temperature_generation: Generation of synthetic temperature records

In chillR: Statistical Methods for Phenology Analysis in Temperate Fruit Trees

Generation of synthetic temperature records

Description

Function to incorporate the temperature generation function of the RMAWGEN weather generator into chillR. The weather generator is calibrated using the weather data.frame (years between years[1] and years[2]), and then generates synthetic weather for a user-defined time frame (bounded by sim_years[1] and sim_years[2]). Monthly change vectors for minimum and maximum temperatures can be specified to allow generation of temperature change scenarios.

Usage

temperature_generation(
  weather,
  years,
  sim_years,
  temperature_scenario = data.frame(Tmin = rep(0, 12), Tmax = rep(0, 12)),
  seed = 99,
  check_temperature_scenario_type = TRUE,
  temperature_check_args = NULL,
  max_reference_year_difference = 5,
  warn_me = TRUE,
  remove_NA_scenarios = TRUE
)

Arguments

weather	daily weather, as produced with the fix_weather function. Can also be generated by other means, but should contain the columns c("Month","Day","Year","Tmin","Tmax").
years	vector of length 2 indicating the start and end year of the time interval to be used for calibrating the temperature generator.
sim_years	vector of length 2 indicating the start and end year of the time interval for which temperatures are to be generated.
temperature_scenario	can be one of three options: 1) a data.frame with two columns Tmin and Tmax and n_intervals (default: 12) rows containing temperature changes for all time intervals, or absolute temperatures for these intervals. 2) a temperature scenario object, consisting of the following elements: 'data' = a data frame with n_intervals elements containing the absolute or relative temperature information (as in input option 1); 'scenario_year' = the year the scenario is representative of; 'reference_year' = the year the scenario is representative of; 'scenario_type' = the scenario type ('absolute' or 'relative' - if NA, this is assigned automatically); 'labels' = and elements attached to the input temperature_scenario as an element names 'labels'. A subset of these elements can also be specified, but 'data' must be present. 3) a (named or unnamed) list containing multiple objects of types 1 and 2. In this case, outputs are generated for all scenarios.
seed	integer specifying the random seed for the weather generation.
check_temperature_scenario_type	boolean variable specifying whether temperature scenarios should be checked - and the scenario_type updated if necessary - with the check_temperature_scenario function.
temperature_check_args	list of arguments to be passed to the check_temperature_scenario function. Check documentation of that function for details.
max_reference_year_difference	for relative temperature scenarios, the maximum difference between the reference years of the scenario and the weather record used for calibration (the median of the two elements in the 'years' argument.
warn_me	boolean variable specifying whether warnings should be shown. Defaults to TRUE.
remove_NA_scenarios	boolean parameter indicating whether temperature scenarios that contain NA values should be removed. Such scenarios would generate an error.

Details

Note that this function uses the temperature generation algorithms of the RMAWGEN package. For more details, refer to the documentation of this package.

Value

list of data.frames containing the simulated weather, with columns c("YEARMODA", "DATE","Year","Month","Day","Tmin","Tmax"). If temperature_scenario is a list, the output list contains simulated temperature records for all scenarios.

Author(s)

Eike Luedeling

Examples

## Examples are #d out to pass CRAN checks. Remove #s to run them. 
# Temp<-temperature_generation(KA_weather,years=c(1999,2001),
#    sim_years = c(2001,2002),temperature_scenario = data.frame(Tmin=c(1,3,2,1,5,7,3,2,1,5,4,3),
#    Tmax=c(1,2,3,2,1,3,2,1,2,3,4,5)))
  
# Temp<-temperature_generation(weather=KA_weather,years=c(1999,2001),
#  sim_years = c(2005,2006),
#   temperature_scenario=data.frame(Tmin=c(1,3,5,8,12,15,15,15,10,8,3,1),
#    Tmax=c(6,8,10,13,17,20,20,20,15,13,8,6)))

 

chillR documentation built on Nov. 28, 2023, 1:09 a.m.