Postprocessing of Numerical Weather Forecasts Using Online Sequential Extreme Learning Machines
In this work, we use a sequential learning algorithm called the online sequential extreme learning machine (OS-ELM) to postprocess NWP model forecasts of the daily maximum and minimum temperatures and the daily quantitative precipitation. Originated from the batch learning extreme learning machine, the OS-ELM can update itself by learning from a single new data point or multiple new data points, then discard the data. Four different postprocessing methods were tested for forecast days 1-8. The four methods were the OS-ELM, the simple moving-average method, the Kalman filter and the online multiple linear regression. The methods were tested on daily temperature and quantitative precipitation forecasts over 19 stations on complex terrain in southwestern British Columbia, Canada.