[Eoas-seminar] Visiting research scientist Aviatar Bach (today and tomorrow)

[eoas-seminar at lists.fsu.edu]

Hello everyone,


Aviatar Bach (https://eviatarbach.com/cv/) will be visiting the 
Department of Scientific Computing March 1st and 2nd.

He will a talk **at the colloquium of SC with the title *Towards the 
combination of physical and data-driven forecasts for Earth system 
prediction*  today Wednesday March 1st starting at 3:30 in 499DSL.


If you would like to join by zoom: https://fsu.zoom.us/j/94273595552


He is also available to talk with other scientist during his visit. If 
you are interested please contact Olmo Zavala osz09 at fsu.edu ASAP.


*Abstract:*

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.



Thanks,

Olmo


-- 

/*Olmo Zavala-Romero*/

*Assistant Professor*

*http://olmozavala.com*

*Department of Scientific Computing*

*Florida State University*

400 Dirac Science Library

Tallahassee, FL 32306-4120

*Tel:   850-346-9101*

*Email: osz09 at fsu.edu*
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