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Effect of lateral boundary perturbations on the breeding method and the local ensemble transform Kalman filter for mesoscale ensemble prediction Cover

Effect of lateral boundary perturbations on the breeding method and the local ensemble transform Kalman filter for mesoscale ensemble prediction

Open Access
|Dec 2012

Figures & Tables

Fig. 1. 

Schematic diagrams of (a) the MBD method and (b) the LET method.

Table 1. List of data assimilated in the Meso 4D-VAR and LETKF analyses

LETKF Meso 4DVAR GTS data Radiosonde Radiosonde Pilot balloon Pilot balloon Aircraft (AMDAR) Aircraft (AMDAR) Ship Buoy QuikSCAT sea surface winds Domestic data Non Wind profiler in Japan Aircraft (domestic ACARS) Radar-AMeDAS analysed rainfall in Japan One-hour precipitation amount and total precipitable water retrieved from SSM/I, TMI and AMSR-E. Radial velocity data of operational Doppler radars in Japan
Fig. 2. 

Schematic diagram of the preparation procedure for LBCs and LBPs.

Fig. 3. 

Time sequence of the statistical ensemble spread of the 500 hPa height filed in the JMA's global one-week EPS. A line indicated by triangles is the spread by the global BGM before October 2007, whereas squares indicate the spread by the global SV method after November 2007. Courtesy of Ryouta Sakai of JMA.

Fig. 4. 

Sea level pressure (contours) and accumulated 3-h precipitation (colour scale) predicted by the control run. Initial time is 12 UTC, 4 July 2008. The colour bar indicates precipitation intensity in mm. (a) FT=3. (b) FT=24.

Table 2. List of experiments

Name Initial condition of the control run Initial perturbations LBPs in breeding/EnKF cycles to produce initial perturbations LBPs in the ensemble forecast NIP_lbpf 4DVAR No – Yes MBD_nlbp 4DVAR BGM No No MBD_lbpf 4DVAR BGM No Yes MBD_lbpfc 4DVAR BGM Yes Yes LET_lbpf 4DVAR LETKF No Yes LET_lbpfc 4DVAR LETKF Yes Yes LET_kfbf LETKF LETKF No Yes LET_kfbfc LETKF LETKF Yes Yes
Fig. 5. 

(a) Ensemble spread for meridional horizontal wind (V) at 850 hPa in the experiment with LBP only (NIP_lbpf). Initial time is 12 UTC 4 July 2008. (b) Ensemble spread at FT=36. (c) Time sequence of the ensemble spreads of surface elements in the common verification area.

Fig. 6. 

Similar to Fig. 5 but for the MBD experiment without LBPs (MBD_nlbp).

Fig. 7. 

Similar to Fig. 6 but with LBPs in the ensemble forecast (MBD_lbpf).

Fig. 8. 

Similar to Fig. 7 but with LBPs in both breeding cycles and the ensemble forecast (MBD_lbpfc).

Fig. 9. 

Three-hour accumulated precipitation at 12UTC 5 July (FT=24) predicted by each member. From left, member p05, member m05, the ensemble mean and the ensemble spread. (a) MBD method without LBPs (MBD_nlbp). (b) MBD method with LBPs in both breeding cycles and the ensemble forecast (MBD_lbpfc).

Fig. 10. 

(a) Averaged RMSEs against initial conditions for 3–4 July 2008 for surface variables. From left to right, Psea, T, RH and V. (b) Same as in (a) but for 500 hPa variables.

Fig. 11. 

Similar to Fig. 7 but for the LET method (LET_lbpf).

Fig. 12. 

Similar to Fig. 11 but with LBPs in both forecast analysis cycles and the ensemble forecast (LET_lbpfc).

Fig. 13. 

Similar to Fig. 10 but for the LET method.

Fig. 14. 

(a) Time series of the TE norm. Averages of two EPSs with initial times of 12 UTC, 3 July and 12 UTC, 4 July 2008. (b) Time series of growth rate of TE norm.

Fig. 15. 

Brier Skill Scores against different 6-h precipitation intensity thresholds for the five initial perturbation methods over two 36-h EPSs with initial times of 12 UTC, 3 July, and 12 UTC, 4 July 2008.

Table 3. (a) Similarity indexes between bred vectors. Upper triangular matrix components indicate the case without LBPs in the breeding cycles (MBD_lbpf), and lower triangular matrix components show the case with LBPs in the breeding cycles (MBD_lbpfc). Values less than −0.4 or greater than 0.4 are indicated in boldface. (b) Same as (a) but for LET initial perturbations. Upper triangular matrix components indicate the case without LBPs in EnKF cycles (LET_lbpf), whereas lower triangular matrix components indicate the case with LBPs in EnKF cycles (LET_lbpfc)

P1 p2 p3 P4 p5 m1 m2 m3 m4 m5 (a) p1 1.00 0.25 0.50 0.18 0.21 −1.00 −0.25 −0.49 −0.18 −0.21 p2 0.09 1.00 0.04 0.02 0.62 −0.25 −0.99 −0.03 −0.01 −0.61 p3 0.39 0.28 1.00 0.65 −0.05 −0.49 −0.03 −0.990.64 0.06 p4 0.21 0.03 0.20 1.00 −0.18 −0.18 0.00 −0.640.98 0.19 p5 −0.07 0.53 0.00 0.25 1.00 −0.20 −0.61 0.06 0.19 −0.99 m1 −0.99 −0.08 −0.37 −0.21 0.07 1.00 0.25 0.50 0.19 0.21 m2 −0.08 −0.98 −0.25 −0.02 −0.52 0.08 1.00 0.03 0.00 0.62 m3 −0.37 −0.26 −0.97 −0.19 0.01 0.37 0.27 1.000.65 −0.06 m4 −0.20 −0.02 −0.18 −0.99 −0.24 0.21 0.02 0.19 1.00 −0.19 m5 0.07 −0.52 0.02 −0.24 −0.99 −0.07 0.53 −0.01 0.24 1.00
M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 (b) M1 1.00 0.07 −0.23 −0.18 0.01 0.09 −0.03 −0.46 −0.30 −0.19 M2 −0.10 1.00 −0.11 −0.07 0.01 0.12 −0.31 −0.22 −0.33 −0.36 M3 0.19 −0.51 1.00 −0.06 −0.40 −0.34 −0.12 −0.03 0.27 0.03 M4 −0.18 −0.25 0.01 1.00 −0.22 −0.23 −0.02 −0.11 0.03 −0.15 M5 −0.10 0.32 −0.37 −0.26 1.00 0.06 −0.16 −0.01 −0.25 −0.02 M6 −0.56 0.12 −0.19 −0.02 0.06 1.00 −0.11 −0.07 −0.36 −0.18 M7 −0.01 −0.07 −0.07 −0.11 −0.17 −0.09 1.00 −0.14 0.10 −0.07 M8 0.03 −0.14 0.07 −0.10 −0.17 −0.17 −0.31 1.00 0.00 0.11 M9 −0.02 −0.27 0.09 −0.07 −0.15 −0.20 −0.20 −0.17 1.00 −0.02 M10 −0.23 −0.07 −0.26 −0.03 −0.14 0.07 0.00 −0.22 0.04 1.00
Fig. 16. 

Similar to Fig. 4 but by the control run with LETKF analysis (LET_kfbfc).

Fig. 17. 

(a) RMSEs of FT=24 forecast fields from 4 July 2008 against the Meso 4D-VAR analysis of 5 July 2008 for surface variables. 4DVAR is the forecast from the 4DVAR analysis, whereas LET_kfbf and LET_kfbfc are forecasts from the LETKF analyses without and with LBPs in EnKF cycles, respectively. LET_kfbf_em and LET_kfbfc_em are their ensemble mean without and with LBPs in EnKF cycles. (b) Same as (a) but for 500 hPa variables.

ARPA-SMR Agenzia Regionale Prevenzione e Ambiennte Romagna-Servizio Meteo Regionale BGM Breeding of Growing Modes BSS Brier Skill Score B08FDP Beijing 2008 Olympics Forecast Demonstration Project B08RDP Beijing 2008 Olympics Research and Development Project CAMS Chinese Academy of Meteorological Sciences CMA China Meteorological Administration COSMO-LEPS COnsortium for Small-scale MOdeling-Limited area Ensemble Prediction System EnKF Ensemble Kalman Filter EPS Ensemble Prediction System ETKF Ensemble Transform Kalman Filter FT Forecast Time GPV Grid Point Value GSM Global Spectral Model GSV Global model Singular Vector JMA Japan Meteorological Agency LAEF Limited Area Ensemble Forecasting LET Local Ensemble Transform (method) LETKF Local Ensemble Transform Kalman Filter MBD Mesoscale BreeDing growing mode (method) MOGREPS Met Office Global and Regional Ensemble Prediction System MRI Meteorological Research Institute MSC Meteorological Service of Canada MSM MesoScale Model NCEP National Centers for Environmental Prediction NHM Non-Hydrostatic Model NMC National Meteorological Center NPD Numerical Prediction Division NWP Numerical Weather Prediction RSMC Regional Specialized Meteorological Center RMSE Root Mean Square Error SREF Short-Range Ensemble Forecast SV Singular Vector TE Total Energy WMO World Meteorological Organization WWRP World Weather Research Programme ZAMG Zentralanstalt für Meteorologie und Geodynamik
Language: English
Page range: 11594 - 11594
Submitted on: Feb 18, 2011
Published on: Dec 1, 2012
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2012 Kazuo Saito, Hiromu Seko, Masaru Kunii, Takemasa Miyoshi, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.