[R-sig-Geo] How to calculate climatology in rasterbricks
Vijay Lulla
vijaylulla at gmail.com
Fri Jun 3 02:02:01 CEST 2016
I think the following StackOverflow question has the answer:
http://stackoverflow.com/questions/16135877/applying-a-function-to-a-multidimensional-array-with-grouping-variable/16136775#16136775
Following the instructions listed on that page for your case might go
something like below:
> idxYM <- as.integer(strftime(idx,"%Y%m"))
> idxM <- unique(idxYM)%%100
> meanYM <- calc(s,fun=function(x) { by(x, idxYM, mean) })
> meanYM
class : RasterBrick
dimensions : 20, 20, 400, 360 (nrow, ncol, ncell, nlayers)
resolution : 18, 9 (x, y)
extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
data source : in memory
names : X196101, X196102, X196103, X196104, X196105, X196106,
X196107, X196108, X196109, X196110, X196111, X196112, X196201,
X196202, X196203, ...
min values : 0.3728, 0.2725, 0.3421, 0.3652, 0.3342, 0.3185,
0.3130, 0.3780, 0.3376, 0.3727, 0.3537, 0.3737, 0.3515, 0.3588,
0.3334, ...
max values : 0.6399, 0.6652, 0.6583, 0.6640, 0.6359, 0.6761,
0.6442, 0.6800, 0.6397, 0.6769, 0.6489, 0.6388, 0.6471, 0.6661,
0.6255, ...
> meanM <- calc(meanYM, fun=function(x) { by(x, idxM, mean) })
> meanM
class : RasterBrick
dimensions : 20, 20, 400, 12 (nrow, ncol, ncell, nlayers)
resolution : 18, 9 (x, y)
extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
data source : in memory
names : X1, X2, X3, X4, X5, X6, X7,
X8, X9, X10, X11, X12
min values : 0.4645, 0.4715, 0.4768, 0.4717, 0.4749, 0.4705, 0.4697,
0.4724, 0.4629, 0.4774, 0.4736, 0.4708
max values : 0.5274, 0.5275, 0.5293, 0.5259, 0.5285, 0.5276, 0.5269,
0.5260, 0.5256, 0.5281, 0.5279, 0.5286
>
I'm not sure how [in]efficient this is for actual (i.e. not toy
example) data. Maybe others more experienced, and knowledgeable,
members can provide better answers.
HTH,
Vijay.
On Thu, Jun 2, 2016 at 4:30 PM, Thiago V. dos Santos via R-sig-Geo
<r-sig-geo at r-project.org> wrote:
> Dear all,
>
> I am working with daily time series of meteorological variables. This is an example of the dataset:
>
> library(raster)
>
> # Create date sequence
> idx <- seq(as.Date("1961/1/1"), as.Date("1990/12/31"), by = "day")
>
> # Create raster stack and assign dates
> r <- raster(ncol=20, nrow=20)
> s <- stack(lapply(1:length(idx), function(x) setValues(r, runif(ncell(r)))))
> s <- setZ(s, idx)
>
>
> Now, let's assume those values represent daily precipitation. What I need to do is to integrate daily to monthly values,
> and then take a monthly climatology. Climatology in this case means multi-year average of selected months, e.g., an average of the 30 Octobers from 1961 to 1990, an average of the 30 Novembers from 1961 to 1990 and etc.
>
> On the other hand, let's assume the raster values represent daily temperature. Integrating daily to monthly temperature doesn't make sense. Hence, instead of integrating daily values, I need to take monthly means (e.g. mean value of all days in every month), and then calculate the climatology.
>
> What would be the best approach to achieve that using the raster package?
>
> Greetings,
> -- Thiago V. dos Santos
>
> PhD student
> Land and Atmospheric Science
> University of Minnesota
>
> _______________________________________________
> R-sig-Geo mailing list
> R-sig-Geo at r-project.org
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
More information about the R-sig-Geo
mailing list