Yuchuan Lai

Statistical forecasting of city-specific temperature and precipitation

• Historical-observation-based forecasting methods were developed
• Annual temperature and preciptation including extreme events can be forecasted
• Daily temperature and precipitation can be generated given with future conditions

Statistical forecasts of local temperature and precipitation indices can be obtained using the autoregressive integrated moving average (ARIMA) model. Using the long-term historical temeprature and precipitation records as presented in the other page, the ARIMA model was used to extend the existing long-term climate change trend and to acquire the future forecasts.

The ARIMA model is a statistical forecasing method and therefore is only appropriate to make near-term forecasts in the future (up to 20 years ahead in this case). The ARIMA forecasts generally are the extension of long-term climate change trend exhibited in historical records and are largely affected by the most recent average historical level. The ARIMA model can be directly used to forecast different annual temeprature and precipitation indices, with the additional use of the Box-Cox transformation to consider skewed distributions. The descriptions and discussions of the ARIMA model can be found in Lai, Y. and D.A. Dzombak. 2020. Use of the Autoregressive Integrated Moving Average (ARIMA) Model to Forecast Near-term Regional Temperature and Precipitation. Weather and Forecasting, 35, 959–976, https://doi.org/10.1175/WAF-D-19-0158.1.

The ARIMA forecasting model is also available at https://github.com/yuchuan-lai/scifi as a R package.

Interactive plot for the ARIMA forecasts

The interactive graph below presents some of our work on the use of the ARIMA model to forecast temperature and precipitation records at different U.S. cities. The forecast results are also available for downloads using the side panel.
Note that: 1) the ARIMA forecasts of the annual indices are provided by directly forecasting the time series of the annual indies; 2) the ARIMA annual forecasts of some indicies such as those related to the number of days are subject to greater uncertainty as these indices are in integer values; 3) the orders of the ARIMA model (for the autoregressive and moving average terms) were determined by fitting the historical period of records each time and consequently the different lengths of historical records (even under the same indices for the same cities) can have the different ARIMA orders, leading to likely different ARIMA forecasting results.
It may take up to a minute for the graphs to be loaded. View the webpage with the desktop version is recommended.

Acknowledgement The research was supported by a Carnegie Mellon College of Engineering Dean’s Fellowship to Yuchuan Lai, and by the Hamerschlag Chair of Professor Dzombak.