Abstract
Recent activities on ensemble data assimilation and its application to observing-system research at the Japan Agency for Marine-Earth Science and Technology are reviewed. A revised version of an ensemble-based data assimilation system for global atmospheric data has been developed on the second-generation Earth Simulator. This system assimilates conventional atmospheric observations and satellite-based wind data into an atmospheric general circulation model using the local ensemble transform Kalman filter (LETKF), a deterministic ensemble Kalman filter algorithm that is extremely efficient with parallel computer architecture. The updated system incorporates improvements to the previous system in the forecast model, data assimilation algorithm and input data. Using the LETKF system, observations taken during field campaigns are evaluated by data assimilation experiments involving adding or removing observations. The results of these observing-system experiments successfully demonstrate the value of the observations and are highly useful for exploring the predictability of atmospheric disturbances.
Keywords
- Field Campaign
- Global Precipitation Climatology Project
- Ensemble Spread
- Earth Simulator
- Atmospheric Model Intercomparison Project
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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- 1.
Currently PREPBUFR observations are available continuously from 1 May 1997.
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Enomoto, T. et al. (2013). Observing-System Research and Ensemble Data Assimilation at JAMSTEC. In: Park, S., Xu, L. (eds) Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35088-7_21
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