Valid Error Bars for Neural Weather Models using Conformal Prediction
Neural weather models have shown immense potential as inexpensive and accurate alternatives to physics-based models. However, most models trained to perform weather forecasting do not quantify the uncertainty associated with their forecasts. This limits the trust in the model and the usefulness of t...
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Main Authors | , , , , , , , |
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Format | Journal Article |
Language | English |
Published |
20.06.2024
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Subjects | |
Online Access | Get full text |
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Summary: | Neural weather models have shown immense potential as inexpensive and
accurate alternatives to physics-based models. However, most models trained to
perform weather forecasting do not quantify the uncertainty associated with
their forecasts. This limits the trust in the model and the usefulness of the
forecasts. In this work we construct and formalise a conformal prediction
framework as a post-processing method for estimating this uncertainty. The
method is model-agnostic and gives calibrated error bounds for all variables,
lead times and spatial locations. No modifications are required to the model
and the computational cost is negligible compared to model training. We
demonstrate the usefulness of the conformal prediction framework on a limited
area neural weather model for the Nordic region. We further explore the
advantages of the framework for deterministic and probabilistic models. |
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DOI: | 10.48550/arxiv.2406.14483 |