Post-Processing

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.
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.

BACKGROUND

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.

Figure 2: Quantile regression applied to Kosi river (India) basin-wide rainfall forecasts during 2015-6, blending forecasts from five global weather centers into one “grand global ensemble” for this particular basin, enhancing forecast skill out 2 to 3 additional days longer.
Figure 2: Quantile regression applied to Kosi river (India) basin-wide rainfall forecasts during 2015-6, blending forecasts from five global weather centers into one “grand global ensemble” for this particular basin, enhancing forecast skill out 2 to 3 additional days longer.

QR is also a powerful approach for combining different forecast model outputs to generate one coherent and reliable probability distribution function of what the future weather will be. RAL scientists have merged medium-range ensemble rainfall forecasts from five global weather centers (Canada, China, ECMWF, US, Brazil) to calibrate and enhance the accuracy of rainfall forecasts over both East Africa and the Indian subcontinent, gaining 2- to 3-days in additional forecasting skill in the process.

ACCOMPLISHMENTS IN FY2016

  • 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
  • QR was used to generate ensemble predictions of river flow at hydrologic gauging stations in the Ganges and Brahmaputra river basins within India
  • QR was applied to medium-, monthly-, and seasonal-range ensemble prediction of rainfall falling within select river basins in East Africa for water resource planning purposes

 

PLANS FOR FY2017

  • 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.