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
PRISM Spatial Climate Layers
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 distance-weighting
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 GIScompatible. 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 open-ended 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
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independent variables and climate. Instead, much effort has gone into controlling for 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 = 1 X + 0
where Y is the predicted climate element, 1 and 0 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 low 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.
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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 into
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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.
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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.
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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.
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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 effective 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 background 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.
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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.
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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.
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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)
Figure 1-10.
(b)
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 pseudo
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 anchor 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 modest variation in
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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 grown 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. superregion 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 state-specific 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 acid 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.
Final 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
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minimum and July maximum temperature maps for each of the climatological regions in the
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., 1st
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., 1st International Conference on
Geospatial Information in Agriculture and Forestry, Lake Buena Vista, FL, June 1-3, II255 - 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
al
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):
ga mi ny ri vt
fl ky me nj va
ct
in
ma
nc
oh
sc
wv
de
md
nh
pa
tn
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
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
jun
nov
*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.04g
us_ppt.05 us_ppt.05g us_ppt.06
us_ppt.07g us_ppt.08 us_ppt.08g
us_ppt.09
us_ppt.10g us_ppt.11 us_ppt.11g us_ppt.12
3) region
us_ppt.03 us_ppt.03g us_ppt.04
us_ppt.06g us_ppt.07
us_ppt.09g us_ppt.10
us_ppt.12g
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
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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
2. Arc themes
3. Annotation
pat.adf
aat.adf
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.
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