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

TL;DR

This paper demonstrates the use of quantile machine learning and conformal prediction to forecast extreme air temperatures in Paris, providing accurate two-week ahead predictions with valid uncertainty measures.

Contribution

It introduces a novel application of quantile machine learning combined with conformal prediction for forecasting extreme temperatures with valid uncertainty quantification.

Findings

Forecasting accuracy is promising for diurnal and nocturnal temperatures.

Adaptive conformal prediction provides valid finite-sample coverage.

The approach supports decision-making for heat-related policies.

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.