Stanback Postdoctoral Fellow at the Division of Geological and Planetary Sciences,
California Institute of Technology (Caltech)
"Towards the combination of physical and data-driven forecasts for Earth system prediction"
Mar 1, 2023, Schedule:
- Nespresso & Teatime 417 DSL - Commons
- 03:00 to 03:30 PM Eastern Time (US and Canada)
- Colloquium - F2F 499 DSL / Virtual Zoom
- 03:30 to 04:30 PM Eastern Time (US and Canada)
Due to the recent success of machine learning (ML) in many prediction problems, there is a high degree of interest in applying ML to Earth system prediction. However, because of the high dimensionality of the system, it is critical to use hybrid methods which combine data-driven models, physical models, and observations. I will present two such hybrid methods: Ensemble Oscillation Correction (EnOC) and the multi-model ensemble Kalman filter (MM-EnKF).
Oscillatory modes of the climate system are one of its most predictable features, especially at intraseasonal timescales. It has previously been shown that these oscillations can be predicted well with statistical methods, often with better skill than dynamical models. However, they only represent a portion of the signal, and a method for beneficially combining them with dynamical forecasts of the full system has not previously been developed. Ensemble Oscillation Correction (EnOC) is a method which corrects oscillatory modes in ensemble forecasts from dynamical models. I will show results of EnOC applied to forecasts of South Asian monsoon rainfall, outperforming the state-of-the-art forecasts on subseasonal-to-seasonal timescales.
A more general method for combining multiple models and observations is multi-model data assimilation (MM-DA). MM-DA generalizes the variational, Bayesian, and minimum variance formulation of the Kalman filter. Here, I will show how multiple model ensembles can be combined for both DA and forecasting in a flow-dependent manner using a multi-model ensemble Kalman filter (MM-EnKF). This methodology is applied to multiscale chaotic models and results in significant error reductions compared to the best model and to an unweighted multi-model ensemble. Lastly, I will discuss the prospects of using the MM-EnKF for hybrid forecasting.