Developments of a local ensemble transform Kalman filter with JMA global model
Takemasa Miyoshi* † , Yoshiaki Sato**, Takashi Kadowaki*, Masahiro Kazumori*,Ryota Sakai*, and Munehiko Yamaguchi**Numerical Prediction Division, Japan Meteorological Agency**Environmental Modeling Center, National Centers for Environmental Prediction, NOAA

Miyoshi and Sato (2007) reported their successful implementation of the local ensemble transform
Kalman filter (LETKF, Hunt et al. 2007) with the JMA global model (GSM). They also succeeded inassimilating satellite radiances, which indicated positive impact. After the report by Miyoshi and Sato(2007), the following major upgrades have been made to the GSM-LETKF system:
1) Removing local patches as in Miyoshi et al. (2007)
2) Applying an additive covariance inflation method as in Whitaker et al. (2007), but using JRA-25(Onogi et al. 2007) instead of NCEP/NCAR reanalysis (Kalnay et al. 1996)
3) Applying an efficient parallel algorithm
4) Applying an adaptive bias correction for satellite radiances to simulate the operational variational
bias correction within 4D-Var (Sato 2007)
The details are described in a separate paper, which is in preparation at the moment. This short reportpresents recent results of the comparison between LETKF and the JMA operational 4D-Var (Kadowaki2005) and ensemble prediction system (EPS).
LETKF performed well in typhoon track forecast. The typhoon Rananim case in 2004 showed an
excellent performance of LETKF (Fig. 1). LETKF captured the westward movement successfully,
although most operational systems at that time predicted the northward movement, leading to false
caution to Japan. Even with JMA’s latest operational EPS with singular vectors centered at 4D-Varanalysis (Sakai et al. 2008), just a few ensemble members out of 51 captured the westward movement.
Not just in this case but on average over many cases, we obtained improvements of the track forecast.

4D-Var + SV

LETKF

Fig. 1. 51-member typhoon track
ensemble forecast in the typhoon
Rananim case initialized at 12 UTC
8 August 2004. Left and right panels
indicate operational singular-vector
ensemble prediction system centeredat 4D-Var analysis and LETKF,
respectively. The thick black line
indicates the best track. The
red/orange lines indicate ensemble
members. The blue and green lines
show control forecast and ensemble
mean, respectively.

To make an overall comparison between LETKF and 4D-Var, forecast anomaly correlations arecompared; Figure 2 shows the relative improvements of LETKF over 4D-Var. Without the adaptive bias
correction for satellite radiances, high temperature bias existed in the LETKF analysis, which leads tolarge degradation of forecasts. This problem was found to be caused by the fact that RTTOV-8 has been
applied to LETKF rather than RTTOV-7 which is used in the operational 4D-Var and quality controlsystems. It turned out that the surface emissivity model FASTEM-2 of RTTOV-7 contained some known
bugs causing spurious positive bias in the calculated surface emissivity. As a result, radiances arecomputed to be spuriously large for satellite sensors sensitive to the surface emissivity such as AMSU-Ach.4, which are also sensitive to the lower tropospheric temperature. Therefore, the observations are too†

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

low compared to the computed radiances; the variational bias correction scheme in 4D-Var adds positive
value to the observation to remove the biased differences. Without the adaptive bias correction within
LETKF, the same bias correction has been applied. However in LETKF, RTTOV-8 does not contain thebugs in FASTEM-2. Therefore, the observations after the “wrong” bias correction are too high, which
results in the high temperature bias in the lower troposphere.
After applying the adaptive bias correction, the estimated bias was drifted rapidly away from theoptimal value for 4D-Var; it happened only to the satellite channels sensitive to the surface emissivity.
Then, we obtained much improvement as shown in Fig. 2 (b). However, the positive bias in the analysis
was not completely removed. We still need to improve the SH and extratropical surface pressure
forecasts.
LETKF indicated advantages in probabilistic forecasts compared to the operational EPS. LETKF
showed better brier skill scores than the singular-vector or bred-vector EPS centered at 4D-Var analysis in
shorter leads up to 72 hours (Fig. 3). This tendency that LETKF probabilistic forecast is advantageous inshorter leads is true for other verification measures including ROC area.
Several major upgrades have been made to the LETKF system and resulted in steady improvements.
Now it is said that overall LETKF performs comparable to 4D-Var. LETKF outperforms 4D-Var in theTropics, and slightly outperforms in the NH in general. However, further improvements are desired forextratropical surface pressure forecasts and general performance in the SH. We will continue thedevelopment to improve the LETKF performance, so that it becomes a possible operational choice in nearfuture.
(a)(b)

Fig. 2. Relative improvements (%) of 1- to 9-day forecast
anomaly correlations of LETKF over 4D-Var, averaged over
31 days in August, 2004. Positive values indicate that LETKF
outperforms 4D-Var. (a) and (b) show the cases without andwith the adaptive bias correction for satellite radiances,respectively.

Fig. 3. Brier skill scores of LETKF (red), SV (green), and BV(blue) for the event that temperature at 850 hPa is greater than
2 K than climatology.

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