Applying a local ensemble transform Kalman filter to the JMA nonhydrostatic model
Takemasa Miyoshi* † and Kohei Aranami**Numerical Prediction Division, Japan Meteorological Agency

In the 2006 issue of the WGNE Blue Book, Miyoshi and Yamane (2006) reported their successful
implementation of the local ensemble transform Kalman filter (LETKF, Hunt 2005) with the AFES model(AGCM for the Earth Simulator). They made further comprehensive investigations; the results are now inpress in Monthly Weather Review (Miyoshi and Yamane 2007). In the meantime, based on theinvestigations on the Earth Simulator, LETKF has been developed with the JMA’s nonhydrostatic
mesoscale model (NHM). Miyoshi and Aranami (2006, hereafter “MA06”) published results of perfectmodel twin experiments with the NHM-LETKF. In this short report, we briefly overview our preliminary
results with real observations.
After the successful investigations by MA06, real observations used in JMA’s operational
mesoscale analysis are assimilated with the NHM-LETKF. The model domain is chosen to be the same asthe JMA operational mesoscale system (about 3600km x 2900km). The resolution is reduced to be 20-km
grid spacing with 181x145x50 grid points, instead of the operational 5-km grid spacing which requires 16
times more grid points. The model parameters and physical processes of the 20-km NHM are chosen to be
essentially the same as the operational settings. Differences are found in the orography, land-surface,finite-differencing time, and buffer area near the boundaries, all of which are minimum required changes
by the resolution difference. The ensemble size is chosen to be 20. The data assimilation cycle begins on
June 25, 2004.
Figure 1 shows 6-hour forecast fields (i.e., first guess) of the NHM-LETKF and JMA operational
mesoscale NWP systems. The operational mesoscale NWP system as of June and July 2004 was not
NHM but a hydrostatic spectral model with 10-km grid spacing, thus it is not straightforward to comparethem precisely. Although NHM-LETKF indicates a little higher pressure, the position of the low pressure
system and the precipitation pattern show good agreements.
Figure 2 shows analysis ensemble spreads after the first analysis step and a few week cycle
processes. Since a large number of observations are available over Japan, the area with small spreads
indicates the shape of Japan after the first analysis step. Due to the fixed boundary conditions for all
ensemble members, spreads are artificially small near the boundaries, especially about 20 grids (400 km)
which corresponds to the damping area. Still, we see large spreads beyond 7 m/s in the region; the errors
are actually growing and not damped by the fixed boundaries. The ensemble spreads vary dynamically toshow flow-dependent behaviors. The stable LETKF performance would explain that the error growth
inside the region is not significantly affected by the fixed boundary conditions.
Although it is difficult to compare the analysis accuracy, the NHM-LETKF appears to generatereasonable analysis. Miyoshi et al. (2007) developed a new version of the LETKF that does not use localpatches, which requires less computational time and generates better analysis without discontinuities atthe edges of local patches. Future plans include applying the new version without local patches, as well as
further investigations of the verifications and ensemble forecast experiments.

†Corresponding author address: Numerical Prediction Division, Japan Meteorological Agency, 1-3-4
Otemachi, Chiyoda-ku, Tokyo 100-8122, Japan. E-mail: miyoshi@naps.kishou.go.jp

Figure 1. 3-hour accumulated precipitation (mm, shades) and sea-level pressure (contour) of NHM-LETKF 6-hour forecast
ensemble mean (left) and JMA operational mesoscale 6-hour forecast (right) valid on 06Z July 4, 2004.

Figure 2. Horizontal pattern of the analysis ensemble spreads of wind speed (m/s) at the 21st vertical level (about 4000 metershigh), after first analysis step (00Z June 25, 2004, left) and a few week cycle processes (06Z July 4, 2004, right). Horizontal
and vertical axes indicate grid numbers.

Acknowledgements
We thank Prof. Eugenia Kalnay for fruitful discussions.
References
Hunt, B. R., 2005: Efficient Data Assimilation for Spatiotemporal Chaos: a Local Ensemble Transform
Kalman Filter. arXiv:physics/0511236v1, 25pp.
Miyoshi, T. and S. Yamane, 2006: Local ensemble transform Kalman filtering with an AGCM at a
T159/L48 resolution. CAS/JAC WGNE Research Activities in Atmospheric and Oceanic Modelling,35, 1-19.
Miyoshi, T. and S. Yamane, 2007: Local ensemble transform Kalman filtering with an AGCM at aT159/L48 resolution. Mon. Wea. Rev., in press.
Miyoshi, T. and K. Aranami, 2006: Applying a Four- dimensional Local Ensemble Transform Kalman
Filter (4D-LETKF) to the JMA Nonhydrostatic Model (NHM), SOLA, 2, 128-131.
Miyoshi, T., S. Yamane, and T. Enomoto, 2007: Localizing the error covariance by physical distances
within a local ensemble transform Kalman filter. in preparation.