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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-252
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-252
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: development and technical paper 07 Nov 2019

Submitted as: development and technical paper | 07 Nov 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

An ensemble Kalman filter data assimilation system for the whole neutral atmosphere

Dai Koshin1, Kaoru Sato1, Kazuyuki Miyazaki2,3, and Shingo Watanabe3 Dai Koshin et al.
  • 1Department of Earth Planetary Science, The University of Tokyo, Tokyo, Japan
  • 2Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
  • 3Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

Abstract. A data assimilation system with a four-dimensional local ensemble transform Kalman filter (4D-LETKF) is developed to make a new analysis data for the atmosphere up to the lower thermosphere using the Japanese Atmospherics General Circulation model for Upper Atmosphere Research. The time period from 10 January 2017 to 20 February 2017, when an international radar network observation campaign was performed, is focused on. The model resolution is T42L124 which can resolve phenomena at synoptic and larger scales. A conventional observation dataset provided by National Centers for Environmental Prediction, PREPBUFR, and satellite temperature data from the Aura Microwave Limb Sounder (MLS) for the stratosphere and mesosphere are assimilated. First, the performance of the forecast model is improved by modifying the vertical profile of the horizontal diffusion coefficient and modifying the source intensity in the non-orographic gravity wave parameterization, by comparing it with radar wind observations in the mesosphere. Second, the MLS observational bias is estimated as a function of the month and latitude and removed before the data assimilation. Third, data assimilation parameters, such as the degree of gross error check, localization length, inflation factor, and assimilation window are optimized based on a series of sensitivity tests. The effect of increasing the ensemble member size is also examined. The obtained global data are evaluated by comparison with the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) reanalysis data covering pressure levels up to 0.1 hPa and by the radar mesospheric observations which are not assimilated.

Dai Koshin et al.
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Status: open (until 02 Jan 2020)
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Data sets

MLS/Aura Level 2 Temperature V004 M. Schwartz, N. Livesey, and W. Read https://doi.org/10.5067/Aura/MLS/DATA2021

NCEP ADP Global Upper Air and Surface Weather Observations (PREPBUFR format) National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce https://doi.org/10.5065/Z83F-N512

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Short summary
A new data assimilation system with a 4D local ensemble transform Kalman filter for the whole neutral atmosphere is developed using a T42L124 general circulation model. A conventional observation dataset and bias-corrected satellite temperature data are assimilated. After the improvements of the forecast model, the assimilation parameters are optimized. The minimum optimal number of ensembles is also examined. Results are evaluated using the reanalysis data and independent radar observations.
A new data assimilation system with a 4D local ensemble transform Kalman filter for the whole...
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