Probabilistic prediction of the time to hard freeze using seasonal weather forecasts and survival time methods

TL;DR

This paper introduces a probabilistic forecasting framework combining seasonal climate predictions and survival analysis to predict the timing of the first hard freeze, improving accuracy over climatology in specific regions.

Contribution

It develops a novel probabilistic prediction method for the end of freeze-free seasons using multi-model seasonal forecasts and survival analysis techniques.

Findings

Forecasts outperform climatology when the predicted time to freeze is under 40 days.

Post-processing of temperature forecasts improves prediction accuracy.

Method is validated with data from Fennoscandia (Norway) 1993-2020.

Abstract

Agricultural food production and natural ecological systems depend on a range of seasonal climate indicators that describe seasonal patterns in climatological conditions. This paper proposes a probabilistic forecasting framework for predicting the end of the freeze-free season, or the time to a mean daily near-surface air temperature below 0 $^\circ$C (here referred to as hard freeze). The forecasting framework is based on the multi-model seasonal forecast ensemble provided by the Copernicus Climate Data Store and uses techniques from survival analysis for time-to-event data. The original mean daily temperature forecasts are statistically post-processed with a mean and variance correction of each model system before the time-to-event forecast is constructed. In a case study for a region in Fennoscandia covering Norway for the period 1993-2020, the proposed forecasts are found to outperform a climatology forecast from an observation-based data product at locations where the average predicted time to hard freeze is less than 40 days after the initialization date of the forecast on October 1.