Evaluation and Statistical Correction of Area-Based Heat Index Forecasts That Drive a Heatwave Warning Service

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. Significance Statement Heatwaves have terrible impacts on society. Understanding the performance of heatwave forecasts and warnings is important so that we can understand how they can be improved to reduce negative health impacts. We demonstrate techniques to evaluate the performance, biases, and stability of area-based forecasts that are used by a warning service. We illustrate how area-based forecasts can be improved through conditional bias correction. Additionally, we conduct a synthetic experiment that shows that deriving an area-based warning from a perfectly calibrated gridded forecast may lead to biased warnings.