Greetings,
I am interested in calculating precipitation averages globally. However, I only
want to isolate land and/or oceanic areas to compute the mean of those
separately. What I would like to do is somehow isolate those grid cells whose
centers overlap with either land or ocean and then compute the annual mean. I
already first created a raster stack, called "RCP1pctCO2Mean", which contains
the mean values of interest. There are 138 layers, with each layer representing
one year. This raster stack has the following attributes:
class : RasterStack
dimensions : 64, 128, 8192, 138 (nrow, ncol, ncell, nlayers)
resolution : 2.8125, 2.789327 (x, y)
extent : -181.4062, 178.5938, -89.25846, 89.25846 (xmin, xmax, ymin,
ymax)
coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
names : layer.1, layer.2, layer.3, layer.4, layer.5,
layer.6, layer.7, layer.8, layer.9, layer.10, layer.11,
layer.12, layer.13, layer.14, layer.15, ...
min values : 0.42964514, 0.43375653, 0.51749371, 0.50838983, 0.45366730,
0.53099146, 0.49757186, 0.45697752, 0.41382199, 0.46082401, 0.45516687,
0.51857087, 0.41005131, 0.45956529, 0.47497867, ...
max values : 96.30350, 104.08584, 88.92751, 97.49373, 89.57201,
90.58570, 86.67651, 88.33519, 96.94720, 101.58247, 96.07792,
93.21948, 99.59785, 94.26218, 90.62138, ...
Previously, I tried isolating a specific region by specifying a range of
longitudes and latitudes to obtain the means and medians for that region, just
like this:
>expansion1<-expand.grid(103:120, 3:15) #This is a range of longitudes and
then latitudes
>lonlataaa<-extract(RCP1pctCO2Mean, expansion1)
>Columnaaa<-colMeans(lonlataaa)
#Packages loaded library(raster)
library(maps)
library(maptools)
library(rasterVis)
However, with this approach, too much water can mix with land areas, and if I
narrow the latitude/longitude range on land, I might miss too much land to
compute the mean meaningfully.
Therefore, with this RasterStack, would it be possible to create a condition
that tells R that if the "center point" or centroid of each grid cell (with
each grid cell center representing a specific latitude/longitude coordinate)
happens to fall on land, then it would be considered as land (i.e. that would
be TRUE - if not, then FALSE, or maybe somehow use 0s or 1s)? Even if a grid
cell happens to have water mixed with land, but the center point/centroid of
the grid is on land, that would be considered as land. I would like to do this
for specific countries, too.
I want the individual 138 layers/years to be retained, so that all the Year 1s
can be averaged across all relevant grid cells, then all Year 2s, then all Year
3s, then all Year 4s, etc. (to create a time series later). I'm not sure if
this is the correct way to do this, but what I did first was take the
"RCP1pctCO2Mean" RasterStack and created a SpatialPolygonsDataframe using:
>trans <- raster::rasterToPolygon(RCP1pctCO2Mean) >trans
class : SpatialPolygonsDataFrame
features : 8192
extent : -181.4062, 178.5938, -89.25846, 89.25846 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
variables : 138
names : layer.1, layer.2, layer.3,
layer.4, layer.5, layer.6, layer.7,
layer.8, layer.9, layer.10, layer.11,
layer.12, layer.13, layer.14, layer.15, ...
min values : 0.429645141288708, 0.433756527757047, 0.517493712584313,
0.508389826053265, 0.453667300300907, 0.530991463885754, 0.4975718597839,
0.456977516231847, 0.413821991744321, 0.460824014230889, 0.45516687008843,
0.518570869929649, 0.410051312472821, 0.459565291388527, 0.474978673081429, ...
max values : 96.3034965348338, 104.085840813594, 88.9275127089197,
97.4937326695693, 89.5720053000712, 90.5857030396531, 86.6765123781949,
88.3351859796546, 96.947199473011, 101.582468961459, 96.0779212204739,
93.2194802269814, 99.5978503841538, 94.2621847475029, 90.6213755054263, ...
Could generating an id value for each of those land (or water) polygons, such
that the center of those grid cells (i.e. latitude/longitude coordinates) on
land are only counted, be a logical next step to do something like this? Is it
possible to somehow just isolate an all-land polygon, and then somehow specify
which cells are considered land, and then compute averages for each year across
the grid cells? If so, there are a lot of cells to assign a weight
individually, so I am not sure if there is a way to do this quickly?
Thank you, and I would greatly appreciate any assistance! I look forward to
your response!
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