IMILAST: A Community Effort to Intercompare Extratropical Cyclone Detection and Tracking Algorithms

Author:

Neu Urs1,Akperov Mirseid G.2,Bellenbaum Nina3,Benestad Rasmus4,Blender Richard5,Caballero Rodrigo6,Cocozza Angela7,Dacre Helen F.8,Feng Yang9,Fraedrich Klaus5,Grieger Jens10,Gulev Sergey11,Hanley John6,Hewson Tim12,Inatsu Masaru13,Keay Kevin14,Kew Sarah F.15,Kindem Ina16,Leckebusch Gregor C.17,Liberato Margarida L. R.18,Lionello Piero19,Mokhov Igor I.2,Pinto Joaquim G.3,Raible Christoph C.20,Reale Marco7,Rudeva Irina21,Schuster Mareike10,Simmonds Ian22,Sinclair Mark23,Sprenger Michael24,Tilinina Natalia D.11,Trigo Isabel F.25,Ulbrich Sven3,Ulbrich Uwe10,Wang Xiaolan L.9,Wernli Heini24

Affiliation:

1. ProClim, Swiss Academy of Sciences, Bern, Switzerland

2. A.M. Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, Moscow, Russia

3. Institute of Geophysics and Meteorology, University of Cologne, Cologne, Germany

4. Norwegian Meteorological Institute, Oslo, Norway

5. Institute of Meteorology, University of Hamburg, Hamburg, Germany

6. Department of Meteorology, Stockholm University, Stockholm, Sweden

7. Di.S.Te.B.A., University of Salento, Lecce, Italy

8. Department of Meteorology, University of Reading, Reading, United Kingdom

9. Climate Research Division, Environment Canada, Toronto, Ontario, Canada

10. Institute of Meteorology, Freie Universität Berlin, Berlin, Germany

11. P. P. Shirshov Institute of Oceanology, Russian Academy of Sciences, and Moscow State University, Moscow, Russia

12. European Centre for Medium-Range Weather Forecasts, Reading, and Met Office, Exeter, United Kingdom

13. Graduate School of Science, Hokkaido University, Sapporo, Japan

14. School of Earth Sciences, University of Melbourne, and Bureau of Meteorology, Melbourne, Victoria, Australia

15. Royal Netherlands Meteorological Institute, De Bilt, Netherlands

16. Bjerknes Centre for Climate Research, Bergen, Norway

17. School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom

18. Instituto Dom Luiz, University of Lisbon, Lisbon, and School of Sciences and Technology, University of Trás-os-Montes and Alto Douro (UTAD), Vila Real, Portugal

19. Di.S.Te.B.A., University of Salento, and CMCC, EuroMediterranea Center on Climate Change, Lecce, Italy

20. Climate and Environmental Physics, and Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland

21. P. P. Shirshov Institute of Oceanology, Russian Academy of Sciences, Moscow, Russia, and School of Earth Sciences, University of Melbourne, Melbourne, Victoria, Australia

22. School of Earth Sciences, University of Melbourne, Melbourne, Victoria, Australia

23. Embry-Riddle Aeronautical University, Prescott, Arizona

24. Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland

25. Instituto Dom Luiz, University of Lisbon, and Institute of Meteorology I. P., Lisbon, Portugal

Abstract

The variability of results from different automated methods of detection and tracking of extratropical cyclones is assessed in order to identify uncertainties related to the choice of method. Fifteen international teams applied their own algorithms to the same dataset—the period 1989–2009 of interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERAInterim) data. This experiment is part of the community project Intercomparison of Mid Latitude Storm Diagnostics (IMILAST; see www.proclim.ch/imilast/index.html). The spread of results for cyclone frequency, intensity, life cycle, and track location is presented to illustrate the impact of using different methods. Globally, methods agree well for geographical distribution in large oceanic regions, interannual variability of cyclone numbers, geographical patterns of strong trends, and distribution shape for many life cycle characteristics. In contrast, the largest disparities exist for the total numbers of cyclones, the detection of weak cyclones, and distribution in some densely populated regions. Consistency between methods is better for strong cyclones than for shallow ones. Two case studies of relatively large, intense cyclones reveal that the identification of the most intense part of the life cycle of these events is robust between methods, but considerable differences exist during the development and the dissolution phases.

Publisher

American Meteorological Society

Subject

Atmospheric Science

Reference49 articles.

1. Probability distributions for cyclones and anticyclones from the NCEP/NCAR reanalysis data and the INM RAS climate model;Akperov;Izv., Atmos. Oceanic Phys.,2007

2. Explosive cyclogenesis: A global climatology comparing multiple reanalyses;Allen;J. Climate,2010

3. North Atlantic oscillation and synoptic variability in the European- Atlantic region in winter;Bardin;Izv., Atmos. Oceanic Phys.,2005

4. The use of a calculus-based cyclone identification method for generating storm statistics;Benestad;Tellus,2006

5. Will extratropical storms intensify in a warmer climate?;Bengtsson;J. Climate,2009

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