Colorado Monthly Meteorological Data: Monthly surface meterology for Colorado 1895-1997

Description

Source: These is a group of R data sets for monthly min/max temperatures and precipitation over the period 1895-1997. It is a subset extracted from the more extensive US data record described in at http://www.image.ucar.edu/Data/US.monthly.met. Observed monthly precipitation, min and max temperatures for the conterminous US 1895-1997. See also http://www.image.ucar.edu/Data/US.monthly.met/CO.shtml for an on line document of this Colorado subset. Temperature is in degrees C and precipitation is total monthly accumulation in millimeters. Note that minimum (maximum) monthly tempertuare is the mean of the daily minimum (maximum) temperatures.

Data domain: A rectagular lon/lat region [-109.5,-101]x [36.5,41.5] larger than the boundary of Colorado comprises approximately 400 stations. Although there are additional stations reported in this domain, stations that only report preicipitation or only report temperatures have been excluded. In addition stations that have mismatches between locations and elevations from the two meta data files have also been excluded. The net result is 367 stations that have colocated temperatures and precipitation.

Arguments

Creation of data subset

Here is the precise R script used to create this data subset from the larger US monthly data set. This parent data set is available from http://www.image.ucar.edu/public/Data with a general description at http://www.image.ucar.edu/Data/US.monthly.met.

These technical details are not needed for casual use of the data -- skip down to examples for some R code that summarizes these data. attach("RData.USmonthlyMet.bin")

#To find a subset that covers Colorado (with a bit extra):

indt<- UStinfo$lon< -101 & UStinfo$lon > -109.5 indt<- indt & UStinfo$lat<41.5 &="" ustinfo$lat="">36.5

# check US(); points( UStinfo[indt,3:4])

#find common names restricting choices to the temperature names tn<- match( UStinfo$station.id, USpinfo$station.id) indt<- !is.na(tn) & indt

# compare metadata locations and elevations. # initial matches to precip stations CO.id<- UStinfo[indt,1] CO.names<- as.character(UStinfo[indt,5]) pn<- match( CO.id, USpinfo$station.id)

loc1<- cbind( UStinfo$lon[indt], UStinfo$lat[indt], UStinfo$elev[indt]) loc2<- cbind( USpinfo$lon[pn], USpinfo$lat[pn], USpinfo$elev[pn])

abs(loc1- loc2) -> temp indbad<- temp[,1] > .02 | temp[,2]> .02 | temp[,3] > 100

# tolerance at 100 meters set mainly to include the CLIMAX station # a high altitude station.

data.frame(CO.names[ indbad], loc1[indbad,], loc2[indbad,], temp[indbad,] )

# CO.names.indbad. X1 X2 X3 X1.1 X2.1 X3.1 X1.2 X2.2 X3.2 #1 ALTENBERN -108.38 39.50 1734 -108.53 39.58 2074 0.15 0.08 340 #2 CAMPO 7 S -102.57 37.02 1311 -102.68 37.08 1312 0.11 0.06 1 #3 FLAGLER 2 NW -103.08 39.32 1519 -103.07 39.28 1525 0.01 0.04 6 #4 GATEWAY 1 SE -108.98 38.68 1391 -108.93 38.70 1495 0.05 0.02 104 #5 IDALIA -102.27 39.77 1211 -102.28 39.70 1208 0.01 0.07 3 #6 KARVAL -103.53 38.73 1549 -103.52 38.80 1559 0.01 0.07 10 #7 NEW RAYMER -103.85 40.60 1458 -103.83 40.58 1510 0.02 0.02 52

# modify the indt list to exclude these mismatches (there are 7 here)

badones<- match( CO.id[indbad], UStinfo$station.id) indt[ badones] <- FALSE

###### now have working set of CO stations have both temp and precip ##### and are reasonably close to each other.

N<- sum( indt) # put data in time series order instead of table of year by month. CO.tmax<- UStmax[,,indt] CO.tmin<- UStmin[,,indt]

CO.id<- as.character(UStinfo[indt,1]) CO.elev<- UStinfo[indt,2] CO.loc <- UStinfo[indt,3:4] CO.names<- as.character(UStinfo[indt,5])

CO.years<- 1895:1997

# now find precip stations that match temp stations pn<- match( CO.id, USpinfo$station.id) # number of orphans sum( is.na( pn))

pn<- pn[ !is.na( pn)] CO.ppt<- USppt[,,pn]

# checks --- all should zero

ind<- match( CO.id[45], USpinfo$station.id) mean( abs( c(USppt[,,ind]) - c(CO.ppt[,,45]) ) , na.rm=TRUE)

ind<- match( CO.id[45], UStinfo$station.id) mean( abs(c((UStmax[,,ind])) - c(CO.tmax[,,45])), na.rm=TRUE)

mean( abs(c((UStmin[,,ind])) - c(CO.tmin[,,45])), na.rm=TRUE)

# check order ind<- match( CO.id, USpinfo$station.id) sum( CO.id != USpinfo$station.id[ind]) ind<- match( CO.id, UStinfo$station.id) sum( CO.id != UStinfo$station.id[ind])

# (3 4 5) (6 7 8) (9 10 11) (12 1 2) N<- ncol( CO.tmax)

CO.tmax.MAM<- apply( CO.tmax[,3:5,],c(1,3), "mean")

CO.tmin.MAM<- apply( CO.tmin[,3:5,],c(1,3), "mean")

CO.ppt.MAM<- apply( CO.ppt[,3:5,],c(1,3), "sum")

# Now average over 1961-1990 ind<- CO.years>=1960 & CO.years < 1990

temp<- stats( CO.tmax.MAM[ind,]) CO.tmax.MAM.climate<- ifelse( temp[1,] >= 15, temp[2,], NA)

temp<- stats( CO.tmin.MAM[ind,]) CO.tmin.MAM.climate<- ifelse( temp[1,] >= 15, temp[2,], NA)

CO.tmean.MAM.climate<- (CO.tmin.MAM.climate + CO.tmin.MAM.climate)/2

temp<- stats( CO.ppt.MAM[ind,]) CO.ppt.MAM.climate<- ifelse( temp[1,] >= 15, temp[2,], NA)

save( list=c( "CO.tmax", "CO.tmin", "CO.ppt", "CO.id", "CO.loc","CO.years", "CO.names","CO.elev", "CO.tmin.MAM", "CO.tmax.MAM", "CO.ppt.MAM", "CO.tmin.MAM.climate", "CO.tmax.MAM.climate", "CO.ppt.MAM.climate", "CO.tmean.MAM.climate"), file="COmonthlyMet.rda")

Examples

Run this code

data(COmonthlyMet)

#Spatial plot of 1997 Spring average daily maximum temps
 quilt.plot( CO.loc,CO.tmax.MAM[103,]  )
 US( add=TRUE)
 title( "Recorded MAM max temperatures (1997)")

# min and max temperatures against elevation

matplot( CO.elev, cbind( CO.tmax.MAM[103,], CO.tmin.MAM[103,]),
  pch="o", type="p",
  col=c("red", "blue"), xlab="Elevation (m)", ylab="Temperature (C)")
title("Recorded MAM max (red) and min (blue) temperatures 1997")

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