read_ccmp | R Documentation |
RSS CCMP_RT V2.1 derived surface winds (Level 3.0)
read_ccmp(
date,
time.resolution = c("6hourly"),
xylim = NULL,
lon180 = FALSE,
magonly = FALSE,
dironly = FALSE,
uonly = FALSE,
vonly = FALSE,
nobsonly = FALSE,
latest = TRUE,
returnfiles = FALSE,
...,
inputfiles = NULL
)
date |
date or dates of data to read, see Details |
time.resolution |
time resolution (6hourly) |
xylim |
spatial extents to crop from source data, can be anything accepted by |
magonly |
return just the magnitude from the U and V components |
dironly |
return just the direction from the U and V, in degrees N=0, E=90, S=180, W=270 |
uonly |
return just the U component of velocity |
vonly |
return just the V component of velocity components, in degrees (0 north, 90 east, 180 south, 270 west) |
latest |
if TRUE (and date not supplied) return the latest time available, otherwise the earliest |
returnfiles |
ignore options and just return the file names and dates |
... |
passed to brick, primarily for |
CCMP wind data is read from files managed by
ccmp_files
. Dates are matched to file names by
finding the nearest match in time within a short duration. By
default only one time step is returned with both U and V
components. Multiple dates can be returned for magnitude or
direction, U or V only or N-obs only.
This is the " RSS VAM 6-hour analyses starting from the NCEP GFS wind analyses"
See References.
icefiles
for details on the repository of
data files, raster
for the return value
## read a single time slice, direction only
x <- read_ccmp(dironly = TRUE)
## get a local extent for a zoom plot and plot the directions [0,360) as an image with arrows
e <- extent(projectExtent(raster(extent(130, 150, -50, -30), crs = "+proj=longlat"), projection(x)))
x <- crop(read_ccmp(), e)
crds <- coordinates(x)
scale <- 0.05
vlen <- function(x) sqrt(x[[1]]^2 + x[[2]]^2)
plot(vlen(crop(x, e)))
x1 <- crds[,1]
y1 <- crds[,2]
x2 <- crds[,1] + values(x[[1]]) * scale
y2 <- crds[,2] + values(x[[2]]) * scale
arrows(x1, y1, x2, y2, length = 0.02)
## faster if we get the file list first
ccfiles <- ccmp_files()
earliest <- read_ccmp(ccfiles$date[1:16], xylim = e, magonly = TRUE, inputfiles = ccfiles)
plot(earliest, col = hcl.colors(64), zlim = c(0, 20))
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