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Quantification of forecast uncertainty using neural networks
Abstract : Uncertainty quantification in numerical weather and climate prediction is usually achieved using a Monte Carlo estimation (i.e., ensemble forecasting) of the forecast probability distribution function of the state of the system. In this work, we present a method for uncertainty quantification based on neural networks and using a likelihood-based loss function to train the network. This provides state dependent uncertainty estimation, without the need of integrating an ensemble of forecasts. The method is evaluated with a chaotic low-dimensional model in two scenarios: with stochastic errors only (SE) and systematic and stochastic errors (SSE).
Cited literature [3 references]
https://hal-imt-atlantique.archives-ouvertes.fr/hal-02942802
Contributor : Pierre Tandeo <>
Submitted on : Friday, September 18, 2020 - 12:00:53 PM
Last modification on : Thursday, May 20, 2021 - 9:34:42 AM
Long-term archiving on: : Thursday, December 3, 2020 - 12:27:58 PM
Contributor : Pierre Tandeo <>
Submitted on : Friday, September 18, 2020 - 12:00:53 PM
Last modification on : Thursday, May 20, 2021 - 9:34:42 AM
Long-term archiving on: : Thursday, December 3, 2020 - 12:27:58 PM
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CI2020_Sacco_et_al.pdf
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