Daymet: Monthly Climate Summaries on a 1-km Grid for North America, Version 3

This dataset provides Daymet Version 3 monthly summary climatologies at a 1-km x 1-km spatial resolution for four Daymet variables; minimum and maximum temperature, precipitation, and vapor pressure. Monthly averages are provided for minimum and maximum temperature and vapor pressure, and monthly totals are provided for the precipitation variable. These single month summary data products are produced for each individual month within a calendar year and cover the same period of record as the Daymet V3 daily data. The monthly climatology summaries are derived from the much larger dataset of daily weather parameters (Thornton et al., 2016), produced on a 1-km x 1-km grid over North America including Canada, the United States, and Mexico. Separate monthly summary files are provided for the land areas of Hawaii and Puerto Rico, which are also available as gridded daily files as part of the Daymet V3 dataset. Data are in a Lambert Conformal Conic projection for North America and are distributed in a netCDF file (version 1.6) format compliant to Climate and Forecast (CF) metadata conventions and geotiff file formats.

There are 960 files in this dataset; 480 netCDF files and 480 geotiff files. The netCDF and geotiff files contain the same data in different file formats for user convenience. Each file contains data for a single year and Daymet parameter (prcp, tmin, tmax, and vp), with 12 monthly time bands per file. There are separate files for each of the three spatial areas: continental North America, Hawaii, and Puerto Rico.

Project: Daymet

Daymet is a model-produced gridded estimate 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 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 dataset) produced on a 1-km x 1-km gridded surface in Lambert Conformal Conic projection over continental North America, Hawaii, and Puerto Rico. 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., 2016) 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 grid cell.

Daymet V3 daily gridded data are available for download from the ORNL DAAC through website search and order tools or directly by browsing the Daymet data directories. Data are also accessible through a THREDDS Data Server. The ORNL DAAC also supports a separate Daymet Project web site (https://daymet.ornl.gov/) which provides customized tools for accessing the data.

Related Publications:

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: 204-251. 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. 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

Related Datasets:

Thornton, P.E., M.M. Thornton, B.W. Mayer, Y. Wei, R. Devarakonda, R.S. Vose, and R.B. Cook. 2016. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 3. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1328

Thornton, M.M., P.E. Thornton, Y. Wei, B.W. Mayer, R.B. Cook, and R.S. Vose. 2016. Daymet: Annual Climate Summaries on a 1-km Grid for North America, Version 3. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1343

Thornton, P.E., M.M. Thornton, and R.S. Vose. 2016. Daymet: Annual Tile Summary Cross-Validation Statistics for North America, Version 3. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1348

Thornton, M.M., P.E. Thornton, Y. Wei, R.S. Vose, and A.G. Boyer. 2017. Daymet: Station-Level Inputs and Model Predicted Values for North America, Version 3. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1391

Acknowledgements:

The development and distribution of the Daymet model and data products has been supported by the NASA Terrestrial Ecology Program.

This dataset contains monthly climatological summaries at a 1-km x 1-km spatial resolution derived from the daily gridded Daymet Version 3 dataset. Data are available for 4 parameters, minimum and maximum temperature, precipitation, and vapor pressure. Data are available as separate files for each year and parameter in both netCDF and geotiff file formats. Each annual file contains 12 time steps for each of the 12 months. Three file sets with separate spatial areas are available: a North American continental file set that includes continuous surfaces of Canada, the United States, Mexico; a file set for Hawaii; and one for Puerto Rico.

Spatial Coverage (all files): North America, including: Canada, Mexico, and the United States of America, and the island areas of Hawaii and Puerto Rico.

Spatial Resolution: 1000 m
Temporal Coverage: 1980-01-01 to 2019-12-31
Temporal Resolution: Monthly

Site boundaries: (All latitude and longitude given in degrees and fractions)

Site (Region)	Westernmost Longitude	Easternmost Longitude	Northernmost Latitude	Southernmost Latitude	Geodetic Datum
North America, Puerto Rico, and Hawaii	-179	-52	83	14	WGS_1984

Data File Information:
Data are assembled by parameter and year with each yearly file containing twelve time steps. NetCDF files are in CF-compliant netCDF 4 format. Data are available as both netCDF and GeoTIFF file formats. In the Geotiff files, months are provided as 12 separate bands. Band 1 = January, 2 = February, and so on, such that 12 = December.

Filenames follow this syntax:
daymet_v3_pppp_method_yyyy_area.format

Where:
pppp is the parameter abbreviation (prcp, tmax, tmin, or vp)
method indicates the monthly summary where 'monavg' is the average over the monthly period and 'monttl' is the total over the monthly period.
yyyy is the year
area indicates the spatial area where 'na' is continental North America, 'hi' is Hawaii, and 'pr' is Puerto Rico
format is either nc4 for netCDF version 4 or or tif for geotiff

For example, the file daymet_v3_prcp_monttl_1980_hi.nc4 is a netCDF file for Hawaii that contains 12 time steps for each of the 12 months in 1980. For each month, each grid cell is the total precipitation in mm for that month in 1980.

Parameters, Parameter abbreviations, Units, and Descriptions:

Parameter	Abbr	Units	Description
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 high temperature for a 24-hour period during the month
Minimum air temperature	tmin	degrees C	The average minimum temperature for a 24-hour period during the month
Water vapor pressure	vp	Pa	The average partial pressure of water vapor for a 24-hour period during the month

Version Information:
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:
In the daily data from which the annual summaries are derived, the Daymet calendar is based on a standard calendar year. All Daymet years, including leap years, have 1 - 365 days. For leap years, the Daymet database includes leap day. Values for December 31 are discarded from leap years to maintain a 365-day year.

Spatial Reference Properties (for all files, all areas):
Type: Projected
Geographic Coordinate Reference: WGS_1984
Projection: Lambert Conformal Conic

The North American Daymet projection system and parameters:
Projection System: Lambert Conformal Conic
Parameters:
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

Spatial data properties (for all files, all areas):
Spatial Representation Type: Raster
Pixel Depth: 32 bit
Pixel Type: float
Number of Bands: 12
Band Information: time
Raster Format: netCDF
Source Type: continuous
No Data Value: -9999
Scale Factor: none
Offset: none
Endian Type: NA

Continental North America (na) Data

Number Columns: 7,814
Column Resolution: 1,000 meter
Number Rows: 8,075
Row Resolution: 1,000 meter
Extent in the items coordinate system
North: 4984500
South: -3090500
West: -4560750
East: 3253250
xll corner:-4560750.0
yll corner:-3090500.0
Cell Geometry: area
Point in Pixel: corner

Hawaii (hi) Data

Number Columns: 284
Column Resolution: 1,000 meter
Number Rows: 584
Row Resolution: 1,000 meter
Extent in the items coordinate system
North: -38500
South: -622500
West: -5802750
East: -5518750
xll corner:-5802750.0
yll corner:-622500.0
Cell Geometry: area
Point in Pixel: corner

 

Puerto Rico (pr) Data

Number Columns: 364
Column Resolution: 1,000 meter
Number Rows: 231
Row Resolution: 1,000 meter
Extent in the items coordinate system
North: -1764500
South: -1995500
West: 3445250
East: 3809250
xll corner:3445250.0
yll corner:-1995500.0
Cell Geometry: area
Point in Pixel: corner

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 dataset 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. In it, 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.

Tile Quality
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. Annual tile-wide summary cross-validation statistics for input data parameters; minimum temperature (tmin), maximum temperature (tmax), and daily total precipitation (prcp) are computed within the model routines. These cross validation statistics are derived from the station-based daily observations and predictions and summarized for each of the 2-degree by 2-degree tiles. Average and period-of-record mean absolute error (MAE) and bias statistics for observations of tmin, tmax, and prcp are calculated. In addition, tile-wide values of total ground weather stations per tile and total station days per tile are determined.

Model Inputs

Version 3.0 Daymet model inputs of spatially referenced ground observations of daily maximum and minimum temperature and precipitation were obtained from the NOAA National Centers for Environmental Information's Global Historical Climatology Network (GHCN)-Daily dataset (Menne et al., 2012).  For years 1980 - 2013 through additional NASA support, the sparse network of Mexican stations available through the GHCN-Daily network was augmented with additional stations provided by the Servicio Meteorológico Nacional of Mexico.  These data were obtained through an agreement with NOAA/GHCN-Daily in which GHCN-D acquired the station data directly from the Servicio Meteorológico Nacional and processed this data through the same QA/QC measures as all GHCN-D data is subject ensuring credibility to the data provenance of these additional data. The additional Mexican data were then provided to the NASA Daymet processing group.

GHCN-Daily Version Download Dates:

Daymet V3 for years 1980 - 2014 used GHCN-D data set version 3.22 downloaded on September 16, 2015.
Daymet V3 for year 2015 used GHCN-D data set version 3.22 downloaded on February 29, 2016.
Daymet V3 for year 2016 used GHCN-D data set version 3.22 downloaded on March 13, 2017.
Daymet V3 for year 2017 used GHCN-D data set version 3.23 downloaded on February 6, 2018.
Daymet V3 for year 2018 used GHCN-D data set version 3.25 downloaded on March 18, 2019.
Daymet V3 for year 2019 used GHCN-D data set version 3.27 downloaded on February 16, 2020.

Additional inputs for the Daymet algorithm are a digital elevation model (DEM) and Land Mask. The DEM used in Daymet v3.0 processing is a North American subset of the NASA SRTM near-global 30 arc second DEM version 2.1 (SRTM DEM). This DEM was reprojected and resampled from a geographic coordinate system (GCS_WGS_84) to the Daymet Lambert Conformal Conic projection. The resampling method used a cubic convolution interpolation with an output cell size set to 1,000 m. Slope and aspect grids are derived from the DEM within the Daymet algorithm. Horizon files were separately generated within the SVF model using the GRASS GIS software.
The land/water mask for Daymet processing was derived from the MODIS 250 meter Land-Water Mask; MOD44W_v2 (NASA LP DAAC, 2016). The North American study area of Daymet v3.0 was clipped out and the data were resampled and reprojected to the Daymet projection system at a 1 km x 1 km spatial resolution. Inland water was converted to land, retaining only the coast line as the Daymet land/water mask division.

Daymet Algorithm
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 which are processed individually through the Daymet software. The set of ground surface observation stations that are input for the interpolation methods is collected from the heterogeneously spaced stations of the input data from three separate input files of minimum temperature, maximum temperature, and precipitation. The interpolation method at each prediction point is accomplished through an iterative estimation of local station density using the spatial convolution of a truncated Gaussian filter as further described in Thornton et al., (1997). In it, 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 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 for adjacent tiles. 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.

NASA LP DAAC, 2016, MODIS/Terra Land Water Mask Derived from MODIS and SRTM L3 Global 250m Grid (MOD44W). NASA EOSDIS Land Processes DAAC, USGS Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota (https://lpdaac.usgs.gov).

Menne, M.J., I. Durre, R.S. Vose, B.E. Gleason, and T.G. Houston, 2012a: An overview of the Global Historical Climatology Network-Daily Database. Journal of Atmospheric and Oceanic Technology, 29, 897-910, doi:10.1175/JTECH-D-11-00103.1. http://dx.doi.org/10.1175/JTECHD-11-00103.1

Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Houston, 2012b: Global Historical Climatology Network -Daily (GHCN-Daily), Version 3.22. NOAA National Climatic Data Center. http://doi.org/10.7289/V5D21VHZ February 29, 2016.

Shuttle Radar Topography Mission (SRTM) Near-global Digital Elevation Models (DEM). Produced from a collaborative mission by the National Aeronautics and Space Administration (NASA), the National Imagery and Mapping Agency (NIMA), the German Aerospace Center (DLR, Deutsches Zentrum fur Luft-und Raumfart), and the Italian Space Agency (ASI, Agenzia Spaziale Italiana). Available at [https://lta.cr.usgs.gov/SRTM] from the U.S. Department of the Interior, U.S. Geological Survey, Earth Resources Observation Systems (EROS) Data Center (EDC), Distributed Active Archive Center (DAAC), Sioux Falls, South Dakota, USA.

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., M.M. Thornton, B.W. Mayer, Y. Wei, R. Devarakonda, R.S. Vose, and R.B. Cook. 2016. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 3. ORNL DAAC, Oak Ridge, Tennessee, USA. http://dx.doi.org/10.3334/ORNLDAAC/1328