numslist_to_grd: Convert a list of numbers to a grid

Description

Take a cloudcount raster and a number-of-observations raster and make a fraction cloud raster.

Usage

numslist_to_grd(numslist, grd, ydim_new, xdim_new)

Arguments

numslist

The list of numbers, each corresponding to a pixel

grd

A grid object, from which the projection and extend of the new grid will be taken

ydim_new

Number of rows (number of pixels in height)

xdim_new

Number of columns (number of pixels in width)

Value

grd_final, as SpatialPixelsDataFrame object

References

Ackerman S, Frey R, Strabala K, Liu Y, Gumley L, Baum B and Menzel P (2010). "Discriminating clear-sky from cloud with MODIS algorithm theoretical basis document (MOD35)." MODIS Cloud Mask Team, Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin - Madison. .

GoldsmithMatzkeDawson2013

Examples

Run this code

#######################################################
# Load some bit TIFs (TIFs with just the cloud indicators extracted)
# and add up the number of cloudy days, out of the total
# number of observation attempts
#######################################################
# Code excluded from CRAN check because it depends on modiscdata
## Not run: 
# library(devtools)
# # The modiscdata (MODIS c=cloud data=data) package is too big for CRAN (60 MB); so it is available on github:
# # https://github.com/nmatzke/modiscdata
# # If we can't get install_github() to work, try install_url():
# # install_github(repo="modiscdata", username="nnmatzke")
# install_url(url="https://github.com/nmatzke/modiscdata/archive/master.zip")
# library(modiscdata)
# tifsdir = system.file("extdata/2002_bit/", package="modiscdata")
# tiffns = list.files(tifsdir, pattern=".tif", full.names=TRUE)
# tiffns
# 
# library(rgdal)	# for readGDAL
# 
# # numpixels in subset
# xdim = 538
# ydim = 538
# 
# # Read the grid and the grid metadata
# coarsen_amount = 1
# xdim_new = xdim / floor(coarsen_amount)
# ydim_new = ydim / floor(coarsen_amount)
# 
# 
# sum_nums = NULL
# for (j in 1:length(tiffns))
# 	{
# 	fn = tiffns[j]
# 
# 	grd = readGDAL(fn, output.dim=c(ydim_new, xdim_new))
# 
# 	grdr = raster(grd)
# 	pointscount_on_SGDF_points = coordinates(grd)
# 	grdr_vals = extract(grdr, pointscount_on_SGDF_points)
# 
# 	# Convert to 1/0 cloudy/not
# 	data_grdr = grdr_vals
# 	data_grdr[grdr_vals > 0] = 1
# 
# 	grdr_cloudy = grdr_vals
# 	grdr_cloudy[grdr_vals < 4] = 0
# 	grdr_cloudy[grdr_vals == 4] = 1
# 
# # Note: Don't run the double-commented lines unless you want to collapse different bit values.
# 	# grdr_clear = grdr_vals
# 	# grdr_clear[grdr_vals == 4] = 0
# 	# grdr_clear[grdr_vals == 3] = 1
# 	# grdr_clear[grdr_vals == 2] = 1
# 	# grdr_clear[grdr_vals == 1] = 1
# 	# grdr_clear[grdr_vals == 0] = 0
# 	#
# 
# if (j == 1)
# 	{
# 	sum_cloudy = grdr_cloudy
# 	#sum_not_cloudy = grdr_clear
# 	sum_data = data_grdr
# 	} else {
# 	sum_cloudy = sum_cloudy + grdr_cloudy
# 	sum_data = sum_data + data_grdr
# 	}
# 
# 	}
# 
# 
# ## Calculate percentage cloudy
# sum_nums = sum_cloudy / sum_data
# 
# grd_final = numslist_to_grd(numslist=sum_nums, grd=grd, ydim_new=ydim_new, xdim_new=xdim_new)
# 
# # Display the image (this is just the sum of a few images)
# image(grd_final)
# 
# ## End(Not run)