search for: regride

summary: spatial data to be input to a regional-scale environmental model must (1) be converted to netCDF and then (2) "regridded" (cropped, projected, increased resolution). In a public git repository https://github.com/TomRoche/GEIA_to_NetCDF I have R code (with bash drivers) that does the conversion step, and plots the converted output, apparently correctly. However attempts to plot

Hello R Users, I am new to R. I have two data sets i) TOMS aerosol optical depth(AOD) and ii) TOMS ozone(O3). > > AOD data is on 1x1 grid and O3 data is on 5x5 grid. > > First I want to read AOD and O3 as it is and then I want to regrid AOD on > 5x5 grid as O3. > > Reading is first problem. > > FIRST PROBLEM READING AOD: > > AOD data is in following format: >

Hello R Users, I have two data sets i) TOMS aerosol optical depth(AOD) and ii) TOMS ozone(O3). AOD data is on 1x1 grid and O3 data is on 5x5 grid. First I want to read AOD and O3 as it is and then I want to regrid AOD on 5x5 grid as O3. Reading is first problem. FIRST PROBLEM READING AOD: AOD data is in following format: ######### Latitute: 89.5 167 0 0 0 0 0 182 0 0 0 0 0 0 0 0 0 0 0 0 200

Dear all, I have a grid (data frame) dataset at 0.5 x 0.5 degrees spatial resolution (720 columns x 360 rows; regular spacing) and wish to coarsen this to a resolution of 2.5 x 2.5 degrees. A simple calculation which takes the mean of a block of points to form the regridded values would do the trick. Values which should be excluded from the calculation are -9999 (unless all points within a block

...for this is: a <- rep(1,100)+rnorm(100) b <- rep(2,100)+rnorm(100) c <- rep(1.5,100)+rnorm(100) n.start <- 50 coefs <- array(NA,c(51,4)) for (i in (n.start:100)) coefs[i-n.start+1,] <- coef(lm(a ~ b + c, subset=1:i)) I want to avoid loops, so I was trying to make something like: regride <- function(formula,dat,sub,w) { window <- c(1:sub) w[1:sub-1] <- w[1:sub-1]*beta^sub coefs(lm(formula=formula,data=dat,subset=window,weights=w)) } coefs <- sapply(n_start:dim(ind)[1],regride,formula,dat,x) But it's not working. Best regards, Caio Casimiro -- Caio Ra...

Hello, we have 80 text files with matrices. Each matrix represents a map (rows for latitude and columns for longitude), the 80 maps represent steps in time. In addition, we have a vector x of length 80. We would like to compute a regression between matrices (response through time) and x and create maps representing coefficients, r2 etc. Problem: the 80 matrices are of the size 4000 x 3500 and we

SUMMARY: Specific problem: I'm regridding biomass-burning emissions from a global/unprojected inventory to a regional projection (LCC over North America). I need to have boundaries for Canada, Mexico, and US (including US states), but also Caribbean and Atlantic nations (notably the Bahamas). I would also like to add Canadian provinces and Mexican states. How to put these together? General

Dear list, I'm writing a function to re-grid a data set from finer to coarser resolutions in R as follows (I use this function with sapply/apply): gridResize <- function(startVec = stop("What's your input vector"), to = stop("Missing 'to': How long do you want the fnial vector to be?")){ from <- length(startVec) shortVec<-numeric() tics <- from*to