Title: A Cross-Scale Analysis Tool for Model-Observation Visualization and Integration
Version: 0.7.1
Description: Integration of Earth system data from various sources is a challenging task. Except for their qualitative heterogeneity, different data records exist for describing similar Earth system process at different spatio-temporal scales. Data inter-comparison and validation are usually performed at a single spatial or temporal scale, which could hamper the identification of potential discrepancies in other scales. 'csa' package offers a simple, yet efficient, graphical method for synthesizing and comparing observed and modelled data across a range of spatio-temporal scales. Instead of focusing at specific scales, such as annual means or original grid resolution, we examine how their statistical properties change across spatio-temporal continuum.
Depends: R (≥ 3.4.0)
Imports: grDevices, stats, ggplot2, data.table, scales, reshape2, moments, Lmoments, foreach, ggpubr, raster, doParallel, parallel
License: GPL-2
Encoding: UTF-8
LazyData: true
URL: https://github.com/imarkonis/csa
BugReports: https://github.com/imarkonis/csa/issues
RoxygenNote: 7.2.3
Suggests: testthat (≥ 2.1.0), colorspace
NeedsCompilation: no
Packaged: 2023-10-23 15:21:23 UTC; mirovago
Author: Yannis Markonis [aut, cre], Christoforos Pappas [aut], Mijael Vargas [ctb], Simon Papalexiou [ctb], Martin Hanel [ctb]
Maintainer: Yannis Markonis <imarkonis@gmail.com>
Repository: CRAN
Date/Publication: 2023-10-24 13:40:11 UTC

Simulation data (CNRM)

Description

Model cnrm-cm3; scenario 20c3m; variable pr. 24 h 2.8 degree x 2.8 degree for Holland at daily time step for period 1961-01-01 to 2000-12-31. Spatial Region: 1 grid cell at latitude: 51.625, longitude: 5.625

Usage

data(cnrm_nl)

Format

An object of class data.table (inherits from data.frame) with 14610 rows and 2 columns.

Source

KNMI explorer

Examples

str(cnrm_nl)

Estimate and print the temporal CSA plot

Description

The function csa computes (and by default plots) the aggregation curve of a given statistic in a single dimension, e.g., time.

Usage

csa(
  x,
  stat = "var",
  std = TRUE,
  threshold = 30,
  plot = TRUE,
  fast = FALSE,
  chk = FALSE,
  ...
)

Arguments

x

A numeric vector.

stat

The statistic which will be estimated across the cross-scale continuum. Suitable options are:

  • "var" for variance,

  • "sd" for standard deviation,

  • "skew" for skewness,

  • "kurt" for kurtosis,

  • "l2" for L-scale,

  • "t2" for coefficient of L-variation,

  • "t3" for L-skewness,

  • "t4" for L-kurtosis.

std

logical. If TRUE (the default) the CSA plot is standardized to unit, i.e., zero mean and unit variance in the original time scale.

threshold

numeric. Sample size of the time series at the last aggregated scale.

plot

logical. If TRUE (the default) the CSA plot is printed.

fast

logical. If TRUE the CSA plot is estimated only in logarithmic scale; 1, 2, 3, ... , 10, 20, 30, ... , 100, 200, 300 etc.

chk

logical. If TRUE the number of cores is limited to 2.

...

log_x and log_y (default TRUE) for setting the axes of the CSA plot to logarithmic scale. The argument wn (default FALSE) is used to plot a line presenting the standardized variance of the white noise process. Therefore, it should be used only with stat = "var" and std = T.

Value

If plot = TRUE, the csa returns a list containing:

If plot = FALSE, then it returns only the matrix of the timeseries values for the selected stat at each scale.

References

Markonis et al., A cross-scale analysis framework for model/data comparison and integration, Geoscientific Model Development, Submitted.

Examples

## Not run: 
csa(rnorm(1000), wn = TRUE)
data(gpm_nl, knmi_nl, rdr_nl, ncep_nl, cnrm_nl, gpm_events)
csa(knmi_nl$prcp, threshold = 10, fast = TRUE, chk = TRUE)

csa(gpm_nl$prcp, stat = "skew", std = FALSE, log_x = FALSE, log_y = FALSE,
smooth = TRUE, chk = TRUE)

gpm_skew <- csa(gpm_nl$prcp, stat = "skew", std = FALSE, log_x = FALSE, log_y = FALSE,
smooth = TRUE, plot = FALSE, chk = TRUE)
rdr_skew <- csa(rdr_nl$prcp, stat = "skew", std = FALSE, log_x = FALSE, log_y = FALSE,
smooth = TRUE, plot = FALSE, chk = TRUE)
csa.multiplot(rbind(data.frame(gpm_skew, dataset = "gpm"), data.frame(rdr_skew,
dataset = "rdr")), log_x = FALSE, log_y = FALSE, smooth = TRUE)

set_1 <- data.frame(csa(gpm_nl$prcp, plot = FALSE, fast = TRUE), dataset = "gpm")
set_2 <- data.frame(csa(rdr_nl$prcp, plot = FALSE, fast = TRUE), dataset = "radar")
set_3 <- data.frame(csa(knmi_nl$prcp, plot = FALSE, fast = TRUE), dataset = "station")
set_4 <- data.frame(csa(ncep_nl$prcp, plot = FALSE, fast = TRUE), dataset = "ncep")
set_5 <- data.frame(csa(cnrm_nl$prcp, plot = FALSE, fast = TRUE), dataset = "cnrm")
csa.multiplot(rbind(set_1, set_2, set_3, set_4, set_5))

## End(Not run)

Multiple CSA plotting

Description

Function for plotting multiple CSA curves in a single plot.

Usage

csa.multiplot(df, log_x = TRUE, log_y = TRUE, wn = FALSE, smooth = FALSE)

Arguments

df

A matrix or data.frame composed of three columns; scale for the temporal or spatial scale; value for the estimate of a given statistic (e.g., variance) at the given aggregated scale and variable for defining the corresponding dataset.

log_x

logical. If TRUE (the default) the x axis of the CSA plot is set to the logarithmic scale.

log_y

logical. If TRUE (the default) the y axis of the CSA plot is set to the logarithmic scale.

wn

logical. The argument wn (default FALSE) is used to plot a line presenting the standardized variance of the white noise process. Therefore, it should be used only with stat = "var" and std = T in the csa/csas functions.

smooth

logical. If TRUE (the default) the aggregation curves are smoothed (loess function).

Value

The CSA plot as a ggplot object.

Examples


aa <- rnorm(1000)
csa_aa <- data.frame(csa(aa, plot = FALSE, chk = TRUE), variable = 'wn')
bb <- as.numeric(arima.sim(n = 1000, list(ar = c(0.8897, -0.4858), ma = c(-0.2279, 0.2488))))
csa_bb <- data.frame(csa(bb, plot = FALSE, chk = TRUE), variable = 'arma(2, 2)')
csa.multiplot(rbind(csa_aa, csa_bb), wn = TRUE)
csa.multiplot(rbind(csa_aa, csa_bb), wn = TRUE, smooth = TRUE)

CSA curve plotting

Description

Function for plotting single CSA curves.

Usage

csa.plot(x, log_x = TRUE, log_y = TRUE, smooth = FALSE, wn = FALSE)

Arguments

x

A matrix or data.frame composed of two columns; scale for the temporal or spatial scale and value for the estimate of a given statistic (e.g., variance) at the given aggregated scale.

log_x

logical. If TRUE (the default) the x axis of the CSA plot is set to the logarithmic scale.

log_y

logical. If TRUE (the default) the y axis of the CSA plot is set to the logarithmic scale.

smooth

logical. If TRUE (the default) the aggregation curves are smoothed (loess function).

wn

logical. The argument wn (default FALSE) is used to plot a line presenting the standardized variance of the white noise process. Therefore, it should be used only with stat = "var" and std = T in the csa/csas functions.

Value

The CSA plot as a ggplot object.

Examples

## Not run: 
aa <- rnorm(1000)
csa_aa <- csa(aa, chk = TRUE, plot = FALSE)
csa.plot(csa_aa)

## End(Not run)

Estimate and print the spatial CSA plot

Description

The function csa computes (and by default plots) the aggregation curve of a given statistic in two dimensions, e.g., space.

Usage

csas(
  x,
  stat = "var",
  std = TRUE,
  plot = TRUE,
  threshold = 30,
  chk = FALSE,
  ...
)

Arguments

x

A raster or brick object.

stat

The statistic which will be estimated across the cross-scale continuum. Suitable options are:

  • "var" for variance,

  • "sd" for standard deviation,

  • "skew" for skewness,

  • "kurt" for kurtosis,

  • "l2" for L-scale,

  • "t2" for coefficient of L-variation,

  • "t3" for L-skewness,

  • "t4" for L-kurtosis.

std

logical. If TRUE (the default) the CSA plot is standardized to unit, i.e., zero mean and unit variance in the original time scale.

plot

logical. If TRUE (the default) the CSA plot is printed

threshold

numeric. Sample size of the time series at the last aggregated scale.

chk

logical. If TRUE the number of cores is limited to 2.

...

log_x and log_y (default TRUE) for setting the axes of the CSA plot to logarithmic scale. The argument wn (default FALSE) is used to plot a line presenting the standardized variance of the white noise process. Therefore, it should be used only with stat = "var" and std = T.

Value

If plot = TRUE, the csa returns a list containing:

If plot = FALSE, then it returns only the matrix of the timeseries values for the selected stat at each scale.

References

Markonis et al., A cross-scale analysis framework for model/data comparison and integration, Geoscientific Model Development, Submitted.

Examples

## Not run: 
data(gpm_events)
event_dates <- format(gpm_events[, unique(time)], "%d-%m-%Y")
gpm_events_brick <- dt.to.brick(gpm_events, var_name = "prcp")
plot(gpm_events_brick, col = rev(colorspace::sequential_hcl(40)),
     main = event_dates)
csas(gpm_events_brick, chk = TRUE)

gpm_sp_scale <- csas(gpm_events_brick, plot = FALSE, chk = TRUE)
gpm_sp_scale[, variable := factor(variable, labels = event_dates)]
csa.multiplot(gpm_sp_scale, smooth = TRUE, log_x = FALSE, log_y = FALSE)

## End(Not run)

Transform data.table to brick

Description

The function dt.to.brick transforms a data.table object to brick (raster) format

Usage

dt.to.brick(dt, var_name)

Arguments

dt

The data table object to be transformed. It must be in a four-column format, with the coordinate columns named as "lat" & "lon" and time values as "time".

var_name

The name (chr) of the column in the data table (dt) which holds the values of the variable, e.g., "temperature".

Value

dt as a brick object.

Examples

## Not run: 
aa <- expand.grid(lat = seq(40, 50, 1),
                 lon = seq(20, 30, 1),
                 time = seq(1900, 2000, 1))
aa$anomaly = rnorm(nrow(aa))
aa <- brick(dt.to.brick(aa, "anomaly"))

## End(Not run)

GPM-IMERG precipitation events over 10 mm/day

Description

GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree for Holland at daily time step for period 2014-03-12 to 2018-05-15. Spatial averaged over: latitude: 50.75, 53.55, longitude: 3.45, 7.15

Usage

data(gpm_events)

Format

An object of class data.table (inherits from data.frame) with 6612 rows and 6 columns.

Source

KNMI explorer

Examples

str(gpm_events)

Satellite data (GPM-IMERG)

Description

GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree for Holland at daily time step for period 2014-03-12 to 2018-05-15. Spatial averaged over: latitude: 50.75, 53.55, longitude: 3.45, 7.15

Usage

data(gpm_nl)

Format

An object of class data.table (inherits from data.frame) with 1526 rows and 2 columns.

Source

KNMI explorer

Examples

str(gpm_nl)

Station data (KNMI)

Description

240 homogenized stations 1951-now. 24 h point data for Holland at daily time step for period 1950-12-31 to 2018-04-29. Spatial Region: latitude: 50.78, 53.48, longitude: 3.4, 7.11

Usage

data(knmi_nl)

Format

An object of class data.table (inherits from data.frame) with 24592 rows and 2 columns.

Source

KNMI explorer

Examples

str(knmi_nl)

Reanalysis data (NCEP/NCAR)

Description

NMC reanalysis 24 h 2.5 degree x 2.5 degree for Holland at daily time step for period 1948-01-01 to 2018-06-05. Spatial Region: 1 grid cell at latitude: 52.38, longitude: 5.625

Usage

data(ncep_nl)

Format

An object of class data.table (inherits from data.frame) with 25601 rows and 2 columns.

Source

KNMI explorer

Examples

str(ncep_nl)

Radar data (KNMI)

Description

RAD_NL25_RAC_MFBS_24H_NC 24 h 1 km x 1 km for Holland at daily time step for period 2014-03-11 to 2018-03-30. Spatial Region: latitude: 50.76, 53.56, longitude: 3.37, 7.22

Usage

data(rdr_nl)

Format

An object of class data.table (inherits from data.frame) with 1472 rows and 2 columns.

Source

KNMI explorer

Examples

str(rdr_nl)