Abstract. A data assimilation system with a four-dimensional local
ensemble transform Kalman filter (4D-LETKF) is developed to make a new
analysis dataset 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 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 the 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.