search for: regridded

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 the output of the regridding step...

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 are -9999, in which case -9999 should be returned as the 'new' cell). How would I go about achieving this in R? Any help or guidelines would be very much appreciated...

Hi Caio, I just replied to a fellow who needed to run a series of regressions changing the dependent variables for the same period. I believe your solution might be similar to that one, just making the window dynamic instead. I used a lapply to store regressions and sapplys to extract stats from the lm()s. Please check the attachment and hope it?s useful. Filipe Botelho -----Mensagem

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

...egional data (i.e., data that is multinational but not global). My specific problem is https://bitbucket.org/tlroche/gfed-3.1_global_to_aqmeii-na/downloads/GFED-3.1_2008_N2O_monthly_emissions_regrid_20130404_1344.pdf which plots N2O concentrations from a global inventory of fire emissions (GFED) regridded to a North American projection. (See https://bitbucket.org/tlroche/gfed-3.1_global_to_aqmeii-na for details.) The plot currently includes boundaries for Canada, Mexico, and US (including US states, since this is being done for a US agency), which are being gotten calling code from package=M3 htt...

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