# PRISM Spatial Climate Layers

An Overview of the USDA-NRCS Spatial Climate MappingProject, 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 themPRISM Spatial Climate Layers

12/3/98 Page 2 of 49 

## 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 PR ISM 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

................................ ................. 48PRISM Spatial Climate Layers

12/3/98 Page 3 of 49 _____________________________________________________________________ 

### 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 mainPRISM Spatial Climate Layers

12/3/98 Page 4 of 49 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 isgathered 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 Functio n 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 inPRISM 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 forPRISM Spatial Climate Layers

12/3/98 Page 5 of 49 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 = J 1 

### X + J 0 where Y is the predicted climate element, J 1 and J 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 { W

d 

### , W z 

### , W c 

### , W l 

### , W f 

### , W p 

### , W e 

### }
where 

### W

d 

### , W z 

### , W c 

### , W l 

### , W f 

### , W p and 

### W e 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 A low relief/high relief

@ question. 3D interpolation is always assumed for temperature, because even minor terrain features are important to the thermal climate.PRISM Spatial Climate Layers

12/3/98 Page 6 of 49 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.PRISM Spatial Climate Layers

12/3/98 Page 7 of 49 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.PRISM Spatial Climate Layers

12/3/98 Page 8 of 49 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 2.5-3.0

E C/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 regressionPRISM Spatial Climate Layers

12/3/98 Page 9 of 49 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 g rid 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).PRISM Spatial Climate Layers

12/3/98 Page 10 of 49 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.PRISM Spatial Climate Layers

12/3/98 Page 11 of 49 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.PRISM Spatial Climate Layers

12/3/98 Page 12 of 49 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 A

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 A 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. 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).PRISM Spatial Climate Layers

12/3/98 Page 13 of 49 (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.PRISM Spatial Climate Layers

12/3/98 Page 14 of 49 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 20 E C 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).PRISM Spatial Climate Layers

12/3/98 Page 15 of 49 (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 A 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 dataPRISM Spatial Climate Layers

12/3/98 Page 16 of 49 were provided by Myron Molnau and John James, State Climatologists of Idaho and Nevada, respectively. April 1 st

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 A 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 relativelyPRISM Spatial Climate Layers

12/3/98 Page 17 of 49 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 A 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. 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 verylittle 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.PRISM Spatial Climate Layers

12/3/98 Page 18 of 49 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 A

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. A

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 minimum and July maximum temperature maps for each of the climatological regions in thePRISM Spatial Climate Layers

12/3/98 Page 19 of 49 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.PRISM Spatial Climate Layers

12/3/98 Page 20 of 49 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.PRISM Spatial Climate Layers

12/3/98 Page 21 of 49 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.PRISM Spatial Climate Layers

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### Section 2. PRISM Digital Climate La yers--How to Access and

Manipulate Them
2.A. Climate Elements Available
The USDA-Natural Res ources 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 dif ferent 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,PRISM Spatial Climate Layers

12/3/98 Page 23 of 49 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.PRISM Spatial Climate Layers

12/3/98 Page 24 of 49 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.PRISM Spatial Climate Layers

12/3/98 Page 25 of 49 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).PRISM Spatial Climate Layers

12/3/98 Page 26 of 49 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.PRISM Spatial Climate Layers

12/3/98 Page 27 of 49 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.PRISM Spatial Climate Layers

12/3/98 Page 28 of 49 Figure 2.8. Mean annual total snow water equivalent (in.) for Oregon, based on a methodology combining PRISM precipitation and temperature digital layers.PRISM Spatial Climate Layers

12/3/98 Page 29 of 49 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.PRISM Spatial Climate Layers

12/3/98 Page 30 of 49 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.PRISM Spatial Climate Layers

12/3/98 Page 31 of 49 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).PRISM Spatial Climate Layers

12/3/98 Page 32 of 49 Figure 2.14. Mean daily maximum dewpoint temperature (F) for the western U.S. in the month of January.PRISM Spatial Climate Layers

12/3/98 Page 33 of 49 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.PRISM Spatial Climate Layers

12/3/98 Page 34 of 49 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.PRISM Spatial Climate Layers

12/3/98 Page 35 of 49 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.PRISM Spatial Climate Layers

12/3/98 Page 36 of 49 

### 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. PRI SM 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 mappin g 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 andPRISM Spatial Climate Layers

12/3/98 Page 37 of 49 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.PRISM Spatial Climate Layers

12/3/98 Page 38 of 49 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.PRISM Spatial Climate Layers

12/3/98 Page 39 of 49 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.PRISM Spatial Climate Layers

12/3/98 Page 40 of 49 Figure 2.21. Example of PRISM map products used for local scale planning. Mean annual precipitation (in.) for Tillamook county, on the Oregon coast.PRISM Spatial Climate Layers

12/3/98 Page 41 of 49 

### 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.PRISM Spatial Climate Layers

12/3/98 Page 42 of 49 Figure 2.22. Examples of the PRISM web sites at the Oregon Climate Service, Oregon State University (top) and the USDA-NRCS (bottom).PRISM Spatial Climate Layers

12/3/98 Page 43 of 49 A new 3-CD-ROM set of all PRISM precipita tion 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 S W 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 Informatio n 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 abovePRISM Spatial Climate Layers

12/3/98 Page 44 of 49 Software

• aesetup.exe The executabl e file that installs ArcExplorer on your pc. You will need W indows95, or W indowsNT 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 DC W state boundary* counties DC W County boundaries roads DC W Roads hydro DC W Hydrology countyseat US Census annual Annual precip polygons w/arc isolines jan thru dec Monthly precip polygons
*Note: DC W 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:PRISM Spatial Climate Layers

12/3/98 Page 45 of 49 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 ea st_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.PRISM Spatial Climate Layers

12/3/98 Page 46 of 49 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 menuPRISM Spatial Climate Layers

12/3/98 Page 47 of 49 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.PRISM Spatial Climate Layers

12/3/98 Page 48 of 49 

### 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.PRISM Spatial Climate Layers

12/3/98 Page 49 of 49 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.