The easiest approach would be to create a separate aligned raster layer for land vs water. There are plenty of coastline polygons available out there (e.g., maptools, rworldmap, rnaturalearth packages): choose one in your raster CRS (or choose one and spTransform() it). Then, use a grid version of your raster to extract values from that land/water SpatialPolygons object.
1: Your idea of extracting the land/water value at each grid cell centroid gives one estimate. Instead of TRUE/FALSE, think of the numeric equivalents 1,0, then using those as weights for averaging across your grid cells. 2: A "better" estimate would be to compute the fraction of each grid cell that is land, then use those fractional [0, 1] values as weights to compute a weighted average of precipitation over land. At 2.8deg grid cells, a lot of heavy rainfall coastal areas would have the grid cell centroid offshore and be omitted by approach #1. 3: I recommend that you think hard about averaging across cells in Lat Lon to estimate average precipitation over land. The actual area of a ~2.8 by 2.8 deg grid cell at the equator is much larger than the same at 70 deg N. I would spend the extra hour computing the actual area (in km^2) of land in each of your 8192 grid cells, then using those in a raster as weights for whatever calculations you do on the raster stack. [Or you can cheat, as the area of a grid cell in degrees is a function of only the latitudes, and your required weights are multiplicative.] Your mileage may vary... Tom On Wed, Nov 6, 2019 at 6:18 PM rain1290--- via R-sig-Geo < r-sig-geo@r-project.org> wrote: > Hi there, > I am interested in calculating precipitation medians globally. However, I > only want to isolate land areas to compute the median. I already first > created a raster stack, called "RCP1pctCO2median", which contains the > median values. 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 was isolating a specific region by specifying a range of > longitudes and latitudes to obtain the medians for that region, like this: > expansion1<-expand.grid(103:120, 3:15)lonlataaa<-extract(RCP1pctCO2Median, > expansion1)Columnaaa<-colMeans(lonlataaa) > > 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 median meaningfully. > Therefore, with this data, would it be possible to use an IF/ELSE > statement to tell R that if the "center point" of each grid cell happens to > fall on land, then it would be considered as land (i.e. that would be TRUE > - if not, then FALSE)? Even if a grid cell happens to have water mixed with > land, but the center point of the grid is on land, that would be considered > land. But can R even tell what is land or water in this case? > Thank you, and I would greatly appreciate any assistance! > > [[alternative HTML version deleted]] > > _______________________________________________ > R-sig-Geo mailing list > R-sig-Geo@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-geo > [[alternative HTML version deleted]] _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo