Daymet annual 1-km precipitation for SW USA
TIF files: ---> DEM, ---> Lat, Lon, ---> Mask, ---> DayMET precipitation Daymet (annual) precipitation SVR processing example Daymet data set provides annual and summary climate data for minimum and maximum temperature, precipitation, and vapor pressure (Thornton et al. 2014). Daymet data consider the total accumulated precipitation over the annual period of the daily total precipitation. Precipitation is the sum of all forms of precipitation converted to water equivalent (mm/yr) (Thornton et al. 2014). The Daymet data layers are produced on a 1-km x 1-km gridded surface over the conterminous United States in Lambert Conformal Conic projection (units are meters) with the following parameters, a) horizontal datum: WGS 84, b) 1st standard parallel= 25o, 2nd standard parallel= 60o, c) Central meridian= -100o, and Latitude of origin= 42.5o, d) false easting= 0, false northing= 0. X is in the range -1949774 to -725054 m, and Y is in the range -1144097 to 222385 m. So the data is projected to a rectangular grid instead of a geographic grid. Thus, the 3 independent variables will be h, X (Easting) and Y (Northing) instead of h, ö and ë. The 12 Daymet gridded annual precipitation (P) images for the period 2003 to 2014 are used. Thornton, P.E., Thornton, M.M., Mayer, B.W., Wilhelmi, N., Wei, Y., Devarakonda, R., & Cook, R.B. (2014), Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 2. ORNL DAAC, Oak Ridge, Tennessee, USA. http://dx.doi.org/10.3334/ORNLDAAC/1277.
Steps to reproduce
Miliaresis G. 2016. Spatial decorrelation stretch of annual (2003-2014) Daymet precipitation summaries on a 1-km grid for California, Nevada, Arizona and Utah. Environmental Monitoring & Assessment, 188 (article 361) 1-21 DOI: 10.1007/s10661-016-5365-5. Available for free read from Springer Nature at http://rdcu.be/nrdt Miliaresis G., 2016. Revealing the precipitation dependency of regional in time and in space thermal anomaly peaks in SW USA. Modeling Earth Systems & Environment, vol. 2, no 1 (article 34), 1-10 p. DOI: 10.1007/s40808-016-0093-y Available for free at: http://link.springer.com/article/10.1007/s40808-016-0093-y Miliaresis G., 2016. An Unstandardized Selective Variance Reduction Script for Elevation, Latitude & Longitude Decorrelation Stretch of Multi-temporal LST Imagery. Modeling Earth Systems & Environment, 2, (1) (Article 41), 1-13 p. DOI: 10.1007/s40808-016-0103-0. Available for free at http://link.springer.com/article/10.1007/s40808-016-0103-0