Probabilistic prediction of the time to hard freeze using seasonal weather forecasts and survival time methods. (7th December 2022)

• Record Type: Journal Article
• Authors: Roksvåg, Thea; Lenkoski, Alex; Scheuerer, Michael; Heinrich‐Mertsching, Claudio; Thorarinsdottir, Thordis L.
• Is Part Of: Quarterly journal of the Royal Meteorological Society. Volume 149:Number 750(2023)
• Page Start: 211
• Page End: 230
• Publication Date: 2022-12-07
• Subjects: hard freeze -- seasonal forecasting -- survival analysis -- time‐to‐event prediction; Meteorology -- Periodicals; 551.5
• DOI: 10.1002/qj.4403
• Languages: English
• ISSNs: 0035-9009

Abstract

Abstract: Agricultural food production and natural ecological systems depend on a range of seasonal climate indicators that describe seasonal patterns in climatological conditions. This article 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°C (referred to here as hard freeze). The forecasting framework is based on the multimodel 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 postprocessed and downscaled 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. Abstract : We propose a probabilistic framework for predicting the time to hard freeze. It is based on using models from survival analysis with a multimodel seasonal forecast ensemble as input. The figure shows example forecasts, issued October 1, based on a raw ensemble, a postprocessed and downscaled ensemble, and climatology. We show that, for Norway, the proposed postprocessed forecasts outperform climatology forecasts at locations where the average predicted time to hard freeze is less than 40 days after the issue date.