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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

A framework for probabilistic weather forecast post-processing across models and lead times using machine learning.

Charlie Kirkwood¹, Theo Economou¹, Henry Odbert²

¹College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences

|February 15, 2021

Summary

This summary is machine-generated.

This study introduces a machine learning framework to combine multiple numerical weather prediction (NWP) models. The method generates well-calibrated probabilistic forecasts for improved decision-making in weather forecasting.

Keywords:

artificial intelligence data integration decision theory model stacking quantile regression uncertainty quantification

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Last Updated: Nov 17, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Area of Science:

Meteorology
Machine Learning
Data Science

Background:

Numerical weather prediction (NWP) models are increasing in complexity and number.
Combining forecasts from multiple NWP models with unique biases presents a challenge for operational meteorologists.
There is a need for well-calibrated probabilistic forecasts for effective decision support.

Purpose of the Study:

To demonstrate a machine learning framework for post-processing weather forecasts from multiple NWP models.
To bridge the gap between raw NWP model outputs and decision-support-ready probabilistic forecasts.
To improve the calibration and utility of weather forecasts for stakeholders.

Main Methods:

A three-stage machine learning framework was developed.
Quantile regression forests were used to learn individual NWP model error profiles.
Probabilistic forecasts were combined using quantile averaging and interpolated to form a predictive distribution.

Main Results:

The framework effectively learns and corrects for individual model biases.
Quantile averaging successfully combined probabilistic forecasts from different models.
The generated predictive distribution demonstrated properties suitable for decision support.

Conclusions:

The proposed machine learning framework offers an effective and operationally viable method for cohesive post-processing of weather forecasts.
This approach yields well-calibrated probabilistic outputs across multiple models and lead times.
The methodology enhances the usability of weather forecasts for critical decision-making.