arima_temperature() creates the temperature-focused future-weather method
described by Arima et al. (2024). It requires a baseline EPW, matching
historical and future daily model tas, and multi-year observed daily tas
for the target location.
For each calendar month, the method calculates historical and future model inverse CDFs and subtracts values at common percentiles. The resulting change function is smoothed with the published endpoint-aware nine-point moving mean repeated three times. Each baseline EPW daily mean is located in the observed monthly empirical CDF, and the corresponding additive factor is applied to all 24 hours of that baseline day.
The publications do not specify empirical plotting positions, quantile interpolation, or endpoint evaluation. This implementation records its deterministic midpoint probability grid, type-7 quantiles, linear factor interpolation, and endpoint clamping in result provenance.
The "paper_faithful" policy changes dry-bulb temperature while preserving
baseline humidity fields. The "harmonized" policy instead retains feasible
baseline specific humidity and recomputes relative humidity and dew point.
arima_temperature(
reference = NULL,
observed_reference = NULL,
policy = c("paper_faithful", "harmonized")
)A required historical_reference(),
shift_reference_plan(), or extracted ShiftClimate stage containing
historical daily model output.
A required shift_reference_plan() or extracted
ShiftClimate stage containing multi-year observed daily weather.
Physical execution policy: "paper_faithful" preserves
baseline humidity fields; "harmonized" applies shared specific-humidity
closure.
A complete ShiftMorphMethod for shift_future_epw().
Arima, Y., Ozaki, A., Kuma, Y., Iseda, H., and Abe, G. (2024). Development of Future Weather Data Using the Quantile Mapping Technique and its Application in Japan. doi:10.69357/asim2024.1178