Climate data--including 30-Year-normal data--provided by PRISM Climate Group at Oregon State University. Data is in raster formats.

This OSU PRISM Group web site provides access to the highest-quality spatial climate data sets currently available. These data sets were created using the PRISM climate mapping system, developed by Dr. Christopher Daly, PRISM Group director. PRISM is unique in that it incorporates a spatial climate knowledge base that accounts for rain shadows, temperature inversions, coastal effects, and more in the climate mapping process. Daily minimum temperature [averaged over all days in the month].

Yearly effective energy and mass transfer (EEMT) (MJ m−2 yr−1) was calculated for the Catalina Mountains by summing the 12 monthly values. Effective energy and mass flux varies seasonally, especially in the desert southwestern United States where contemporary climate includes a bimodal precipitation distribution that concentrates in winter (rain or snow depending on elevation) and summer monsoon periods. This seasonality of EEMT flux into the upper soil surface can be estimated by calculating EEMT on a monthly basis as constrained by solar radiation (Rs), temperature (T), precipitation (PPT), and the vapor pressure deficit (VPD): EEMT = f(Rs,T,PPT,VPD). Here we used a multiple linear regression model to calculate the monthly EEMT that accounts for VPD, PPT, and locally modified T across the terrain surface. These EEMT calculations were made using data from the PRISM Climate Group at Oregon State University (www.prismclimate.org). Climate data are provided at an 800-m spatial resolution for input precipitation and minimum and maximum temperature normals and at a 4000-m spatial resolution for dew-point temperature (Daly et al., 2002). The PRISM climate data, however, do not account for localized variation in EEMT that results from smaller spatial scale changes in slope and aspect as occurs within catchments. To address this issue, these data were then combined with 10-m digital elevation maps to compute the effects of local slope and aspect on incoming solar radiation and hence locally modified temperature (Yang et al., 2007). Monthly average dew-point temperatures were computed using 10 yr of monthly data (2000–2009) and converted to vapor pressure. Precipitation, temperature, and dew-point data were resampled on a 10-m grid using spline interpolation. Monthly solar radiation data (direct and diffuse) were computed using ArcGIS Solar Analyst extension (ESRI, Redlands, CA) and 10-m elevation data (USGS National Elevation Dataset [NED] 1/3 Arc-Second downloaded from the National Map Seamless Server at seamless.usgs.gov). Locally modified temperature was used to compute the saturated vapor pressure, and the local VPD was estimated as the difference between the saturated and actual vapor pressures. The regression model was derived using the ISOHYS climate data set comprised of approximately 30-yr average monthly means for more than 300 weather stations spanning all latitudes and longitudes (IAEA).

This storymap communicates the access to jobs by different transport modes against population demographics data. To request access contact the Data Insight Team.

The downloadable ZIP file contains an Esri grid. These data were created as part of a graduate thesis at the University of Idaho. The Koppen Climate Classification was produced using gridded estimates of precipitation, temperature, and elevation from the PRISM (Parameter-elevation Regressions on Independent Slopes Model). PRISM was developed at Oregon State University and information about the gridded ASCII data sets can be obtained from: https://prism.oregonstate.edu/.These data were created as part of this thesis: https://alliance-primo.hosted.exlibrisgroup.com/permalink/f/m1uotc/CP71174200670001451These data were contributed to INSIDE Idaho at the University of Idaho Library in 1999.

Yearly topographically modified effective energy and mass transfer (EEMT-topo) (MJ m−2 yr−1) was calculated for the Valles Caldera, upper part of the Jemez River basin by summing the 12 monthly values. Effective energy and mass flux varies seasonally, especially in the desert southwestern United States where contemporary climate includes a bimodal precipitation distribution that concentrates in winter (rain or snow depending on elevation) and summer monsoon periods. This seasonality of EEMT-topo can be estimated by calculating monthly values using topographic variations of solar radiation, temperature, precipitation, evapotranspiration and surface wetting as described by Rasmussen et al. (2015). The following datasets were used to compute EEMT-topo: the precipitation climatology (1981-2010) data from the PRISM Climate Group at Oregon State Universityat an 800-m spatial resolution; the Jemez River Basin 2010 LiDARbased DEM dataset was up-scaled to 10 m DEM; the local meteorological data (Temperature, RH, Wind Speed and Pressure) downloaded for the Valles Caldera National Preserve Climate Stationsfrom 2003 to 2012; 2011 National Agriculture Imagery Program (NAIP) multispectral (4-band) images for the Valles Caldera downloaded from the USGS Seamless Data Distribution; and MODIS Albedo 16-Day L3 Global 500m data (MCD43A3) obtained from theLand Processes Distributed Active Archive Center (LP DAAC).

This is a dataset download, not a document. The Open button will start the download.This data layer is an element of the Oregon GIS Framework. Monthly 30-year "normal" dataset covering Oregon, averaged over the climatological period 1991-2020. Contains spatially gridded average annual total precipitation at 800m (30 arc-second) grid cell resolution. Distribution of the point measurements to the spatial grid was accomplished using the PRISM model, developed and applied by Dr. Christopher Daly of the PRISM Climate Group at Oregon State University. This dataset is available free-of-charge on the PRISM website.

This data layer is an element of the Oregon GIS Framework. Monthly 30-year "normal" dataset covering Oregon, averaged over the climatological period 1991-2020. Contains spatially gridded average daily maximum temperature at 800m grid cell resolution. Distribution of the point measurements to the spatial grid was accomplished using the PRISM model, developed and applied by Dr. Christopher Daly of the PRISM Climate Group at Oregon State University. This dataset is available free-of-charge on the PRISM website.

The 13 Weather Service (NWS) River Forecast Centers (RFC) produce Quantitative Precipitation Estimates (QPE) for their individual RFC areas. The RFCs produce the data using a multi-sensor approach utilizing NWS 88D radar estimates of precipitation, automated and manual precipitation gauges and satellite estimates of precipitation. These QPEs are used as input into their hydrologic models to produce NWS river forecasts and guidance products. The QPEs from each RFC are combined into a single mosaic to create a QPE product that covers the lower 48 states, Alaska and Puerto Rico. These QPE 's measuring units are in inches. The data are on an approximate 4km x 4km grid cell scale.The individual hourly data products (labeled Since 12Z Observed and those labeled Last X hours) contain data for the labeled time frame. These products are updated every hour to incorporate the most recent data.The individual daily data products (Today's Analysis Observed, those labeled Last X Days Observed, and those labeled X To Date Observed.) represent a 24 hour total ending at 12UTC on the indicated date. These 24-hour data are then summed together to produce multi-day precipitation totals. Normal precipitation data are also produced for the Today's Analysis Observed and multi-day summations of 7 days or greater using data from the PRISM Climate Group. Percent of normal and departure from normal comparisons are also available by comparing the QPE data with the PRISM normal data. The daily data may be updated several times between 12UTC and 21UTC each day as updated data becomes available.Link to graphical web page: Web SiteLink to data download (National Water Prediction Service (NWPS) Precipitation Downloads): DownloadsLinks to metadata:ObservedNormalDeparture from NormalPercent of Normal PrecipitationQuestions/Concerns about the service, please contact the DISS GIS teamTime Information:This service is not time enabled

This OSU PRISM Group web site provides access to the highest-quality spatial climate data sets currently available. These data sets were created using the PRISM climate mapping system, developed by Dr. Christopher Daly, PRISM Group director. PRISM is unique in that it incorporates a spatial climate knowledge base that accounts for rain shadows, temperature inversions, coastal effects, and more in the climate mapping process. Daily maximum temperature [averaged over all days in the month].

(Link to Metadata) Nitrate Leaching Index data for the state of Vermont. This is a derivative product based on the SSURGO soils data for all counties except Essex Co., which does not yet have SSURGO soils data. Precipitation data from PRISM averaged over the 1971-2000 30-year span was used in the Leaching Index formulae. Layer was dissolved so that soil polygons with the same leaching index category were merged. 68 polygons in this layer.

The downloadable ZIP file contains an Esri grid. These data were created as part of a graduate thesis at the University of Idaho to 1.) demonstrate that a combination of geographic information systems (GIS) and multivariate statistical procedures can be used to map climate using data from the Parameter-elevation Regressions on Independent Slopes Model (PRISM), and to 2). delineate agroclimate zones for the purpose of applying successful dryland agricultural research management practices throughout areas of relative climatic uniformity. No responsibility is assumed by Idaho State Climate Services in the use of these data.Multivariate statistical analysis and geographic information systems were used to delineate homogeneous agroclimate zones for Idaho for the purpose of applying successful dryland agricultural research practices and management decisions throughout these areas of relative climatic uniformity. Data used to produce the classification are from the Parameter-elevation Regressions on Independent Slopes Model (PRISM), developed at Oregon State University. PRISM has produced gridded estimates of mean monthly and annual climatic parameters from point data and a digital elevation model (DEM). Principal components analysis was performed on fifty-five variables including various temperature and precipitation parameters, the number of growing-degree days, the mean annual number of freeze-free days, the mean annual day of first freeze in the fall, and the mean annual day of last freeze in the spring. Cluster analysis was used to identify sixteen agroclimate zones each having similar climatic conditions regardless of its spatial _location. As a result, successful dryland agricultural practices and management decisions that are based on new technologies and developed for one part of the state may potentially be applied to other parts of the state that fall within the same agroclimate zone.These data were created as part of this thesis: https://alliance-primo.hosted.exlibrisgroup.com/permalink/f/m1uotc/CP7117420067000145136" x 48" PDF map: https://insideidaho.org/data/ago/ics/agroclimate-zones.pdfThese data were contributed to INSIDE Idaho at the University of Idaho Library in 1999.

​Important Note: This item is in mature support as of April 2024 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.This layer portrays 141 forest types across the contiguous United States and Alaska. This 250m raster was derived from MODIS composite images from the 2002 and 2003 growing seasons in combination with nearly 100 other geospatial data layers, including elevation, slope, aspect, ecoregions, and PRISM climate data. The purpose of this layer is to portray broad distribution patterns of forest cover in the United States and provide input to national scale modeling projects. Knowing where various forest types occur can be used to predict wildlife movements or design corridors, or to predict the effect of climate change on forest species.Dataset Summary The dataset was developed as a collaborative effort between the USFS Forest Inventory and Analysis and Forest Health Monitoring programs and the USFS Remote Sensing Applications Center. The source format is 250-meter raster. This layer covers the entire United States.The original forest type layer is available from the USDA portal.What can you do with this layer?This layer has query, identify, and export image services available. The layer is restricted to a 24,000 x 24,000 pixel limit, which represents an area of nearly 450 miles on a side.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.

This is software and data to support the manuscript "Variations in Tropical Cyclone Size and Rainfall Patterns based on Synoptic-Scale Moisture Environments in the North Atlantic," which we are submitting to the journal, Journal of Geophysical Research Atmospheres.The MIT license applies to all source code and scripts published in this dataset.The software includes all code that is necessary to follow and evaluate the work. Public datasets include (1) the Atlantic hurricane database HURDAT2 (https://www.nhc.noaa.gov/data/#hurdat), (2) NASA’s Global Precipitation Measurement IMERG final precipitation (https://catalog.data.gov/dataset/gpm-imerg-final-precipitation-l3-half-hourly-0-1-degree-x-0-1-degree-v07-gpm-3imerghh-at-g), (3) the Tropical Cyclone Extended Best Track Dataset (https://rammb2.cira.colostate.edu/research/tropical-cyclones/tc_extended_best_track_dataset/), (4) the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5) (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5), and (5) the Statistical Hurricane Intensity Prediction Scheme (SHIPS) dataset (https://rammb.cira.colostate.edu/research/tropical_cyclones/ships/data/). We are also including four datasets generated by the code that will be helpful in evaluating the work. Lastly, we used the eofs software package, a python package for computing empirical orthogonal functions (EOFs), available publicly here: https://doi.org/10.5334/jors.122.All figures and tables in the manuscript are generated using Python, ArcGIS Pro, and GraphPad/Prism 10 Software:ArcGIS Pro used to make Figures 5GraphPad/Prism 10 Software used to make box plots in Figures 6-9Python used to make Figures 1-4, 10-11, and Tables 1-5Public Datasets:HURDAT2: Landsea, C. and Beven, J., 2019: The revised Atlantic hurricane database (HURDAT2). March 2022, https://www.aoml.noaa.gov/hrd/hurdat/hurdat2-format.pdfIMERG:NASA EarthData: GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06. 9 December 2024, https://catalog.data.gov/dataset/gpm-imerg-final-precipitation-l3-half-hourly-0-1-degree-x-0-1-degree-v07-gpm-3imerghh-at-g. Note that this dataset is not longer publicly available, as it has been replaced with IMERG version 7: https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGHH_07/summary?keywords="IMERG final"Extended Best Track:Regional and Mesoscale Meteorology Branch, 2022: The Tropical Cyclone Extended Best Track Dataset (EBTRK). March 2022, https://rammb2.cira.colostate.edu/research/tropical-cyclones/tc_extended_best_track_dataset/ERA5: Guillory, A. (2022). ERA5. Ecmwf [Dataset]. https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5. (Accessed March 2, 2023). Also: Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146, 1999–2049, https://doi.org/10.1002/qj.3803SHIPS:Ships Predictor Files - Colorado State University (2022). Statistical Tropical Cyclone Intensity Forecast Technique Development. https://rammb.cira.colostate.edu/research/tropical_cyclones/ships/data/ships_predictor_file_2022.pdf. Also: DeMaria, M., and J. Kaplan, 1994: A Statistical Hurricane Intensity Prediction Scheme (SHIPS) for the Atlantic Basin. Weather and Forecasting, 9, 209–220, https://doi.org/10.1175/1520-0434(1994)0092.0.CO;2.Public Software: Dawson, A., 2016: eofs: A Library for EOF Analysis of Meteorological, Oceanographic, and Climate Data. JORS, 4, 14, https://doi.org/10.5334/jors.122.van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., et al. (2014). Scikit-image: Image processing in Python [Software]. PeerJ, 2, e453. https://doi.org/10.7717/peerj.453

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for Olympic National Park. The vegetation map is a geotiff raster, and at 67MB may be difficult to download. An ArcGIS file geodatabase contains plot data and lookup tables that relate map class units to mapping associations. The geodatabase includes a vegetation Feature dataset with the park boundary and project boundary used in the map. The map development process was organized around the random forests machine learning algorithm. The modeling used 2,519 plots representing 150 vegetation associations and 50 map classes. Imagery from the National Agriculture Imagery Program and the Sentinel-2 and Landsat 8 satellites, airborne lidar bare earth and canopy height data, elevation data from the U.S. Geological Survey 3D Elevation Program, and climate normals from the PRISM Climate Group were used to develop a variety of predictor metrics. The predictors and the map class calls at each plot were input to a process in which each map class was modeled against every other map class in a factorial random forests scheme. We used the plot-level modeling outcomes and species composition data to adjust the crosswalk between association and map class so that floristic consistency and model accuracy were jointly optimized across all classes. The map was produced by predicting the factorial models and selecting the overall best-performing class at each 3-meter pixel. The final vegetation map, including a buffer surrounding the park, contains 43 natural vegetated classes, seven mostly unvegetated natural classes, and four classes representing burned areas or anthropogenic disturbance.

This OSU PRISM Group web site provides access to the highest-quality spatial climate data sets currently available. These data sets were created using the PRISM climate mapping system, developed by Dr. Christopher Daly, PRISM Group director. PRISM is unique in that it incorporates a spatial climate knowledge base that accounts for rain shadows, temperature inversions, coastal effects, and more in the climate mapping process. Daily mean temperature [averaged over all days in the month].

FIA Modeled Abundance:�This dataset portrays the live tree mean basal area (square feet per acre) of the species across the contiguous United States. The underlying data publication contains raster maps of live tree basal area for each tree species along with corresponding assessment data. An efficient approach for mapping multiple individual tree species over large spatial domains was used to develop these raster datasets. The method integrates vegetation phenology derived from MODIS imagery and raster data describing relevant environmental parameters with extensive field plot data of tree species basal area to create maps of tree species abundance and distribution at a 250-meter (m) pixel size for the contiguous United States. The approach uses the modeling techniques of k-nearest neighbors and canonical correspondence analysis, where model predictions are calculated using a weighting of nearest neighbors based on proximity in a feature space derived from the model. The approach also utilizes a stratification derived from the 2001 National Land-Cover Database tree canopy cover layer.�This data depicts current species abundance and distribution across the contiguous United States, modeled by using FIA field plot data. Although the absolute values associated with the maps differ from species to species, the highest values within each map are always associated with darker colors. The Little's Range Boundaries show the historical tree species ranges across North America. This is a digital representation of maps by Elbert L. Little, Jr., published between 1971 and 1977. These maps were based on botanical lists, forest surveys, field notes and herbarium specimens.Forest-type Groups:This dataset portrays the forest type group. Each group is a subset of the National Forest Type dataset which portrays 28 forest type groups across the contiguous United States. These data were derived from MODIS composite images from the 2002 and 2003 growing seasons in combination with nearly 100 other geospatial data layers, including elevation, slope, aspect, ecoregions, and PRISM climate data.Harvest Growth:This data shows the percentage of timber that is harvested when compared to the total live volume, at a county-by-county level. Timber volume in forests is constantly in flux, and harvest plays an important role in shaping forests. While most counties have some timber harvest, harvest volumes represent low percentages of standing timber volume.Carbon Harvest:The Carbon Harvest raster dataset represents Mg of annual pulpwood harvested (carbon) by county, derived from the Forest Inventory Analysis in 2016.

PRISM Climate Group, Oregon State University, https://prism.oregonstate.edu, data created 4 Feb 2014, accessed 22 March 2022. Downloaded temperature and precipitation annual data for 2001 through 2021 (2021 is provisional). Downloaded the 30-year normal for both temp and precipitation. Within the zone of the AVCA, we averaged (mean) the raster cells contained in each separate dataset. From this data we drew the numbers for the yearly averages (below). Departure from the 30-year mean was performed by determining each cell's difference from the 30-year nromal and then dividing that difference by the 30-year normal for a percentage.YearPrecipitation (mm_Temperature (C)2001359.84618.0200352002229.75618.3439742003393.40318.7412852004346.24317.8430822005319.17218.5252312006301.08418.4154542007378.8418.4689672008358.57118.1235822009238.64518.8193392010419.9518.1028592011297.75218.080462012303.15518.6249812013329.0118.030712014356.63118.846922015446.58918.4795552016359.17418.7654472017295.39319.4173322018439.66418.882942019476.38117.8856122020182.90619.1412332021420.26418.60969430Year normal389.37418.59009207

The Highway Assignment Model (HAM) represents an average weekday for three time periods; the AM average hour from 0700 to 0930, the IP average hour from 0930 to 1530 and the average PM hour from 1530 to 1900. Five user-classes are modelled; Car Business, Car Work, Car Other, LGV and HGV. The models use an equilibrium assignment procedure that incorporates detailed junction modelling and blocking back within the Area of Detailed Modelling and is in Visum 16 software. The Public Transport Assignment Model (PTAM) represents an average weekday for three time periods; the AM average 2 hours from 0700 to 0930, the IP average 2 hours from 0930 to 1530 and the PM average 2 hours from 1530 to 1900. The PT demand is assigned with three user classes; PT Fare, PT No Fare and Train Long Distance. The assignment methodology makes use of a timetable-based assignment and is in Visum 2020 software. The new trip matrices were assigned to the HAM and PTAM networks and comparisons made to observed data. A process was then undertaken to calibrate the models, including adjustment to the networks and matrices. A matrix estimation process was then undertaken to adjust the trip matrices to better match observed data. Checks showed that the matrix estimation process did not distort the matrices beyond acceptable limits. The HAM and PTAM models validate well against observed data and achieve the targets recommended in TAG. Checks undertaken on the demand model show that the re-calibration of the model reflects observed data well. Realism tests were carried out to demonstrate that the response of the demand model to changes in car fuel cost, public transport fares and car journey time are realistic and within ranges recommended in TAG.

Extreme temperatures can vary greatly across communities due to differences in land use, shade availability, proximity to water, and elevation. Spatially detailed estimates of temperature are difficult to find - often they are stations that are not regularly spaced or are from satellite observations, which estimate only the surface temperature, which can be quite different from air temperature. The PRISM Climate Group at the Oregon State University have developed an 800-meter resolution climatology of temperature for the United States that provides enough detail for intra-city temperature comparisons. It is created by a downscaling model, Parameter-elevation Regressions on Independent Slopes Model (PRISM).The 1991-2020 climate normal for maximum temperature for the month of July was downloaded and analyzed in ArcGIS Pro. Zonal Statistics provide min, max, and mean summaries for county and census tracts (2020 version) geometries. All temperatures were converted from degrees Celsius to Fahrenheit. Additionally, in each layer the mean of the maximum temperature analysis for the next order of geometry is provided (e.g., county data in the tracts layer), which allows comparison of the observed temperature to a larger geographic average. Data Source: https://www.prism.oregonstate.edu/normals/Citation: PRISM Climate Group, Oregon State University, https://prism.oregonstate.edu, data created 10 June 2022, accessed 10 June 2022