Visualization of model biases
model_biases.RdAutomatically compute the difference between observations and the historical experiment
Usage
model_biases(
data,
bias.correction = F,
uppert = NULL,
lowert = NULL,
season,
consecutive = F,
frequency = F,
n.sessions = 1,
duration = "max",
method = "eqm",
cross_validation = "none",
window = "monthly"
)Arguments
- data
output of load_data
- bias.correction
logical
- uppert
numeric of length=1, upper threshold
- lowert
numeric of length=1, lower threshold
- season
list, containing seasons to select. For example, list(1:6, 7:12)
- consecutive
logical, to use in conjunction with lowert or uppert
- frequency
logical. Used only when consecutive is TRUE and duration is not "max". For example, to know the number of heatwaves defined as the number of days with Tmax higher than 35 for at least 3 consecutive days, specify uppert=35, consecutive =T and duration=3, frequency=T
- n.sessions
numeric, number of sessions to use, default is one. Parallelization can be useful when multiple scenarios are used (RCPS, SSPs). However, note that parallelizing will increase RAM usage
- duration
either "max" or specify a number. Used only when consecutive is TRUE. For example, to know the number of consecutive days with tmax above 35, lasting more than 3 days, specify uppert=35, consecutive =T and duration=3
- method
character, bias-correction method to use. One of eqm (Empirical Quantile Mapping), qdm (Quantile Delta Mapping) or scaling. Default to eqm. When using the scaling method, the multiplicative approach is automatically applied only when the variable is precipitation.
- cross_validation
character, one of none or 3fold. Whether 3-fold cross validation should be used to avoid overfitting during bias-correction. Default to "none"
- window
character, one of none or monthly. Whether bias correction should be applied on a monthly or annual basis. Monthly is the preferred option when performing bias-correction using daily data