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CYCLONE CENTER Can Citizen Scientists Improve Tropical Cyclone Intensity Records? CYCLONE CENTER Can Citizen Scientists Improve Tropical Cyclone Intensity Records? by Christopher C. h en n o n , Ken n et h r. Knapp, Car l J. s Ch r eCK iii, sCo t t e. stevens, James p. K o ssin , pet er W. t horne, paul a a. hennon, miCh ael C. KruK, Jar ed r ennie, Jea n -maur iCe Gadéa, max imil ian strieGl, and ian Car l ey Crowdsourcing could help alleviate the uncer tainty inherent in the modern global tropical cyclone record. ne of the fundamental challenges in tropical 2010), and evaluation of reanalysis datasets (Schenkel cyclone (TC) analysis and forecasting is and Hart 2012). O accurately determining the storm’s sustained Unfortunately, when tropical cyclones are tracked maximum wind speed (or “intensity”) in an area by more than one agency, best-track data frequently with little or no in situ obser vations. Forecast centers, disagree. For example, Webster et al. (2005) showed relying primarily on interpretation of geostation- that the frequency of category 4 and 5 typhoons in ary satellite imagery as well as any other available the northwestern Pacific Ocean increased by 41% data during operational activities, create postseason between the 1975–89 and 1990–2004 periods. But “best track” datasets. Best-track data contain (at Wu et al. (2006) reported that those severe storms minimum) information on TC track and intensity. actually decreased in frequency between 10% and 16% They are widely used in a large number of research over the same time periods. This contradiction arises applications, including trend analysis (e.g., Emanuel from the source of the best-track data used by each 2005; Webster et al. 2005; Wu et al. 2006), forecast group. Webster et al. used data from the Joint Typhoon verification (e.g., Sievers et al. 2000; Poroseva et al. Warning Center (JTWC); Wu et al. used best-track data AFFILIATION S: h en n o n —University of North Carolina at *CU RREN T A FFILIAT IO N : Maynooth Univer sit y Depar tment of Asheville, Asheville, North Carolina; Knapp—NOAA/National Geography, Maynooth Ireland Climatic Data Center, Asheville, North Carolina; sCh r eCK, stevens, CO RRESPO N DIN G AU T H O R: Christopher C. Hennon, University p. a . h en n o n , and r ennie—Cooperative Institute for Climate and of North Carolina at Asheville, 1 University Heights, CPO #2450, Satellites (CICS-NC), North Carolina State University, Asheville, Asheville, NC 28804 North Carolina; Ko ssin —NOAA/National Climatic Data Center, E-mail: [email protected] Asheville, Nor th Carolina, and NOAA/Cooper ative Institute for The abstract for this article can be found in this issue, following the table Meteorological Satellite Studies, Madison, Wisconsin; t horne*— of content s. Nansen Environmental and Remote Sensing Center, Bergen, DOI:10.1175/BAMS-D-13-00152.1 Norway; KruK—ERT, Inc. at NOAA/National Climatic Data Center, Asheville, N or t h Carolina; Gadéa—Citizen Scientist, Montpellier, In final form 18 July 2014 ©2015 Amer ican Met eor ological Societ y Fr ance; strieGl—Citizen Scientist, Schortens, Germany; Car l ey— Citizen Scientist, Brisbane, Queensland, Australia AMERICAN METEOROLOGICAL SOCIETY APRIL 2015 | 591 from the Regional Specialized Meteorological Centre The conflicting conclusions regarding western (RSMC) Tokyo and t he Hong Kong Obser vator y. To Pacific TC intensity trends, significant differences further complicate matters, Kossin et al. (2007) applied found in other basins (Schreck et al. 2014), and the an objective intensity algorithm to homogeneous changes in technology and observation practices that infrared (IR) satellite data and found no significant have led to these discrepancies significantly lower our change in severe typhoons from 1983 to 2005. confidence in the TC record. In fact, recent assess- Kossin et al. (2013) provide a good discussion on the ment work by the World Meteorological Organization causes of differences in global best-track data. They (WMO) (Knutson et al. 2010) and the Intergovern- can be roughly classified into two categories: changes mental Panel on Climate Change conclude there is in technology and data availability and diverse “low confidence that any observed long-term (i.e. 40 analysis methods at RSMCs. Aircraft reconnaissance years or more) increases in TC activity are robust, provides forecasters with the highest amount of after accounting for past changes in observing capa- confidence for a storm’s position and intensity. Data bilities” (Seneviratne et al. 2012). are recorded at flight level and GPS dropsondes are This statement and the work summarized above routinely deployed during missions to provide surface suggest that a comprehensive global reanalysis of TC and vertical-profile information. Missions began intensity is needed. There are a number of recent and during the 1940s in the Atlantic and North Pacific; ongoing projects in this area, but they are restricted unfortunately, routine missions stopped in the North to analyses of single storms (e.g., Landsea et al. 2004), Pacific in 1987 and have never been regularly flown specific time periods (e.g., Landsea et al. 2008), or in any other ocean basin besides the North Atlantic. regions (Diamond et al. 2012; T. Kimberlain and Shoemaker et al. (1990), Gray et al. (1991), and Martin P. Caroff 2014, personal communication). Although and Gray (1993) provide quantitative evidence for the all of these efforts are valuable, they are not ideal effectiveness of aircraft reconnaissance in reducing for two reasons. First, they are necessarily restric- TC initial and forecast error. Furthermore, in a re- tive because of the large amount of time required to cent survey of National Hurricane Center specialists, analyze voluminous amounts of data and imagery. Landsea and Franklin (2013) found that intensity and For example, here we are analyzing nearly 300,000 position uncertainty decreases substantially when images from a 32-yr TC data record. Assuming 5 min aircraft data are available. per analysis, it would take one person 25,000 h (~12 Because reconnaissance data are rarely available years) to analyze all of the images one time—and that [even in the North Atlantic, only about 30% of best- is with no time off for holidays or vacation. Second, track times include aircraft data Rappaport et al. regional reanalyses risk exacerbating interbasin (2009)], analysts must turn to geostationary satellites, differences because they are not applied globally or microwave satellites, and sparse in situ measurements consistently. such as buoys or nearby ships; it is then that the deter- We present a new approach to TC reanalysis that mination of intensity becomes subject to analyst inter- is global in scope, can be completed in a reason- pretation and RSMC rules. Wu et al. (2006) attribute able amount of time, and shows promising skill differences between JTWC and RSMC Tokyo to wind in estimating TC intensity. Cyclone Center is an speed time averaging (10 versus 1 min), the discrete Internet portal that provides global, homogeneous, nature of the Saffir–Simpson scale (Kantha 2006), and TC-centric IR satellite imagery to “citizen scien- an “in-house” change to the application of the Dvorak tists.” Instead of using a small number of experts, technique at RSMC Tokyo (Koba et al. 1990). The we tap into the scientific curiosity of thousands of Dvorak technique (Dvorak 1973, 1984; Velden et al. ordinary people, using their enthusiasm, time, and 2006) is a procedure that provides an analyst with an pattern recognition skills to eventually answer these estimate of the maximum sustained winds of a TC scientific questions: based on its cloud pattern, cloud-top temperature considerations, and recent intensity trend. It is used 1) Can citizen scientists provide more certainty in at all RSMCs and is widely considered the best avail- TC intensity when forecast agencies disagree? able tool for determining TC intensity in the absence 2) Can citizen scientists provide more skill than of direct observations. However, the method is also current approaches in determining TC intensity, inherently subjective and its rules have been changed particularly with difficult patterns where auto- at various RSMCs to conform to perceived regional mated techniques struggle? intensity differences. This is discussed in more detail 3) Has TC intensity responded to recent changes in in the section on “Data and design.” climate? 592 | APRIL 2015 Our intent is not to replace or change best-track data; EN GAGIN G TH E CROW D. Cr owdsour ci ng rather, we intend to provide a global record of TC st rat egies. Crowdsourcing offers the ability to quickly intensity as determined by a consistent algorithm analyze vast amounts of data. Naturally, that ad- applied to a homogeneous record of satellite data. vantage relies on attracting and maintaining a large Such a data record could then be used as one impor- community of citizen scientists. One might expect tant tool in any future global TC reanalysis effort. that the destructive power of tropical cyclones would Therefore, this paper will not completely answer be enough to attract and maintain a large number of the questions above but will describe the ideas and users, but Cyclone Center still had to be carefully de- motivation behind Cyclone Center, the developments signed to do so. The development team at the Citizen in data that have made it possible, the process of Science Alliance (CSA; the parent organization of soliciting and collecting citizen scientist responses, Zooniverse) was a valuable resource during this and some of the early results that begin to address process, since they could call on past experiences these questions. Interested readers can participate in from other projects to estimate the abilities of our Cyclone Center at ht tp://cyclonecenter.org. volunteers. Citizen scientists, especially nonexperts, hold valid THE CROW DSOURCING OF SCIENCE.
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