Evaluation and statistical correction of area-based heat index forecasts that drive a heatwave warning service

TL;DR

This paper assesses the accuracy of district-level heatwave forecasts used in Australian warnings, identifies biases at different lead times, and demonstrates how isotonic regression can improve forecast calibration and reliability.

Contribution

It introduces a novel application of the FIRM scoring framework for multicategorical heatwave forecasts and demonstrates bias correction using isotonic regression.

Findings

Longer lead times show overforecast bias in heatwave severity.

Forecast revisions tend to downgrade heatwave categories at shorter lead times.

Bias correction with isotonic regression improves forecast calibration.

Abstract

This study evaluates the performance of the area-based, district heatwave forecasts that drive the Australian heatwave warning service. The analysis involves using a recently developed approach of scoring multicategorical forecasts using the FIxed Risk Multicategorical (FIRM) scoring framework. Additionally, we quantify the stability of the district forecasts between forecast updates. Notably, at longer lead times, a discernible overforecast bias exists that leads to issuing severe and extreme heatwave district forecasts too frequently. Consequently, at shorter lead times forecast heatwave categories are frequently downgraded with subsequent revisions. To address these issues, we demonstrate how isotonic regression can be used to conditionally bias correct the district forecasts. Finally, using synthetic experiments, we illustrate that even if an area warning is derived from a perfectly calibrated gridded forecast, the area warning will be biased in most situations. We show how these biases can also be corrected using isotonic regression which could lead to a better warning service. Importantly, the evaluation and bias correction approaches demonstrated in this paper are relevant to forecast parameters other than heat indices.