Quantile Regression

BACKGROUND 

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.

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: Web display screenshot of our rainfall forecast visualizations provided to Sudan – individual and multi-model rainfall forecasts come from models from eight global forecasting centers.
Figure 2: Web display screenshot of our rainfall forecast visualizations provided to Sudan – individual and multi-model rainfall forecasts come from models from eight global forecasting centers.

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 FY2018

  • The prediction of river water level across the Brahmaputra and Ganges catchments was expanded utilizing QR with a combination of river width measurements and hydrologic multi-model forecasting
  • A combined QR-logistic regression approach has been further refined in the application of post-processing of numerical weather forecasts for the Army Test and Evaluation Command at the Dugway Testing Range in Utah
  • QR was also applied to blend ensemble rainfall forecasts from eight weather forecasting centers for the Indian Bihar State to optimize rainfall prediction ensembles that feed into hydrologic models for the Kosi and Bagmati river basins
  • A training was carried out on QR and other techniques for Bihar-based Indian hydrologists and water engineers working on hydrologic forecasts, so they can apply these techniques locally
  • QR was utilized to provide ensemble rainfall forecasts at 1-day to 6-week time scales for basins in Ethiopia as well as for the Blue Nile above Sudan, utilizing ensemble rainfall data from a variety of global circulation models and geophysical regressors
  • Two separate trainings were carried out on QR and other techniques in Addis Ababa, Ethiopia and in Khartoum, Sudan for hydrologists and water engineers working on hydrologic forecasts, so they can apply these techniques locally

PLANS FOR FY2019

  • Transfer our rainfall forecasting technology for basins in Ethiopia to run on NASA SERVIR servers for permanent availability for Ethiopian, East Africa, and NASA applications