Package 'rIACI' reference manual

Title:	Iberian Actuarial Climate Index Calculations for R
Description:	Provides functions to calculate the Iberian Climate Index (IACI) and its components, including temperature, precipitation, wind power, and sea level data. Designed to support climate change analysis and risk assessment.
Authors:	Nan Zhou [aut, cre] (<https://orcid.org/0009-0002-5166-6753>, With gratitude to Jose Luis Vilar-Zanon, Jose Garrido, and Antonio Jose Heras Martinez for their essential support.)
Maintainer:	Nan Zhou <[email protected]>
License:	GPL-3
Version:	1.0.0
Built:	2025-03-18 06:12:44 UTC
Source:	https://github.com/nan-z-byte/riaci

Calculate Consecutive Dry Days (CDD)

Description

Calculates the maximum length of consecutive dry days.

Usage

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

Arguments

`ci`	List. Climate input object created by `climate_input`.
`spells_can_span_years`	Logical. Whether spells can span across years (default is TRUE).
`monthly`	Logical. Whether to interpolate annual data to monthly data (default is TRUE).

Value

Data frame with dates and calculated CDD values.

Examples

## Not run: 
# Calculate annual CDD index
cdd_values <- cdd(ci, monthly = FALSE)

## End(Not run)
## Not run: 
# Calculate annual CDD index
cdd_values <- cdd(ci, monthly = FALSE)

## End(Not run)

Calculate Standardized CDD Index

Description

Calculates the standardized consecutive dry days index.

Usage

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

Arguments

`ci`	List. Climate input object.
`freq`	Character. Frequency of calculation, either "monthly" or "seasonal".

Value

Data frame with dates and standardized CDD values.

Examples

## Not run: 
# Calculate standardized CDD index on a monthly basis
cdd_std_values <- cdd_std(ci, freq = "monthly")

## End(Not run)
## Not run: 
# Calculate standardized CDD index on a monthly basis
cdd_std_values <- cdd_std(ci, freq = "monthly")

## End(Not run)

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
)
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

`tmax`	Numeric vector. Maximum temperature data.
`tmin`	Numeric vector. Minimum temperature data.
`prec`	Numeric vector. Precipitation data.
`wind`	Numeric vector. Wind speed data.
`dates`	Date vector. Dates corresponding to the data.
`base.range`	Numeric vector of length 2. Base range years for calculations (default is c(1961, 1990)).
`n`	Integer. Window size for running averages (default is 5).
`quantiles`	List. Pre-calculated quantiles (optional).
`temp.qtiles`	Numeric vector. Temperature quantiles to calculate (default is c(0.10, 0.90)).
`wind.qtile`	Numeric. Wind quantile to calculate (default is 0.90).
`max.missing.days`	Named numeric vector. Maximum allowed missing days for annual and monthly data (default is c(annual = 15, monthly = 3)).
`min.base.data.fraction.present`	Numeric. Minimum fraction of data required in base range (default is 0.1).

Value

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

Examples

## Not run: 
# Create climate input object
ci <- climate_input(
  tmax = tmax,
  tmin = tmin,
  prec = prec,
  wind = wind,
  dates = dates
)

## End(Not run)
## Not run: 
# Create climate input object
ci <- climate_input(
  tmax = tmax,
  tmin = tmin,
  prec = prec,
  wind = wind,
  dates = dates
)

## End(Not run)

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)
csv_to_netcdf(csv_dir, output_file, freq)

Arguments

`csv_dir`	Character. Directory containing CSV files, each file representing a single latitude-longitude point. The filename format should be 'lat_lon.csv'.
`output_file`	Character. Path to the output NetCDF file.
`freq`	Character. Frequency of the data, either ''monthly'‘ or '’seasonal''. - ''monthly'‘ data uses date format '’YYYY-MM''. - ''seasonal'‘ data uses date format like '’YYYY-SSS'‘ (e.g., '’1961-DJF'').

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)
## 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
)
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

`start_year`	Integer. The starting year for data download.
`end_year`	Integer. The ending year for data download.
`start_month`	Integer. The starting month (default is 1).
`end_month`	Integer. The ending month (default is 12).
`variables`	Character vector. Variables to download. Default includes common variables: `c("10m_u_component_of_wind", "10m_v_component_of_wind", "2m_temperature", "total_precipitation")`.
`dataset`	Character. Dataset short name (default is "reanalysis-era5-land").
`area`	Numeric vector. Geographical area specified as `c(North, West, South, East)`.
`output_dir`	Character. Directory to save the downloaded data (default is "cds_data").
`user_id`	Character. Your ECMWF user ID.
`user_key`	Character. Your ECMWF API key.
`max_retries`	Integer. Maximum number of retry attempts in case of download failure (default is 3).
`retry_delay`	Numeric. Delay between retry attempts in seconds (default is 5).
`timeout`	Numeric. Timeout duration for each request in seconds (default is 7200, i.e., 2 hours).

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)
## 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)

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)
export_data_to_csv(nc_file, output_dir)

Arguments

`nc_file`	Character. Path to the NetCDF file.
`output_dir`	Character. Output directory where CSV files will be saved.

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)
## 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)

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

Arguments

`ci`	List. Climate input object.
`si`	Data frame. Sea level input data.
`freq`	Character. Frequency of calculation, either "monthly" or "seasonal".

Value

Data frame with dates and IACI values.

Examples

## Not run: 
# Assume we have a climate input object 'ci' and sea level data 'si'
# ci should be created using 'climate_input' function (not provided here)
ci <- list(base_range = c(1980, 1990))
si <- sea_input(
  Date = c("1980-01", "1980-02", "1980-03"),
  Value = c(1.2, 1.3, 1.4)
)
# Calculate the IACI with monthly frequency
result <- iaci_output(ci, si, freq = "monthly")

## End(Not run)
## Not run: 
# Assume we have a climate input object 'ci' and sea level data 'si'
# ci should be created using 'climate_input' function (not provided here)
ci <- list(base_range = c(1980, 1990))
si <- sea_input(
  Date = c("1980-01", "1980-02", "1980-03"),
  Value = c(1.2, 1.3, 1.4)
)
# Calculate the IACI with monthly frequency
result <- iaci_output(ci, si, freq = "monthly")

## End(Not run)

Convert Monthly Data to Seasonal Data

Description

Aggregates monthly data into seasonal averages.

Usage

monthly_to_seasonal(data)
monthly_to_seasonal(data)

Arguments

data

Data frame. Monthly data with Date and Value columns.

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)
## 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)

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

Arguments

`si`	Data frame. Sea level input data.
`input_dir`	Character. Directory containing input CSV files.
`output_dir`	Character. Directory to save output files.
`freq`	Character. Frequency of calculation, either "monthly" or "seasonal".
`base.range`	Numeric vector. Base range years (default is c(1961, 1990)).
`time.span`	Numeric vector. Time span for output data (default is c(1961, 2022)).

Value

None. Results are saved to the output directory.

Examples

## Not run: 
# Assume we have sea level data 'si' and input/output directories
si <- sea_input(
  Date = c("1980-01", "1980-02", "1980-03"),
  Value = c(1.2, 1.3, 1.4)
)
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)
## Not run: 
# Assume we have sea level data 'si' and input/output directories
si <- sea_input(
  Date = c("1980-01", "1980-02", "1980-03"),
  Value = c(1.2, 1.3, 1.4)
)
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)

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)
process_data(input_dir, output_dir, save_merged = FALSE)

Arguments

`input_dir`	Character. Directory containing input NetCDF files.
`output_dir`	Character. Directory to save output files.
`save_merged`	Logical. If TRUE, saves the merged NetCDF file. Default is FALSE.

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)
## 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)

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

Arguments

`ci`	List. Climate input object created by `climate_input`.
`freq`	Character. Frequency of calculation, either "monthly" or "annual".
`center_mean_on_last_day`	Logical. Whether to center the mean on the last day (default is FALSE).

Value

Data frame with dates and calculated Rx5day values.

Examples

## Not run: 
# Calculate monthly Rx5day index
rx5day_values <- rx5day(ci, freq = "monthly")

## End(Not run)
## Not run: 
# Calculate monthly Rx5day index
rx5day_values <- rx5day(ci, freq = "monthly")

## End(Not run)

Calculate Standardized Rx5day Index

Description

Calculates the standardized Rx5day index.

Usage

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

Arguments

`ci`	List. Climate input object.
`freq`	Character. Frequency of calculation, either "monthly" or "seasonal".

Value

Data frame with dates and standardized Rx5day values.

Examples

## Not run: 
# Calculate standardized Rx5day index on a monthly basis
rx5day_std_values <- rx5day_std(ci, freq = "monthly")

## End(Not run)
## Not run: 
# Calculate standardized Rx5day index on a monthly basis
rx5day_std_values <- rx5day_std(ci, freq = "monthly")

## End(Not run)

Sea Level Input Function

Description

Creates a data frame for sea level data input.

Usage

sea_input(Date = levels(ci$date_factors$monthly), Value = NA)
sea_input(Date = levels(ci$date_factors$monthly), Value = NA)

Arguments

`Date`	Character vector. Dates in "YYYY-MM" format.
`Value`	Numeric vector. Sea level values (default is NA).

Value

Data frame with Date and Value columns.

Examples

## Not run: 
# Create a sea level input data frame
dates <- c("1980-01", "1980-02", "1980-03")
values <- c(1.2, 1.3, 1.4)
sea_data <- sea_input(Date = dates, Value = values)
# If Value is not provided, it defaults to NA
sea_data_na <- sea_input(Date = dates)

## End(Not run)
## Not run: 
# Create a sea level input data frame
dates <- c("1980-01", "1980-02", "1980-03")
values <- c(1.2, 1.3, 1.4)
sea_data <- sea_input(Date = dates, Value = values)
# If Value is not provided, it defaults to NA
sea_data_na <- sea_input(Date = dates)

## End(Not run)

Calculate Standardized Sea Level Index

Description

Calculates the standardized sea level index.

Usage

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

Arguments

`si`	Data frame. Sea level input data created by `sea_input`.
`ci`	List. Climate input object containing the base range (e.g., created by `climate_input`).
`freq`	Character. Frequency of calculation, either "monthly" or "seasonal".

Value

Data frame with dates and standardized sea level values.

Examples

## Not run: 
# Assume you have a climate input object 'ci' and sea level data 'si'
# ci should be created using 'climate_input' function
ci <- climate_input(...)
si <- sea_input(Date = c("1980-01", "1980-02", "1980-03"), Value = c(1.2, 1.3, 1.4))
# Calculate standardized sea level index with monthly frequency
sea_std_values <- sea_std(si, ci, freq = "monthly")

## End(Not run)
## Not run: 
# Assume you have a climate input object 'ci' and sea level data 'si'
# ci should be created using 'climate_input' function
ci <- climate_input(...)
si <- sea_input(Date = c("1980-01", "1980-02", "1980-03"), Value = c(1.2, 1.3, 1.4))
# Calculate standardized sea level index with monthly frequency
sea_std_values <- sea_std(si, ci, freq = "monthly")

## End(Not run)

Calculate T10p Index

Description

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

Usage

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

Arguments

`ci`	List. Climate input object.
`freq`	Character. Frequency of calculation, either "monthly" or "annual".

Value

Data frame with dates and calculated T10p values.

Examples

## Not run: 
# Calculate monthly T10p index
t10p_values <- t10p(ci, freq = "monthly")

## End(Not run)
## Not run: 
# Calculate monthly T10p index
t10p_values <- t10p(ci, freq = "monthly")

## End(Not run)

Calculate Standardized T10p Index

Description

Calculates the standardized T10p index.

Usage

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

Arguments

`ci`	List. Climate input object.
`freq`	Character. Frequency of calculation, either "monthly" or "seasonal".

Value

Data frame with dates and standardized T10p values.

Examples

## Not run: 
Calculate standardized T10p index on a monthly basis
t10p_std_values <- t10p_std(ci, freq = "monthly")

## End(Not run)
## Not run: 
Calculate standardized T10p index on a monthly basis
t10p_std_values <- t10p_std(ci, freq = "monthly")

## End(Not run)

Calculate T90p Index

Description

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

Usage

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

Arguments

`ci`	List. Climate input object.
`freq`	Character. Frequency of calculation, either "monthly" or "annual".

Value

Data frame with dates and calculated T90p values.

Examples

## Not run: 
# Calculate monthly T90p index
t90p_values <- t90p(ci, freq = "monthly")

## End(Not run)
## Not run: 
# Calculate monthly T90p index
t90p_values <- t90p(ci, freq = "monthly")

## End(Not run)

Calculate Standardized T90p Index

Description

Calculates the standardized T90p index.

Usage

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

Arguments

`ci`	List. Climate input object.
`freq`	Character. Frequency of calculation, either "monthly" or "seasonal".

Value

Data frame with dates and standardized T90p values.

Examples

## Not run: 
# Calculate standardized T90p index on a monthly basis
t90p_std_values <- t90p_std(ci, freq = "monthly")

## End(Not run)
## Not run: 
# Calculate standardized T90p index on a monthly basis
t90p_std_values <- t90p_std(ci, freq = "monthly")

## End(Not run)

Calculate TN10p Index

Description

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

Usage

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

Arguments

`ci`	List. Climate input object created by `climate_input`.
`freq`	Character. Frequency of calculation, either "monthly" or "annual".

Value

Data frame with dates and calculated TN10p values.

Examples

## Not run: 
# Calculate monthly TN10p index
tn10p_values <- tn10p(ci, freq = "monthly")

## End(Not run)
## Not run: 
# Calculate monthly TN10p index
tn10p_values <- tn10p(ci, freq = "monthly")

## End(Not run)

Calculate TN90p Index

Description

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

Usage

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

Arguments

`ci`	List. Climate input object created by `climate_input`.
`freq`	Character. Frequency of calculation, either "monthly" or "annual".

Value

Data frame with dates and calculated TN90p values.

Examples

## Not run: 
# Calculate monthly TN90p index
tn90p_values <- tn90p(ci, freq = "monthly")

## End(Not run)
## Not run: 
# Calculate monthly TN90p index
tn90p_values <- tn90p(ci, freq = "monthly")

## End(Not run)

Calculate TX10p Index

Description

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

Usage

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

Arguments

`ci`	List. Climate input object created by `climate_input`.
`freq`	Character. Frequency of calculation, either "monthly" or "annual".

Value

Data frame with dates and calculated TX10p values.

Examples

## Not run: 
# Calculate monthly TX10p index
tx10p_values <- tx10p(ci, freq = "monthly")

## End(Not run)
## Not run: 
# Calculate monthly TX10p index
tx10p_values <- tx10p(ci, freq = "monthly")

## End(Not run)

Calculate TX90p Index

Description

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

Usage

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

Arguments

`ci`	List. Climate input object created by `climate_input`.
`freq`	Character. Frequency of calculation, either "monthly" or "annual".

Value

Data frame with dates and calculated TX90p values.

Examples

## Not run: 
# Calculate monthly TX90p index
tx90p_values <- tx90p(ci, freq = "monthly")

## End(Not run)
## Not run: 
# Calculate monthly TX90p index
tx90p_values <- tx90p(ci, freq = "monthly")

## End(Not run)

Calculate W90p Index

Description

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

Usage

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

Arguments

`ci`	List. Climate input object created by `climate_input`.
`freq`	Character. Frequency of calculation, either "monthly" or "annual".

Value

Data frame with dates and calculated W90p values.

Examples

## Not run: 
# Calculate monthly W90p index
w90p_values <- w90p(ci, freq = "monthly")

## End(Not run)
## Not run: 
# Calculate monthly W90p index
w90p_values <- w90p(ci, freq = "monthly")

## End(Not run)

Calculate Standardized W90p Index

Description

Calculates the standardized W90p index.

Usage

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

Arguments

`ci`	List. Climate input object.
`freq`	Character. Frequency of calculation, either "monthly", "seasonal".

Value

Data frame with dates and standardized W90p values.

Examples

## Not run: 
# Calculate standardized W90p index on a monthly basis
w90p_std_values <- w90p_std(ci, freq = "monthly")

## End(Not run)
## Not run: 
# Calculate standardized W90p index on a monthly basis
w90p_std_values <- w90p_std(ci, freq = "monthly")

## End(Not run)

Package 'rIACI'

Help Index

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Sea Level Input Function

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