Probabilistic prediction of the time to hard freeze using seasonal weather forecasts and survival time methods
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 tim...
| Published in: | Quarterly Journal of the Royal Meteorological Society |
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| Main Authors: | , , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Wiley
2022
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.1002/qj.4403 https://onlinelibrary.wiley.com/doi/pdf/10.1002/qj.4403 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/qj.4403 https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/qj.4403 |
| Summary: | 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. |
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