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Forecast error growth: A dynamic-stochastic model.
Eviatar Bach1,2,3, Dan Crisan4, Michael Ghil4,5,6
1Department of Environmental Science and Engineering and Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California 91125, USA.
This study introduces a new nonlinear stochastic differential equation (SDE) model for forecast error growth in numerical weather prediction (NWP). The model accurately captures both mean and probabilistic error growth features.
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Area of Science:
- Atmospheric sciences
- Meteorology
- Data science
Background:
- Numerical weather prediction (NWP) models have historically used simple error growth models.
- Existing models capture key properties but can be improved with advanced techniques.
Purpose of the Study:
- To propose a novel dynamic-stochastic scalar model for forecast error growth.
- To incorporate multiplicative noise within a nonlinear stochastic differential equation (SDE).
Main Methods:
- Developed a nonlinear stochastic differential equation (SDE) incorporating multiplicative noise.
- Analyzed the SDE's properties, including error growth curves and stationary distribution.
- Fitted the model to operational NWP error growth data.
Main Results:
- The proposed SDE model demonstrates well-posedness and positivity of solutions.
- The model shows good agreement with both mean and probabilistic aspects of NWP error growth.
- The model's dynamic-stochastic approach offers a robust framework for error prediction.
Conclusions:
- The new dynamic-stochastic error growth model provides an accurate representation of NWP error dynamics.
- This modeling approach has potential applications beyond meteorology in various predictive sciences.