The PRISM daily and monthly datasets are gridded climate datasets for the conterminous United States, produced by the PRISM Climate Group at Oregon State University. Grids are developed using PRISM (Parameter-elevation Regressions on Independent Slopes Model). PRISM interpolation routines simulate how weather and climate vary with elevation, and account for coastal effects, temperature inversions, and terrain barriers that can cause rain shadows. Station data are assimilated from many networks across the country. For more information, see the Descriptions of PRISM Spatial Climate Datasets.

This data set consists of PRSIM mean air temperature climatologies for Alaska in GeoTIFF format. The files in this data set are available from the PRISM Climate Group as text files but have been processed into GeoTIFFs. These are monthly climatologies with a resolution of 771m. Units are degrees Celsius. There are multiple climatological periods currently available through PRISM, but only one is currently available through SNAP in this dataset: 1971-2000.

Linework of assessment units intersected with PRISM 30-year normal mean temperature values for the period of 1981-2010. This is one input dataset in a model of stream temperatures.

Monthly PRISM datasets covering the conterminous U.S., from 1981-2019 were used to calculate yearly average air temperature and spatially averaged yearly precipitation for selected counties in and near the Permian Basin. Distribution of the measurements was accomplished using the PRISM, developed and applied by Dr. Christopher Daly of the PRISM Climate Group at Oregon State University. The aggregated data was used to display and/or analyze spatially distributed yearly average air temperature and spatially averaged yearly precipitation for select counties in and near the Permian Basin from 1981-2019.

Meteorological map products that show annual precipitation (mm/in) and temperature minimum and maximum (C) averages. Please us link to PRISM Home website below for most current PRISM data.

Spatially distributed monthly and annual temperature. Each file represents 1 month of 1 year for the period 1895-1997. Distribution of the point measurements to a spatial grid was accomplished using the PRISM model, developed by Christopher Daly, Director, The PRISM Climate Group, Oregon State University. Care should be taken in estimating temperature values at any single point on the map. Temperature estimated for each grid cell is an average over the entire area of that cell; thus, point temperature can be estimated at a spatial precision no better than half the resolution of a cell. For example, the temperature data were distributed at a resolution of approximately 4km. Therefore, point temperature can be estimated at a spatial precision no better than 2km. However, the overall distribution of temperature features is thought to be accurate. For further information, the online PRISM homepage can be found at URL:http://prism.oregonstate.edu. Further information on the current state of this project can be found at URL:ftp://ftp.ncdc.noaa.gov/pub/data/prism100

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].

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].

Monthly totals of precipitation in millimeters (mm), monthly means of daily maximum air temperature in degrees Celsius (C), and monthly means of daily minimum air temperature (C) were developed at the 5 arc minute grid level for the conterminous United States (US) for the 1940-2006 period. Also, included are computed monthly mean of daily potential evapotranspiration (mm) and mean grid elevation in meters (m). These data were developed from PRISM (Parameter-elevation Regressions on Independent Slopes Model) data at the 2.5 arc minute scale and aggregated to the 5 arc minute grid scale. The county means were computed using a weighted mean of the 5 arc minute grids within the county.The USDA Forest Service (USFS) produces a periodic assessment of the condition and trends of the Nation's renewable resources as required by the Forest and Rangeland Renewable Resources Planning Act (RPA) of 1974. This RPA Assessment provides a snapshot of current US forest and rangeland conditions and trends on all ownerships, identifies drivers of change, and projects 50 years into the future (//www.fs.fed.us/research/rpa/, accessed 8/16/2009). For 2010 RPA Assessment, an integrated modeling framework is being used in which the potential implications of climate change can be analyzed across some resource areas (Langner in review). The nature of the climate variables needed to address climate change impacts for these resource analyses in the 2010 RPA Assessment were determined to be monthly precipitation and temperature variables at the county level spatial scale and for some resource analyses at the 5 arc minute grid scale.Original metadata date was 08/02/2010. Metadata modified on 04/22/2011 to adjust citation to include the addition of a DOI (digital object identifier). Minor metadata updates on 02/20/2013. Metadata modified on 07/22/2015 to update cross-reference citations and other minor updates. Additional minor metadata updates on 12/13/2016 and 04/19/2018.

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].

This feature layer contains the gridded one month PRISM Temperature Normals from Oregon State University on a 0.5 x 0.5 degree grid for the contiguous United States. The data was originally created in February 2018. These climatologies will be updated along with the drought outlook tools.The one month climatology has the same time period as the one month lead for the Climate Prediction Center's One Month Outlook. This climatology is for the current one month forecast released on the third Thursday of every month and updated on the last day of the month for the following month. This is a tool for the Drought Outlook Interactive Web Map and Drought Outlook Interactive Experience.The Climate Prediction Center uses climatologies with a base period from 1981 to 2010.For more information visit the PRISM Climate Group, Oregon State University, https://prism.oregonstate.edu

This Resource serves to explain and contain the methodology, R codes, and results of the PRISM freshwater supply key indicator analysis for my thesis. For more information, see my thesis at the USU Digital Commons.

Freshwater availability in the state can be summarized using streamflow, reservoir level, precipitation, and temperature data. Climate data for this study have a period of record greater than 30 years, preferably extending beyond 1950, and are representative of natural conditions at the county-level.

Oregon State University, Northwest Alliance for Computational Science and Engineering PRISM precipitation and temperature gridded data are representative of statewide, to county-level, from 1895-2015. These data are available online from the PRISM Climate Group. Using the R ‘prism’ package, monthly PRISM 4km raster grids were downloaded. Boundary shapefiles of Utah state, and each county, were obtained online from the Utah Geospatial Resource Center webpage. Using the R ‘rgdal’ and ‘sp’ packages, these shapefiles were transformed from their native World Geodetic System 1984 coordinate system to match the PRISM BIL raster’s native North American Datum 1983 coordinate system. Using the R ‘raster’ package, medians of PRISM precipitation grids at each spatial area of interest were calculated and summed for water years and seasons. Medians were also calculated for PRISM temperature grids and averaged over water years and seasons. For analysis of single months, the median results were used for all PRISM indicators. Seasons were analyzed for the calendar year which they are in, Winter being the first season of each year. Freshwater availability key indicators were non-parametrically separated per temporal/spatial delineation into quintiles representing Very Wet/Very High/Hot (top 20% of values), Wet/High/Hot (60-80%), Moderate/Mid-level (40-60%), Dry/Low/Cool (20-40%), to Very Dry/Very Low/Cool (bottom 20%). Each quintile bin was assigned a rank value 1-5, with ‘5’ being the value of the top quintile, in preparation for the Kendall Tau-b correlation analysis. These results, along with USGS irrigation withdrawal and acreage data, were loaded into R. State-level quintile results were matched according to USGS report year. County quintile results were matched with corresponding USGS irrigation withdrawal and acreage county-level data per report year for all other areas of interest. Using the base R function cor(), with the “kendall” method selected (which is, by default, the Kendall Tau-b calculation), relationship correlation matrices were produced for all areas of interest. The USGS irrigation withdrawal and acreage data correlation analysis matrices were created using the R ‘corrplot’ package for all areas of interest.

See Word file for an Example PRISM Analysis, made by Alan Butler at the United States Bureau of Reclamation, which was used as a guide for this analysis.

This dataset was created using the PRISM (Parameter-elevation Regressions on Independent Slopes Model) climate mapping system, developed by Dr. Christopher Daly, PRISM Climate Group director. PRISM is a unique knowledge-based system that uses point measurements of precipitation, temperature, and other climatic factors to produce continuous, digital grid estimates of monthly, yearly, and event-based climatic parameters. Continuously updated, this unique analytical tool incorporates point data, a digital elevation model, and expert knowledge of complex climatic extremes, including rain shadows, coastal effects, and temperature inversions. PRISM data sets are recognized world-wide as the highest-quality spatial climate data sets currently available. PRISM is the USDA's official climatological data. The latest snapshot of PRISM available free of charge and hosted here was developed with the AN81m method documented here: http://www.prism.oregonstate.edu/documents/PRISM_datasets.pdf

Monthly 30-year "normal" dataset covering the conterminous U.S., including the Russian River watershed, averaged over the climatological period 1981-2010. Contains spatially gridded average monthly and average annual precipitation, maximum temperature, and minimum 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 was heavily peer reviewed, and is available free-of-charge on the PRISM website. The dataset was downloaded from the PRISM website in 2019

Annual dataset covering the conterminous U.S., from 1981 to now. Contains spatially gridded annual average daily mean temperature at 4km 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.

description: Climate data (Standard Deviation of Average Annual Temperature for 1968-1999) were created by PRISM (Parameter-elevation Regressions on Indepenent Slopes Model - prism.oregonstate.edu) on a 2.5 arc-minute lat-lon grid. They are based on historical observations from 1968-1999. We created mean monthly climatologies for that period from the PRISM data, and reprojected the results to the BLM Albers 4km grid. We used these results as a historical baseline climate to de-bias RegCM3 projections. We also compiled annual and seasonal summaries of precipitation and temperature from the PRISM data to allow for simple comparisons with other climatologies. Units are degrees celsius.; abstract: Climate data (Standard Deviation of Average Annual Temperature for 1968-1999) were created by PRISM (Parameter-elevation Regressions on Indepenent Slopes Model - prism.oregonstate.edu) on a 2.5 arc-minute lat-lon grid. They are based on historical observations from 1968-1999. We created mean monthly climatologies for that period from the PRISM data, and reprojected the results to the BLM Albers 4km grid. We used these results as a historical baseline climate to de-bias RegCM3 projections. We also compiled annual and seasonal summaries of precipitation and temperature from the PRISM data to allow for simple comparisons with other climatologies. Units are degrees celsius.

Monthly totals of precipitation in millimeters (mm), monthly means of daily maximum air temperature in degrees Celsius (C), and monthly means of daily minimum air temperature (C) were developed at the 5 arc minute grid level for the conterminous United States (US). Also, included are computed monthly mean of daily potential evapotranspiration (mm) and mean grid elevation in meters (m). These data were developed from climate scenarios used in the Fourth Assessment of the Intergovernmental Panel on Climate Change, specifically the A1B and the A2 SRES (Special Report on Emissions Scenarios) scenarios as modeled by these climate models: CGCM3.1MR, CSIRO-MK3.5, and MIROC3.2MR. The monthly change factors were developed from global model output and downscaled to the 5 arc minute spatial grid using ANUSPLIN. The 30 year mean climatology (1961-1990) was developed from PRISM (Parameter-elevation Regressions on Independent Slopes Model) data at the 2.5 arc minute scale and aggregated to the 5 arc minute grid scale. The change factors were imposed upon the 30-year period (1961-1990) to develop the projections for each climate scenario.The USDA Forest Service (USFS) produces a periodic assessment of the condition and trends of the Nation's renewable resources as required by the Forest and Rangeland Renewable Resources Planning Act (RPA) of 1974. This RPA Assessment provides a snapshot of current US forest and rangeland conditions and trends on all ownerships, identifies drivers of change, and projects 50 years into the future (//www.fs.fed.us/research/rpa/, accessed 07/06/2015). For 2010 RPA Assessment, an integrated modeling framework is being used in which the potential implications of climate change can be analyzed across some resource areas (Langner et al. 2012). The nature of the climate variables needed to address climate change impacts for these resource analyses in the 2010 RPA Assessment were determined to be monthly precipitation and temperature variables at the 5 arc minute grid level spatial scale.Original metadata dated 08/02/2010. Minor modifications made to Attribute Accuracy section of metadata on 09/17/2010. Metadata modified on 02/22/2012 to adjust citation to include the addition of a DOI (digital object identifier) and update to the cross-reference section. Minor metadata updates on 02/20/2013. Metadata modified on 07/22/2015 to update cross-reference citations and other minor updates. Additional minor metadata updates on 12/13/2016.

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 daily minimum 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.

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. Year Precipitation (mm_ Temperature (C)

2001 359.846 18.020035

2002 229.756 18.343974

2003 393.403 18.741285

2004 346.243 17.843082

2005 319.172 18.525231

2006 301.084 18.415454

2007 378.84 18.468967

2008 358.571 18.123582

2009 238.645 18.819339

2010 419.95 18.102859

2011 297.752 18.08046

2012 303.155 18.624981

2013 329.01 18.03071

2014 356.631 18.84692

2015 446.589 18.479555

2016 359.174 18.765447

2017 295.393 19.417332

2018 439.664 18.88294

2019 476.381 17.885612

2020 182.906 19.141233

2021 420.264 18.609694

30Year normal 389.374 18.59009207

This study presents a comprehensive comparison of gridded datasets for the Great Salt Lake (GSL) basin, focusing on precipitation and temperature as the main inputs for hydrological balances. The evaluated gridded datasets include PRISM, DAYMET, GRIDMET, NLDAS-2, and CONUS404, with in-situ data used for assessing alignment and accuracy. Key metrics such as Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (CC) were employed to evaluate gridded dataset performance. Spatial and temporal accuracy analyses were conducted across different GSL basin regions to understand variations in accuracy. DAYMET emerged as the leading dataset for precipitation across most metrics, demonstrating consistent performance. For temperature, GRIDMET and PRISM ranked higher, indicating better representation of temperature patterns in the GSL basin. Spatial analysis revealed variability in accuracy for both temperature and precipitation data, emphasizing the importance of selecting suitable datasets for different regions to enhance overall accuracy. The insights from this study can inform environmental forecasting and water resource management in the GSL basin, assisting researchers and decision-makers in choosing reliable gridded datasets for hydrological studies.