Start Submission Become a Reviewer

Reading: Verification and intercomparison of mesoscale ensemble prediction systems in the Beijing 200...

Download

A- A+
Alt. Display

Original Research Papers

Verification and intercomparison of mesoscale ensemble prediction systems in the Beijing 2008 Olympics Research and Development Project

Authors:

Masaru Kunii ,

Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, JP
X close

Kazuo Saito,

Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, JP
X close

Hiromu Seko,

Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, JP
X close

Masahiro Hara,

Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, JP
X close

Tabito Hara,

Numerical Prediction Division, Japan Meteorological Agency, Tokyo, JP
X close

Munehiko Yamaguchi,

Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, JP
X close

Jiandong Gong,

National Meteorological Center, Chinese Meteorological Administration, Beijing, CN
X close

Martin Charron,

Environment Canada, Dorval, Québec, CA
X close

Jun Du,

National Centers for Environmental Prediction, Washington D.C., US
X close

Yong Wang,

Zentralanstalt für Meteorologie und Geodynamik, Wien, AT
X close

Dehui Chen

National Meteorological Center, Chinese Meteorological Administration, Beijing, CN
X close

Abstract

During the period around the Beijing 2008 Olympic Games, the Beijing 2008 Olympics Research and Development Project (B08RDP) was conducted as part of the World Weather Research Program short-range weather forecasting research project. Mesoscale ensemble prediction (MEP) experiments were carried out by six organizations in nearreal time, in order to share their experiences in the development of MEP systems. The purpose of this study is to objectively verify these experiments and to clarify the problems associated with the current MEP systems through the same experiences.

Verification was performed using the MEP outputs interpolated into a common verification domain with a horizontal resolution of 15 km. For all systems, the ensemble spreads grew as the forecast time increased, and the ensemble mean improved the forecast errors compared with individual control forecasts in the verification against the analysis fields. However, each system exhibited individual characteristics according to the MEP method.

Some participants used physical perturbation methods. The significance of these methods was confirmed by the verification. However, the mean error (ME) of the ensemble forecast in some systems was worse than that of the individual control forecast. This result suggests that it is necessary to pay careful attention to physical perturbations.

How to Cite: Kunii, M., Saito, K., Seko, H., Hara, M., Hara, T., Yamaguchi, M., Gong, J., Charron, M., Du, J., Wang, Y. and Chen, D., 2011. Verification and intercomparison of mesoscale ensemble prediction systems in the Beijing 2008 Olympics Research and Development Project. Tellus A: Dynamic Meteorology and Oceanography, 63(3), pp.531–549. DOI: http://doi.org/10.1111/j.1600-0870.2011.00512.x
9
Citations
  Published on 01 Jan 2011
 Accepted on 13 Jan 2011            Submitted on 20 Apr 2010

References

  1. Brier , G ., 1950 . Verification of forecasts expressed in terms of probabil-ity. Mon . Wea. Re v ., 78 , 1 – 3 .  

  2. Buizza , R. , Tribbia , J ., Molteni , E and Palmer , T. N. , 1993. Computation of optimal unstable structures for a numerical weather prediction model. Tellus , 45A , 388 - 407 .  

  3. Casati , B. , Wilson , L. J. , Stephenson , D. B. , Nurmi , R , Ghelli , A. and co-authors , 2008. Forecast verification: current status and future di-rections. Meteor AppL , 15 , 3 - 18 .  

  4. Charron , M. , Pellerin , G. , Spacek , L. , Houtekamer , P. L. , Gagnon , N. and co-authors . 2010 . Toward random sampling of model error in the Canadian ensemble prediction system . Mon. Wea. Rev ., 138 , 1877– 1901 .  

  5. Chen D H , Xue , J. S. and Yang , X. S ., 2008 . New generation of multi-scale NVVP system (GRAPES): general scientific design . China Sci. Bull ., 53 ( 22 ): 3433 – 3445  

  6. Cote , J. , Gravel , S. , Methot , A. , Patoine, A. Roch , M. and co-authors. 1998a. The operational CMC-MRB Global Environmental Multiscale (GEM) model , Part I: design considerations and formulation. Mon. Wea. Rev ., 126 , 1373-139 5 .  

  7. Cote , J. , Desmarais , J.-G. Gravel, S. Methot , A. Patoine , A. and co-authors. 1998b. The operational CMC-MRB Global Environmen-tal Multiscale (GEM) model , part II: results. Mon. Wea. Rev ., 126 , 1397-141 8 .  

  8. Derkova. , M. and Bellus , M . 2007 . Various applications of the blending by digital filter technique in the ALADIN numerical weather predic-tion system. Meteorologic14 casopis , 10 , 27 - 36. Available online at http://wwwsc-lace.eu.  

  9. Du , J . 2004 . Hybrid ensemble prediction system: a new ensem-bling approach. Preprints, Symposium on the 50th Anniversary of Operational Numerical Weather Prediction , University of Mary-land, College Park, Maryland, June 14-17, 2004, Amer. Me-teor. Soc., CD-ROM (paper p4.2, 5pp). Available online at http://www.emc.ncep.noaa.gov/mmb/SREF/reference.html.  

  10. Ebisuzalci , W. and Kalnay , E . 1991 . Ensemble experiments with a new lagged average forecasting scheme. WMO Research Activities in At-mospheric and Oceanic Modeling Rep . 15 , Geneva, Switzerland , 6.31 - 6.32 .  

  11. Evensen , G ., 1994 . Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statis-tics. J. Geophys. Res ., 99 ( C5 ), 10 143-10 162 .  

  12. Harvey , L. O. , Hammond , K. R. , Lusk , C. M. and Mross , E. F. 1992. The application of signal detection theory to weather forecasting behavior. Mon. Wea. Rev ., 120 , 863 - 883 .  

  13. Hou , D. , Kalnay , E. and Drogemeier , K . 2001 . Objective verification of the SAMEX98 ensemble forecasts. Mon . Wea. Re v ., 129 , 73 – 91 .  

  14. Janjié , Z. I. , Gerrity Jr., J. P . and Nickovic , S. 2001 . An alternative ap-proach to nonhydrostatic modeling . Mon. Wea. Rev ., 129 , 1164 - 1178 .  

  15. Keenan , T. , Joe , P. , Wilson , J. , Collier , C. , Golding , B. and co-authors . 2003 . The Sydney 2000 World Weather Research Programme Forecast Demonstration Project. Bull. A. Met. Soc ., 84 , 1041 - 1054 .  

  16. Kunii , M. , Saito , K. and Seko , H . 2010 . Mesoscale data assimilation experiment in the WWRP BO8RDP . SOLA , 6 , 33 – 36 .  

  17. Mullen , S. L. , and Baumhefner , D. P . 1989 . The impact of initial con-dition uncertainty on numerical simulations of large-scale explosive cyclogenesis. Mon . Wea. Re v ., 117 , 2800 – 2821 .  

  18. Murphy , A. H ., 1973 . A new vector partition of the probability score . J. AppL Meteor , 12 , 595 – 600 .  

  19. Murphy , A. H ., 1988 . Skill scores based on the mean square error and their relationships to the correlation coefficient. Mon . Wea. Re v ., 116 , 2417 – 2424 .  

  20. Richardson , D. S ., 2000 . Skill and relative economic value of the ECMWF ensemble prediction system . Q. J. R. Meteor Soc ., 126 , 649 – 667 .  

  21. Saito , K. , Fujita , T. , Yamada , Y. , Ishida , J. , Kumagai , Y. and co-authors . 2006 . The operational JMA nonhydrostatic mesoscale model. Mon. Wea. Rev ., 134 , 1266 - 1298 .  

  22. Saito , K. , Ishida , J. , Aranami , K. , Hara , T. , Segawa , T. and co-authors . 2007 . Nonhydrostatic atmospheric models and operational develop-ment at .TMA. J. Meteor Soc. Japan , 85B , 271 - 304 .  

  23. Saito , K. , Seko , H. , Kunii , M. and Hara , M . 2008 . Mesoscale ensem-ble prediction experiment for WWRP Beijing Olympic 2008 RDP: 2007 preliminary experiment. CAS/JSC WGNE Research Activities in Atmospheric and Oceanic Modelling , 37 , 1.23 - 1.24 .  

  24. Saito , K. , Kunii , M. , Hara , M. , Seko , H. , Hara , T. and co-authors . 2010 . WWRP Beijing Olympics 2008 Forecast Demon-stration/Research and Development Project (B08FDP/RDP). Tech. Rep. MRI , 62 , 1 - 146. Available online at http://www.mri-jma.go. jp/Publish/Technical/DATANOL_62/62_en.html.  

  25. Saito , K. , Hara , M. , Kunii , M. , Seko , H. and Yamaguchi , M . 2011 . Com-parison of initial perturbation methods for the mesoscale ensemble prediction system of the Meteorological Research Institute for the WWRP Beijing 2008 Olympics Research and Development Project (B08RDP) . Tellus , 63A .  

  26. Skamarock , W. C. , Klemp , J. B. , Dudhia , J. , Gill , D. O. , Barker , D. M. and co-authors. 2005. A description of the Advanced Research WRF, Version 2. NCAR Technical Note.  

  27. Toth , Z. , and Kalnay , E . 1993 . Ensemble forecasting at NMC: the gen-eration of perturbations . Bull. Am. Meteor. Soc ., 74 , 2317 – 2330 .  

  28. Toth , Z. , and Kalnay , E . 1997 . Ensemble forecasting at NCEP: the breeding method. Mon . Wea. Re v ., 125 , 3297 – 3318 .  

  29. Wei , M. , Toth , Z. , Wobus , R. and Zhu , Y . 2008 . Initial perturbations based on the ensemble transform (ET) technique in the NCEP global operational forecast system . Tellus , 60A , 62 – 79 .  

  30. Yamaguchi , M. , Sakai , R. , Kyoda , M. Komori , T. and Kadowaki , T. 2009. Typhoon ensemble prediction system developed at the Japan Meteorological Agency. Mon. Wea. Rev ., 137 , 2592 - 2604 .  

  31. Yeh , K.-S. , Cote , J. , Gravel , S. , Methot , A. , Patoine , A. and co-authors . 2002 . The CMC-MRB global environmental multiscale (GEM) model, Part HL nonhydrostatic formulation. Mon. Wea. Rev ., 130 , 339 - 356 .  

  32. Zhou , B. and Du , J . 2010 . Fog prediction from a multimodel mesoscale ensemble prediction system . Weather and Forecasting , 25 , 302 – 322 .  

comments powered by Disqus