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Reading Data from NetCDF Files for 'REddyProc'
Extension to 'REddyProc' that allows reading data from netCDF files.
Tools to Visualize CM SAF NetCDF Data
The Satellite Application Facility on Climate Monitoring (CM SAF) is a ground segment of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) and one of EUMETSATs Satellite Application Facilities. The CM SAF contributes to the sustainable monitoring of the climate system by providing essential climate variables related to the energy and water cycle of the atmosphere (< https://www.cmsaf.eu>). It is a joint cooperation of eight National Meteorological and Hydrological Services. The 'cmsafvis' R-package provides a collection of R-operators for the analysis and visualization of CM SAF NetCDF data. CM SAF climate data records are provided for free via (< https://wui.cmsaf.eu/safira>). Detailed information and test data are provided on the CM SAF webpage (< http://www.cmsaf.eu/R_toolbox>).
Extract Metadata from 'NetCDF' Files as Data Frames
Tools for handling 'NetCDF' metadata in data frames. The metadata is provided as relations in tabular form, to avoid having to scan printed header output or to navigate nested lists of raw metadata.
Generating Rainfall Rasters from IMD NetCDF Data
The developed function is a comprehensive tool for the analysis of India Meteorological Department (IMD) NetCDF rainfall data. Specifically designed to process high-resolution daily
gridded rainfall datasets. It provides four key functions to process IMD NetCDF rainfall data and create rasters for various temporal scales, including annual, seasonal, monthly, and weekly
rainfall. For method details see, Malik, A. (2019).
Easy Access to NetCDF Files with CF Metadata Conventions
Network Common Data Form ('netCDF') files are widely used for scientific data. Library-level access in R is provided through packages 'RNetCDF' and 'ncdf4'. Package 'ncdfCF' is built on top of 'RNetCDF' and makes the data and its attributes available as a set of R6 classes that are informed by the Climate and Forecasting Metadata Conventions. Access to the data uses standard R subsetting operators and common function forms.
Makes it Easier to Work with Daily 'netCDF' from EURO-CORDEX RCMs
Daily 'netCDF' data from e.g. regional climate models (RCMs) are not trivial to work with. This package, which relies on 'data.table', makes it easier to deal with large data from RCMs, such as from EURO-CORDEX (< https://www.euro-cordex.net/>, < https://cordex.org/data-access/>). It has functions to extract single grid cells from rotated pole grids as well as the whole array in long format. Can handle non-standard calendars (360, noleap) and interpolate them to a standard one. Potentially works with many CF-conform 'netCDF' files.
General Purpose Client for 'ERDDAP™' Servers
General purpose R client for 'ERDDAP™' servers. Includes functions to search for 'datasets', get summary information on 'datasets', and fetch 'datasets', in either 'csv' or 'netCDF' format. 'ERDDAP™' information: < https://upwell.pfeg.noaa.gov/erddap/information.html>.
Interface to the 'Daymet' Web Services
Programmatic interface to the 'Daymet' web services (< http://daymet.ornl.gov>). Allows for easy downloads of 'Daymet' climate data directly to your R workspace or your computer. Routines for both single pixel data downloads and gridded (netCDF) data are provided.
Helper Functions for Use with the 'ncdf4' Package
Contains a collection of helper functions for dealing with 'NetCDF' files < https://www.unidata.ucar.edu/software/netcdf/> opened using 'ncdf4', particularly 'NetCDF' files that conform to the Climate and Forecast (CF) Metadata Conventions < http://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/cf-conventions.html>.
Hierarchical Climate Regionalization
A tool for Hierarchical Climate Regionalization applicable to any correlation-based clustering.
It adds several features and a new clustering method (called, 'regional' linkage) to hierarchical
clustering in R ('hclust' function in 'stats' library): data regridding, coarsening spatial resolution,
geographic masking, contiguity-constrained clustering, data filtering by mean and/or variance
thresholds, data preprocessing (detrending, standardization, and PCA), faster correlation function
with preliminary big data support, different clustering methods, hybrid hierarchical clustering,
multivariate clustering (MVC), cluster validation, visualization of regionalization results, and
exporting region map and mean timeseries into NetCDF-4 file.
The technical details are described in Badr et al. (2015)