Package {climatestatsr}

Type:	Package
Title:	Statistical Tools for Climate Change Analysis
Version:	0.1.2
Date:	2026-05-21
Maintainer:	Sadikul Islam <sadikul.islamiasri@gmail.com>
Description:	A comprehensive collection of statistical functions for climate change research. Provides tools for temporal trend detection based on the Mann-Kendall (MK) test (Mann 1945 <doi:10.2307/1907187>; Kendall 1975, ISBN:0852641990) and Sen's slope (Sen 1968 <doi:10.2307/2285891>), spatial autocorrelation using Moran's I (Moran 1950 <doi:10.2307/2332142>), extreme value analysis using the Generalised Extreme Value (GEV) distribution and Peaks-Over-Threshold (POT) method (Coles 2001 <doi:10.1007/978-1-4471-3675-0>), standardised drought indices including the Standardised Precipitation Index (SPI; McKee et al. 1993) and the Standardised Precipitation Evapotranspiration Index (SPEI; Vicente-Serrano et al. 2010 <doi:10.1175/2009JCLI2909.1>), and formal detection-attribution methods via optimal fingerprint regression and Empirical Orthogonal Function (EOF) analysis (Allen and Tett 1999 <doi:10.1007/s003820050291>), and apparent temperature via the heat index (Steadman 1979 <doi:10.1175/1520-0450(1979)018%3C0861:TAOSPI%3E2.0.CO;2>). Suitable for both station-level time series and gridded climate fields.
License:	GPL-3
Encoding:	UTF-8
Language:	en-US
Depends:	R (≥ 4.0.0)
Imports:	stats, graphics, utils
Suggests:	ggplot2 (≥ 3.4.0), sf (≥ 1.0-0), ncdf4 (≥ 1.21), testthat (≥ 3.0.0), knitr (≥ 1.42), rmarkdown (≥ 2.20)
VignetteBuilder:	knitr
NeedsCompilation:	no
ByteCompile:	true
RoxygenNote:	7.3.3
Packaged:	2026-06-11 12:11:32 UTC; acer
Author:	Sadikul Islam [aut, cre], Rajesh Kaushal [aut]
Repository:	CRAN
Date/Publication:	2026-06-18 14:20:14 UTC

Statistical Tools for Climate Change Analysis

Description

A comprehensive collection of statistical functions for climate change research. Provides tools for temporal trend detection, spatial analysis, extreme event statistics, standardised climate indices, and formal detection-attribution methods.

Details

The package is organised into five function families:

• Temporal analysis: mk_test, sens_slope, change_point_detection, seasonal_decompose_climate, rolling_trend, temporal_homogeneity, trend_significance, autocorrelation_climate

• Spatial analysis: morans_i, hot_cold_spots, spatial_interpolate, spatial_trend_field, cluster_climate_zones, spatial_anomaly, elevation_lapse_rate

• Extreme events: fit_gev, return_period, peaks_over_threshold, heat_wave_detection, cold_spell_detection, drought_spell, extreme_value_index

• Climate indices: spi, spei, pdsi_simple, heat_index, wind_chill, frost_days, growing_degree_days, diurnal_temp_range

• Attribution and utilities: detection_attribution, fingerprint_analysis, optimal_fingerprint, fill_gaps_climate, homogenize_series, aggregate_climate, anomaly_baseline, standardize_climate, climate_summary

Author(s)

Sadikul Islam sadikul.islam@climate-research.org (ORCID)

Maintainer: Sadikul Islam sadikul.islam@climate-research.org

References

Mann, H. B. (1945). Nonparametric tests against trend. Econometrica 13(3), 245–259. doi:10.2307/1907187

Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall's tau. Journal of the American Statistical Association 63(324), 1379–1389. doi:10.2307/2285891

Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer-Verlag, London. doi:10.1007/978-1-4471-3675-0

McKee, T. B., Doesken, N. J. and Kleist, J. (1993). The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th Conference on Applied Climatology, 17–22 January 1993, Anaheim, California, pp. 179–184. American Meteorological Society.

Vicente-Serrano, S. M., Begueria, S. and Lopez-Moreno, J. I. (2010). A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index. Journal of Climate 23(7), 1696–1718. doi:10.1175/2009JCLI2909.1

Aggregate Climate Data to Coarser Time Steps

Description

Aggregates sub-monthly climate data (e.g., daily) to monthly, seasonal (DJF, MAM, JJA, SON), or annual statistics using a user-supplied aggregation function.

Usage

aggregate_climate(x, dates, to = c("monthly", "seasonal", "annual"),
                  FUN = mean)

Arguments

Value

A data.frame with columns period (character labels such as "2000", "2000-01", or "2000-JJA") and value.

See Also

anomaly_baseline, standardize_climate.

Examples

dates <- seq(as.Date("2000-01-01"), by = "day", length.out = 365 * 5)
temp  <- stats::rnorm(length(dates), 15, 5)
ann   <- aggregate_climate(temp, dates, to = "annual")
head(ann)
seas  <- aggregate_climate(temp, dates, to = "seasonal")
head(seas)

Climate Anomaly Relative to a Baseline Period

Description

Computes absolute or standardised anomalies for a climate series relative to a user-defined baseline (reference) period.

Usage

anomaly_baseline(x, time_index, baseline_start, baseline_end,
                 type = c("absolute", "standardised"))

Arguments

Value

Numeric vector of anomalies.

See Also

spatial_anomaly, standardize_climate.

Examples

years <- 1950:2020
temp  <- 14 + 0.02 * (years - 1950) + stats::rnorm(71)
anom  <- anomaly_baseline(temp, years, 1961, 1990)
## Mean over baseline period should be zero
cat("Baseline mean:", round(mean(anom[years >= 1961 & years <= 1990]), 10), "\n")

Autocorrelation Analysis for Climate Series

Description

Computes the autocorrelation function (ACF) and partial autocorrelation function (PACF) for a climate series, and performs the Ljung-Box portmanteau test for significant autocorrelation.

Usage

autocorrelation_climate(x, max.lag = NULL, plot = TRUE)

Arguments

Value

A list with:

acf

Numeric vector: sample ACF at lags 1 to max.lag.

pacf

Numeric vector: sample PACF at lags 1 to max.lag.

lags

Integer vector: lag indices.

ljung_box

An "htest" object from Box.test.

ar1

Numeric: sample ACF at lag 1.

See Also

mk_test.

Examples

set.seed(5)
temp <- as.numeric(stats::arima.sim(list(ar = 0.7), n = 100)) + 15
ac <- autocorrelation_climate(temp, plot = FALSE)
cat("AR(1) coefficient:", round(ac$ar1, 3), "\n")
print(ac$ljung_box)

Change-Point Detection for Climate Series

Description

Detects an abrupt shift (change point) in the mean of a climate time series using either Pettitt's non-parametric test or a CUSUM-based approach with bootstrap significance assessment.

Usage

change_point_detection(x, method = c("pettitt", "cusum"), alpha = 0.05)

Arguments

Value

A list containing:

method

Character: name of the test applied.

change_point

Integer: index of the detected change point.

p.value

Numeric: approximate p-value.

U_stat

Numeric: test statistic (K for Pettitt; max|CUSUM| for CUSUM).

mean_before

Numeric: mean of observations up to the change point.

mean_after

Numeric: mean after the change point.

significant

Logical: whether the shift is significant at alpha.

n

Integer: number of observations used.

(Pettitt only) U_series

Integer vector of Pettitt U statistics.

(CUSUM only) cusum

Numeric vector: cumulative sum series.

References

Pettitt, A.N. (1979). A non-parametric approach to the change-point problem. Applied Statistics 28, 126–135. doi:10.2307/2346729

Page, E.S. (1954). Continuous inspection schemes. Biometrika 41, 100–115.

See Also

temporal_homogeneity for SNHT-based inhomogeneity detection; mk_test for gradual trend testing.

Examples

## Known shift at observation 30
set.seed(3)
x  <- c(stats::rnorm(30, 14, 1), stats::rnorm(30, 16, 1))
cp <- change_point_detection(x, method = "pettitt")
cat("Change point at index:", cp$change_point, "\n")
cat("Mean before:", round(cp$mean_before, 2),
    " / after:", round(cp$mean_after, 2), "\n")

## CUSUM method
change_point_detection(x, method = "cusum")

Comprehensive Climate Series Summary

Description

Prints a formatted statistical summary of a climate series including descriptive statistics, Mann-Kendall trend test results, and Sen's slope.

Usage

climate_summary(x, dates = NULL, variable_name = "Climate Variable")

Arguments

Value

Invisibly returns a named list with elements mean, sd, mk_tau, mk_p, sens_slope, slope_decade, and trend.

See Also

mk_test, sens_slope.

Examples

set.seed(99)
years <- 1970:2020
temp  <- 14 + 0.025 * (years - 1970) + stats::rnorm(51, 0, 0.4)
res   <- climate_summary(temp, variable_name = "Annual Mean Temperature (C)")

S3 Methods for "climate_test" Objects

Description

Print, summary, and plot methods for objects of class "climate_test", which is the base class returned by mk_test and seasonal_decompose_climate.

Usage

## S3 method for class 'climate_test'
print(x, ...)
## S3 method for class 'climate_test'
summary(object, ...)
## S3 method for class 'climate_test'
plot(x, ...)

Arguments

Details

print and summary display a formatted table of the test statistic, p-value, and interpretation.

plot behaviour depends on the subclass:

"mk_test"

Two-panel plot: (1) time series with Sen's slope trend line; (2) histogram with mean line.

"climate_decomp"

Four-panel plot: original series, trend, seasonal, and remainder components.

Value

print and summary return x invisibly. plot returns x invisibly.

See Also

mk_test, seasonal_decompose_climate.

Examples

set.seed(1)
r <- mk_test(1:50 + stats::rnorm(50, 0, 3))
print(r)

plot(r)

K-Means Climate Zone Classification

Description

Partitions a set of locations into climate zones using k-means clustering on multi-variable climate normals (e.g., mean temperature and total precipitation).

Usage

cluster_climate_zones(clim_matrix, k = 5, scale = TRUE, seed = 42)

Arguments

Value

A list with:

cluster

Integer vector: cluster assignment (1 to k) for each location.

centers

Numeric matrix: cluster centres in the (possibly scaled) variable space.

within_ss

Numeric: total within-cluster sum of squares.

between_ss

Numeric: between-cluster sum of squares.

total_ss

Numeric: total sum of squares.

k

Integer: number of clusters used.

See Also

spatial_anomaly, fingerprint_analysis.

Examples

set.seed(99)
cm <- matrix(c(stats::rnorm(100, 20, 5),
               stats::rnorm(100, 800, 200)), ncol = 2)
cz <- cluster_climate_zones(cm, k = 3)
table(cz$cluster)

Cold Spell Detection

Description

Identifies cold spell events as periods during which daily minimum temperature falls below a threshold for a minimum number of consecutive days. This is the low-temperature counterpart of heat_wave_detection.

Usage

cold_spell_detection(tmin, dates, threshold = "p10", min_days = 3)

Arguments

Value

A data.frame with columns start_date, end_date, duration, min_temp, mean_temp, and intensity (mean deficit below the threshold).

See Also

heat_wave_detection, frost_days.

Examples

set.seed(9)
dates <- seq(as.Date("2000-01-01"), by = "day", length.out = 365 * 5)
doy   <- as.integer(format(dates, "%j"))
tmin  <- 5 - 12 * sin(2 * pi * doy / 365) +
         stats::rnorm(length(dates), 0, 2)
cs <- cold_spell_detection(tmin, dates, threshold = "p10")
cat("Cold spell events:", nrow(cs), "\n")

Climate Change Detection and Attribution Test

Description

Tests whether an observed climate change can be statistically distinguished from natural internal variability using a signal-to-noise ratio approach based on projections onto a forced signal.

Usage

detection_attribution(observed, natural_ensemble,
                      forced_signal = NULL, conf.level = 0.95)

Arguments

Value

A list with:

detected

Logical: whether the signal is detected at conf.level.

z_score

Numeric: standardised score relative to natural variability.

p.value

Numeric: two-tailed p-value.

attribution_fraction

Numeric: fraction of observed variance explained by the forced signal.

noise_sd

Numeric: mean standard deviation of the natural ensemble.

projection_observed

Numeric: Pearson correlation of observed with the forced signal.

projection_natural

Numeric vector: correlations for each ensemble member.

conf.level

Numeric: confidence level used.

method

Character: "Optimal Fingerprint Detection".

References

Hasselmann, K. (1979). On the signal-to-noise problem in atmospheric response studies. In: Shaw, D. B. (ed.) Meteorology Over the Tropical Oceans, pp. 251–259. Royal Meteorological Society, Bracknell.

Allen, M. R. and Tett, S. F. B. (1999). Checking for model consistency in optimal fingerprinting. Climate Dynamics 15(6), 419–434. doi:10.1007/s003820050291

Hegerl, G. C. and Zwiers, F. (2011). Use of models in detection and attribution of climate change. Wiley Interdisciplinary Reviews: Climate Change 2(4), 570–591. doi:10.1002/wcc.121

See Also

fingerprint_analysis, optimal_fingerprint.

Examples

set.seed(10)
obs  <- cumsum(stats::rnorm(50, 0.025, 0.15))
nat  <- matrix(stats::rnorm(50 * 20, 0, 0.5), ncol = 20)
sig  <- seq(0, 1.2, length.out = 50)
da   <- detection_attribution(obs, nat, sig)
cat("Signal detected:", da$detected, "\n")
cat("Attribution fraction:", round(da$attribution_fraction, 2), "\n")

Diurnal Temperature Range

Description

Computes the mean Diurnal Temperature Range (DTR = Tmax - Tmin) aggregated by year or calendar month. DTR is widely used as an indicator of cloud cover, land use change, and climate variability.

Usage

diurnal_temp_range(tmax, tmin, dates, by = c("year", "month"))

Arguments

Value

Named numeric vector of mean DTR values.

See Also

growing_degree_days, frost_days.

Examples

dates <- seq(as.Date("1980-01-01"), by = "day", length.out = 365 * 10)
tmax  <- rep(25, length(dates))
tmin  <- rep(12, length(dates))
dtr   <- diurnal_temp_range(tmax, tmin, dates)
cat("Mean DTR:", round(mean(dtr), 1), "\u00b0C\n")

Drought Spell Detection from Standardised Indices

Description

Identifies drought episodes as periods during which a standardised drought index (SPI, SPEI, or any continuous series) remains below a threshold for a minimum number of consecutive time steps. The severity integrates the area between the index series and the threshold.

Usage

drought_spell(index_series, dates, threshold = -1.0, min_duration = 2)

Arguments

Value

A data.frame with columns start, end, duration, min_index, mean_index, and severity (positive number: sum of threshold - index over the event). Returns an empty data frame if no events are found.

References

McKee, T.B., Doesken, N.J. and Kleist, J. (1993). The relationship of drought frequency and duration to time scales. Proceedings of the 8th Conference on Applied Climatology, 179–184.

See Also

spi, spei.

Examples

set.seed(7)
spi_vals <- stats::rnorm(360)
dates_m  <- seq(as.Date("1990-01-01"), by = "month",
                length.out = 360)
droughts <- drought_spell(spi_vals, dates_m, threshold = -1)
cat("Drought events:", nrow(droughts), "\n")

Environmental Lapse Rate Estimation

Description

Estimates the temperature lapse rate (change in temperature per 1000 m elevation gain) from station temperature and elevation data via ordinary least squares regression. Useful for statistical downscaling and data quality assessment.

Usage

elevation_lapse_rate(temp, elevation)

Arguments

Value

A list with:

lapse_rate_per_m

Numeric: OLS slope (^\circC m^{-1}).

lapse_rate_per_1000m

Numeric: lapse rate per 1000 m (^\circC km^{-1}).

intercept

Numeric: OLS intercept.

r_squared

Numeric: coefficient of determination.

data

Data frame with columns elevation, temp, and fitted.

Examples

elev <- seq(100, 3000, by = 100)
temp <- 25 - 0.0065 * elev + stats::rnorm(30, 0, 0.5)
lr   <- elevation_lapse_rate(temp, elev)
cat("Lapse rate:", round(lr$lapse_rate_per_1000m, 2),
    "\u00b0C / 1000 m\n")
cat("R-squared:", round(lr$r_squared, 3), "\n")

Extreme Value Index (Hill Estimator)

Description

Estimates the tail index of a heavy-tailed climate variable (e.g., wind speed, precipitation intensity) using the Hill estimator, with a stability plot for choosing the order statistic k.

Usage

extreme_value_index(x, k = NULL)

Arguments

Value

A list with:

hill_index

Numeric: Hill estimate of the tail index at k.

xi_estimate

Numeric: same as hill_index (GEV shape parameterisation).

k_used

Integer: order statistic used.

n

Integer: sample size.

hill_plot

Data frame with columns k and hill: Hill estimates across a range of k for stability assessment.

References

Hill, B.M. (1975). A simple general approach to inference about the tail of a distribution. Annals of Statistics 3, 1163–1174. doi:10.1214/aos/1176343247

See Also

fit_gev, peaks_over_threshold.

Examples

set.seed(9)
ws <- abs(stats::rnorm(500, 10, 4))^1.5
ev <- extreme_value_index(ws)
cat("Hill tail index:", round(ev$hill_index, 3), "\n")

plot(ev$hill_plot$k, ev$hill_plot$hill, type = "l",
     xlab = "k", ylab = "Hill estimate")

Gap-Filling for Climate Series

Description

Fills missing values in a climate series using linear interpolation, monthly climatological means, or regression against a nearby reference station.

Usage

fill_gaps_climate(x, method = c("linear", "climatology", "reference"),
                  dates = NULL, ref = NULL)

Arguments

Value

Numeric vector with missing values filled.

See Also

homogenize_series, standardize_climate.

Examples

x  <- c(10, NA, NA, 13, 14, NA, 16)
xf <- fill_gaps_climate(x)
print(xf)

EOF-Based Spatial Fingerprint Analysis

Description

Extracts the leading Empirical Orthogonal Functions (EOFs) and corresponding Principal Components (PCs) from a climate anomaly field as spatial fingerprints of climate change or variability modes.

Usage

fingerprint_analysis(data, n_eof = 3)

Arguments

Value

A list with:

eof

Numeric matrix (p \times n_eof): spatial EOF patterns.

pc

Numeric matrix (n \times n_eof): principal component time series.

var_explained

Numeric vector: fraction of variance explained by each EOF.

cumvar

Numeric vector: cumulative explained variance.

n_eof

Integer: number of EOFs extracted.

References

Lorenz, E. N. (1956). Empirical Orthogonal Functions and Statistical Weather Prediction. Scientific Report No. 1, Statistical Forecasting Project. Massachusetts Institute of Technology, Department of Meteorology, Cambridge, Massachusetts.

Von Storch, H. and Zwiers, F. W. (1999). Statistical Analysis in Climate Research. Cambridge University Press, Cambridge. doi:10.1017/CBO9780511612336

See Also

detection_attribution, optimal_fingerprint.

Examples

set.seed(5)
mat <- matrix(stats::rnorm(50 * 100), nrow = 50)
## Add a forced spatially coherent signal
mat[, 1:30] <- mat[, 1:30] +
               outer(seq(0, 1, length.out = 50), rep(1, 30))
fp <- fingerprint_analysis(mat, n_eof = 3)
cat("Variance explained by EOF1:",
    round(fp$var_explained[1] * 100, 1), "%\n")

plot(fp$pc[, 1], type = "l", main = "Leading PC",
     xlab = "Year", ylab = "PC score")

Fit Generalised Extreme Value Distribution

Description

Fits the three-parameter Generalised Extreme Value (GEV) distribution to a vector of block maxima (e.g., annual maximum daily temperature or precipitation) via maximum likelihood estimation (MLE).

Usage

fit_gev(x, conf.level = 0.95)

Arguments

Details

The GEV cumulative distribution function is

F(x;\mu,\sigma,\xi) = \exp\!\left\{-\left[1 + \xi\!\left(\frac{x-\mu}{\sigma}\right)\right]^{-1/\xi}\right\}

for 1 + \xi(x-\mu)/\sigma > 0. When \xi \to 0 this reduces to the Gumbel distribution.

Starting values for optimisation are obtained by Gumbel method-of-moments. The Hessian at the MLE is used to construct the parameter covariance matrix and delta-method confidence intervals.

Value

An object of class c("gev_fit", "climate_test") (a list) with:

mu

Numeric: location parameter \mu.

sigma

Numeric: scale parameter \sigma > 0.

xi

Numeric: shape parameter \xi.

se

Numeric vector of length 3: standard errors.

ci

3-by-2 matrix: lower and upper confidence limits.

loglik

Numeric: log-likelihood at the MLE.

aic

Numeric: Akaike information criterion.

bic

Numeric: Bayesian information criterion.

n

Integer: number of block maxima used.

data

Numeric: the input data.

cov_mat

3-by-3 numeric matrix: parameter covariance.

method

Character: "GEV maximum likelihood".

conf.level

Numeric: the confidence level used.

Calling print() on this object shows a formatted parameter table.

References

Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer, London. doi:10.1007/978-1-4471-3675-0

Jenkinson, A.F. (1955). The frequency distribution of the annual maximum (or minimum) values of meteorological elements. Quarterly Journal of the Royal Meteorological Society 81, 158–171.

See Also

return_period for return-level estimation; peaks_over_threshold for the POT alternative; rgev_sim for simulating GEV random variates.

Examples

set.seed(42)
ann_max <- rgev_sim(50, mu = 35, sigma = 4, xi = 0.1)
gev <- fit_gev(ann_max)
print(gev)

Frost Day Count

Description

Counts the number of frost days (days on which daily minimum temperature falls below 0^\circC) aggregated by year or calendar month.

Usage

frost_days(tmin, dates, by = c("year", "month"))

Arguments

Value

Named integer vector of frost day counts.

See Also

growing_degree_days, cold_spell_detection.

Examples

dates <- seq(as.Date("2000-01-01"), by = "day", length.out = 365)
tmin  <- rep(5, 365)
tmin[1:30] <- -2   ## 30 frost days in January
fd <- frost_days(tmin, dates, by = "year")
cat("Annual frost days:", fd["2000"], "\n")

Growing Degree Days

Description

Computes daily Growing Degree Days (GDD) as the amount by which the daily mean temperature exceeds a base threshold, with an optional upper temperature cap.

Usage

growing_degree_days(tmax, tmin, base_temp = 10,
                    upper_temp = Inf, dates = NULL,
                    cumulative = FALSE)

Arguments

Value

Numeric vector of daily GDD (or cumulative GDD if cumulative = TRUE).

See Also

frost_days, diurnal_temp_range.

Examples

n    <- 365
tmax <- rep(25, n)
tmin <- rep(15, n)
## Mean = 20, base = 10 -> 10 GDD per day
gdd <- growing_degree_days(tmax, tmin, base_temp = 10)
cat("Annual GDD:", sum(gdd), "\n")   ## should be 3650

Heat Index (Apparent Temperature)

Description

Computes the Heat Index (apparent temperature, also called the “feels like” temperature) from air temperature and relative humidity using the Rothfusz regression equation (National Weather Service).

Usage

heat_index(temp, rh, unit = "C")

Arguments

Value

Numeric vector of heat index values in the same unit as temp.

References

Rothfusz, L. P. (1990). The Heat Index Equation. Technical Attachment SR/SSD 90-23. National Weather Service Southern Region, Fort Worth, Texas.

Steadman, R. G. (1979). The assessment of sultriness. Part I: A temperature-humidity index based on human physiology and clothing science. Journal of Applied Meteorology 18(7), 861–873. doi:10.1175/1520-0450(1979)018<0861:TAOSPI>2.0.CO;2

See Also

wind_chill, heat_wave_detection.

Examples

## At 35 C, RH 80%
hi <- heat_index(temp = 35, rh = 80)
print(paste("Heat index:", round(hi, 1), "deg C"))
## Higher humidity feels hotter
heat_index(35, 90) > heat_index(35, 40)

Heat Wave Detection

Description

Identifies heat wave events as periods during which daily maximum temperature exceeds a threshold for a minimum number of consecutive days.

Usage

heat_wave_detection(tmax, dates, threshold = "p90", min_days = 3)

Arguments

Value

A data.frame with one row per event and columns:

start_date

Date: first day of the event.

end_date

Date: last day.

duration

Integer: length in days.

peak_temp

Numeric: maximum temperature during the event.

mean_temp

Numeric: mean temperature.

intensity

Numeric: mean excess above the threshold (^\circC-days).

If no events are found, an empty data.frame with the above columns is returned invisibly.

See Also

cold_spell_detection, heat_index.

Examples

set.seed(6)
dates <- seq(as.Date("1990-01-01"), by = "day", length.out = 365 * 10)
doy   <- as.integer(format(dates, "%j"))
tmax  <- 25 + 10 * sin(2 * pi * doy / 365) +
         stats::rnorm(length(dates), 0, 3)
hw <- heat_wave_detection(tmax, dates, threshold = "p90", min_days = 3)
cat("Heat wave events detected:", nrow(hw), "\n")
head(hw)

Homogenise a Climate Series Using SNHT

Description

Applies a mean-shift correction to a climate series based on the break point detected by the Standard Normal Homogeneity Test (SNHT). The segment before the detected break is shifted upward or downward so that its mean equals the mean of the segment after the break.

Usage

homogenize_series(x, alpha = 0.05)

Arguments

Value

Numeric vector: the homogenised series (same length as x).

See Also

temporal_homogeneity, fill_gaps_climate.

Examples

set.seed(20)
x_inhom <- c(stats::rnorm(40, 0, 1), stats::rnorm(40, 1.5, 1))
x_hom   <- homogenize_series(x_inhom)
cat("Mean before correction:", round(mean(x_inhom[1:40]), 2), "\n")
cat("Mean after  correction:", round(mean(x_hom[1:40]), 2), "\n")

Local Spatial Hot-Spot and Cold-Spot Detection (Getis-Ord Gi*)

Description

Identifies statistically significant spatial clusters of high values (hot spots) or low values (cold spots) in a climate field using the Getis-Ord G_i^* local statistic.

Usage

hot_cold_spots(x, coords, dist_threshold = NULL, alpha = 0.05)

Arguments

Details

G_i^* includes the focal location itself in the sum, making it suited to detecting local hot and cold spots rather than spatial outliers. Z scores are derived under the assumption of normality.

Value

A data.frame with one row per location and columns:

index

Integer: location index.

x

Numeric: original climate value.

lon, lat

Numeric: coordinates.

gi_star

Numeric: G_i^* Z score.

p.value

Numeric: two-tailed p-value.

n_neighbours

Numeric: sum of spatial weights (number of neighbours including self).

classification

Character: "hot spot", "cold spot", or "not significant".

References

Getis, A. and Ord, J.K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis 24, 189–206. doi:10.1111/j.1538-4632.1992.tb00261.x

See Also

morans_i, spatial_anomaly.

Examples

set.seed(3)
n      <- 50
coords <- data.frame(x = stats::runif(n, 0, 10),
                     y = stats::runif(n, 0, 10))
vals   <- ifelse(coords$x > 7,
                 stats::rnorm(n, 30, 2),
                 stats::rnorm(n, 15, 2))
hs <- hot_cold_spots(vals, coords, dist_threshold = 2)
table(hs$classification)

Mann-Kendall Trend Test

Description

Performs the non-parametric Mann-Kendall test for a monotonic trend in a climate time series. Missing values are silently removed. An optional AR(1) pre-whitening step (Yue and Wang 2002) removes serial autocorrelation before the test statistic is computed.

Usage

mk_test(x, prewhiten = FALSE, conf.level = 0.95,
        alternative = c("two.sided", "greater", "less"))

Arguments

Details

The Mann-Kendall statistic S is the sum of signs of all pairwise differences \mathrm{sgn}(x_j - x_i) for j > i. Under the null hypothesis of no trend, S is asymptotically normal with mean zero and variance adjusted for tied values.

The standardised statistic Z is obtained by continuity correction (\pm 1 adjustment to S).

When prewhiten = TRUE and the sample AR(1) coefficient exceeds 0.05, the series is filtered as x'_t = x_t - \hat\rho\, x_{t-1} before computing S.

Value

An object of class c("mk_test", "climate_test") (a list) containing:

statistic

Named numeric: the standardised Z statistic.

p.value

Numeric: p-value for the chosen alternative.

tau

Numeric: Kendall's tau.

S

Integer: Mann-Kendall score.

var.S

Numeric: variance of S adjusted for ties.

n

Integer: number of non-missing observations used.

trend

Character: "increasing", "decreasing", or "no trend".

alternative

Character: the alternative hypothesis.

conf.level

Numeric: the confidence level used.

prewhiten

Logical: whether pre-whitening was applied.

data

Numeric: the original input vector (including NAs).

method

Character: description of the method.

References

Mann, H.B. (1945). Nonparametric tests against trend. Econometrica 13, 245–259. doi:10.2307/1907187

Kendall, M.G. (1975). Rank Correlation Methods. Griffin, London.

Yue, S. and Wang, C.Y. (2002). Applicability of prewhitening to eliminate the influence of serial correlation on the Mann-Kendall test. Water Resources Research 38, 1068. doi:10.1029/2001WR000861

See Also

sens_slope for the slope magnitude; trend_significance for simultaneous testing of multiple stations.

Examples

## Basic usage on a warming temperature series
set.seed(42)
years <- 1971:2020
temp  <- 14.0 + 0.025 * (years - 1971) + stats::rnorm(50, 0, 0.4)
result <- mk_test(temp)
print(result)

## One-sided test
mk_test(temp, alternative = "greater")

## Pre-whitened variant for autocorrelated series
ar_temp <- as.numeric(stats::arima.sim(list(ar = 0.6), n = 60)) +
           seq(0, 3, length.out = 60) + 14
mk_test(ar_temp, prewhiten = TRUE)


## Plot the result (time series + distribution)
plot(result)

Moran's I Test for Spatial Autocorrelation

Description

Computes the global Moran's I statistic for spatial autocorrelation in a climate field (e.g., temperature or precipitation anomalies at observation stations), with both analytical and permutation-based p-values.

Usage

morans_i(x, coords, dist_threshold = NULL, n_perm = 999)

Arguments

Details

Moran's I is defined as

I = \frac{n}{S_0} \frac{\mathbf{z}^{\top} W \mathbf{z}}{\mathbf{z}^{\top}\mathbf{z}}

where \mathbf{z} are mean-centred values, W is the row-standardised binary spatial weight matrix, and S_0 is the sum of all weights.

Under the randomisation assumption the expected value is E[I] = -1/(n-1). The analytical variance uses the formulas of Cliff and Ord (1981). The permutation p-value randomly reassigns values to locations n_perm times.

Value

A list with:

I

Numeric: Moran's I statistic.

expected

Numeric: expected value -1/(n-1).

variance

Numeric: analytical variance.

Z

Numeric: standardised Z score.

p.value

Numeric: analytical two-tailed p-value.

p.perm

Numeric: permutation p-value.

n_perm

Integer: number of permutations used.

dist_threshold

Numeric: neighbourhood radius used.

interpretation

Character: plain-English summary.

References

Moran, P.A.P. (1950). Notes on continuous stochastic phenomena. Biometrika 37, 17–23. doi:10.2307/2332142

Cliff, A.D. and Ord, J.K. (1981). Spatial Processes: Models and Applications. Pion, London.

See Also

hot_cold_spots for local spatial clustering; spatial_trend_field for per-location trend analysis.

Examples

set.seed(7)
n      <- 30
coords <- data.frame(lon = stats::runif(n, -10, 10),
                     lat = stats::runif(n, 40, 60))
## Temperature decreasing with latitude -> positive autocorrelation
x <- 25 - 0.3 * coords$lat + stats::rnorm(n, 0, 1)
mi <- morans_i(x, coords, n_perm = 199)
cat("Moran's I:", round(mi$I, 3), "  p =", round(mi$p.value, 4), "\n")
cat(mi$interpretation, "\n")

Optimal Fingerprint Regression

Description

Estimates scaling factors for anthropogenic (ANT) and natural (NAT) climate signals by regressing observed climate changes onto model-simulated response patterns using Generalised Least Squares (GLS), with an optional noise covariance matrix estimated from control runs.

Usage

optimal_fingerprint(obs, all_forcing, nat_forcing = NULL,
                    noise_cov = NULL)

Arguments

Value

A list with:

beta_all

Numeric: scaling factor for the ALL-forcing signal (or ANT if nat_forcing is supplied).

beta_nat

Numeric: scaling factor for the NAT signal (NA if nat_forcing is NULL).

residual_variance

Numeric: residual variance per degree of freedom.

scaling_factors

Numeric vector or matrix: all estimated scaling factors.

method

Character: "Optimal Fingerprint (GLS)".

References

Allen, M.R. and Tett, S.F.B. (1999). Checking for model consistency in optimal fingerprinting. Climate Dynamics 15, 419–434. doi:10.1007/s003820050291

See Also

detection_attribution, fingerprint_analysis.

Examples

set.seed(42)
obs_c <- cumsum(stats::rnorm(50, 0.03, 0.1))
all_c <- cumsum(stats::rnorm(50, 0.025, 0.05)) +
         cumsum(stats::rnorm(50, 0.01, 0.05))
nat_c <- cumsum(stats::rnorm(50, 0, 0.1))
ofp   <- optimal_fingerprint(obs_c, all_c, nat_c)
cat("ANT scaling factor:", round(ofp$beta_all, 2), "\n")
cat("NAT scaling factor:", round(ofp$beta_nat, 2), "\n")

Simplified Palmer Drought Severity Index

Description

Computes a simplified, self-calibrated Palmer Drought Severity Index (PDSI) from monthly mean temperature and precipitation using the Thornthwaite potential evapotranspiration method.

Usage

pdsi_simple(temp, precip, lat = 40, awc = 150)

Arguments

Value

Numeric vector of simplified PDSI values. Negative values indicate drought; positive values indicate wet conditions.

References

Palmer, W. C. (1965). Meteorological Drought. Research Paper No. 45. US Weather Bureau, Washington, D.C.

Thornthwaite, C. W. (1948). An approach toward a rational classification of climate. Geographical Review 38(1), 55–94. doi:10.2307/210739

See Also

spi, spei.

Examples

set.seed(1)
n    <- 360
doy  <- rep(1:12, 30)
temp <- 10 + 12 * sin(pi * doy / 6) + stats::rnorm(n, 0, 1)
pr   <- pmax(0, 50 + 20 * cos(pi * doy / 6) + stats::rnorm(n, 0, 15))
pdsi_vals <- pdsi_simple(temp, pr, lat = 40)
plot(pdsi_vals, type = "l", ylab = "PDSI",
     xlab = "Month", main = "Simplified PDSI")
abline(h = 0, lty = 2)

Peaks-Over-Threshold Analysis

Description

Selects independent peak exceedances above a user-defined threshold, fits the Generalised Pareto Distribution (GPD) to the excesses, and estimates return levels.

Usage

peaks_over_threshold(x, threshold, min_sep = 3,
                     return_periods = c(10, 50, 100),
                     n_years = NULL)

Arguments

Value

A list with:

sigma

Numeric: GPD scale parameter.

xi

Numeric: GPD shape parameter.

threshold

Numeric: the threshold used.

n_excess

Integer: number of peaks retained.

lambda

Numeric: average number of exceedances per year.

n_years

Numeric: record length in years.

return_levels

Data frame with columns T and level.

excess

Numeric vector: retained peak excesses above the threshold.

References

Davison, A.C. and Smith, R.L. (1990). Models for exceedances over high thresholds (with discussion). Journal of the Royal Statistical Society B 52, 393–442. doi:10.1111/j.2517-6161.1990.tb01796.x

See Also

fit_gev, extreme_value_index.

Examples

set.seed(2)
daily_precip <- stats::rexp(365 * 30, rate = 1 / 5)
pot <- peaks_over_threshold(daily_precip, threshold = 20,
                             n_years = 30,
                             return_periods = c(10, 50, 100))
print(pot$return_levels)

Print Method for GEV Fit Objects

Description

Prints a formatted parameter table for objects of class "gev_fit" returned by fit_gev.

Usage

## S3 method for class 'gev_fit'
print(x, ...)

Arguments

Value

x, invisibly.

See Also

fit_gev.

Examples

set.seed(2)
gev <- fit_gev(rgev_sim(40, mu = 30, sigma = 5, xi = 0.1))
print(gev)

Return Periods and Return Levels

Description

Computes return levels (values expected to be exceeded on average once every T years) from a fitted GEV object with delta-method confidence intervals, or empirically using the Gringorten plotting position.

Usage

return_period(fit, return_periods = c(2, 5, 10, 25, 50, 100, 200),
              conf.level = 0.95)

Arguments

Value

A data.frame with columns T (return period), level (return level), lower, and upper (confidence bounds; NA for empirical method).

References

Gringorten, I.I. (1963). A plotting rule for extreme probability paper. Journal of Geophysical Research 68, 813–814. doi:10.1029/JZ068i003p00813

See Also

fit_gev, peaks_over_threshold.

Examples

set.seed(1)
am  <- rgev_sim(50, mu = 30, sigma = 5, xi = 0.05)
gev <- fit_gev(am)
rp  <- return_period(gev, c(2, 10, 50, 100))
print(rp)

Simulate GEV Random Variates

Description

Generates random variates from the Generalised Extreme Value distribution using the probability-integral transform. Primarily intended for testing, simulation studies, and vignette examples.

Usage

rgev_sim(n, mu = 0, sigma = 1, xi = 0)

Arguments

Value

Numeric vector of length n.

See Also

fit_gev.

Examples

set.seed(1)
x <- rgev_sim(100, mu = 30, sigma = 5, xi = 0.1)
hist(x, main = "GEV sample", col = "lightblue")

Rolling-Window Trend Analysis

Description

Applies Sen's slope estimator over a moving window to identify periods of accelerating or decelerating warming (or other climate trends).

Usage

rolling_trend(x, window = 20, step = 1)

Arguments

Value

A data.frame with one row per window position and columns:

start_index

Integer: first index of the window.

end_index

Integer: last index of the window.

mid_index

Numeric: mid-point of the window.

slope

Numeric: Sen's slope within the window.

slope_decade

Numeric: slope scaled to change per decade.

See Also

sens_slope, mk_test.

Examples

set.seed(1)
years <- 1950:2020
temp  <- 14 + 0.015 * (years - 1950) + stats::rnorm(71, 0, 0.5)
rt <- rolling_trend(temp, window = 20, step = 2)
plot(rt$mid_index, rt$slope_decade, type = "l",
     xlab = "Mid-window index", ylab = "Trend per decade")
abline(h = 0, lty = 2)

Seasonal Climate Decomposition

Description

Decomposes a regularly-spaced climate series into trend, seasonal, and irregular (remainder) components using STL (Seasonal and Trend decomposition using Loess) or classical additive decomposition.

Usage

seasonal_decompose_climate(x, frequency = 12,
                           method = c("stl", "classical"),
                           s.window = "periodic")

Arguments

Value

An object of class c("climate_decomp", "climate_test") (a list) with elements:

method

Character: decomposition method used.

frequency

Integer: cycle length.

x

Numeric: original (possibly interpolated) series.

trend

Numeric: trend component.

seasonal

Numeric: seasonal component.

remainder

Numeric: irregular / residual component.

Calling plot() on this object produces a four-panel time series display (original, trend, seasonal, remainder).

See Also

rolling_trend to analyse trend acceleration; mk_test to test for monotonic trend in the extracted trend component.

Examples

## 30 years of synthetic monthly temperature
set.seed(7)
n     <- 360
t_idx <- seq_along(numeric(n))
temp  <- 15 + 0.002 * t_idx + 8 * sin(2 * pi * t_idx / 12) +
         stats::rnorm(n, 0, 0.5)
dc <- seasonal_decompose_climate(temp, frequency = 12)

## Inspect components

plot(dc)

Sen's Slope Estimator

Description

Computes Sen's slope — the median of all pairwise slopes — as a robust, non-parametric estimator of linear trend magnitude for a climate time series. An optional confidence interval is derived via a normal approximation on the rank of the sorted pairwise slopes.

Usage

sens_slope(x = NULL, y, conf.level = 0.95)

Arguments

Details

The estimator computes all n(n-1)/2 pairwise slopes Q_{ij} = (x_j - x_i)/(t_j - t_i) for j > i and takes their median. The intercept is computed as \hat\beta_0 = \mathrm{median}(y) - \hat\beta_1\, \mathrm{median}(x).

The confidence interval uses a normal approximation based on the Mann-Kendall variance to identify the lower and upper rank positions in the sorted slope vector (Sen 1968).

The convenience field slope_decade multiplies the slope by 10, giving the trend per decade — a standard reporting unit in climate science.

Value

A list with elements:

slope

Numeric: Sen's slope estimate.

intercept

Numeric: corresponding intercept.

slope_ci

Named numeric vector c(lower, upper): confidence interval for the slope.

slope_decade

Numeric: slope scaled to change per decade (slope * 10).

n

Integer: number of complete pairs used.

conf.level

Numeric: the confidence level used.

x

Numeric: time indices used (after NA removal).

y

Numeric: observations used (after NA removal).

fitted

Numeric: fitted values \hat\beta_0 + \hat\beta_1 x.

References

Sen, P.K. (1968). Estimates of the regression coefficient based on Kendall's tau. Journal of the American Statistical Association 63, 1379–1389. doi:10.2307/2285891

See Also

mk_test for the associated significance test; rolling_trend for moving-window application.

Examples

## Annual mean temperature 1970-2020
set.seed(1)
years <- 1970:2020
temp  <- 14.0 + 0.03 * (years - 1970) + stats::rnorm(51, 0, 0.4)
ss <- sens_slope(years, temp)
cat("Warming rate:", round(ss$slope_decade, 3), "\u00b0C per decade\n")
cat("95% CI:     [", round(ss$slope_ci["lower"], 3), ",",
                      round(ss$slope_ci["upper"], 3), "]\n")

## Without explicit time index (uses 1:n)
sens_slope(y = temp)$slope_decade

Spatial Climate Anomaly Field

Description

Computes standardised anomalies for each grid cell or station column relative to a user-defined climatological baseline period.

Usage

spatial_anomaly(data, time_index, baseline_start, baseline_end)

Arguments

Value

Numeric matrix of standardised anomalies with the same dimensions as data. Columns with zero baseline standard deviation are returned as NA.

See Also

anomaly_baseline for a single time series.

Examples

set.seed(4)
mat <- matrix(stats::rnorm(50 * 20, 15, 3), nrow = 50)
idx <- 1971:2020
anm <- spatial_anomaly(mat, idx, 1971, 2000)
cat("Dimensions:", dim(anm), "\n")

Spatial Interpolation for Climate Data

Description

Interpolates scattered station observations onto a regular grid using Inverse Distance Weighting (IDW) or a two-dimensional loess spline.

Usage

spatial_interpolate(obs_coords, obs_values, grid_coords,
                    method = c("idw", "spline"), power = 2)

Arguments

Value

Numeric vector of length m: interpolated values at grid_coords.

References

Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. Proceedings of the 23rd ACM National Conference, 517–524. doi:10.1145/800186.810616

See Also

elevation_lapse_rate, spatial_anomaly.

Examples

set.seed(5)
obs  <- matrix(stats::runif(40), ncol = 2)
vals <- sin(obs[, 1] * 3) + cos(obs[, 2] * 3)
grd  <- as.matrix(expand.grid(x = seq(0.1, 0.9, 0.1),
                               y = seq(0.1, 0.9, 0.1)))
pred <- spatial_interpolate(obs, vals, grd, method = "idw")
cat("Interpolated", length(pred), "grid points\n")

Spatial Field of Climate Trends

Description

Applies the Mann-Kendall test and Sen's slope independently to each column (grid cell or station) of a space-time matrix, producing a map of trend magnitudes and significance.

Usage

spatial_trend_field(data_3d, ...)

Arguments

Value

A data.frame with one row per location and columns loc, slope, slope_decade, tau, p.value, and trend.

See Also

mk_test, trend_significance.

Examples

set.seed(11)
mat <- matrix(stats::rnorm(50 * 100), nrow = 50)
## Impose warming in second half of locations
mat[, 51:100] <- mat[, 51:100] +
                 outer(seq_len(50), rep(0.03, 50))
sf <- spatial_trend_field(mat)
cat("Significant cells:", sum(sf$p.value < 0.05, na.rm = TRUE), "\n")

Standardised Precipitation-Evapotranspiration Index (SPEI)

Description

Computes the Standardised Precipitation-Evapotranspiration Index by fitting a three-parameter log-logistic distribution to the accumulated climatic water balance (precipitation minus potential evapotranspiration). If PET is not supplied directly, it is estimated using the Hargreaves-Samani formula from monthly minimum/maximum temperature and latitude.

Usage

spei(precip, pet = NULL, tmin = NULL, tmax = NULL,
     lat = 45, scale = 3, dates = NULL)

Arguments

Value

Numeric vector of SPEI values (same length as precip).

References

Vicente-Serrano, S.M., Begueria, S. and Lopez-Moreno, J.I. (2010). A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index. Journal of Climate 23, 1696–1718. doi:10.1175/2009JCLI2909.1

Hargreaves, G.H. and Samani, Z.A. (1985). Reference crop evapotranspiration from temperature. Applied Engineering in Agriculture 1, 96–99.

See Also

spi, drought_spell.

Examples

set.seed(5)
n    <- 240
pr   <- stats::rgamma(n, 5, 0.04)
tmin <- abs(stats::rnorm(n, 8, 3)) + 2
tmax <- tmin + stats::runif(n, 6, 12)
sp6  <- spei(precip = pr, tmin = tmin, tmax = tmax, lat = 45, scale = 6)
cat("SPEI-6 SD:", round(stats::sd(sp6, na.rm = TRUE), 3), "\n")

Standardised Precipitation Index (SPI)

Description

Computes the Standardised Precipitation Index at any accumulation time scale from a monthly precipitation series. A two-parameter gamma distribution is fitted to the positive accumulated values over a reference period, and probabilities are transformed to Z scores.

Usage

spi(precip, scale = 3, dates = NULL,
    ref_start = NULL, ref_end = NULL)

Arguments

Details

An n-month accumulation is computed by summing n consecutive monthly values, introducing n - 1 leading NAs. The proportion of zero-precipitation months is modelled explicitly as a point mass at zero; the gamma CDF is used for positive values. SPI values are obtained by inverting the normal distribution.

Typical SPI classifications (McKee et al. 1993):

Value

Numeric vector of SPI values (same length as precip). The first scale - 1 elements are NA.

References

McKee, T.B., Doesken, N.J. and Kleist, J. (1993). The relationship of drought frequency and duration to time scales. Proceedings of the 8th Conference on Applied Climatology, 179–184.

See Also

spei, drought_spell.

Examples

set.seed(3)
precip <- stats::rgamma(360, shape = 2, rate = 0.05)
spi3   <- spi(precip, scale = 3)
## Distribution should be approximately standard normal
cat("Mean:", round(mean(spi3, na.rm = TRUE), 3),
    "SD:", round(stats::sd(spi3, na.rm = TRUE), 3), "\n")


plot(spi3, type = "h",
     col  = ifelse(spi3 >= 0, "steelblue", "firebrick"),
     xlab = "Month", ylab = "SPI-3")
abline(h = c(-1, 1), lty = 2)

Standardise a Climate Variable

Description

Applies Z-score standardisation to a climate variable, optionally performing the standardisation separately within each calendar month to remove seasonality before further analysis (e.g., trend testing).

Usage

standardize_climate(x, by_month = FALSE, dates = NULL)

Arguments

Value

Numeric vector of standardised values.

See Also

anomaly_baseline.

Examples

x <- stats::rnorm(120, 20, 5)
z <- standardize_climate(x)
cat("Mean:", round(mean(z), 10), "  SD:", round(stats::sd(z), 6), "\n")

Standard Normal Homogeneity Test (SNHT)

Description

Applies Alexandersson's Standard Normal Homogeneity Test to detect a single inhomogeneity (e.g., a station relocation or instrument change) in a climate record.

Usage

temporal_homogeneity(x, alpha = 0.05)

Arguments

Value

A list with elements:

method

Character: "Standard Normal Homogeneity Test (SNHT)".

T0

Numeric: maximum SNHT statistic.

break_index

Integer: index of the detected break.

critical

Numeric: critical value from Alexandersson (1986).

significant

Logical: TRUE if T0 > critical.

T_series

Numeric vector: SNHT statistic at each candidate break position.

n

Integer: sample size.

References

Alexandersson, H. (1986). A homogeneity test applied to precipitation data. International Journal of Climatology 6, 661–675. doi:10.1002/joc.3370060607

See Also

homogenize_series to apply the detected break correction; change_point_detection for the Pettitt / CUSUM alternatives.

Examples

set.seed(10)
x <- c(stats::rnorm(40, 0, 1), stats::rnorm(40, 0.8, 1))
snht <- temporal_homogeneity(x)
cat("Break at index:", snht$break_index,
    " Significant:", snht$significant, "\n")

Multiple-Station Trend Significance with FDR/Bonferroni Correction

Description

Runs the Mann-Kendall test on each column of a climate data matrix (stations or grid cells) simultaneously and applies a false discovery rate (FDR) or Bonferroni correction to adjust for multiple comparisons.

Usage

trend_significance(data, correction = c("fdr", "bonferroni"), alpha = 0.05)

Arguments

Value

A data.frame with one row per station / grid cell and columns:

station

Integer: column index.

tau

Numeric: Kendall's tau.

p.raw

Numeric: unadjusted p-value.

p.adjusted

Numeric: adjusted p-value.

significant

Logical: whether the adjusted p-value is below alpha.

trend

Character: "increasing", "decreasing", or "no trend".

References

Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate. Journal of the Royal Statistical Society B 57, 289–300. doi:10.1111/j.2517-6161.1995.tb02031.x

See Also

mk_test, spatial_trend_field.

Examples

set.seed(42)
mat <- matrix(stats::rnorm(50 * 20), nrow = 50)
## Impose a trend in first 5 stations
mat[, 1:5] <- mat[, 1:5] + outer(seq_len(50), rep(0.05, 5))
ts_result <- trend_significance(mat)
table(ts_result$trend)

Wind Chill Temperature

Description

Computes the Wind Chill Temperature using the 2001 North American Joint Action Group for Temperature Indices (JAG/TI) formula, valid for air temperatures at or below 10 ^\circC and wind speeds above 4.8 km/h.

Usage

wind_chill(temp, wind)

Arguments

Value

Numeric: Wind Chill Temperature (^\circC).

References

Environment Canada and US National Weather Service (2001). Report on Wind Chill Temperature and Extreme Heat Indices: Evaluation and Improvement Projects. Office of the Federal Coordinator for Meteorological Services and Supporting Research (OFCM), Washington, D.C. Publication FCM-R19-2003.

Siple, P. A. and Passel, C. F. (1945). Measurements of dry atmospheric cooling in subfreezing temperatures. Proceedings of the American Philosophical Society 89(1), 177–199.

See Also

heat_index.

Examples

wind_chill(temp = -10, wind = 30)
## Should be substantially colder than -10
wind_chill(-10, 30) < -10