................................................................................. 19

II...........PRISM Digital Climate Layers--How to Access and Manipulate Them............ 22

II.A........Climate Elements Available................................................................ 22

II.B........Spatial and Temporal Resolution of PRISM Products................................. 36

II.C........Data Formats of PRISM Products......................................................... 36

II.D........Access to PRISM Data Products .......................................................... 41

II.E........Future PRISM Spatial Climate Products................................................. 48
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_____________________________________________________________________

Section 1: PRISM Spatial Climate Layers

INSTRUCTORS:Christopher Daly and Gregory Johnson

COURSE: PRISM Spatial Climate Layers

_____________________________________________________________________

OBJECTIVES: Review PRISM climate layer development, and demonstrate

how to access and manipulate these layers

___________________________________________________________________________

At the completion of this lesson, the participant should:

(1) Understand the important factors that affect the spatial patterns of climate, and how the

PRISM climate layers incorporate them. 

(2) Be able to access and manipulate PRISM digital climate layers available on the Web and

CD-ROM

(3) Have first-hand knowledge of the latest PRISM products under development.

___________________________________________________________________________

I. Development of the PRISM Climate Layers

The overall objective in developing the PRISM climate layers for the United States was to use

station data and other spatial data sets to estimate patterns of climate in a spatially

representative and physically meaningful way. The resulting climate layers are unprecedented

in their combination of physically realistic detail and comprehensive spatial extent. In this

section, we give an overview of the PRISM modeling system and how it addresses major

climate mapping issues; then summarize the methods used to construct the PRISM climate

grids, which are the basis for all of the digital map layers.

I.A. Background

PRISM was developed to help meet the rising demand for spatial data sets of climate elements

in digital form. This demand has been fueled by the maturation of computer technology,

enabling a variety of hydrologic, ecological, and natural resource models to be linked to

geographic information systems (GIS). In turn, the use of such model/GIS linkages has

stemmed partially from the increasingly complex nature of today=s environmental issues,

requiring multiple layers of spatial information to be analyzed in a relational manner.

Historically, methods for mapping climate from point observations have fallen into two main
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categories: geographical and statistical. Geographical techniques dominated climate mapping

for the first three-fourths of this century. They involve the manual preparation of climate

maps, primarily of precipitation, and topographic analyses involving the correlation of point

climate data with an array of topographic and synoptic parameters. During the late 1970s,

methods of climate distribution became largely statistical. Such methods include distanceweighting algorithms, kriging, splining, and multivariate analyses. The switch to statistical

methods corresponded to the advent of the computer as a common workplace tool.

In 1991, computerized GIS and visualization technology had developed sufficiently to allow the

development of PRISM (Parameter-elevation Regressions on Independent Slopes Model), a

hybrid statistical-geographic approach to mapping climate (Daly and Neilson 1992, Daly et al.

1994, 1997). PRISM retains many of the predictive advantages of statistical techniques, while

emphasizing a geographic approach that is lacking in the more generalized statistical methods.

 This technique seeks to integrate into a predictive system the vast store of information

concerning climate processes, variation and pattern accumulated from geographical studies. 

I.B. PRISM Overview

PRISM uses point data, a digital elevation model (DEM), and other spatial data sets to

generate estimates of annual, monthly and event-based climatic elements that are gridded and

GIS-compatible. PRISM is not a static system of equations; rather, it is a coordinated set of

rules, decisions, and calculations, designed to accommodate the decision-making process an

expert climatologist would invoke when creating a climate map. Because information is

gathered each time PRISM is applied to a new region or climatic element, it is kept as openended and flexible as possible to reflect our current state of knowledge. 

1.B.1 Governing Equation - The General Elevation Regression Function

The strong variation of climate with elevation is the main premise underlying the model

formulation. PRISM adopts the assumption that for a localized region, elevation is the most

important factor in the distribution of temperature and precipitation. Observations from many

parts of the world show the altitudinal variations of temperature and precipitation to

approximate a linear form. 

Available station data often do not span the complete range of elevations in an area, especially

in mountainous regions. Therefore, vertical extrapolation is required. This is accomplished in

PRISM at each DEM grid cell (termed the target grid cell) through a simple linear climateelevation regression. This regression function serves as the main predictive equation in the

model. A linear regression was chosen over nonlinear methods such as polynomial regression

and curve-fitting functions such as splining, because: (1) altitudinal variations of climate often

approximate a linear form; (2) the linear function can be extrapolated in a stable fashion far

beyond the elevational range of the data; and (3) the linear function can be easily manipulated

to compensate for inadequacies in the data, which are rarely sufficient to fully represent the

vertical distribution of the climate element. A simple, rather than multiple, regression model

was chosen because it is difficult to control and interpret the complex relationships between

multiple independent variables and climate. Instead, much effort has gone into controlling for
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the effects of variables other than elevation by weighting the data points based on numerous

factors, as will be discussed later. 

The simple linear regression has the form

Y = J1 X + J0

where Y is the predicted climate element, J1 and J0 are the regression slope and intercept,

respectively, and X is the DEM elevation at the target grid cell. The climate-elevation

regression is developed from x,y pairs of elevation and climate observations supplied by

station data in the local area.

1.B.2 Station Weighting

Upon entering the regression function, each station is assigned a weight that is based on

several factors. The combined weight W of a station is a function of the following:

W = f { Wd , Wz , Wc , Wl , Wf , Wp , We }

where Wd , Wz , Wc , Wl , Wf , Wp and We are the distance, elevation, cluster, vertical layer,

topographic facet, coastal proximity, and effective terrain weights, respectively. Distance,

elevation, and cluster weighting are relatively straightforward in concept. A station is downweighted when it is relatively distant or at a much different elevation than the target grid cell,

or when it is clustered with other stations (which leads to over-representation). Vertical layer,

topographic facet, coastal proximity and effective terrain weights are discussed below.

I.C. Climate Mapping Issues

The level of sophistication necessary to accurately map a climate element depends on the

characteristics of the region and the element to be mapped. Below is a list of questions that

were asked when developing and applying PRISM for precipitation and temperature for the

United States. Accompanying each question is a description of how PRISM handles the

situation. 

1. Does the region contain any hills or mountains?

This is the most basic question to be asked; it determines whether a two-dimensional mapping

approach is adequate, or whether a three-dimensional approach is required. We have found

that most regions have at least some terrain features, thus requiring a 3D approach. In fact,

the governing equation in PRISM is a climate-elevation regression function. When low-relief

(2D) situations are encountered within a region and precipitation is being modeled, PRISM

transitions to a 2D interpolater. This is discussed further under the Alow relief/high relief@

question. 3D interpolation is always assumed for temperature, because even minor terrain

features are important to the thermal climate. 
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If yes to Question 1:

1a. Does the climate-elevation relationship vary across the region?

It is uncommon to encounter a region of any size that can be easily characterized with

one overall relationship between climate and elevation, or a even multi-variate

relationship between climate and elevation, latitude, and longitude. There are always

numerous additional factors that need to be considered, such as coastal influence,

barriers to moisture, etc. that are difficult to account for in a domain-wide manner. 

PRISM deals with this by localizing the climate-elevation relationship to a relatively

small moving window. Each grid cell has its own, unique relationship (Figure 1-1).

Figure 1-1. Screen capture from the PRISM graphical user interface (GUI) showing a local

mean annual precipitation-elevation relationship for a grid cell in the Green

Mountains of Vermont. Sizes of the dots on the scatterplot indicate a station=s

relative weight in the regression function. 
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1b. Is there evidence of sharply-defined climate regimes delineated by terrain features?

The most common of these features is the rain shadow, caused by blockage of moisturebearing air flow by topographic features. Rain shadows occur on the lee sides of many

mountain ranges in the western U.S., as well as in interior valleys of the Appalachians.

 Mixing stations from windward and leeward exposures when creating the local climateelevation regression function gives a very muddied and inaccurate picture of the

situation. To identify stations that have a similar exposure as the target grid cell,

PRISM divides the terrain into major topographic orientations, or facets, based on an

eight-point compass. Facets are delineated at six different spatial scales to

accommodate varying station density and terrain complexity (Figure 1-2). Stations on

the same facet as the modeled grid cell are given the highest weight in the regression

function. Others are downweighted accordingly (Figure 1-3).

(a) (b)

Figure 1-2. Topographic facet grids for northwestern Oregon delineated at two spatial scales:

a) 2.5 minutes (~4 km); and b) 40 minutes

(~60 km). The 8-point facet orientations are condensed into 4 classes - north, south, east,

and west - to increase readability of the diagram. 
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Figure 1-3. PRISM scatterplots and estimated regression lines for 1961-90 mean annual

precipitation vs. elevation for a windward grid cell near the crest of the Coast

Range in northwestern Oregon; a) without facet weighting; b) with facet

weighting.

1c. Is the climate-elevation relationship largely monotonic?

It is not always sufficient to assume a monotonic change in climate with elevation. In

coastal regions, orographic precipitation may result from uplift of a shallow boundary

layer, causing mid-slope precipitation maxima to occur, with drying at higher altitudes.

This has been documented in subtropical locations dominated by the trade wind

inversion (e.g., Hawaii), but is also thought to occur in mid-latitude coastal areas,

where the moist, marine layer is relatively shallow. Mid-elevation maxima can also

occur in the case of temperature. For example, inland valleys often experience

persistent temperature inversions during winter. In Colorado's San Luis Valley and

Wyoming's Bighorn Valley, increases in mean January minimum and maximum

temperatures of 2.5-3.0EC/100 m are not uncommon. If one were to extrapolate these

lapse rates upwards into the surrounding mountains, the predicted temperature would be

wildly unrealistic.

To simulate these situations, PRISM divides climate stations entering the regression
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into two vertical layers. Layer 1 represents the boundary layer and layer 2 the free

atmosphere above it. Stations in the same layer as the target grid cell receive full

weight, while those in the adjacent layer receive lower weights. In essence, the layer

weighting scheme limits the ability of stations in one layer to influence the regression

function of the other.

Simple methods were used to create a potential wintertime inversion height grid for the

U.S., which was used in the temperature mapping height grid was created by

generating a grid of smoothed "base", or valley-bottom elevations, then adding a

constant climatological inversion height to the base elevations.

Analyses of radiosonde data from several cities in the United States with persistent

wintertime inversions indicated that the inversion top typically occurred at 200-300 m

above ground level. The inversion top was quite consistent; when an inversion formed,

it tended to do so at about the same height each time. Therefore, 250 m was added to

the base elevation at each pixel to obtain the potential inversion height above sea level.

For precipitation mapping, a constant height of 2500 m was used for marine-dominated

regions along the West Coast. In recent mapping work in Hawaii, a constant height of

1000 m was used (Figure 1-5, Figure 1-6).

Figure 1-4. Estimated wintertime inversion layer grid for the U.S. Shaded areas denote

terrain estimated to be in the free atmosphere (layer 2) under winter inversion

conditions, should they develop. Unshaded areas are expected to be within the

boundary layer (layer 1). Grid resolution is 2.5 minutes (~ 4 km). 
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Figure 1-5. PRISM mean annual precipitation for Hawaii. Magenta area on the eastern side

of the Mauna Kea and Mauna Loa volcanos is the area of maximum

precipitation. Mauna Kea is the northern volcano. Black lines are elevation

contours; dots are climate station locations. 
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Figure 1-6. PRISM scatterplot and estimated regression lines for 1961-90 mean annual

precipitation vs. elevation for the windward (eastern) slope of Mauna Kea,

island of Hawaii; a) without vertical layer weighting; b) with vertical layer

weighting; c) without vertical layer weighting and the wettest station omitted

from the data set; d) with vertical layer weighting and the wettest station

omitted from the data set. The wettest station is predicted accurately only when

layer weighting is used. Size of symbol indicates the relative total weight of the

observation.
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1d. Does the region contain low relief as well as high-relief topography?

Not all terrain features are alike in their ability to produce orographic enhancement of

precipitation. Conceptually, the effectiveness of a terrain feature in amplifying

precipitation depends on its ability to block and uplift moisture-bearing air. One might

imagine a spectrum of Aeffective@ terrain heights, ranging from large features that

produce highly three-dimensional precipitation patterns, to a nearly flat condition which

exhibits two-dimensional patterns only. 

Simple methods have been developed for producing effective terrain height grids for a

given region, and passing this information to PRISM. The effective terrain height for

each pixel on a DEM is estimated by comparing the height of the DEM pixel to that

same pixel on a smoothed, large-scale representation of the terrain. Features rising

only slightly above the large-scale Abackground@ terrain are considered to have little

effect on precipitation, while those rising far above the background field are assumed to

have a significant effect. (Figure 1-7). In areas of orographically-effective terrain,

PRISM operates in a full 3D fashion. In non-effective terrain situations (low relief),

PRISM becomes a 2D interpolater, for which all station weighting factors become

meaningless, except for distance, clustering, and coastal proximity. The result is

precipitation maps that vary smoothly in flat terrain, and increase in complexity only

when the terrain is significant (Figure 1-8). 

Figure 1-7. Effective terrain grid for the U.S. Shaded areas denote terrain features that are

expected to produce significant terrain-induced (3D) precipitation patterns. Grid

resolution is 2.5 minutes (~ 4 km).
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(a)

(b)

Figure 1-8. PRISM contour maps of mean annual precipitation for southeastern Colorado a)

in 3D mode everywhere; b) in varying 2D/3D mode using the effective terrain

height grid. 
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2. Does the region contain climatically-significant water bodies?

Sites near a large water body such as the oceans or Great Lakes may experience climatic

conditions that are significantly different than those just a short distance inland. For example,

summer maximum temperature gradients can exceed 20EC within thin coastal strips, and

coastal precipitation is sometimes different than over adjacent inland areas. Therefore, simple

coastal proximity grids have been developed that estimate the proximity of each pixel to the

water (Figure 1-9). PRISM uses this information to select and weight stations according to

their similarity in coastal proximity to the target grid cell. The result is a more consistent

coastal regime that is less sensitive to relative differences in the density and placement of

coastal and inland stations (Figure 1-10). The current proximity measure defines coastal

influence only as the shortest distance from a site to the water, but more sophisticated

measures that accommodate the effects of complex terrain are under development.

Figure 1-9. Coastal proximity grid for the U.S. Shaded areas denote zone that may

experience significant gradients in spatial climate associated with coastline

proximity. Grid resolution is 2.5 minutes (~ 4 km).
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(a) (b)

Figure 1-10. PRISM map of 1961-90 mean July maximum temperature for the coast of

central California; a) without coastal proximity weighting; and b) with coastal

proximity weighting. White squares denote locations of coastal stations. Black

dots denote inland stations. Grid resolution is 2.5 minutes (~ 4 km).

I.D. Construction of the PRISM Climate Grids

The preparation of the basic PRISM precipitation and temperature grids for the conterminous

United States consisted of a sequence of steps that were iterated several times: (1) collect

station data; (2) develop PRISM climate grids; and (3) subject PRISM grids to peer review and

make improvements. These steps are discussed below. 

I.D.1 Collect Station Data

Observed precipitation data were collected from the National Weather Service cooperative

network, the NRCS Snotel network, storage gauges, snow courses, and estimated Apseudo@

stations. The coop data were provided by the National Climatic Data Center (NCDC) and the

SNOTEL data by the NRCS National Water and Climate Center (NWCC). Storage gauge data
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were provided by Myron Molnau and John James, State Climatologists of Idaho and Nevada,

respectively. April 1st mean snow water equivalent measurements from snow course sites,

processed into estimates of mean annual precipitation, were available from Myron Molnau, and

Phil Farnes, former NRCS snow hydrologist in Montana. Additional snow course data for

California were obtained from the Department of Water Resources and processed into annual

precipitation estimates at Oregon State University (OSU). All observations were provided to

OSU as 1961-90 monthly or annual normals. A small number of estimated data points, or

pseudo-stations, were used in areas where either a state climatologist or OSU personnel

believed the station data to be insufficient to adequately represent a precipitation regime. 

Typically, pseudo-station estimates were based on vegetation type, short-term observations, or

stream flow records. 

Annual-only precipitation data from storage gauges, snow courses, and pseudo-stations were

processed into mean monthly values based on a subjectively chosen Aanchor@ station, a nearby

monthly station that was believed to possess a representative monthly precipitation distribution.

 These annual-only stations were used sparingly, when the benefits of a better-defined

precipitation regime far outweighed the costs associated with potentially inaccurate values. 

Temperature data were collected from the NWS cooperative network, the NRCS Snotel

network, and the Global Gridded Upper Air Statistics (GGUAS) data set. The coop and

Snotel data were provided as 1961-90 mean monthly minimum and maximum temperatures

from NCDC and NWCC, respectively. The GGUAS upper-air data, available on CD-ROM

from NCDC, were 2.5-degree grid-cell estimates of mean monthly temperature from the

ECMWF model. These estimates were given as 1980-1995 means. Grid cell values for the

500-mb surface were used over the western U.S., and the 700-mb surface was used over the

central and eastern parts of the country. The pressure levels were chosen to be far above the

highest terrain, so that the grid cell estimates never conflicted with the surface-based

observations. The GUASS data provided useful high-altitude anchor points for the estimation

of temperature in the mountains during months when there was little communication between

PRISM layers 1 and 2 for vertical extrapolation (i.e., winter inversions), and when 

observations were sparse. The GGUAS mean monthly temperatures were processed into

minimum and maximum temperatures by developing spatial grids of monthly temperature

range for stations residing in layer 2 and applying these ranges to the gridded mean

temperatures. 

I.D.2 Develop PRISM Climate Grids

Precipitation

The conterminous United States was divided into the following regions to be modeled

individually with PRISM: Each of the eleven western states, including separate northern and

southern California regions; central U.S.; eastern U.S.; and New England. Choosing these

regions was based on several considerations. First, the PRISM evaluation process was

performed on a state-by-state basis in the West, so the mapping naturally began there as a

state-oriented activity. Second, the great variation in precipitation patterns and station density

and placement among states made it preferable to parameterize the model runs at the state

level. The central and eastern U.S. could be done in larger chunks because of the relatively
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modest variation in precipitation pattern and complexity. The exception was New England,

where better results were obtained through a somewhat different parameterization than was

used for the greater eastern U.S. region. 

Spatial input data sets necessary for modeling were prepared for the conterminous United

States super-region, then windowed for each modeling region using GIS. A 2.5-minute DEM

served as the base gridded coverage. It was carefully constructed by using a Gaussian filter to

degrade the resolution of a quality-controlled 15-sec DEM obtained from the USGS EROS

Data Center. A mask that delineated the boundaries of the modeling area was made through a

combination of political and physical boundary files. This mask was then Agrown@ by several

km to provide a buffer of valid precipitation values in near-shore coastal areas and along

political boundaries with Canada and Mexico. Topographic facet grids were prepared using

PRISM software. A layer 1/layer 2 boundary height grid was developed that estimated the

height of the moist marine layer at 2500 m for areas west of the Cascades and Sierra Nevada. 

All other areas were assumed to be within the boundary layer (layer 1) for precipitation. A

coastal proximity grid included the Atlantic and Pacific Oceans and the Great Lakes. An

effective terrain height grid was also prepared. 

Model runs were made repeatedly for each region over a period of several years as the model

was developed and reviewed, and additional data were collected. Monthly grids for the U.S.

super-region were prepared by knitting together output from the many modeling regions. Each

state region was modeled with 1 degree of overlap with the adjacent region to minimize edges

effects. PRISM predictions were highly consistent between one region and another, so very

little overlap was actually used in the knitting process. This was fortunate, because the statespecific review process in the West made getting approval of overlapping changes to the grids

awkward. Annual precipitation grids were generated by summing the monthly grids. 

Temperature

The conterminous United States was divided into three overlapping regions to be modeled

individually with PRISM: western, central, and eastern. Just three regions were justified

because temperature is a more straightforward element to model than precipitation, and the

map review was to be done on a regional, rather than state, basis. 

Most of the same spatial input grids used in precipitation modeling were used here. The

exception was replacing the layer 1/layer 2 boundary grid with a potential wintertime inversion

height grid.

Model runs for mean monthly minimum and maximum temperature were made over a period

of several months. Monthly grids from each of the three regions were knitted together with at

least 0.5-degree of overlap. Annual temperature grids were produced by averaging the

monthly grids.
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I.D.3. Subject Grids to Peer Review and Make Improvements

From the outset, it was recognized that the final PRISM digital climate layers would quickly

become the baseline climatology for a variety of applications, including natural resources

management, hydrologic modeling and forecasting, ecological simulations, and educational

activities. It was, therefore, of the utmost importance that the PRISM technology and resulting

map products represent the state-of-the-science. The way to ensure this was through rigorous

and repeated peer-review of all map products. Peer-review of digital climate maps is

uncommon; in fact, digital GIS layers of any kind are rarely subjected to a formal peer-review

process. However, as will be seen in the discussion below, many significant enhancements to

PRISM were made as a result of peer-review responses to the map products. 

In 1993, a committee of climatologists and hydrologists from several state and federal agencies

was formed by USDA-NWCC to evaluate the PRISM methodology for mapping precipitation

in the United states. Four western states were chosen as test areas: Oregon, Idaho, Nevada,

and Utah. Each of these states was represented by State Climatologists who had a strong

interest in the process, and, in the cases of Idaho and Utah, had recently-completed state

precipitation maps to serve as evaluation benchmarks. It was believed that the West would

serve as an Aacid test@ for the U.S., because it possessed the most spatially complex

precipitation regimes in the country. 

Each member of the committee, termed the PRISM Evaluation Group (PEG), was asked to

evaluate a draft PRISM mean annual precipitation map for his or her area of interest by doing

the following: (1) inspect precipitation patterns for accuracy, reasonableness and detail; (2)

inspect high and low precipitation amounts and their locations; (3) inspect the interrelationships

between extremes; and (4) produce factual evidence supporting major differences between

PRISM and PEG member viewpoints. 

The evaluation process lasted two years, and resulted in several significant improvements in

the PRISM methodology, including increasing the grid resolution from 5 minutes to 2.5

minutes, and delineating topographic facet orientation on a 8-point compass, rather than 4. At

the conclusion of the PEG review, all members formally sanctioned PRISM as able to produce

precipitation maps of equivalent or superior quality than precipitation maps manually prepared

by expert climatologists for the regions studied. 

After all suggestions and comments from the PEG evaluation had been addressed and

incorporated, PRISM draft monthly and annual precipitation maps for the entire U.S. were

produced by OSU. A mean annual precipitation map for each state was sent to climatologists

in that state for review. This review, begun in 1995, required an additional two years, and

resulted in useful responses from most states. Improvements made to PRISM based on these

responses included adding the effective terrain height algorithms and the coastal proximity

measure. AFinal@ precipitation grids were produced during summer 1997. These were updated

one additional time in spring 1998 when efforts to contour-plot monthly values produced

blocky contours. This was remedied by producing PRISM grids at higher numerical precision.

Peer review of the temperature maps was conducted at the regional level. Mean January

minimum and July maximum temperature maps for each of the climatological regions in the
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U.S., and one representative state within each region, were sent to the six Regional Climate

Offices for review. These reviews were incorporated into the final PRISM temperature maps

produced in January 1999 at OSU. 

I.E. Bibliography

Bishop, G.D., M. R. Church, and C. Daly. 1998. Effects of improved precipitation estimates

on automated runoff mapping: eastern United States. Journal of the American Water

Resources Association, 34(1): 159-166.

Daly, C., W. P. Gibson, G.H. Taylor, G. L. Johnson, P. Pasteris. In review. New methods

for mapping temperature and precipitation in complex regions. Journal of Applied

Meteorology. 

Daly, C., G.H. Taylor, and W.P. Gibson. 1998. The PRISM approach to mapping

precipitation and temperature. In: Proc., Modeling for Crop-Climate-Soil-Pest System

and Its Applications in Sustainable Crop Production, Jaingsu Academy of Agricultural

Sciences, Nanjing, China, 22-26 June, 150-152.

Daly, C., G.H. Taylor, W.P. Gibson, T. Parzybok, G.L. Johnson, and P.A. Pasteris. 1998. 

Development of high-quality spatial climate datasets for the United States. In: Proc.,

1

st International Conference on Geospatial Information in Agriculture and Forestry,

Lake Buena Vista, FL, June 1-3, I-512 - I-519. 

Daly, C., G.H. Taylor, and W.P. Gibson. 1997. The PRISM approach to mapping

precipitation and temperature. In: Proc., 10th AMS Conf. on Applied Climatology,

Amer. Meteorological Soc., Reno, NV, Oct. 20-23, 10-12.

Daly, C. and G.H. Taylor. 1996. Development of a new Oregon precipitation map using the

PRISM model. In GIS and Environmental Modeling: Progress and Research Methods,

eds. Goodchild, M.F., Stayaert, L.T., Parks, B.O., Johnston, C., Maidment, D.R.,

Crane, M. and S. Glendenning, 91-92. Fort Collins, GIS World, Inc.

Daly, C., R.P. Neilson, and D.L. Phillips. 1994. A statistical-topographic model for

mapping climatological precipitation over mountainous terrain. Journal of Applied

Meteorology 33: 140-158.
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Daly, C., and R.P. Neilson. 1992. A digital topographic approach to modeling the

distribution of precipitation in mountainous terrain. In: Jones, M.E., and Laenen, A.

(eds.) Interdisciplinary Approaches in Hydrology and Hydrogeology. American

Institute of Hydrology, pp. 437-454.

Gibson, W.P., C. Daly, and G.H. Taylor, 1997. Derivation of facet grids for use with the

PRISM model. In: Proc., 10th AMS Conf. on Applied Climatology, Amer.

Meteorological Soc., Reno, NV, Oct. 20-23, 208-209.

Johnson, G.L., C. Daly, C.L. Hanson, Y.Y. Lu and G.H. Taylor. In review. Spatial

variability and interpolation of stochastic weather simulation model parameters. 

Journal of Applied Meteorology.

Johnson, G.L., C. Daly and C.L. Hanson. 1998. Weather generator parameter spatial

variability and mapping. Presented at ASAE Annual Intl. Meeting, Orlando, FL, July

11-16, 1998. ASAE Paper No. 982006, 5pp.

Johnson, G.L., P.A. Pasteris, C. Daly, and G.H. Taylor. 1998. Climate information for

natural resource management in a spatial world. In: Proc., 1

st International Conference

on Geospatial Information in Agriculture and Forestry, Lake Buena Vista, FL, June 1-

3, II-255 - II-257.

Johnson, G.L., C. Daly, G.H. Taylor, C.L. Hanson, and Y.Y. Lu, 1997. GEM model

temperature and precipitation parameter variability, and distribution using PRISM. In:

Proc., 10th AMS Conf. on Applied Climatology, Amer. Meteorological Soc., Reno,

NV, Oct. 20-23, 210-214.

Johnson, G.L., C.L. Hanson, C. Daly, G.H. Taylor, and C.W. Richardson. 1995. The

development and testing of a spatially-relevant stochastic weather simulation model. 

Proceedings of the USDA-ARS Workshop of Weather and Climate Research, July,

1995, Denver, CO.

Kittel, T.G.F., J.A. Royle, C. Daly, N.A. Rosenbloom, W.P. Gibson, H.H. Fisher, D.S.

Schimel, L.M. Berliner, and VEMAP2 Participants, 1997. A gridded historical (1895-

1993) bioclimate dataset for the conterminous United States. In: Proc., 10th AMS

Conf. on Applied Climatology, Amer. Meteorological Soc., Reno, NV, Oct. 20-23,

219-222.

Parzybok, T., W.P. Gibson, C. Daly, G.H. Taylor, 1997. Quality assurance of climatological

data for the VEMAP project. In: Proc., 10th AMS Conf. on Applied Climatology,

Amer. Meteorological Soc., Reno, NV, Oct. 20-23. 215-216.
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Taylor, G.H., C. Daly, W.P. Gibson, and J. Sibul-Weisberg, 1997. Digital and map products

produced using PRISM. In: Proc., 10th AMS Conf. on Applied Climatology, Amer.

Meteorological Soc., Reno, NV, Oct. 20-23, 217-218.

Taylor, G.H., C. Daly and W.P. Gibson, 1995: A Study of Snowfall Distribution in Oregon. 

Oregon Water Resources Research Institute, Oregon State University, Corvallis,

Oregon.

Taylor, G.H., C. Daly and W.P. Gibson, 1993: Development of an Isohyetal Analysis for

Oregon Using the PRISM Model. The State Climatologist, National Climatic Data

Center, NOAA, Asheville, NC.Vogel, R.M., I. Wilson, and C. Daly. In Press. 

Regional regression models of annual streamflow for the United States. ASCE Journal

of Irrigation and Drainage Engineering.
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Section 2. PRISM Digital Climate Layers--How to Access and

Manipulate Them

2.A. Climate Elements Available

The USDA-Natural Resources Conservation Service’s (USDA-NRCS) National

Water and Climate Center in Portland, Oregon is leading the PRISM climate mapping

project for the United States. The chief function of this project is to produce base

climate layers for the multitude of climate-related projects required for administration by

the USDA. These include programs that protect wetlands, and those that take highly

erodible (from either water or wind) agricultural lands out of production.

The NRCS project is using a number of different delivery mechanisms to get

spatial climate products into the hands of the users who need them. This includes

delivery via the Internet (and ftp), traditional hard copy maps of selected products (only

mean annual precipitation maps of each state at this time), and compact discs (CD’s). 

Hardcopy mean annual precipitation maps can be obtained from the NRCS Climate

Data Liaison in each state, typically at the NRCS state office. CD’s containing

precipitation layers for the continental U.S., in all the formats described above (GEO

projection only), are now available from the NRCS-National Cartography and

Geospatial Center (NCGC).

The following is an overview of products that are presently available either via

the Internet, or on CD-ROM. Unless noted differently, all products are for the

contiguous 48 United States, are at approximately 4 km horizontal resolution, and are

means for the 1961-90 climatic normal period.

Reviewed and Final:

• Monthly and Annual Mean Total Precipitation, all 50 states

• Monthly and Annual Mean Tmax

• Monthly and Annual Mean Tmin

• Monthly and Annual Mean Tavg

• Monthly and Annual Mean Diurnal Temperature Range

Preliminary Output; In Review:

• Median Last Spring and First Fall Frost Dates (32F)

• Frost-free Season Length

• Monthly and Annual Mean Growing Degree Days (various base temperatures)

• Monthly and Annual Mean Heating and Cooling Degree Days

• Mean Monthly and Annual Snowfall, and Snowfall Water Equivalent

• Mean Monthly and Annual Daily Maximum and Minimum Dewpoint

In addition, several special projects have been undertaken using PRISM. These

include the mapping of all temperature and precipitation parameters of the Generation

of weather Elements for Multiple applications (GEM) weather generator model. In a test

of this methodology over a region of Idaho and Oregon PRISM successfully distributed

all of these parameters, producing 58 parameter layers at 4 km horizontal resolution,
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over a region of nearly 8,000 grid cells (Johnson et al. 1997). Example maps of mu,

the mean precipitation on a wet day, and p10, the probability of a dry day following a

wet day, both GEM parameters (and two of the 58 mapped in this test) are shown in

Figures 2.15 and 2.16 on page 44). PRISM has also been used successfully to map

certain elements for time periods shorter than one month, including total precipitation

during several 3 to 5-day flood events in the Pacific Northwest (Taylor et al. 1997;

Johnson et al. 1999).

Shown below are examples of these products that are now, or will soon be, available:

Figure 2.1. Mean annual precipitation (inches) of the contiguous 48 United States

based on an approximate 4 km resolution execution of the PRISM modeling system. 

Based on 1961-1990 normals from the NOAA Cooperative network, and from period of

record measurements from the NRCS SNOTEL network.
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Figure 2.2. Mean annual precipitation for the western United States at a modeling

resolution of approximately 4 km horizontal. Based on 1961-1990 monthly normals,

mostly from NOAA cooperative stations, NRCS SNOTEL and snow course stations,

and some other state and regional networks.
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Figure 2.3. Mean annual precipitation for Oregon. Original modeling resolution was 4

km, which was then filtered to an effective 2 km horizontal resolution. Contours and

shaded polygons were created using scripts built in ArcInfo, including an automated

routine developed by Oregon Climate Service/Oregon State University that creates Arc

polygons from gridded data fields. For more information about this automated

computer code for generating polygon fields contact George Taylor, Oregon State

Climatologist (541-737-5705).
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Figure 2.4. Mean July maximum temperature (F) for the continental United States.

Figure 2.5. Mean January minimum temperature (F) for the continental United States.
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Figure 2.6. Mean maximum temperature (F) for the month of July in Oregon. Both

continental and elevational effects are apparent.

Figure 2.7. Mean minimum temperature (F) for the month of January, Oregon. Coastal

and continental influences dominate in the winter minimums, along with local

elevational effects.
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Figure 2.8. Mean annual total snow water equivalent (in.) for Oregon, based on a

methodology combining PRISM precipitation and temperature digital layers.
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Figure 2.9. Mean date of the first occurrence of a 32 F temperature in the autumn,

based on an algorithm relating mean monthly minimum temperature (for key indicator

months) to frost date, and then examining various local influences on regression

residuals.

Figure 2.10. Mean length of the freeze-free season (median first fall frost date minus

median last spring frost date) for the continental U.S.
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Figure 2.11. The relationship between mean annual temperature and mean annual

accumulated growing degree days (base 50 F) for 1800 climate stations across the

continental United States. The residuals of this cubic regression are mapped by

applying an inverse distance weighting technique to each of the 1800 station residuals,

which showed high spatial autocorrelation. The residual map was then added back to

the original GDD map to produce a final U.S. map, shown

below.

Figure 2.12. Mean annual growing degree days, base 50 F.
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Figure 2.13. Mean daily maximum dewpoint temperature (F) for the month of January for the continental U.S., based on

data from all available ground stations (shown).
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Figure 2.14. Mean daily maximum dewpoint temperature (F) for the western U.S. in the

month of January.
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Figure 2.15. Annual mean of mu, the GEM weather generator parameter denoting the

average precipitation on a wet (>= 0.01 in.) day over southwestern Idaho and

southeastern Oregon. Effective resolution 4 km horizontal. Based on PRISM

distribution of mean monthly mu values at 80 climate stations. This parameter is

strongly dependent on elevation, but with different regression slopes across the

domain.

Figure 2.16. The probability of a dry day following a wet day (GEM weather generator

parameter p10) for the month of December over southwestern Idaho and southeastern

Oregon. 
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Figure 2.17. Example of PRISM modeling of an event. Total precipitation for a six day

period of significant flooding in the northwestern U.S., December 29, 1996 to January 3,

1997.
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Figure 2.18. 25-year, 24-hour precipitation for western Oregon. Statistically-derived

from 4-km resolution gridded mean annual precipitation and a relationship between

mean annual precipitation and extreme precipitation statistics.
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2.B. Spatial and Temporal Resolution of PRISM Products

In nearly all cases, PRISM is being used to model climate elements and derived

variables at a 2.5 minute horizontal resolution using the latitude-longitude (GEO)

coordinate system, resulting in an effective resolution of approximately 4 km in midlatitudes. Many 4 km products are then filtered to an effective resolution twice as fine

(approximately 2 km).

PRISM has been used to model some meteorological elements at much higher

resolution for some special projects (as fine as 250 m horizontal resolution), but these

were for specific scientific studies over small regions.

The spatial resolution (2.5 min.) is a compromise based on several factors,

including experience gained from many modeling simulations at a variety of horizontal

resolutions. In general, many atmospheric processes do not operate at higher

horizontal resolutions at mean monthly and annual time scales. There are also

compelling computational and computer storage space reasons for not using PRISM to

deliver model output at higher spatial resolutions.

PRISM has been used extensively and primarily to deliver digital gridded and

contoured climate map products at monthly to annual time scales. Exceptions to this

include certain event scale mapping efforts, such as the flood event precipitation

mapping effort shown in Figure 2.17. For event mapping, two different methodologies

were investigated. In one case, mean monthly PRISM layers were used as a base

against which anomalous climate element ratios were multiplied to calculate the desired

variable. The other methodology used PRISM to map the raw event precipitation totals

directly. In this case, it appeared that the direct method (shown in Figure 2.17) was

slightly better, but perhaps not significantly so.

In the future, higher spatial and temporal mapping using PRISM will undoubtedly

be conducted, based on demand and scientific study to determine optimal

combinations of resolutions that best describes what is actually occurring in the

atmosphere.

2.C. Data Formats of PRISM Products

PRISM data layers are available in a variety of formats to meet the needs of the

wide spectrum of users of these spatial data sets. It is anticipated that PRISM layers

will be utilized most heavily by persons with Geographic Information Systems (GIS)

capabilities. To meet the expected demand, all gridded PRISM data sets currently in

final or draft final form are available in both ArcInfo and GRASS formats, which are the

two most commonly used GIS’s at the present time. These raster data layers are the 4

km horizontal resolution, latitude/longitude format data described above. At the present

time they are available only at the OSU PRISM web site (see section 2.D., below). 

Precipitation and temperature data are available for the lower 48 states at this time, and

Alaska and Hawaii should be available in the winter or spring of 1999. Data sets at the

OSU PRISM web site are organized into these divisions: national, regional (East,

Central, West U.S.), and by state. Newer coverages, such as growing degree days

(base 50 F), heating and cooling degree days, frost dates, frost-free season length and
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snow water equivalent, are available only as complete, national coverages (data files) at

this time at the OSU site.

Arc polygon coverages, stored in Arc export format (.e00), also are available at

the OSU PRISM web site. These include national, regional and state mean monthly

and annual precipitation coverages. In the future, many temperature and other

products will be available in Arc polygon format, utilizing the automated polygon

creation code developed at OCS/OSU.

Convenient and smaller size .gif image files of national, regional, and state mean

annual precipitation maps also are available at the OSU PRISM web site. These can

be easily downloaded for quick incorporation into documents, for instance.

The NRCS PRISM web page (address given in sec. 2.D., below) has all of the

cartographic-quality state mean annual precipitation maps available for downloading, or

for viewing and downloading as .gif images (such as Figure 2.19, below). Procedures

for creating the cartographic-quality county map cut-outs of the state maps, such as the

one shown in Figure 2.20, below, will also be available from this web site soon. The

NRCS PRISM web site will eventually contain all of the final PRISM data sets

developed under the NRCS project, such as all precipitation, temperature, and many

other files. This is a new web site, and is still under development. Look for many

exciting changes in the coming months. The site already contains a link to the OSU

PRISM web site, for easy access to additional PRISM files.

Both the NRCS and OSU PRISM web sites also contain documentation about

PRISM, including recent and historical papers and articles describing the methodology

of PRISM in detail, relevant research, and most of the promotional materials contained

in this course packet. Documentation is available in a variety of formats for easy

downloading and reading, including Adobe’s pdf format. Both sites also contain

information about who to contact for information about various aspects of the PRISM

project, and instructions on data file downloading via in Internet browser, or through ftp.

Hardcopy maps may be ordered from the OCS/OSU directly for many states. 

The recently completed cartographic-quality state mean annual precipitation maps,

produced by the NRCS-NCGC, can be obtained from the appropriate NRCS state

office, usually by asking for the state climate data liaison.
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Figure 2.19. Mean annual precipitation for Idaho, based on the NCGC cartographic

work described in the county map caption, above. One of these maps was created for

each state, and hardcopies of approximately 24 x 36 inches were made available to

NRCS offices, as well as regional and state climate offices, and others. For a copy of

one of these maps please contact the appropriate NRCS State office.
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Figure 2.20. Mean annual precipitation (in.), Elmore County, southeastern Idaho. 

Example of the cartographic map products being created from the PRISM polygon

layers at the USDA-NRCS National Cartography and Geospatial Center (NCGC), Ft.

Worth, TX. County maps such as these are direct cut-outs from the state polygon

coverages developed using the automated procedures described above, at OSU. 

NCGC developed routines for including roads, road shields, streams, lakes, county

names, county boundaries, county seat names and locations, and legends and other

attribute data, mostly from the 1:1 million Digital Chart of the World coverage.
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Figure 2.21. Example of PRISM map products used for local scale planning. Mean

annual precipitation (in.) for Tillamook county, on the Oregon coast.
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2.D. Access to PRISM Data Products

Primary access to PRISM climate products is through the two PRISM web sites. 

One is maintained by Oregon Climate Service, the home of the PRISM modeling work. 

Here you will find nearly all of the PRISM products, as described above in section 2.C. 

Data can be downloaded through a browser or via ftp at:

http://www.ocs.orst.edu/prism/prism_new.html

The new NRCS PRISM web site has fewer products at this time, but does

contain all of the new state cartographic quality mean annual precipitation maps. It also

has links to other PRISM sites. The address for this site is:

http://www.ftw.nrcs.usda.gov/prism/prism.html

These two web pages are shown in Figure 2.22.
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Figure 2.22. Examples of the PRISM web sites at the Oregon Climate Service, Oregon

State University (top) and the USDA-NRCS (bottom).
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A new 3-CD-ROM set of all PRISM precipitation data sets for the contiguous 48

United States is now available. Here is more complete information about the CD-ROM

set, including how to order them, what they contain, and information about the

ArcExplorer map browser software being included with each CD:

New CD-ROM of PRISM Precipitation Data now available:

A new CD-ROM has been developed by the NRCS and contains mean annual

and monthly PRISM precipitation data for the lower 48 states, with one CD covering

each of three regions of the United States (West, Central, East). The data are

organized in the directory structure shown below. Viewing software included on the CD

is ESRI's ArcExplorer. ArcExplorer project (*.aep) files of each state’s mean annual

precipitation located in the data directory on the CDROM will display the PRISM data

with color files and symbology. Using ArcExplorer the user is able to view, query, and

print the PRISM mean annual precipitation layers. Mean monthly precipitation layers

need to be added to the ArcExplorer view window in order to be displayed. See

information about adding themes in the ae_tips.txt file. To use the PRISM data for

more detailed analysis other software will be needed, such as ESRI’s ArcView.

The original ASCII grids in both ARC/INFO and GRASS format are provided in the grids

directory on the CDROM. Contact Oregon Climate Service, the USDA-NRCS

contractor on this project, at Oregon State University for appropriate use of these grid

data. Main contact at OCS is George Taylor, State Climatologist, Oregon Climate

Service, 326 Strand Ag. Hall, Oregon State University, Corvallis OR 97331. email:

taylor@oce.orst.edu phone: (541) 737-5705. For information about the USDA-NRCS

PRISM Climate Mapping Project, or for inquiries from USDA personnel, users should

contact Greg Johnson, USDA-NRCS Applied Climatologist, National Water and Climate

Center, 101 SW Main St., Suite 1600, Portland OR 97204. email: 

gjohnson@wcc.nrcs.usda.gov; phone: (503) 414-3017.

For more information about the PRISM model and this project please read the PRISM

fact sheet included in the handout materials, or see the przfact.doc or the przfact.pdf

documents on the CD.

Here is a listing of the CD contents:

Documents

• readme.txt The document you are now reading

• ae_tips.txt Information on loading and using ArcExplorer software for viewing

and printing files

• przfact.doc Word 6.0 file describing the PRISM modeling system and this

climate mapping project

• przfact.pdf Adobe Acrobat pdf file of the above
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Software

• aesetup.exe The executable file that installs ArcExplorer on your pc. You will

need Windows95, or WindowsNT with administrator privileges, to run this

program.

Data Directories

1) data Directory containing Arc polygon, ArcExplorer Project, and other files. 

Organized by states. For example, here are the state directories

on the eastern region CD (given by two letter state abbreviation):

al ga mi ny ri vt ct in ma nc oh sc wv de md nh pa tn 

 fl ky me nj va

Here is a sample listing of the data directory for Alabama, and an explanation of

each of these files:

dir = data/al

---------------------------------------------------

al.AEP aug dec jan mar roads

al_poly.lut city feb jul may sep annual counties hydro jun nov

state apr countyseat info log oct

---------------------------------------------------

Coverage

 (File) Description

-------- --------------------------

state DCW state boundary*

counties DCW County boundaries

roads DCW Roads

hydro DCW Hydrology

countyseat US Census

annual Annual precip polygons w/arc isolines

jan thru dec Monthly precip polygons

*Note: DCW refers to coverages directly from the 1:2 million Digital Chart of the

World

2) grids This directory contains the gridded ascii (raw) PRISM coverages, at a

resolution of approximately 2.5 min. Data are in latitude/longitude

(GEO) projection. Both Arc and GRASS header files are included.

 Files contain gridded data for the entire lower 48 US states, as a

whole. Files use the naming convention: us_ppt.xx or us_ppt.xxg,

where xx is the month (01 for January, 12 for December, 14 is for

annual), and ‘g’ files are GRASS-header files. Here is a listing of

these files on this CD:
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us_ppt.01 us_ppt.01g us_ppt.02 us_ppt.02g us_ppt.03 us_ppt.03g us_ppt.04 

us_ppt.04g us_ppt.05 us_ppt.05g us_ppt.06 us_ppt.06g us_ppt.07 

us_ppt.07g us_ppt.08 us_ppt.08g us_ppt.09 us_ppt.09g us_ppt.10 

us_ppt.10g us_ppt.11 us_ppt.11g us_ppt.12 us_ppt.12g

3) region These are the regional Arc polygon and gridded ascii (both Arc and

GRASS) coverages. Naming convention is, again, the region

followed by the month or annual number. For example, here are

the regional Arc polygon files on the eastern region CD:

east_01 east_02 east_03 east_04 east_05 east_06 east_07 east_08 

east_09 east_10 east_11 east_12

east_14

4) metadata

These files contain the FGDC-compliant metadata for all of the PRISM data

layers. This includes relevant metadata for the DEM and the Arc vector files

of each state. One metadata file for the whole U.S. raster (gridded ascii)

coverages is also included.

5) DEM This is the digital elevation model (DEM) of the whole lower 48 U.S. states

used by PRISM.
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More Detailed Information about ESRI’s ArcExplorer browsing software for map

products:

ArcExplorer is a software package freely available for downloading and use, and

developed by the Environmental Systems Research Institute, Inc. (ESRI). For more

information about this software package, or to obtain the latest version of the software,

please go to ESRI’s web site at: http://www.esri.com.

On the precipitation CD each state directory in the data directory contains an

ArcExplorer Project (*.aep) file. These are files that ArcExplorer can load and view

automatically, just by double-clicking on these. One .aep file per state has already

been developed and they reside in each of the state directories in the data directory. 

These are the mean annual precipitation layers from PRISM.

Each .Aep file contains nine Arc/Info coverages as themes. 

1. State Boundary

2. County Boundary

3. County Names

4. County Seats

5. Roads

6. Lakes

7. Streams

8. Annual Contours

9. Annual Legend

Adding Themes

To add a theme click the Theme drop down menu and select the add theme choice.

Navigate to the correct directory and select the theme folder, to display the theme

features.

Choose a theme feature by clicking on the appropriate .adf file and then clicking on the

add theme button on the top of the menu.

Theme Types

1. Polygon themes pat.adf

2. Arc themes aat.adf

3. Annotation txt.adf

Theme Visibility

To make a theme visible check the box next to the theme name.

Theme Legend

To change the Legend double-click on the theme legend to open the legend editor

menu. Colors can be changed by double-clicking on the individual color to access the

Color menu
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Zooming In and Out

To use the zoom functions select the magnifying glass buttons on the tool bar the +

symbol will zoom in and the - symbol will zoom out.

Identifying Features

To identify map features select the theme to identify and click the tools dropdown menu

and select the identify choice. Then move the cursor to the correct location on the map

and click once. A message box will appear displaying the current table information. 

The lower case i button on the tool bar will effect the same command.

Querying Themes

To query themes select the theme to be queried and then click the tools dropdown

menu and select the query builder choice. Identify the fields and values to query by and

click the Execute button. To display the query click the Highlight Results button or the

Zoom to Results button at the bottom of the menu. The Hammer Icon on the tool bar

will execute the same command.

Saving Arc Explorer Project (.aep) files

To save changes to the provided .aep files the directory path and state directory must

be copied from the CD-ROM to the hard drive. The project can then be saved to a new

name. 

 

 Example

 First create a directory called Arc on the C drive then create a directory called

east in the Arc directory. Copy the state directory from the CD-ROM drive to the

directory C:\Arc\east\

 

 CD-ROM drive is D:\Arc\east\al

 Hard Drive C:\Arc\east\al

In this case, the state directory used is al, which is Alabama.

 

See the Arc Explorer help files for more information on any topics. Also, a booklet

entitled “ArcExplorer...Using ArcExplorer” is available for purchase (approx. $20 U.S.)

from ESRI. See their web site, or call them at 909-793-2853, for ordering information.
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2E. Future PRISM Spatial Climate Products

There are many additional spatial climate datasets that are now being planned

for development using the PRISM modeling system, through the USDA-NRCS project

and through other funded projects, including work for the National Climatic Data Center

(NCDC) and the NOAA-NASA Global Change Research Program. Here is a sampling

of products that are planned for the coming 1-2 years:

United States and Possessions:

1. 102-year (1895-1996) time series of monthly precipitation, minimum temperature,

and maximum temperature for the lower 48 states.

2. Mean monthly and annual maximum, minimum and average temperature, and

diurnal temperature range, for AK, HI, the Pacific Basin islands and Puerto Rico.

3. Mean monthly and annual precipitation for AK, HI, the Pacific Basin islands and

Puerto Rico.

4. Mean monthly and annual daily maximum and minimum dewpoint temperature for

AK, HI, the Pacific Basin islands and Puerto Rico.

5. Plant hardiness for all 50 states and possessions (average annual extreme

minimum temperature).

6. Mean monthly and annual daily total direct beam solar radiation for all 50 states and

possessions.

7. Mean monthly and annual potential evapotranspiration for all 50 states and

possessions.

8. New intensity-duration-frequency maps of all 50 states and possessions, including 4

km resolution design storm maps (such as the 100 year, 24-hour storm, the 50 year

1-hour storm, etc.).

9. Annual erosive power of precipitation (R-factor in the RUSLE soil erosion model) for

all 50 states and possessions.

10.Develop methodology to produce growing degree day (GDD) maps for any threshold

through regression analysis between mean temperatures and GDD’s.

11.Mean monthly and annual number of days with maximum temperatures above 90 F

for all 50 states.

12.Mean monthly and annual number of days with minimum temperatures below 32 F

for all 50 states.

13.Mean monthly and annual relative humidity for all 50 states.

14.Mean monthly and annual total snowfall for all 50 states.

15.Mean monthly and annual number of wet days (>= 0.01 in.) for all 50 states.

16.Percentage of hours of temperatures >= 90 F for all months May-October, and for

season.

17.Percentage of hours of temperatures <= 32 F for all months May-October, and for

season.

18.Monthly and annual extreme 24 hour precipitation, maximum temperature and

minimum temperature for all 50 states.

19.Mean monthly (Oct. - April) and annual snowdepth for all 50 states.

20.Mean date of first and last snowfall for all 50 states.
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21.Probability of a white Christmas for all 50 states.

International:

1. Mean monthly precipitation, minimum temperature, and maximum temperature for

British Columbia and Yukon Territory.

2. Mean (and possibly time series) monthly precipitation, minimum temperature, and

maximum temperature for China and Mongolia.

3. Mean monthly precipitation for the European Alps.

4. Mean monthly precipitation for Chile.