fcast_daily: Obtain daily temperature and precipitation simulations with...

In yuchuan-lai/scifi: Statistical Climate Forecasting Integrated for Civil and Environmental Engineering

Obtain daily temperature and precipitation simulations with the ARIMA model

Description

Use the ARIMA model to simulate the future daily maximum and minimum temperature and precipitaiton series for a city based on the compiled long-term location-specific records. The simulations are incorporated with the seasonal forecasts obtained from the SARIMA model and can be seen as a realization of future daily temperature and precipitation.

Usage

fcast_daily(hist.obs.daily, num.fcast.yr = 20, fcast.starting.yr = 2020, num.sets = 1, extra.days.for.bt = 7)

Arguments

hist.obs.daily	A dataframe object that includes the daily dates, maximum temperature, minimum temperature, and precipitation as four columns. It can be a dataframe object that is obtained using download function and with 'daily' argument.
num.fcast.yr	A number of forecasting years. Number of forecasting years is recommended to be less than 20 years as long-term forecasts are unreliable because of climate variability.
fcast.starting.yr	A number specifying the starting year for forecasting (e.g., 2006). The forecasting starting year can be within the period of record to evaluate forecasting accuracy with observations.
num.sets	A number specifying the number of daily simulation sets. One set of simulation represents one realization of future daily temperature and precipitation. A larger number for simulation sets can lead to a longer time for running.
extra.days.for.bt	A number specifying the number of extra days selected from previous and succeeding months when bootstrapping. A larger number will increase the randomness as a result.

Details

Background information about the statistical forecasting models applied can be found at: Lai, Y., & Dzombak, D. A. (in press). Use of the Autoregressive Integrated Moving Average (ARIMA) Model to Forecast Near-term Regional Temperature and Precipitation. Weather and Forecasting.

Additional informaiton about the time series forecasting and bootstrap can be found at: Hyndman, R. J., and G. Athanasopoulos, 2018: Forecasting: principles and practice. OTexts,. ——, and Coauthors, 2018: forecast: Forecasting functions for time series and linear models.

Value

hist.obs	Historical observations of daily temperature and precipitation
fcast.tmax	Simulated future daily maximum temperature
fcast.tmin	Simulated future daily maximum temperature
fcast.temp	Simulated future daily mean temperature
fcast.prcp	Simulated future daily precipitation

Author(s)

Yuchuan Lai

References

Hyndman, R. J., and G. Athanasopoulos, 2018: Forecasting: principles and practice. OTexts,. ——, and Coauthors, 2018: forecast: Forecasting functions for time series and linear models.

Lai, Y., and Dzombak, D. A, 2020. Use of the Autoregressive Integrated Moving Average (ARIMA) Model to Forecast Near-term Regional Temperature and Precipitation. Weather and Forecasting.

See Also

See also fcast_annual and fcast_annual_cl

Examples

# Download the historical daily data for Pittsburgh
pit.daily <- download("Pittsburgh", "daily")

# Obtain 1 set of 20-year simulations of daily temperature and precipitation
# starting from 2020 in Pittsburgh
pit.daily.simu <- fcast_daily(pit.daily)

yuchuan-lai/scifi documentation built on March 29, 2022, 6:24 a.m.