Multi-Model Communication and Data Assimilation for Mitigating Model Error and Improving Forecasts |
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Authors: | Yian CHEN{ and } Samuel N STECHMANN{ |
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Institution: | Department of Mathematics, University of Wisconsin-Madison,Madison, WI 53706, USA; Department of Statistics, University ofChicago, Chicago, IL 60637, USA. and Department of Mathematics and Department of Atmospheric andOceanic Sciences, University of Wisconsin-Madison,Madison, WI 53706, USA. |
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Abstract: | Models for weather and climate prediction are complex, and each
model typi-cally has at least a small number of phenomena that are
poorly represented, such as perhaps the Madden-Julian Oscillation
(MJO for short) or El Ni\~{n}o-Southern Oscillation (ENSO for short)
or sea ice. Furthermore, it is often a very challenging task to
modify and improve a complex model without creating new
deficiencies. On the other hand, it is sometimes possible to design
a low-dimensional model for a particular phenomenon, such as the MJO
or ENSO, with significant skill, although the model may not
represent the dynamics of the full weather-climate system. Here a
strategy is proposed to mitigate these model errors by taking
advantage of each model''s strengths. The strategy involves
inter-model data assimilation, during a forecast simulation, whereby
models can exchange information in order to obtain more faithful
representations of the full weather-climate system. As an initial
investigation, the method is examined here using a simplified
scenario of linear models, involving a system of stochastic partial
differential equations (SPDEs for short) as an imperfect tropical
climate model and stochastic differential equations (SDEs for short)
as a low-dimensional model for the MJO. It is shown that the MJO
prediction skill of the imperfect climate model can be enhanced to
equal the predictive skill of the low-dimensional model. Such an
approach could provide a route to improving global model forecasts
in a minimally invasive way, with modifications to the prediction
system but without modifying the complex global physical model
itself. |
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Keywords: | MJO Multi-Model communication Data assimilation Kalman filteralgorithm |
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