Forecasting Extreme Day and Night Heat in Paris: A Proof of Concept

Abstract

As a form of "small A", quantile machine learning is used to forecast diurnal and nocturnal $Q(.90)$ air temperatures for Paris, France from late spring through the summer months of 2021. The data are provided by the Paris-Montsouris weather station. Rather than trying to directly anticipate the onset and cessation of reported heat waves, Q(.90) values are estimated. The 90th percentile is chosen so that exceedances represent relatively rare and extreme conditions. Predictors include eight routinely available indicators of weather conditions, lagged by 14 days. Using holdout data, the temperature forecasts are produced two weeks in advance. Adaptive conformal prediction regions are computed that, under exchangeability, provide provably valid finite-sample coverage of forecasting uncertainty. For both diurnal and nocturnal temperatures, forecasting accuracy in the holdout data is promising, and sound measures of uncertainty are coupled with a novel decision-making framework. Benefits for policy and practice follow.