Parameter uncertainty in forecast recalibration

TL;DR

This paper reviews methods to incorporate parameter uncertainty into ensemble forecast recalibration, demonstrating that accounting for this uncertainty improves the reliability and skill of probabilistic weather and climate forecasts.

Contribution

It introduces analytic approaches to include parameter uncertainty in recalibration frameworks like NGR, enhancing forecast reliability and skill.

Findings

Accounting for parameter uncertainty improves forecast reliability.

Inclusion of uncertainty enhances tail probability estimates.

Forecasts become more skillful with parameter uncertainty considered.

Abstract

Ensemble forecasts of weather and climate are subject to systematic biases in the ensemble mean and variance, leading to inaccurate estimates of the forecast mean and variance. To address these biases, ensemble forecasts are post-processed using statistical recalibration frameworks. These frameworks often specify parametric probability distributions for the verifying observations. A common choice is the Normal distribution with mean and variance specified by linear functions of the ensemble mean and variance. The parameters of the recalibration framework are estimated from historical archives of forecasts and verifying observations. Often there are relatively few forecasts and observations available for parameter estimation, and so the fitted parameters are also subject to uncertainty. This artefact is usually ignored. This study reviews analytic results that account for parameter uncertainty in the widely used Model Output Statistics recalibration framework. The predictive bootstrap is used to approximate the parameter uncertainty by resampling in more general frameworks such as Non-homogeneous Gaussian Regression. Forecasts on daily, seasonal and annual time scales are used to demonstrate that accounting for parameter uncertainty in the recalibrated predictive distributions leads to probability forecasts that are more skilful and reliable than those in which parameter uncertainty is ignored. The improvements are attributed to more reliable tail probabilities of the recalibrated forecast distributions.