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HiClimR2nc is a helper function that exports region map and mean timeseries into NetCDF-4 file, using the ncdf4 package.

Usage

HiClimR2nc(y = NULL, ncfile = "HiClimR.nc", timeunit = "", dataunit = "")

Arguments

y

a dendrogram tree produced by HiClimR.

ncfile

Path and name of the NetCDF-4 file to be created.

timeunit

an optional character string for the time units, A zero-length string (default: timeunit="") removes units attribute.

dataunit

an optional character string for the data units, A zero-length string (default: timeunit="") removes units attribute.

Value

An object of class ncdf4, which has the fields described in nc_open.

Details

HiClimR2nc function exports region map and mean timeseries from HiClimR tree into NetCDF-4 file, using the ncdf4 package. The NetCDF-4 file will be open to add other variables, if needed. It is important to close the created file using nc_close, which flushes any unwritten data to disk.

References

Hamada S. Badr, Zaitchik, B. F. and Dezfuli, A. K. (2015): A Tool for Hierarchical Climate Regionalization, Earth Science Informatics, 8(4), 949-958, doi:10.1007/s12145-015-0221-7 .

Hamada S. Badr, Zaitchik, B. F. and Dezfuli, A. K. (2014): Hierarchical Climate Regionalization, Comprehensive R Archive Network (CRAN), https://cran.r-project.org/package=HiClimR.

Author

Hamada S. Badr <badr@jhu.edu>, Benjamin F. Zaitchik <zaitchik@jhu.edu>, and Amin K. Dezfuli <amin.dezfuli@nasa.gov>.

See also

Examples

require(HiClimR)
require(ncdf4)
#> Loading required package: ncdf4

## Load test case data
x <- TestCase$x

## Generate longitude and latitude mesh vectors
xGrid <- grid2D(lon = unique(TestCase$lon), lat = unique(TestCase$lat))
lon <- c(xGrid$lon)
lat <- c(xGrid$lat)

## Hierarchical Climate Regionalization
y <- HiClimR(x, lon = lon, lat = lat, lonStep = 1, latStep = 1, geogMask = FALSE,
    continent = "Africa", meanThresh = 10, varThresh = 0, detrend = TRUE,
    standardize = TRUE, nPC = NULL, method = "ward", hybrid = FALSE,
    kH = NULL, members = NULL, validClimR = TRUE, k = 12, minSize = 1,
    alpha = 0.01, plot = TRUE, colPalette = NULL, hang = -1, labels = FALSE)
#> 
#> PROCESSING STARTED
#> 
#> Checking Multivariate Clustering (MVC)...
#> ---> x is a matrix
#> ---> single-variate clustering: 1 variable
#> Checking data...
#> ---> Checking dimensions...
#> ---> Checking row names...
#> ---> Checking column names...
#> Data filtering...
#> ---> Computing mean for each row...
#> ---> Checking rows with mean bellow meanThresh...
#> ---> 4678 rows found, mean ≤  10
#> ---> Computing variance for each row...
#> ---> Checking rows with near-zero-variance...
#> ---> 3951 rows found, variance ≤  0
#> Data preprocessing...
#> ---> Applying mask...
#> ---> Checking columns with missing values...
#> ---> Removing linear trend...
#> ---> Standardizing data...
#> Agglomerative Hierarchical Clustering...
#> ---> Computing correlation/dissimilarity matrix...
#> ---> Starting clustering process...
#> ---> Constructing dendrogram tree...
#> Calling cluster validation...
#> ---> Computing cluster means...
#> ---> Computing inter-cluster correlations...
#> ---> Computing intra-cluster correlations...
#> ---> Computing summary statistics...
#> Generating region map...
#> 
#> PROCESSING COMPLETED
#> 
#> Running Time:
#>    user  system elapsed 
#>   0.391   0.024   0.415 
#> Time difference of 0.4152124 secs

if (FALSE) {

## Export region map and mean timeseries into NetCDF-4 file
y.nc <- HiClimR2nc(y=y, ncfile="HiClimR.nc", timeunit="years", dataunit="mm")
## The NetCDF-4 file is still open to add other variables or close it
nc_close(y.nc)

}