Documentation Revision Date: 20160929
Data Set Version: Version 2
Please note that this version was superseded by Version 3 on 2016/09/27.
Follow this link to the latest version: Thornton, P.E., M.M. Thornton, and R.S. Vose. 2016. Daymet V3: Annual Tile Summary CrossValidation Statistics for North America, Hawaii ORNL DAAC, Oak Ridge, Tennessee, USA. http://dx.doi.org/10.3334/ORNLDAAC/1348 Contact ORNL DAAC User Services (supportornl@earthdata.nasa.gov) if you need additional assistance. 
Summary
Summarized by tile are average and periodofrecord mean absolute error (MAE) and bias statistics for the input weather observations of tmin, tmax, and prcp. Also available are tilewide values of total ground weather stations, total stationdays evaluated, and mean observed input parameter values. Summary statistics are also available for the Gaussian distribution functions, used in the Daymet interpolation method, as mean and standard deviations of the radius of the kernel weights and x, y, and z components of the 3dimensional regression formula.
The data are distributed as shape files that represent the 2degree by 2degree tile structure in which the Daymet model estimates are derived. The annual crossvalidation statistics are provided as a separate shape file for the North American domain for each of the three variables for each year of available Daymet input data (i.e., 3 files/year for 35 years).
Also provided are the complete time series of annual summary crossvalidation statistics for the three Daymet input parameters in comma separated files (*.csv). There is one file for each of the three parameters for each tile.
Figure 1 shows four different annual tilewide summary crossvalidation statistics for 1980 daily precipitation; mean absolute error (MAE) for single day predictions ("daymae"), mean prediction bias ("bias"), mean absolute error for period of record predictions ("pormae"), and mean absolute error as a percentage for period of record predictions (“pormpae”).
Citation
Thornton, P.E., and M.M. Thornton. 2016. Daymet: Annual 2degree Tile Summary CrossValidation Statistics for North America. ORNL DAAC, Oak Ridge, Tennessee, USA. http://dx.doi.org/10.3334/ORNLDAAC/1303
Table of Contents
 Data Set Overview
 Data Characteristics
 Application and Derivation
 Quality Assessment
 Data Acquisition, Materials, and Methods
 Data Access
 References
 Data Set Revisions
Data Set Overview
Project: Daymet
This data set provides annual tilewide summary crossvalidation statistics for minimum temperature (tmin), maximum temperature (tmax), and daily total precipitation (prcp) of the Daymet data set (Daymet: Daily Surface Weather Data on a 1km Grid for North America, Version 2), for the temporal period 1980 through 2014, the most recently processed calendar year of Daymet. The crossvalidation statistics were generated by the Daymet model algorithm from the stationbased daily observations and predictions and summarized for each of the 2degree by 2degree tile regimen in which Daymet is derived.
Average and periodofrecord mean absolute error (MAE) and bias statistics for the input weather observations of tmin, tmax, and prcp were calculated along with tilewide values of total ground weather stations per tile, total station days, and mean observed values per tile. The Gaussian distribution kernel weights and 3dimensional regression components from the interpolation methodology are reported for the three variables.
Related Data Set:
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 1km Grid for North America, Version 2. Data set. Available online [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
Data Characteristics
The annual crossvalidation statistics are provided for North America as a separate shape file for each of the three variables for each year of available Daymet input data (i.e., 3 files/year for 35 years).
Also the complete time series of annual crossvalidation statistics for a variable is provided in a single comma separated file (*.csv). There is one file for each of the three variables.
Shape Files
Daymet crossvalidation data are available as shape files for North America – Mexico, USA, and Canada south of 52N – with a spatial resolution of 2 degrees.
The shape files geometric polygon structure represents the 2 degree x 2 degree tile “grid” in which the Daymet model is processed and output.
Three shape files with crossvalidation statistical information for each of the three Daymet daily weather input variables minimum temperature (tmin), maximum temperature (tmax), and total precipitation (prcp) are available each year of available Daymet data. There are a total of 105 shape files.
The North American shape files are zipped for convenience and contain five files (*.dbf,*.prj,*.shp,*.shp.xml, and *.shx).
File names follow this syntax: xval_pppp_yyyy.shp (*.zip)
Where:
xval distinguishes these as Daymet crossvalidation data files;
pppp is the respective Daymet input meteorological variable (tmin, tmax, and prcp); and
yyyy is year.
Data Dictionary:
Fields within each shape file contain the tilewide summary crossvalidation statistics.
Shape files for temperature (tmin and tmax) CrossValidation Statistics have these attributes.
Field 
Units/format 
Description 
Xmin 
decimal degrees 
Approximate minimum longitude of tile 
Xmax 
decimal degrees 
Approximate maximum longitude of tile 
Ymin 
decimal degrees 
Approximate minimum latitude of tile 
Ymax 
decimal degrees 
Approximate maximum latitude of tile 
year 
YYYY 
Daymet processing year 
tileid 
Daymet Tile ID 

nstns3x3 
stations 
number of stations in 3x3 tile region surrounding the central tile (tileid) 
nstns 
stations 
number of stations evaluated (tileid) 
nstndays 
days 
number of stationdays evaluated (tileid) 
rad90mean 
meter 
mean: radius capturing 90% of filter kernel weight 
rad90std 
meter 
standard deviation: radius capturing 90% of filter kernel weight 
daymae 
degrees Celsius 
mean absolute error for singleday predictions 
pormae 
degrees Celsius 
mean absolute error for periodofrecord predictions 
bias 
degrees Celsius 
mean prediction bias 
tamean 
degrees Celsius 
mean observed temperature (tmin and tmax) 
xlrmean 
degrees C/meter 
3d regression: mean xcomponent 
xlrstdv 
degrees C/meter 
3d regression: amongstation std dev of xcomponent 
ylrmean 
degrees C/meter 
3d regression: mean ycomponent 
ylrstdv 
degrees C/meter 
3d regression: amongstation std dev of ycomponent 
zlrmean 
degrees C/meter 
3d regression: mean zcomponent 
zlrstdv 
degrees C/meter 
3d regression: amongstation std dev of zcomponent 
Shape files for precipitation (prcp) CrossValidation Statistics have these attributes.
Field 
Units/format 
Description 
Xmin 
decimal degrees 
Approximate minimum longitude of tile 
Xmax 
decimal degrees 
Approximate maximum longitude of tile 
Ymin 
decimal degrees 
Approximate minimum latitude of tile 
Ymax 
decimal degrees 
Approximate maximum latitude of tile 
year 
YYYY 
Daymet processing year 
tileid 
Daymet Tile ID 

nstns3x3 
stations 
number of stations in 3x3 tile region surrounding the central tile (tileid) 
nstns 
stations 
number of stations evaluated (tileid) 
nstndays 
days 
number of stationdays evaluated (tileid) 
rad90mean 
meter 
mean: radius capturing 90% of filter kernel weight 
rad90std 
meter 
standard deviation: radius capturing 90% of filter kernel weight 
daymae 
cm/day 
mean absolute error for singleday predictions 
pormae 
cm/day 
mean absolute error for periodofrecord predictions 
pormpae 
% 
mean absolute error as a percentage, for period of record predictions 
bias 
cm/day 
mean prediction bias 
ppmean 
cm/day 
mean observed daily total precipitation 
xlrmean 
1/meter 
3d regression: mean xcomponent 
xlrstdv 
1/meter 
3d regression: amongstation std dev of xcomponent 
ylrmean 
1/meter 
3d regression: mean ycomponent 
ylrstdv 
1/meter 
3d regression: amongstation std dev of ycomponent 
zlrmean 
1/meter 
3d regression: mean zcomponent 
zlrstdv 
1/meter 
3d regression: amongstation std dev of zcomponent 
User’s Notes
 When “nstns” is zero (0), no input weather station data are available within that tile. All attributes are recorded as nodata (9999) or "nan".
 When “nstns” have very low values (e.g. 1, 2, or 3) denoting limited input data available for that tile, values for the 3dimensional regression components may be set to “nan” where the regressions algorithm failed.
 Floating point precision has been carried forward from the Daymet model for all attributes.
Spatial Data Properties
Spatial Representation: vector
Vector Format: shape file
Nodata Value: 9999
Spatial Reference Properties
Type: Geographic
"GEOGCS['GCS_WGS_1984',
DATUM['WGS_1984',
SPHEROID['WGS_84',6378137.0,298.257223563]],
PRIMEM['Greenwich',0.0],
UNIT['Degree',0.0174532925199433]]"
Comma Separated Files
The complete time series (19802014) of annual crossvalidation statistics for a variable is provided in a single comma separated file (*.csv)  one file for each of the three variables.
File names follow this syntax: xval_pppp_yyyyyyyy.csv
Where:
xval distinguishes these as Daymet crossvalidation data files;
pppp is the respective Daymet input meteorological variable (tmin, tmax, and prcp); and
yyyyyyyy is the range of annual summary statistics included in the file.
Data File Columns:
The first column in the *.csv files (pppp) indicates the variable and the remaining columns are the same as the attribute fields in the respective temperature and precipitation shape files.
Example data records: xval_tmin_19802014.csv
pppp,year,tileid,nstns3x3,nstns,nstndays,rad90mean,rad90stdv,daymae,pormae,bias,tamean,xlrmean,xlrstdv,ylrmean,ylrstdv,zlrmean,zlrstdv tmin,1980,9402,274,7,2440,70869.69598,3407.694504,2.247534956,1.569762955,0.036558511,19.78422131,1.63E06,1.84E05,5.50E07,2.05E05,0.005625534,0.00070347 tmin,1980,9403,349,1,365,67391.66637,0,1.408607311,0.362330488,0.362330488,21.44794521,9.75E06,0,1.86E05,0,0.0055475,0 … tmin,2014,12661,23,1,359,269892.1173,0,2.086786393,1.725761521,1.725761521,3.542896936,nan,nan,nan,nan,nan,nan tmin,2014,12662,23,6,2069,222924.8585,26394.05484,1.495518096,0.671129179,0.241127927,1.305219913,7.61E06,2.87E06,8.33E06,2.53E06,0.00783864,0.001134132 tmin,2014,12663,27,0,0,9999,9999,9999,9999,9999,9999,9999,9999,9999,9999,9999,9999 
Example data records: xval_tmax_19802014.csv
pppp,year,tileid,nstns3x3,nstns,nstndays,rad90mean,rad90stdv,daymae,pormae,bias,tamean,xlrmean,xlrstdv,ylrmean,ylrstdv,zlrmean,zlrstdv tmax,1980,9402,268,7,2470,70098.68631,3536.915168,1.598939817,0.705968608,0.152744454,32.04473684,2.16E05,1.58E05,8.47E05,2.29E05,0.007316183,0.00049859 tmax,1980,9403,343,2,698,65768.2673,6.410693902,3.53901007,3.336011606,0.636168483,37.17492837,4.52E06,6.95E06,3.18E05,1.86E05,0.006291728,0.000134242 … tmax,2014,12661,23,1,361,269892.1173,0,1.570174058,0.808686223,0.808686223,5.394459834,nan,nan,nan,nan,nan,nan tmax,2014,12662,23,6,2080,222924.8585,26394.05484,1.581390139,0.61139589,0.066809276,6.296346154,1.34E05,2.98E06,1.39E05,3.08E06,0.006621289,0.001530597 tmax,2014,12663,27,0,0,9999,9999,9999,9999,9999,9999,9999,9999,9999,9999,9999,9999 
Example data records: xval_prcp_19802014.csv
pppp,year,tileid,nstns3x3,nstns,nstndays,rad90mean,rad90stdv,daymae,pormae,pormpae,bias,ppmean,xlrmean,xlrstdv,ylrmean,ylrstdv,zlrmean,zlrstdv prcp,1980,9402,293,8,2783,49416.6094,3629.13058,0.367402143,0.14272381,43.16201199,0.082371536,0.320970176,6.49E09,3.37E06,4.16E07,6.38E06,8.10E05,6.13E05 prcp,1980,9403,371,2,730,51252.8156,18.26042728,0.177868655,0.150577116,638.1771137,0.00203571,0.124616438,7.88E07,5.19E07,4.45E07,9.38E07,4.07E05,3.93E06 … prcp,2014,12661,23,1,356,191164.8662,0,0.261805432,0.096687587,25.53091608,0.096687587,0.378707865,3.01E07,0,4.36E08,0,0.000184898,0 prcp,2014,12662,22,5,1717,161737.6659,26601.34836,0.309491565,0.054964155,18.88746257,0.023351956,0.326435644,3.82E07,8.93E07,7.82E08,4.97E07,0.000305412,0.000173946 prcp,2014,12663,24,0,0,9999,9999,9999,9999,9999,9999,nan,9999,9999,9999,9999,9999,9999 
User’s Notes
 When “nstns” is zero (0), no input weather station data are available within that tile. All attributes are recorded as nodata (9999) or "nan".
 When “nstns” have very low values (e.g. 1, 2, or 3) denoting limited input data available for that tile, values for the 3dimensional regression components may be set to “nan” where the regressions algorithm failed.
 Floating point precision has been carried forward from the Daymet model for all attributes.
Application and Derivation
The Daymet crossvalidation analysis are used to characterize the sensitivity of Daymet model methods to the variation of parameters and to estimate the prediction errors associated with the final selected parameters. The general crossvalidation protocol is to withhold one observation at a time from a sample, generating a prediction error for the withheld case by comparing with the observed value, and repeating over all observations in the sample to generate an average prediction error. The mean absolute error and bias are the basic error prediction error statistics. MAE does not exaggerate the influence of outliers as would a root mean square error and provides a more robust parameterization framework. Both the absolute value and sign of the prediction are considered in the generation of MAE and bias, respectively.
Quality Assessment
Occurrence of No Data and Not A Number (nan) field values
For tiles that had no input weather stations located within the 2 degree by 2 degree tile processing extent (e.g. nstns = 0), there are no crossvalidation data available. For these tiles, the nodata values are represented with 9999 or "nan" values. For tiles with very low weather station inputs (e.g. nstns <= 3), it is often the case that the 3dimensional regression components calculations failed. In those cases, the regression values are represented with “nan” values in the attribute fields.
Figure 2. Daymet crossvalidation showing the number of stationdays evaluated, "nstndays", per 2degree by 2degree Daymet tile for 1980 precipitation.
Figure 3. Daymet crossvalidation showing the number of stationdays evaluated, "nstndays", per 2degree by 2degree Daymet tile for 1980 maximum temperature.
Data Acquisition, Materials, and Methods
Crossvalidation Protocol
The Daymet crossvalidation summary statistics are used to test the sensitivity of Daymet model methods to the variation of parameters and to estimate the prediction errors associated with the final selected parameters.
The general crossvalidation protocol is to withhold one observation at a time from the sample, generating a prediction error for the withheld case by comparing with the observed value, and repeating over all observations in the sample to generate an average prediction error. The mean absolute error and bias are the basic error prediction error statistics. MAE does not exaggerate the influence of outliers as would a root mean square error and provides a more robust parameterization framework. Both the absolute value and sign of the prediction are considered in the generation of MAE and bias, respectively.
The mean absolute error for single prediction days, or "daymae" is determined as below:
The bias for the single prediction days is determined as below:
The mean absolute error for the period of record predictions, or pormae, is determined as below:
Figure 4. Daymet crossvalidation tilewide summary statistics for 1980 maximum temperature  mean absolute error for single day predictions ("daymae").
Figure 5. Daymet crossvalidation tilewide summary statistics for 1980 maximum temperature  mean prediction bias ("bias").
Figure 6. Daymet crossvalidation tilewide summary statistics for 1980 maximum temperature  mean absolute error for period of record predictions ("pormae”).
Data Sources
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 (Menne et al., 2012) 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 GHCNDaily data set.
 The Servicio Meteorológico Nacional provided surface weather observations within Mexico.
Data Access
These data are available through the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).
Daymet: Annual 2degree Tile Summary CrossValidation Statistics for North America
Contact for Data Center Access Information:
 Email: uso@daac.ornl.gov
 Telephone: +1 (865) 2413952
References
Menne, M.J., I. Durre, R.S. Vose, B.E. Gleason, and T.G. Houston, 2012: An overview of the Global Historical Climatology NetworkDaily Database. Journal of Atmospheric and Oceanic Technology, 29, 897910, doi:10.1175/JTECHD1100103.1
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 1km Grid for North America, Version 2. ORNL DAAC, Oak Ridge, Tennessee, USA. http://dx.doi.org/10.3334/ORNLDAAC/1219
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/S00221694(96)031289
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/S01681923(00)001702
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/S01681923(98)001269
Data Set Revisions
The ORNL DAAC is publishing Version 2.0 of the Tile Summary Cross Validation Statistics. Version and change history documentation will be provided.
ORNL DAAC Version Record:
Daymet Product Version 
ORNL DAAC Release Date 
Description 
Version 2, Tile Summary Cross Validation 
April 29, 2016 
ORNL DAAC archived and released Version 2 of Daymet Tile Summary Cross Validation Statistics 