Monthly total precipitation (mm) for August 1997 in the Rocky Mountain Region and some gridded 4km elevation data sets (m).
RMprecip is a useful spatial data set of moderate size consisting of 806
locations. See www.image.ucar.edu/Data for the source of these data.
RMelevation are gridded elevations for the
continental US and Rocky Mountain region at 4km resolution.
Note that the gridded elevations from the PRISM data product are
different than the exact station elevations. (See example below.)
The data set
RMprecip is a list containing the following components:
Longitude-latitude position of monitoring stations. Rows names are station id codes consistent with the US Cooperative observer network. The ranges for these coordinates are [-111, -99] for longitude and [35,45] for latitude.
Station elevation in meters.
Monthly total precipitation in millimeters. for August, 1997
The data sets
RMelevation are lists
in the usual R grid format for images and contouring
They have the following components:
Longitude grid at approximately 4km resolution
Latitude grid at approximately 4km resolution
Average elevation for grid cell in meters
These elevations and the companion grid formed the basis for the 103-Year High-Resolution Precipitation Climate Data Set for the Conterminous United States ftp://ftp.ncdc.noaa.gov/pub/data/prism100 archived at the National Climate Data Center. This work was primarily authored by Chris Daly www.prism.oregonstate.edu and his PRISM group but had some contribution from the Geophysical Statistics Project at NCAR. and is an interpolation of the observational data to a 4km grid that takes into account topography such as elevation and aspect.
The binary file
can be downwloaded from
and also includes information on its source.
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# explicit source code to create the RMprecip data dir <- "" # include path to data file load(paste(dir, "RData.USmonthlyMet.bin", sep="/") #year.id<- 1963- 1895 year.id<- 103 #pptAUG63<- USppt[ year.id,8,] loc<- cbind(USpinfo$lon, USpinfo$lat) xr<- c(-111, -99) yr<- c( 35, 45) station.subset<- (loc[,1]>= xr) & (loc[,1] <= xr) & (loc[,2]>= yr) & (loc[,2]<= yr) ydata<- USppt[ year.id,8,station.subset] ydata <- ydata*10 # cm -> mm conversion xdata<- loc[station.subset,] dimnames(xdata)<- list( USpinfo$station.id[station.subset], c( "lon", "lat")) xdata<- data.frame( xdata) good<- !is.na(ydata) ydata<- ydata[good] xdata<- xdata[good,] test.for.zero.flag<- 1 test.for.zero( unlist(RMprecip$x), unlist(xdata), tag="locations") test.for.zero( ydata, RMprecip$y, "values")
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# this data set was created the # historical data taken from # Observed monthly precipitation, min and max temperatures for the coterminous US # 1895-1997 # NCAR_pinfill # see the Geophysical Statistics Project datasets page for the supporting functions # and details. # plot quilt.plot(RMprecip$x, RMprecip$y) US( add=TRUE, col=2, lty=2) # comparison of station elevations with PRISM gridded values data(RMelevation) interp.surface( RMelevation, RMprecip$x)-> test.elev plot( RMprecip$elev, test.elev, xlab="Station elevation", ylab="Interpolation from PRISM grid") abline( 0,1,col="blue") # some differences with high elevations probably due to complex # topography! # # view of Rockies looking from theSoutheast save.par<- par(no.readonly=TRUE) par( mar=c(0,0,0,0)) # fancy use of persp with shading and lighting. persp( RMelevation, theta=75, phi= 15, box=FALSE, axes=FALSE, xlab="", ylab="", border=NA, shade=.95, lphi= 10, ltheta=80, col= "wheat4", scale=FALSE, expand=.00025) # reset graphics parameters and a more conventional image plot. par( save.par) image.plot(RMelevation, col=topo.colors(256)) US( add=TRUE, col="grey", lwd=2) title("PRISM elevations (m)")
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