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s2dv (version 1.1.0)

A Set of Common Tools for Seasonal to Decadal Verification

Description

The advanced version of package 's2dverification'. It is intended for 'seasonal to decadal' (s2d) climate forecast verification, but it can also be used in other kinds of forecasts or general climate analysis. This package is specially designed for the comparison between the experimental and observational datasets. The functionality of the included functions covers from data retrieval, data post-processing, skill scores against observation, to visualization. Compared to 's2dverification', 's2dv' is more compatible with the package 'startR', able to use multiple cores for computation and handle multi-dimensional arrays with a higher flexibility.

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Version

Install

install.packages('s2dv')

Monthly Downloads

405

Version

1.1.0

License

Apache License 2.0

Maintainer

An-Chi Ho

Last Published

December 17th, 2021

Functions in s2dv (1.1.0)

K-means Clustering

Animate Maps of Forecast/Observed Values or Scores Over Forecast Time

Compute anomalies in cross-validation mode

Compute forecast or observation anomalies

Interpolate arrays with longitude and latitude dimensions using CDO

Compute Bias Corrected Climatologies

Compute the anomaly correlation coefficient between the forecast and corresponding observation

Compute the Atlantic Multidecadal Variability (AMV) index

Compute Brier score, its decomposition, and Brier skill score

ConfigShowTable

Show Configuration Tables And Definitions

Compute trend using only model data for which observations are available

Draws a Color Bar

Compute composites

ConfigShowSimilarEntries

Find Similar Entries In Tables Of Datasets

Functions To Create Open And Save Configuration File

ConfigApplyMatchingEntries

Apply Matching Entries To Dataset Name And Variable Name To Find Related Info

ConfigEditEntry

Add, Remove Or Edit Entries In The Configuration

ConfigEditDefinition

Add Modify Or Remove Variable Definitions In Configuration

Compute the Global Surface Air Temperature (GSAT) anomalies

Chunk long simulations for comparison with hindcasts

Loads Experimental And Observational Data

Average an array along multiple dimensions

Compute effective sample size with classical method

Compute the Global Mean Surface Temperature (GMST) anomalies

Area-weighted empirical orthogonal function analysis using SVD

Compute the correlation coefficient between an array of forecast and their corresponding observation

Filter frequency peaks from an array

Add a named dimension to an array

Checks Whether A Year Is Leap Year

Plots Climatologies

Plot2VarsVsLTime

Plot two scores with confidence intervals in a common plot

Compute persistence

Compute the North Atlantic Oscillation (NAO) Index

Plot Anomaly time series

Arrange and Fill Multi-Pannel Layouts With Optional Colour Bar

Box-And-Whisker Plot of Time Series with Ensemble Distribution

Compute the ratio between the RMSE of two experiments

Plot a score along the forecast time with its confidence interval

RatioPredictableComponents

Calculate ratio of predictable components (RPC)

StatSeasAtlHurr

Compute estimate of seasonal mean of Atlantic hurricane activity

Compute the Tripole Index (TPI) for the Interdecadal Pacific Oscillation (IPO)

Plot Plumes/Timeseries Of Anomaly Correlation Coefficients

Random walk test for skill differences

Compute probabilistic information of a forecast relative to a threshold or a quantile

Compute the South Pacific Ocean Dipole (SPOD) index

Teleconnection indices in European Atlantic Ocean region

Compute seasonal mean

Project anomalies onto modes of variability

Compute the regression of an array on another along one dimension.

Area-weighted empirical orthogonal function analysis with varimax rotation using SVD

Reorder the dimension of an array

Maps A Two-Dimensional Variable On A Cylindrical Equidistant Projection

Plots A Vertical Section

SignalNoiseRatio

Calculate Signal-to-noise ratio

Function to convert any numerical table to a grid of coloured squares.

Maps A Two-Dimensional Variable On A Polar Stereographic Projection

Compute the ratio between the ensemble spread and RMSE

Compute root mean square error

Compute root mean square error skill score

Smooth an array along one dimension

Synthetic forecast generator imitating seasonal to decadal forecasts. The components of a forecast: (1) predictabiltiy (2) forecast error (3) non-stationarity and (4) ensemble generation. The forecast can be computed for real observations or observations generated artifically.

Compute the trend

Estimate frequency spectrum

sampleDepthData

Sample of Experimental Data for Forecast Verification In Function Of Latitudes And Depths

s2dv: A Set of Common Tools for Seasonal to Decadal Verification

Compute interquartile range, maximum-minimum, standard deviation and median absolute deviation

sampleTimeSeries

Sample Of Observational And Experimental Data For Forecast Verification As Area Averages

Sample Of Observational And Experimental Data For Forecast Verification In Function Of Longitudes And Latitudes

Compute Brier scores

Generate Climate Color Palettes