Type:	Package
Title:	Iberian Actuarial Climate Index Calculations
Version:	1.0.0
Description:	Calculates the Iberian Actuarial Climate Index and its components—including temperature, precipitation, wind power, and sea level data—to support climate change analysis and risk assessment. See "Zhou et al." (2023) <doi:10.26360/2023_3> for further details.
License:	GPL-3
Encoding:	UTF-8
Imports:	Rcpp (≥ 1.0.5), dplyr, tidyr, lubridate, magrittr, reticulate, ecmwfr, readr, stats, utils
LinkingTo:	Rcpp
Suggests:	roxygen2, devtools, knitr, rmarkdown, testthat (≥ 3.0.0)
RoxygenNote:	7.3.2
VignetteBuilder:	knitr
Config/testthat/edition:	3
NeedsCompilation:	yes
Packaged:	2025-03-17 14:53:24 UTC; nnnn
Author:	Nan Zhou [aut, cre]
Maintainer:	Nan Zhou <zhounan@ucm.es>
Repository:	CRAN
Date/Publication:	2025-03-17 16:10:02 UTC

Calculate Temperature Quantiles

Description

Computes temperature quantiles based on data within a specified base range.

Usage

calculate_quantiles(
  data_series,
  date_sequence,
  base.range,
  n,
  temp.qtiles,
  min.base.data.fraction.present
)

Arguments

Value

Data frame. Calculated quantiles.

Calculate Standardized Indices

Description

Helper function to calculate standardized indices based on a reference period.

Usage

calculate_standardized(df, freq, base_range)

Arguments

Value

Data frame with standardized values.

Calculate Wind Quantiles

Description

Computes the 90th percentile of wind speed for index calculations.

Usage

calculate_wind_quantiles(
  wind_data_series,
  date_sequence,
  base_range,
  wind_qtile = 0.9
)

Arguments

Value

Data frame. Calculated wind thresholds.

Calculate Consecutive Dry Days (CDD)

Description

Calculates the maximum length of consecutive dry days.

Usage

cdd(ci, spells_can_span_years = TRUE, monthly = TRUE)

Arguments

Value

Data frame with dates and calculated CDD values.

Examples

# 1. Generate a daily date sequence from 1960-01-01 to 2020-12-31
dates <- seq.Date(
  from = as.Date("1960-01-01"),
  to   = as.Date("2020-12-31"),
  by   = "day"
)

# 2. Create random weather data for each date
n <- length(dates)
tmax <- runif(n, min = 5, max = 40)
tmin <- runif(n, min = -10,  max = 5)
# Example: use a Poisson distribution to simulate precipitation
prec <- rpois(n, lambda = 2)
# Random wind speeds, e.g., 0 to 10 m/s
wind <- runif(n, min = 0, max = 10)

# 3. Construct the climate_input object
ci <- climate_input(
  tmax  = tmax,
  tmin  = tmin,
  prec  = prec,
  wind  = wind,
  dates = dates
)
# Then:
# 4. Calculate annual CDD index
cdd_values <- cdd(ci, monthly = FALSE)

Calculate Standardized CDD Index

Description

Calculates the standardized consecutive dry days index.

Usage

cdd_std(ci, freq = c("monthly", "seasonal"))

Arguments

Value

Data frame with dates and standardized CDD values.

Examples

# 1. Generate a daily date sequence from 1960-01-01 to 2020-12-31
dates <- seq.Date(
  from = as.Date("1960-01-01"),
  to   = as.Date("2020-12-31"),
  by   = "day"
)

# 2. Create random weather data for each date
n <- length(dates)
tmax <- runif(n, min = 5, max = 40)
tmin <- runif(n, min = -10,  max = 5)
# Example: use a Poisson distribution to simulate precipitation
prec <- rpois(n, lambda = 2)
# Random wind speeds, e.g., 0 to 10 m/s
wind <- runif(n, min = 0, max = 10)

# 3. Construct the climate_input object
ci <- climate_input(
  tmax  = tmax,
  tmin  = tmin,
  prec  = prec,
  wind  = wind,
  dates = dates
)
# Then:
# 4. Calculate standardized CDD index on a monthly basis
cdd_std_values <- cdd_std(ci, freq = "monthly")

Check Validity of Basic Arguments

Description

Validates the input parameters to ensure they are valid and consistent.

Usage

check_basic_argument_validity(tmax, tmin, prec, wind, dates, base.range, n)

Arguments

Value

None. Stops execution if validation fails.

Climate Input Function

Description

Processes climate data and calculates necessary statistics for climate index calculations.

Usage

climate_input(
  tmax = NULL,
  tmin = NULL,
  prec = NULL,
  wind = NULL,
  dates = NULL,
  base.range = c(1961, 1990),
  n = 5,
  quantiles = NULL,
  temp.qtiles = c(0.1, 0.9),
  wind.qtile = 0.9,
  max.missing.days = c(annual = 15, monthly = 3),
  min.base.data.fraction.present = 0.1
)

Arguments

Value

A list containing processed data and related information for climate index calculations.

Examples

# 1. Generate a daily date sequence from 1960-01-01 to 2020-12-31
dates <- seq.Date(
  from = as.Date("1960-01-01"),
  to   = as.Date("2020-12-31"),
  by   = "day"
)

# 2. Create random weather data for each date
n <- length(dates)
tmax <- runif(n, min = 5, max = 40)
tmin <- runif(n, min = -10,  max = 5)
# Example: use a Poisson distribution to simulate precipitation
prec <- rpois(n, lambda = 2)
# Random wind speeds, e.g., 0 to 10 m/s
wind <- runif(n, min = 0, max = 10)

# 3. Construct the climate_input object
ci <- climate_input(
  tmax  = tmax,
  tmin  = tmin,
  prec  = prec,
  wind  = wind,
  dates = dates
)

# 4. Examine the structure of ci
str(ci)

Create Date Sequence

Description

Generates a complete sequence of dates from the earliest to the latest date provided.

Usage

create_date_sequence(dates)

Arguments

Value

Date vector. A complete sequence of dates.

CSV to NetCDF Function

Description

Merges CSV files in a specified directory into a single NetCDF file, completing the grid by filling missing values.

Usage

csv_to_netcdf(csv_dir, output_file, freq)

Arguments

Value

None. The NetCDF file is saved to the specified location.

Examples

## Not run: 
# Example usage of csv_to_netcdf
csv_directory <- "/path/to/csv_files"
output_netcdf_file <- "/path/to/output_file.nc"
csv_to_netcdf(csv_dir = csv_directory, output_file = output_netcdf_file, freq = "monthly")

## End(Not run)

Download ERA5-Land Data

Description

Downloads ERA5-Land data from the ECMWF Climate Data Store for the specified time range and variables. Implements a retry mechanism to handle transient errors during data download.

Usage

download_data(
  start_year,
  end_year,
  start_month = 1,
  end_month = 12,
  variables = c("10m_u_component_of_wind", "10m_v_component_of_wind", "2m_temperature",
    "total_precipitation"),
  dataset = "reanalysis-era5-land",
  area = c(-90, -180, 90, 180),
  output_dir = "cds_data",
  user_id,
  user_key,
  max_retries = 3,
  retry_delay = 5,
  timeout = 7200
)

Arguments

Value

None. Data is downloaded to the specified output directory.

Examples

## Not run: 
# Set your ECMWF user ID and key
user_id <- "your_user_id"
user_key <- "your_api_key"

# Define the geographical area (North, West, South, East)
area <- c(90, -180, -90, 180) # Global

# Download data for 2020
download_data(
  start_year = 2020,
  end_year = 2020,
  variables = c("2m_temperature", "total_precipitation"),
  area = area,
  user_id = user_id,
  user_key = user_key
)

## End(Not run)

Export Data to CSV Function

Description

Exports data from a NetCDF file to CSV files, one for each latitude and longitude point, including only points where data is present. This function utilizes a Python script to perform the data processing.

Usage

export_data_to_csv(nc_file, output_dir)

Arguments

Details

The function calls a Python script using the 'reticulate' package to process the NetCDF file. The Python script 'data_processing.py' should be located in the 'python' directory of the 'rIACI' package. Only grid points with available data are exported to CSV files. Each CSV file corresponds to a specific latitude and longitude point.

Value

None. CSV files are saved to the specified output directory.

Examples

## Not run: 
# Example usage of export_data_to_csv
netcdf_file <- "/path/to/processed_data.nc"
csv_output_directory <- "/path/to/csv_output"
export_data_to_csv(nc_file = netcdf_file, output_dir = csv_output_directory)

## End(Not run)

Fill Data Series

Description

Aligns the input data with the complete date sequence, filling missing dates with NA.

Usage

fill_data_series(data, dates, date_sequence)

Arguments

Value

Numeric vector. Data series aligned with the date sequence.

Generate NA Masks

Description

Creates masks for annual and monthly data based on maximum allowed missing days.

Usage

generate_na_masks(data_series_list, date_sequence, max.missing.days)

Arguments

Value

List. NA masks for annual and monthly data.

Get Series Lengths at Ends

Description

Calculates the lengths of runs at the ends of a logical vector.

Usage

get.series.lengths.at.ends(x, na.value = FALSE)

Arguments

Value

Numeric vector. Lengths of runs at the ends.

Get Series Lengths

Description

Calculates the lengths of consecutive TRUE values in a logical vector.

Usage

get_series_lengths(bools)

Arguments

Value

Numeric vector. Lengths of consecutive TRUE runs.

Calculate Integrated Iberian Actuarial Climate Index (IACI)

Description

Integrates various standardized indices to compute the IACI.

Usage

iaci_output(ci, si, freq = c("monthly", "seasonal"))

Arguments

Value

Data frame with dates and IACI values.

Examples

# 1. Generate a climate_input object
dates <- seq.Date(
  from = as.Date("1960-01-01"),
  to   = as.Date("2020-12-31"),
  by   = "day"
)
n <- length(dates)
tmax <- runif(n, min = 5, max = 40)
tmin <- runif(n, min = -10,  max = 5)
# Example: use a Poisson distribution to simulate precipitation
prec <- rpois(n, lambda = 2)
# Random wind speeds, e.g., 0 to 10 m/s
wind <- runif(n, min = 0, max = 10)
ci <- climate_input(
  tmax  = tmax,
  tmin  = tmin,
  prec  = prec,
  wind  = wind,
  dates = dates
)
# 2. Create a sea level data frame with sea_input()
monthly_seq <- seq.Date(
  from = as.Date("1960-01-01"),
  to   = as.Date("2020-12-01"),
  by   = "month"
)
sea_dates <- format(monthly_seq, "%Y-%m")
n <- length(sea_dates)
linear_trend <- seq(0, 10, length.out = n)
random_noise <- rnorm(n, mean = 0, sd = 0.3)
sea_values   <- 10 + linear_trend + random_noise  # starts ~10, ends ~20
si <- sea_input(Date = sea_dates, Value = sea_values)
# Then:
# 3. Calculate the IACI with monthly frequency
result <- iaci_output(ci, si, freq = "monthly")

Convert Monthly Data to Seasonal Data

Description

Aggregates monthly data into seasonal averages.

Usage

monthly_to_seasonal(data)

Arguments

Value

Data frame with seasonal data.

Examples

## Not run: 
# Assuming you have monthly data in a data frame 'monthly_data'
# with columns 'Date' (in 'YYYY-MM' format) and 'Value'
seasonal_data <- monthly_to_seasonal(monthly_data)

## End(Not run)

Maximum Precipitation Over Consecutive Days

Description

Calculates maximum precipitation over consecutive n days.

Usage

nday_consec_prec_max(
  daily_prec,
  date_factor,
  ndays,
  center_mean_on_last_day = FALSE
)

Arguments

Value

Numeric vector. Maximum precipitation amounts.

Output IACI of all grids

Description

Processes all CSV files in the input directory and outputs the results to the output directory.

Usage

output_all(
  si,
  input_dir,
  output_dir,
  freq = c("monthly", "seasonal"),
  base.range = c(1961, 1990),
  time.span = c(1961, 2022)
)

Arguments

Value

None. Results are saved to the output directory.

Examples

## Not run: 
# Assume we have sea level data 'si' and input/output directories
input_dir <- "path/to/input/csv/files"
output_dir <- "path/to/save/output/files"
# Run the output_all function with monthly frequency
output_all(si, input_dir, output_dir, freq = "monthly")

## End(Not run)

Percent of Days Above or Below Threshold

Description

Calculates the percentage of days exceeding or below a threshold.

Usage

percent_days_op_threshold(
  temp,
  dates,
  month_day,
  date_factor,
  quantiles_df,
  qtile,
  op = "<",
  max_missing_days
)

Arguments

Value

Numeric vector or data frame with calculated percentages.

Process Data Function

Description

Processes NetCDF files in the input directory and saves merged and processed data to the output directory.

Usage

process_data(input_dir, output_dir, save_merged = FALSE)

Arguments

Value

None. Outputs are saved to the specified directory.

Examples

## Not run: 
# Example usage of process_data
input_directory <- "/path/to/input/netcdf_files"
output_directory <- "/path/to/output"
process_data(input_dir = input_directory, output_dir = output_directory, save_merged = TRUE)

## End(Not run)

Running Mean

Description

Calculates the running mean of a vector over a specified window size.

Usage

running_mean(vec, bin)

Arguments

Value

Numeric vector. Running mean values.

Calculate Rx5day Index

Description

Calculates the maximum consecutive 5-day precipitation amount.

Usage

rx5day(ci, freq = c("monthly", "annual"), center_mean_on_last_day = FALSE)

Arguments

Value

Data frame with dates and calculated Rx5day values.

Examples

# 1. Generate a daily date sequence from 1960-01-01 to 2020-12-31
dates <- seq.Date(
  from = as.Date("1960-01-01"),
  to   = as.Date("2020-12-31"),
  by   = "day"
)

# 2. Create random weather data for each date
n <- length(dates)
tmax <- runif(n, min = 5, max = 40)
tmin <- runif(n, min = -10,  max = 5)
# Example: use a Poisson distribution to simulate precipitation
prec <- rpois(n, lambda = 2)
# Random wind speeds, e.g., 0 to 10 m/s
wind <- runif(n, min = 0, max = 10)

# 3. Construct the climate_input object
ci <- climate_input(
  tmax  = tmax,
  tmin  = tmin,
  prec  = prec,
  wind  = wind,
  dates = dates
)
# Then:
# 4. Calculate monthly Rx5day index
rx5day_values <- rx5day(ci, freq = "monthly")

Calculate Standardized Rx5day Index

Description

Calculates the standardized Rx5day index.

Usage

rx5day_std(ci, freq = c("monthly", "seasonal"))

Arguments

Value

Data frame with dates and standardized Rx5day values.

Examples

# 1. Generate a daily date sequence from 1960-01-01 to 2020-12-31
dates <- seq.Date(
  from = as.Date("1960-01-01"),
  to   = as.Date("2020-12-31"),
  by   = "day"
)

# 2. Create random weather data for each date
n <- length(dates)
tmax <- runif(n, min = 5, max = 40)
tmin <- runif(n, min = -10,  max = 5)
# Example: use a Poisson distribution to simulate precipitation
prec <- rpois(n, lambda = 2)
# Random wind speeds, e.g., 0 to 10 m/s
wind <- runif(n, min = 0, max = 10)

# 3. Construct the climate_input object
ci <- climate_input(
  tmax  = tmax,
  tmin  = tmin,
  prec  = prec,
  wind  = wind,
  dates = dates
)
# Then:
# 4. Calculate standardized Rx5day index on a monthly basis
rx5day_std_values <- rx5day_std(ci, freq = "monthly")

Sea Level Input Function

Description

Creates a data frame for sea level data input.

Usage

sea_input(Date = NULL, Value = NA)

Arguments

Value

Data frame with Date and Value columns.

Examples

# 1. Create a monthly sequence from 1960-01 through 2020-12
monthly_seq <- seq.Date(
  from = as.Date("1960-01-01"),
  to   = as.Date("2020-12-01"),
  by   = "month"
)

# Convert to "YYYY-MM" format, which sea_input() expects
sea_dates <- format(monthly_seq, "%Y-%m")

# 2. Generate random data with an upward linear trend plus some noise
n <- length(sea_dates)
linear_trend <- seq(0, 10, length.out = n)
random_noise <- rnorm(n, mean = 0, sd = 0.3)
sea_values   <- 10 + linear_trend + random_noise  # starts ~10, ends ~20

# 3. Create a sea level data frame with sea_input()
si <- sea_input(Date = sea_dates, Value = sea_values)

# 4. Inspect the first few rows
head(si)

Calculate Standardized Sea Level Index

Description

Calculates the standardized sea level index.

Usage

sea_std(si, ci, freq = c("monthly", "seasonal"))

Arguments

Value

Data frame with dates and standardized sea level values.

Maximum Length of Consecutive Dry Days

Description

Calculates the maximum length of consecutive dry days.

Usage

spell_length_max(daily_prec, date_factor, threshold, op, spells_can_span_years)

Arguments

Value

Numeric vector. Maximum spell lengths.

Calculate T10p Index

Description

Calculates the combined percentage of days when temperature is below the 10th percentile.

Usage

t10p(ci, freq = c("monthly", "annual"))

Arguments

Value

Data frame with dates and calculated T10p values.

Examples

# 1. Generate a daily date sequence from 1960-01-01 to 2020-12-31
dates <- seq.Date(
  from = as.Date("1960-01-01"),
  to   = as.Date("2020-12-31"),
  by   = "day"
)

# 2. Create random weather data for each date
n <- length(dates)
tmax <- runif(n, min = 5, max = 40)
tmin <- runif(n, min = -10,  max = 5)
# Example: use a Poisson distribution to simulate precipitation
prec <- rpois(n, lambda = 2)
# Random wind speeds, e.g., 0 to 10 m/s
wind <- runif(n, min = 0, max = 10)

# 3. Construct the climate_input object
ci <- climate_input(
  tmax  = tmax,
  tmin  = tmin,
  prec  = prec,
  wind  = wind,
  dates = dates
)
# Then:
# 4. Calculate monthly T10p index
t10p_values <- t10p(ci, freq = "monthly")

Calculate Standardized T10p Index

Description

Calculates the standardized T10p index.

Usage

t10p_std(ci, freq = c("monthly", "seasonal"))

Arguments

Value

Data frame with dates and standardized T10p values.

Examples

# 1. Generate a daily date sequence from 1960-01-01 to 2020-12-31
dates <- seq.Date(
  from = as.Date("1960-01-01"),
  to   = as.Date("2020-12-31"),
  by   = "day"
)

# 2. Create random weather data for each date
n <- length(dates)
tmax <- runif(n, min = 5, max = 40)
tmin <- runif(n, min = -10,  max = 5)
# Example: use a Poisson distribution to simulate precipitation
prec <- rpois(n, lambda = 2)
# Random wind speeds, e.g., 0 to 10 m/s
wind <- runif(n, min = 0, max = 10)

# 3. Construct the climate_input object
ci <- climate_input(
  tmax  = tmax,
  tmin  = tmin,
  prec  = prec,
  wind  = wind,
  dates = dates
)
# Then:
# 4. Calculate standardized T10p index on a monthly basis
t10p_std_values <- t10p_std(ci, freq = "monthly")

Calculate T90p Index

Description

Calculates the combined percentage of days when temperature is above the 90th percentile.

Usage

t90p(ci, freq = c("monthly", "annual"))

Arguments

Value

Data frame with dates and calculated T90p values.

Examples

# 1. Generate a daily date sequence from 1960-01-01 to 2020-12-31
dates <- seq.Date(
  from = as.Date("1960-01-01"),
  to   = as.Date("2020-12-31"),
  by   = "day"
)

# 2. Create random weather data for each date
n <- length(dates)
tmax <- runif(n, min = 5, max = 40)
tmin <- runif(n, min = -10,  max = 5)
# Example: use a Poisson distribution to simulate precipitation
prec <- rpois(n, lambda = 2)
# Random wind speeds, e.g., 0 to 10 m/s
wind <- runif(n, min = 0, max = 10)

# 3. Construct the climate_input object
ci <- climate_input(
  tmax  = tmax,
  tmin  = tmin,
  prec  = prec,
  wind  = wind,
  dates = dates
)
# Then:
# 4. Calculate monthly T90p index
t90p_values <- t90p(ci, freq = "monthly")

Calculate Standardized T90p Index

Description

Calculates the standardized T90p index.

Usage

t90p_std(ci, freq = c("monthly", "seasonal"))

Arguments

Value

Data frame with dates and standardized T90p values.

Examples

# 1. Generate a daily date sequence from 1960-01-01 to 2020-12-31
dates <- seq.Date(
  from = as.Date("1960-01-01"),
  to   = as.Date("2020-12-31"),
  by   = "day"
)

# 2. Create random weather data for each date
n <- length(dates)
tmax <- runif(n, min = 5, max = 40)
tmin <- runif(n, min = -10,  max = 5)
# Example: use a Poisson distribution to simulate precipitation
prec <- rpois(n, lambda = 2)
# Random wind speeds, e.g., 0 to 10 m/s
wind <- runif(n, min = 0, max = 10)

# 3. Construct the climate_input object
ci <- climate_input(
  tmax  = tmax,
  tmin  = tmin,
  prec  = prec,
  wind  = wind,
  dates = dates
)
# Then:
# 4. Calculate standardized T90p index on a monthly basis
t90p_std_values <- t90p_std(ci, freq = "monthly")

Fast tapply Function

Description

Provides a faster implementation of tapply for factor-type indices.

Usage

tapply.fast(X, INDEX, FUN = NULL, ..., simplify = TRUE)

Arguments

Value

List or vector. Result of applying FUN to X grouped by INDEX.

Calculate TN10p Index

Description

Calculates the percentage of days when minimum temperature is below the 10th percentile.

Usage

tn10p(ci, freq = c("monthly", "annual"))

Arguments

Value

Data frame with dates and calculated TN10p values.

Examples

# 1. Generate a daily date sequence from 1960-01-01 to 2020-12-31
dates <- seq.Date(
  from = as.Date("1960-01-01"),
  to   = as.Date("2020-12-31"),
  by   = "day"
)

# 2. Create random weather data for each date
n <- length(dates)
tmax <- runif(n, min = 5, max = 40)
tmin <- runif(n, min = -10,  max = 5)
# Example: use a Poisson distribution to simulate precipitation
prec <- rpois(n, lambda = 2)
# Random wind speeds, e.g., 0 to 10 m/s
wind <- runif(n, min = 0, max = 10)

# 3. Construct the climate_input object
ci <- climate_input(
  tmax  = tmax,
  tmin  = tmin,
  prec  = prec,
  wind  = wind,
  dates = dates
)
# Then:
# 4. Calculate monthly TN10p index
tn10p_values <- tn10p(ci, freq = "monthly")

Calculate TN90p Index

Description

Calculates the percentage of days when minimum temperature is above the 90th percentile.

Usage

tn90p(ci, freq = c("monthly", "annual"))

Arguments

Value

Data frame with dates and calculated TN90p values.

Examples

# 1. Generate a daily date sequence from 1960-01-01 to 2020-12-31
dates <- seq.Date(
  from = as.Date("1960-01-01"),
  to   = as.Date("2020-12-31"),
  by   = "day"
)

# 2. Create random weather data for each date
n <- length(dates)
tmax <- runif(n, min = 5, max = 40)
tmin <- runif(n, min = -10,  max = 5)
# Example: use a Poisson distribution to simulate precipitation
prec <- rpois(n, lambda = 2)
# Random wind speeds, e.g., 0 to 10 m/s
wind <- runif(n, min = 0, max = 10)

# 3. Construct the climate_input object
ci <- climate_input(
  tmax  = tmax,
  tmin  = tmin,
  prec  = prec,
  wind  = wind,
  dates = dates
)
# Then:
# 5. Calculate monthly TN90p index
tn90p_values <- tn90p(ci, freq = "monthly")

Calculate TX10p Index

Description

Calculates the percentage of days when maximum temperature is below the 10th percentile.

Usage

tx10p(ci, freq = c("monthly", "annual"))

Arguments

Value

Data frame with dates and calculated TX10p values.

Examples

# 1. Generate a daily date sequence from 1960-01-01 to 2020-12-31
dates <- seq.Date(
  from = as.Date("1960-01-01"),
  to   = as.Date("2020-12-31"),
  by   = "day"
)

# 2. Create random weather data for each date
n <- length(dates)
tmax <- runif(n, min = 5, max = 40)
tmin <- runif(n, min = -10,  max = 5)
# Example: use a Poisson distribution to simulate precipitation
prec <- rpois(n, lambda = 2)
# Random wind speeds, e.g., 0 to 10 m/s
wind <- runif(n, min = 0, max = 10)

# 3. Construct the climate_input object
ci <- climate_input(
  tmax  = tmax,
  tmin  = tmin,
  prec  = prec,
  wind  = wind,
  dates = dates
)
# Then:
# Calculate monthly TX10p index
tx10p_values <- tx10p(ci, freq = "monthly")

Calculate TX90p Index

Description

Calculates the percentage of days when maximum temperature is above the 90th percentile.

Usage

tx90p(ci, freq = c("monthly", "annual"))

Arguments

Value

Data frame with dates and calculated TX90p values.

Examples

# 1. Generate a daily date sequence from 1960-01-01 to 2020-12-31
dates <- seq.Date(
  from = as.Date("1960-01-01"),
  to   = as.Date("2020-12-31"),
  by   = "day"
)

# 2. Create random weather data for each date
n <- length(dates)
tmax <- runif(n, min = 5, max = 40)
tmin <- runif(n, min = -10,  max = 5)
# Example: use a Poisson distribution to simulate precipitation
prec <- rpois(n, lambda = 2)
# Random wind speeds, e.g., 0 to 10 m/s
wind <- runif(n, min = 0, max = 10)

# 3. Construct the climate_input object
ci <- climate_input(
  tmax  = tmax,
  tmin  = tmin,
  prec  = prec,
  wind  = wind,
  dates = dates
)
# Then:
# 5. Calculate monthly TX90p index
tx90p_values <- tx90p(ci, freq = "monthly")

Calculate W90p Index

Description

Calculates the percentage of days when wind speed is above the 90th percentile.

Usage

w90p(ci, freq = c("monthly", "annual"))

Arguments

Value

Data frame with dates and calculated W90p values.

Examples

# 1. Generate a daily date sequence from 1960-01-01 to 2020-12-31
dates <- seq.Date(
  from = as.Date("1960-01-01"),
  to   = as.Date("2020-12-31"),
  by   = "day"
)

# 2. Create random weather data for each date
n <- length(dates)
tmax <- runif(n, min = 5, max = 40)
tmin <- runif(n, min = -10,  max = 5)
# Example: use a Poisson distribution to simulate precipitation
prec <- rpois(n, lambda = 2)
# Random wind speeds, e.g., 0 to 10 m/s
wind <- runif(n, min = 0, max = 10)

# 3. Construct the climate_input object
ci <- climate_input(
  tmax  = tmax,
  tmin  = tmin,
  prec  = prec,
  wind  = wind,
  dates = dates
)
# Then:
# 4. Calculate monthly W90p index
w90p_values <- w90p(ci, freq = "monthly")

Calculate Standardized W90p Index

Description

Calculates the standardized W90p index.

Usage

w90p_std(ci, freq = c("monthly", "seasonal"))

Arguments

Value

Data frame with dates and standardized W90p values.

Examples

# 1. Generate a daily date sequence from 1960-01-01 to 2020-12-31
dates <- seq.Date(
  from = as.Date("1960-01-01"),
  to   = as.Date("2020-12-31"),
  by   = "day"
)

# 2. Create random weather data for each date
n <- length(dates)
tmax <- runif(n, min = 5, max = 40)
tmin <- runif(n, min = -10,  max = 5)
# Example: use a Poisson distribution to simulate precipitation
prec <- rpois(n, lambda = 2)
# Random wind speeds, e.g., 0 to 10 m/s
wind <- runif(n, min = 0, max = 10)

# 3. Construct the climate_input object
ci <- climate_input(
  tmax  = tmax,
  tmin  = tmin,
  prec  = prec,
  wind  = wind,
  dates = dates
)
# Then:
# 4. Calculate standardized W90p index on a monthly basis
w90p_std_values <- w90p_std(ci, freq = "monthly")