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IMILAST a Community Effort to Intercompare Extratropical Cyclone Detection and Tracking Algorithms

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IMILAST a Community Effort to Intercompare Extratropical Cyclone Detection and Tracking Algorithms IMILAST A Community Effort to Intercompare Extratropical Cyclone Detection and Tracking Algorithms BY URS NEU, MIRSEID G. AKPEROV, NIna BELLENBaum, RASmuS BENESTAD, RICHARD BLENDER, RODRIGO CABALLERO, AngELA COCOZZA, HELEN F. DACRE, Yang FEng, KLauS FRAEDRICH, JENS GRIEGER, SERGEY GULEV, JOHN HanLEY, TIM HEWSON, MASARU InaTSU, KEVIN KEAY, SARAH F. KEW, Ina KINDEM, GREGOR C. LECKEBUSCH, MARgaRIDA L. R. LIBERATO, PIERO LIONELLO, IGOR I. MOKHOV, JOAQUIM G. PINTO, CHRISTOPH C. RAIBLE, MARCO REALE, IRIna RUDEVA, MAREIKE SCHUSTER, Ian SImmONDS, MARK SINCLAIR, MICHAEL SPREngER, NATALIA D. TILINIna, ISABEL F. TRIGO, SVEN ULBRICH, UWE ULBRICH, XIAOLan L. Wang, anD HEINI WERNLI An intercomparison experiment involving 15 commonly used detection and tracking algorithms for extratropical cyclones reveals those cyclone characteristics that are robust between different schemes and those that differ markedly. xtratropical cyclones are fundamental Identifying and tracking extratropical cyclones meteorological features and play a key role in might seem, superficially, to be a straightforward E a broad range of weather phenomena. They are activity, but in reality it is very challenging. In this a central component maintaining the global atmo- regard it is useful to compare the situation with spheric energy, moisture, and momentum budgets. tropical cyclones, which possess characteristics that They are on the one hand responsible for an im- make them relatively easy to identify and track: they portant part of our water supply, and on the other occur rarely (making misassociation unlikely), are are intimately linked with many natural hazards generally symmetric and slow moving, and have affecting the middle and high latitudes (wind damage, a relatively unambiguous structure. Extratropical precipitation-related flooding, storm surges, and cyclones are in a sense the “opposite”: they are much marine storminess). Thus, it is important to provide more common, can range greatly in shape and struc- for society an accurate diagnosis of cyclone activity, ture (are often asymmetric), differ rather more in size which includes a baseline climatology of extratropical (with diameters ranging from about 100 to well over storms (e.g., Hoskins and Hodges 2002) and also 1,000 km), and have translational velocities that can estimates of likely future changes therein. While vary greatly. Identifying the same physical feature future changes in some cyclone characteristics such at different times (i.e., tracking) is also complicated as the total number of cyclones might be small, major by the fact that a single cyclone will sometimes split signals may still be expected in specific characteristics into separate features, and sometimes two will merge such as regional storm frequency, intensity, and loca- into one. Furthermore, extratropical cyclones occur tion (e.g., Leckebusch et al. 2006; Wang et al. 2006; in very diverse synoptic situations, with some being Löptien et al. 2008; Bengtsson et al. 2009; Pinto et al. confined to lower-tropospheric levels and others 2009; Raible et al. 2010; Schneidereit et al. 2010). extending through great depth. This great complexity AMERICAN METEOROLOGICAL SOCIETY APRIL 2013 | 529 Unauthenticated | Downloaded 10/11/21 03:59 AM UTC reveals why there is no single commonly agreed upon for example, regarding trends in cyclone intensity. scientific definition of what an extratropical cyclone Raible et al. (2008), for example, demonstrated that is, and also why there exists a range of ideas and three different algorithms applied to the same input concepts regarding how to identify and track them. data showed similar interannual variability but con- It could be argued that an in-depth manual siderable differences in total cyclone numbers. While reanalysis of cyclone trajectories based on weather this comparison showed similar trend patterns in the maps reconstructed using all available data (e.g., Atlantic, trends found for the Pacific even differed Hewson et al. 2000) would provide the best tracks. in sign. Indeed, method-associated uncertainties However, given the lack of data in some regions and in some cases are quite large, such that equivalent the complexity of cyclone development, such activi- scientific studies may find contradictory climate ties inevitably involve some subjective choices being change signals even when using identical input data made by the analyst. So there is no accepted single (Trigo 2006; Ulbrich et al. 2009). Therefore, it is “truth” regarding specific cyclone tracks. Moreover, crucial to know those aspects for which the results while careful manual tracking might nonetheless are robust with regard to the method used, and those be considered optimal, for quantifying the behavior aspects for which there will be large method-related of all cyclones over many decades it is clearly not uncertainties. The project Intercomparison of Mid feasible, and the application of automated detection Latitude Storm Diagnostics (IMILAST) is the first and tracking methods to reanalysis data—the thrust comprehensive assessment focusing on this method- of this paper—is indispensable. related uncertainty. Although automated schemes are objective and reproducible, they are based on different STRATEGY AND METHODS. Over the last understandings of what best characterizes a cyclone. two decades, many numerical identification and Application of different algorithms provides results tracking algorithms have been developed (Murray that are remarkably similar in some aspects but and Simmonds 1991; Hodges 1995; Serreze 1995; may be very different in others. Thus, depending on Blender et al. 1997; Sinclair 1997; Simmonds et al. what one is looking for, the selection of a particular 1999; Lionello et al. 2002; Benestad and Chen 2006; method can significantly affect one’s conclusions, Trigo 2006; Wernli and Schwierz 2006; Akperov AFFILIATIONS: NEU—ProClim, Swiss Academy of Sciences, Dom Luiz, University of Lisbon, Lisbon, and School of Sciences Bern, Switzerland; AKPEROV anD MOKHOV—A.M. Obukhov Institute and Technology, University of Trás-os-Montes and Alto Douro of Atmospheric Physics, Russian Academy of Sciences, Moscow, (UTAD), Vila Real, Portugal; LIONELLO—Di.S.Te.B.A., University of Russia; BELLENBaum, PINTO, anD ULBRICH—Institute of Geophysics Salento, and CMCC, EuroMediterranea Center on Climate Change, and Meteorology, University of Cologne, Cologne, Germany; Lecce, Italy; RAIBLE—Climate and Environmental Physics, and BENESTAD—Norwegian Meteorological Institute, Oslo, Norway; Oeschger Center for Climate Change Research, University of Bern, BLENDER anD FRAEDRICH—Institute of Meteorology, University Bern, Switzerland; RUDEVA—P. P. Shirshov Institute of Oceanology, of Hamburg, Hamburg, Germany; CABALLERO anD HanLEY— Russian Academy of Sciences, Moscow, Russia, and School of Department of Meteorology, Stockholm University, Stockholm, Earth Sciences, University of Melbourne, Melbourne, Victoria, Sweden; COCOZZA anD REALE—Di.S.Te.B.A., University of Salento, Australia; SImmONDS—School of Earth Sciences, University of Lecce, Italy; DACRE—Department of Meteorology, University of Melbourne, Melbourne, Victoria, Australia; SINCLAIR—Embry-Riddle Reading, Reading, United Kingdom; FEng anD Wang—Climate Aeronautical University, Prescott, Arizona; SPREngER anD WERNLI— Research Division, Environment Canada, Toronto, Ontario, Canada; Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, GRIEGER, SCHUSTER, anD ULBRICH—Institute of Meteorology, Freie Switzerland; TRIGO—Instituto Dom Luiz, University of Lisbon, and Universität Berlin, Berlin, Germany; GULEV anD TILINIna— P. P. Institute of Meteorology I. P., Lisbon, Portugal Shirshov Institute of Oceanology, Russian Academy of Sciences, CORRESPONDING AUTHOR: Urs Neu, ProClim, Swiss Academy and Moscow State University, Moscow, Russia; HEWSON—European of Sciences, Schwarztorstrasse 9 CH-3007 Bern, Switzerland Centre for Medium-Range Weather Forecasts, Reading, and Met E-mail: [email protected] Office, Exeter, United Kingdom; InaTSU—Graduate School of Sci- The abstract for this article can be found in this issue, following the table ence, Hokkaido University, Sapporo, Japan; KEAY—School of Earth of contents. Sciences, University of Melbourne, and Bureau of Meteorology, DOI:10.1175 / BAMS - D -11- 0 0154.1 Melbourne, Victoria, Australia; KEW—Royal Netherlands Meteorological Institute, De Bilt, Netherlands; KINDEM—Bjerknes Supplements A and B to this article are available online (10.1175 / BAMS - Centre for Climate Research, Bergen, Norway; LECKEBUSCH—School D -11- 0 0154.2) of Geography, Earth and Environmental Sciences, University of In final form 31 August 2012 Birmingham, Birmingham, United Kingdom; LIBERATO—Instituto ©2013 American Meteorological Society 530 | APRIL 2013 Unauthenticated | Downloaded 10/11/21 03:59 AM UTC et al. 2007; Rudeva and Gulev 2007; Inatsu 2009; that uses 12-hourly data) for a 20-yr period from Kew et al. 2010; Hewson and Titley 2010; Hanley and 1 January 1989 to 31 March 2009. Caballero 2012). According to different perceptions Besides being related to method differences, track of what a cyclone is, tracking may be performed uncertainty can also arise from reanalysis inad- utilizing a number of atmospheric variables equacies. However, this aspect has been discussed (Hoskins and Hodges 2002). One of the most widely elsewhere in the literature (e.g., Wang et al. 2006; discussed algorithmic differences relates to the Benestad and Chen 2006; Raible et al. 2008; Hodges
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