Accurate long-term air temperature prediction with Machine Learning models and data reduction techniques
In one sentence. CNN and ML models predict August fortnight air temperature in Paris and Córdoba months ahead, targeting heatwave-related signals; skill is reported as very good.
Abstract
In this paper, three customised Artificial Intelligence (AI) frameworks, considering Deep Learning, Machine Learning (ML) algorithms and data reduction techniques, are proposed for a problem of long-term summer air temperature prediction. Specifically, the prediction of the average air temperature in the first and second August fortnights, using input data from previous months, at two different locations (Paris, France) and (Córdoba, Spain), is considered. The target variable, mainly in the first August fortnight, can contain signals of extreme events such as heatwaves, like the heatwave of 2003, which affected France and the Iberian Peninsula. Three different computational frameworks for air temperature prediction are proposed: a Convolutional Neural Network (CNN), with video-to-image translation, several ML approaches including Lasso regression, Decision Trees and Random Forest, and finally a CNN with pre-processing step using Recurrence Plots, which convert time series into images. Using these frameworks, a very good prediction skill has been obtained in both Paris and Córdoba regions, showing that the proposed approaches can be an excellent option for seasonal climate prediction problems.
Labels
- Topic
- Sub-seasonal to seasonal, Extremes & hazards
- Type
- New model or method
- Method
- CNN / U-Net, Classical ML (trees, GP, linear, kernels)
- Time scale
- Seasonal to annual
- Domain
- Local / station
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.1016/j.asoc.2023.110118 OpenAlex:W4320498740
- Added
- 2026-10-22
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