Revision Date: September 26, 2016
Please note that this version was superseded by Version 3 on 2016/07/15.
Follow this link to the latest version:
Thornton, M.M., P.E. Thornton, Y. Wei, B.W. Mayer, R.B. Cook, and R.S. Vose. 2016. Daymet: Monthly Climate Summaries on a 1-km Grid for North America, Version 3. ORNL DAAC, Oak Ridge, Tennessee, USA. http://dx.doi.org/10.3334/ORNLDAAC/1345
Contact ORNL DAAC User Services (email@example.com) if you need additional assistance.
This data set provides monthly summary climate data at 1-km x 1-km spatial resolution for four Daymet variables; minimum and maximum temperature, precipitation, and vapor pressure. These single month summary data products are produced for each month for individual years and covers the period 1980 to 2015.
The monthly climatological summaries are derived from the much larger data set of daily weather parameters produced on a 1-km x 1-km grid over the conterminous United States, Southern Canada, and Mexico as station data inputs allow (Thornton, et al., 2014).
Daymet monthly summary data are available from the ORNL DAAC via two download mechanisms:
1. Search and Order or Data Browse: Data files may be obtained through DAAC search and order tools or directly from the HTTP data browse site. Files are in both netCDF version 4.0 format or GeoTIFF file formats. There are a total of 1,728 *.nc4 files and 1,728 *.tif files for the four Daymet parameters (prcp, tmax, tmin, and vp) for 36 years (1980 -2015).
2.THREDDS (Thematic Real-time Environmental Data Services) Data Server: Data can be subset spatially and temporally prior to downloading. THREDDS downloads (http://thredds.daac.ornl.gov/thredds/catalogs/ornldaac/Regional_and_Global_Data/DAYMET_COLLECTIONS/DAYMET_COLLECTIONS.html) are available in various formats: netCDF-3 format (*.nc) or netCDF-4 format (*.nc4) if through NetCDF Subset Service and in ASCII or Binary if through OPeNDAP. Subsetting and downloading of files available through THREDDS has a 2-GB file size limitation.
The ORNL DAAC is publishing Version 2.0 of the North American monthly summary files. Version and change history documentation will be provided.
ORNL DAAC Version Record:
|Daymet Product Version||ORNL DAAC Release Date||Description|
|Version 2, North American monthly summary climate data, 1980-2015||March, 2016||ORNL DAAC released monthly climatologies for 2015 for each Daymet variable.|
|Version 2, North American monthly summary climate data, 1980-2014||July, 2015||First ORNL DAAC archived version.|
Get Data: /cgi-bin/dsviewer.pl?ds_id=1281
The THREDDS Data Server allows users to find and access data sets of interest from within a simple, hierarchical catalog within a Web browser or compatible client software. Data can be subset spatially and temporally prior to downloading. THREDDS supports data downloads in various data formats. Subsetting and downloading of files available through THREDDS has a 2-GB file size limitation.
Companion Documentation for this Data Set:
Daymet_monthlysummary.pdf (this user’s guide)
Cite this data set as follows:
Thornton, P.E., M.M. Thornton, B.W. Mayer, N. Wilhelmi, Y. Wei, R. Devarakonda, and R.B. Cook. 2014. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 2. ORNL DAAC, Oak Ridge, Tennessee, USA. Accessed Month DD, YYYY. Time period: YYYY-MM-DD to YYYY-MM-DD. Spatial range: N=DD.DD, S=DD.DD, E=DDD.DD, W=DDD.DD. http://dx.doi.org/10.3334/ORNLDAAC/1281
For citing specific Daymet downloaded data and subsets used in your analyses and publications document as appropriate:
Date accessed: Date that you downloaded data to account for possible updates within Version 2.
Temporal range: Range of data in year/month/day (YYYY/MM/DD).
Spatial range: Define the bounding box for the data as the Northern- and Southern-most latitudes and Eastern- and Western-most longitudes in decimal degrees. South latitude and West longitude are (-) negative values. Add decimal places as needed for precision.
In addition to the data set citation, the following should be used as the general reference for the methods used to generate Daymet data products:
Thornton, P.E., Running, S.W., White, M.A. 1997. Generating surfaces of daily meteorological variables over large regions of complex terrain. Journal of Hydrology 190: 214 - 251. http://dx.doi.org/10.1016/S0022-1694(96)03128-9
For applications of the radiation and humidity data please include the following citations in addition to the general citation:
Thornton, P.E., H. Hasenauer, and M.A. White. 2000. Simultaneous estimation of daily solar radiation and humidity from observed temperature and precipitation: An application over complex terrain in Austria. Agricultural and Forest Meteorology 104:255-271. http://dx.doi.org/10.1016/S0168-1923(00)00170-2
Thornton, P.E. and S.W. Running. 1999. An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation. Agriculture and Forest Meteorology. 93:211-228. http://dx.doi.org/10.1016/S0168-1923(98)00126-9
Daymet is model-produced gridded estimates of daily weather parameters based on daily meteorological observations. The algorithms and software that generate Daymet data products were developed to fulfill the need for continuous surfaces of daily weather data necessary for plant growth model inputs. These Daymet data also have broad applications over a wide variety of scientific and research fields including hydrology, terrestrial vegetation growth models, carbon cycle science, and regional to large scale climate change analysis. Weather parameters generated include daily surfaces of minimum and maximum temperature, precipitation, humidity, and radiation (not included with this data set) produced on a 1-km x 1-km gridded surface in Lambert Conformal Conic projection over the conterminous United States, Mexico, and Southern Canada. The required model inputs include a digital elevation model and observations of maximum temperature, minimum temperature, and precipitation from ground-based meteorological stations.
Monthly climatological summary files are produced from the daily gridded data (Thornton et al., 2014) by averaging or totaling, on a pixel by pixel basis, the mosaicked daily gridded data files. Each of the summary products is presented as a complete spatial grid, where the summary results have been derived for each gridcell.
Monthly summary files are available for the four output parameters and distributed as individual files by year each in both a spatially referenced GeoTIFF and CF compliant (version 1.4) netCDF file format. The netCDF file format is self-describing and compliant to the CF metadata conventions. Each parameter, as well as the spatial and temporal properties of the data, is defined within the header file.
Data are available for each of the four parameters (minimum and maximum temperature, precipitation, and vapor pressure) at a 1-km x 1-km spatial resolution for the conterminous United States, Mexico, and Southern Canada. The spatial extent is the same for all of the files. See the data spatial properties description below.
The data set begins in 1980 and ends in 2015. There are 1,728 files in netCDF-4 format (*.nc4) and 1,728 files in GeoTIFF (*.tif) for each parameter.
DAAC Data Browse Site:
Data are assembled by parameter and year with each yearly file containing one time dimension. NetCDF files are in CF-compliant netCDF-4 format. Files are assembled by parameter and year in a flat structure. Data files are available as both netCDF and GeoTIFF file formats.
Filenames follow this syntax:
pppp is the respective parameter abbreviation (prcp, tmax, tmin, and vp),
Summary-type indicates that the monthly data have either been totaled- monttl-the total over the monthly period or or monavg-the average over the monthly period.
YYYY is the year,
MN is the numeric month,
.ext will be either .nc4 or .tif.
For example, one month of total precipitation data is prcp_monttl_1980_01.nc4 indicating that each grid cell is the total precipitation value for January 1980.
Another example, tmin_monavg_1980_01.nc4 indicates that each gridcell is the averaged minimum daily temperature for January of 1980.
THREDDS Data Server:
Data can be subset spatially and temporally prior to downloading. THREDDS downloads are available in various formats: netCDF-3 format (*.nc) or netCDF-4 format (*.nc4) if through NetCDF Subset Service and in ASCII or Binary if through OPeNDAP. Subsetting and downloading of files available through THREDDS has a 2-GB file size limitation.
Data User Note: The data files on the THREDDS Data Server are the same as on the FTP site and are individually named the same. The current THREEDS Data Server NetCDF Subset Service provides output files in both netCDF-3 (*.nc) and netCDF-4 (*.nc4) formats.
Parameters, Parameter abbreviations, Units, and Descriptions:
|Precipitation||prcp||mm/mon||The total accumulated precipitation over the monthly period of the daily total precipitation. Precipitation is the sum of all forms of precipitation converted to water equivalent|
|Maximum air temperature||tmax||degrees C||The average over the monthly period of high temperature for a 24-hour period|
|Minimum air temperature||tmin||degrees C||The average over the monthly period of minimum temperature for a 24-hour period|
|Water vapor pressure||vp||Pa||The average over the monthly period of the daily average partial pressure of water vapor.|
The most current Daymet data are being delivered to the user in terms of both Daymet software and Daymet data versions. Version information is recorded in the header file of each of the CF-netCDF files within the Global Attribute fields; Version_software and Version_data. All Daymet data are provisional and subject to revision.
The Daymet Calendar:
The Daymet calendar is based on a standard calendar year in which all years including leap years, have 1 - 365 days. For leap years, Daymet includes leap day February 29. Values for December 31 are discarded from leap years to maintain a 365-day year. February and December have different numbers of days in leap years relative to non-leap years. For non-leap years, the month of February (02) includes 28 days. For leap years, the month of February (02), includes 29 days and December 31 is not available as described above.
Spatial Data Properties:
Spatial Representation Type: Raster
Pixel Depth: 32 bit
Pixel Type: float
Number of Bands: 1
Band Information: time
Raster Format: netCDF
Source Type: continuous
No Data Value: -9999
Scale Factor: none
Endian Type: NA
Number Columns: 5,268
Column Resolution: 1,000 meter
Number Rows: 4,823
Row Resolution: 1,000 meter
Extent in the items coordinate system
xll corner: -2015000
yll corner: -3037000
Cell Geometry: area
Point in Pixel: corner
Spatial Reference Properties:
Geographic Coordinate Reference: WGS_1984
Projection: Lambert Conformal Conic
The North American Daymet projection system and parameters:
Projection System: Lambert Conformal Conic
projection units: meters
datum (spheroid): WGS_84
1st standard parallel: 25 deg N
2nd standard parallel: 60 deg N
Central meridian: -100 deg (W)
Latitude of origin: 42.5 deg N
false easting: 0
false northing: 0
Site boundaries:(All latitude and longitude given in degrees and fractions)
|Site (Region)||Westernmost Longitude||Easternmost Longitude||Northernmost Latitude||Southernmost Latitude||Geodetic Datum|
•The data set covers the period 1980 to 2015
The Daymet data have broad applications over a wide variety of research fields including hydrology, terrestrial vegetation growth models, carbon cycle science, and regional to large scale climate change analysis. Measurements of near-surface meteorological conditions are made at many locations, but researchers are often faced with having to perform ecosystem process simulations in areas where no meteorological measurements have been taken. The continuous gridded surfaces of the Daymet data set were developed to overcome these limitations.
The Daymet method is based on the spatial convolution of a truncated Gaussian weighting filter with the set of station locations. Sensitivity to the typical heterogeneous distribution of stations in complex terrain is accomplished with an iterative station density algorithm. A system is established in which the search radius of stations is reduced in data-rich regions and increased in data-poor regions. This is accomplished by specifying an average number of observations to be included at each point. The average number of stations (n) for temperature extrema is 25; for precipitation, n = 15. The search distance of stations is then varied as a smooth function of the local station density. The result is a seamless match of gridded daily data.
In the Daymet algorithm, spatially and temporally explicit empirical analyses of the relationships of temperature and precipitation to elevation are performed. In addition, a daily precipitation occurrence algorithm is introduced, as a precursor to the prediction of daily precipitation amount. Surfaces of humidity (water vapor pressure) are generated as a function of the predicted daily minimum temperature and the predicted daily average daylight temperature.
The monthly summary files are generated from daily data inputs and therefore follow the same quality assessment of the data from which it is derived.
In the Daymet data processing, binary output files from the Daymet model are passed through a number of QA/QC checks that include annual and monthly climate summary evaluations and minimum/maximum value checks. The Daymet algorithm manages the large number of input data and large spatial extent of the study area by creating a system of 2-degree x 2-degree “tiles” that are processed individually through the Daymet software. These tiles are identified by a TileID, which is derived within the Daymet algorithm and is consistent throughout the temporal period of the Daymet record. There are a small number of Daymet tiles that do not run properly through the Daymet algorithm or that do run but have been removed from the Daymet data collection due to poor quality results for all or some of the associated Daymet parameters. The cause of the failure of these tiles is almost exclusively due to low surface observation station density in areas that are sparsely populated.
No Data Tiles
In order to maintain the integrity of the Daymet product, any tile that was not produced because of quality reasons (i.e., low station density) has been replaced with NoData netCDF files for each of the parameters associated with that tile. For these tiles that were not produced, there are NoData netCDF files for each of the parameters: dayl.nc, prcp.nc, tmax.nc, tmin.nc, and vp.nc. These NoData netCDF files will have the appropriate spatial and temporal extents for its specific tile location, but contain only the NoData Value of -9999.
Note that meteorological station data for Mexico is available only for the period January 1980 through December 2011. Because Daymet tiles have not been produced for Mexico since January 2012, Mexican tiles for 2012 and 2013 are included in the distribution but are NoData netCDF files.
A list of Daymet NoData Tiles by year can be found at: http://daymet.ornl.gov/datasupport.html.
The Daymet model requires spatially referenced ground observations of daily maximum and minimum temperature and precipitation. These observations have been obtained from a number of sources throughout this current Daymet campaign. The ground observations for the United States came from two main sources. The first is the Cooperative Summary of the Day network of weather stations archived and distributed by the National Climate Data Center (NCDC). These data have recently come under the umbrella and are distributed as part of the Global Historical Climatology Network (GHCN)-Daily data set. The second source of surface observation data for the United States is the SNOwpack and TELemetry (SNOTEL) data set managed and distributed by the Natural Resources Conservation Service (NRCS). These stations are primarily in high elevation regions in the Western US and Alaska and are principle in maintaining critical snow pack information. Canadian surface observations were provided by the Government of Canada (Environment Canada) and through the GHCN-Daily data set. The Servicio Meteorológico Nacional provided surface weather observations within Mexico.
Additional inputs for the Daymet algorithm are a digital elevation model (DEM) and Land Mask. The DEM used in this version of Daymet is a North American subset of the NASA SRTM near-global 30 arc second DEM. This DEM was reprojected and resampled from a geographic coordinate system (GCS_WGS_84) to the Daymet Lambert Conformal Conic projection as outlined below. The resampling method used a cubic convolution interpolation with an output cell size set to 1,000 m and a file extent as recorded below. Although Daymet data are not currently available for regions north of 60 degrees North, the SRTM DEM was augmented with GTOPO30 data above this latitude. Slope, aspect, and horizon grids are derived from the DEM within the Daymet algorithm. A Daymet North American land “mask” file was derived in order to allow Daymet processing to occur for “land” pixels including shallow inland water ways, coastlines, and lake shorelines while excluding only very large bodies of water such as the Great Lakes. The SRTM DEM included the Great Lakes and tended to over-estimate large river and coastal water and was therefore not a good candidate for deriving the Daymet land mask. A reclassification was performed on the MODIS Nadir BRDF-Adjusted Reflectances (NBAR) MODIS land-water mask (Salomon, 2004) as outlined below.
|Old Classification||New Classification|
|0 – Shallow Ocean||Water|
|1 - Land||Land|
|2 – Shallow Inland Water||Land|
|3 – Ocean Coastline and Lake Shorelines||Land|
|5 – Deep Inland Water||Land|
|6 – Shallow Ocean||Water|
|7 – Deep Ocean||Water|
This data is available through the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).
Telephone: +1 (865) 241-3952
Thornton, P.E., S.W. Running, and M.A. White. 1997. Generating surfaces of daily meteorological variables over large regions of complex terrain. Journal of Hydrology 190: 214 - 251. http://dx.doi.org/10.1016/S0022-1694(96)03128-9
Thornton, P.E., H. Hasenauer, and M.A. White. 2000. Simultaneous estimation of daily solar radiation and humidity from observed temperature and precipitation: An application over complex terrain in Austria. Agriculturaland Forest Meteorology 104:255 - 271. http://dx.doi.org/10.1016/S0168-1923(00)00170-2
Thornton, P.E. and S.W. Running. 1999. An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation. Agriculture and Forest Meteorology. 93:211 - 228. http://dx.doi.org/10.1016/S0168-1923(98)00126-9
Thornton, P.E., M.M. Thornton, B.W. Mayer, N. Wilhelmi, Y. Wei, R. Devarakonda, and R.B. Cook. 2014. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 2. Data set. Available on-line [http://daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA. http://dx.doi.org/10.3334/ORNLDAAC/1219