Figure 1: Quantile regression applied to dewpoint temperature at one station at the Army Test and Evaluation Command at the Dugway Testing Range in Utah, providing a probabilistic range that the dew point may fall within at a lead-time of 42-hr.

Post-processing ensemble forecasts is generally a necessary requirement to provide meaningful probabilistic guidance to users.  One approach that has been used for a variety of applications is quantile regression (QR). RAL scientists are applying a novel statistical correction approach by combining QR with other post-processing approaches (e.g. analog, logistic regression) to calibrate at the specific probability intervals required by the user. Some of the benefits of this approach are that no assumptions are required on the form of the forecast probability distribution function to attain optimality; the resultant forecast skill is no worse than a forecast of either climatology or persistence; and the generated ensembles have dispersive properties directly related to the uncertainty in the forecast that one would expect.

• QR was merged with the analog approach to post-process numerical weather prediction ensembles of wind prediction of wind turbines within China
• QR was similarly merged with the analog approach to post-process weather station forecasts for the Army Test and Evaluation Command at the Dugway Testing Range in Utah

PLANS

• Utilize QR to generate ensemble predictions at hydrologic stations in the Ganges and Brahmaputra rivers within India
• Apply QR to medium-, monthly-, and seasonal-range ensemble prediction of river discharge flowing into hydro-electric reservoirs within East Africa to provide probabilistic guidance on water release decision-making