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remote

R EMpirical Orthogonal TEleconnections

for detailed descriptions of the algorithm & methodolgy please have a look at:

Empirical Orthogonal Teleconnections
H. M. van den Dool, S. Saha, Å Johansson
Journal of Climate, Volume 13, Issue 8 (April 2000) pp. 1421-1435
http://journals.ametsoc.org/doi/abs/10.1175/1520-0442%282000%29013%3C1421%3AEOT%3E2.0.CO%3B2

Empirical methods in short-term climate prediction
H. M. van den Dool
Oxford University Press, Oxford, New York (2007)
https://global.oup.com/academic/product/empirical-methods-in-short-term-climate-prediction-9780199202782?cc=de&lang=en&

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Installation

install and load the devtools package.

For the stable release version of remote use

install.packages("remote")

To install the development version use

install_github("environmentalinformatics-marburg/remote", ref = "develop")

Contact

Please file bug reports and feature requests at https://github.com/environmentalinformatics-marburg/remote/issues

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Version

Install

install.packages('remote')

Monthly Downloads

1,231

Version

1.2.1

License

GPL (>= 3) | file LICENSE

Maintainer

Last Published

September 17th, 2016

Functions in remote (1.2.1)

covWeight

Create a weighted covariance matrix
deg2rad

Convert degrees to radians
deseason

Create seasonal anomalies
EotCycle

Calculate a single EOT
australiaGPCP

Monthly GPCP precipitation data for Australia
nmodes

Number of modes of an EotStack
getWeights

Calculate weights from latitude
nXplain

Number of EOTs needed for variance explanation
lagalize

Create lagged RasterStacks
pacificSST

Monthly SSTs for the tropical Pacific Ocean
plot

Plot an Eot* object
subset

Subset modes in EotStacks
predict

EOT based spatial prediction
remote-package

R EMpirical Orthogonal TEleconnections
writeEot

Write Eot* objects to disk
longtermMeans

Calculate long-term means from a 'RasterStack'
names

Names of Eot* objects
cutStack

Shorten a RasterStack
geoWeight

Geographic weighting
calcVar

Calculate space-time variance of a RasterStack or RasterBrick
anomalize

Create an anomaly RasterStack
denoise

Noise filtering through principal components