plot_annual: Plot regional climate forecasts of annual indices from...

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

Plot regional climate forecasts of annual indices from statistical forecasting model

Description

Plot the temperature or precipitation forecasts of annual indices obtained using statistical forecasting models and fcast_annual.

Usage

plot_annual(fcast.output)

Arguments

fcast.output

A dataframe object that is produced by the fcast_annual as the output of statistical forecasting in annual resolution.

Details

Historical observations are presented as points, while historical fitting is shown as the line during the historical period of record. Point forecasts and interval forecasts (80th and 95th intervals) are presented as the line and shaded are in the forecasting period.

Value

A ggplot object.

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.

Lai, Y., and Dzombak, D. A., 2021: Use of Integrated Global Climate Model Simulations and Statistical Time Series Forecasting to Project Regional Temperature and Precipitation. Journal of Applied Meteorology and Climatology.

See Also

See Also as fcast_annual, plot_annual_cl, and plot_daily

Examples

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

# Process the annual data of Pittsburgh to obtain annual average temperature
pit.temp <- select(pit.annual, "Avg.Temp")

# Obtain 20-year forecasts of annual average temperature
# starting from 2020 in Pittsburgh using the ARIMA model
pit.fcast.temp <- fcast_annual(pit.temp)

# Plot the obtained 20-year forecasts of annual average temperature
plot_annual(pit.fcast.temp)

# Obtain 15-year forecasts of annual maximum 1-day precipitation
# starting from 2006 in Chicago using the non-stationary GEV model

chi.annual <- download("Chicago", "annual")
chi.prcp.ex <- select(chi.annual, "Max.1.day.P")
chi.fcast.prcp.ex <- fcast_annual(chi.prcp.ex, 15, 2006, "gev")
plot_annual(chi.fcast.prcp.ex)

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