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| Multi-Model Communication and Data Assimilation for Mitigating Model Error and Improving Forecasts |
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Citation： |
Yian CHEN{,} Samuel N. STECHMANN{.Multi-Model Communication and Data Assimilation for Mitigating Model Error and Improving Forecasts[J].Chinese Annals of Mathematics B,2019,40(5):689~720 |
| Page view： 519
Net amount： 529 |
Authors： |
Yian CHEN{; } Samuel N. STECHMANN{ |
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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. |
Keywords： |
MJO, Multi-Model communication, Data assimilation, Kalman filteralgorithm |
Classification： |
60H10, 60H15, 86A10, 62M20 |
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