Beta Test of the Systematic Approach Expert System Prototype as a Tropical Cyclone Track Forecasting Aid

Carr, Lester E.; Elsberry, Russell L.; Peak, James E.

doi:10.1175/1520-0434(2001)016<0355:btotsa>2.0.co;2

Cited by 35 publications

(32 citation statements)

References 8 publications

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“…The type of joint, human-model forecasting behavior described above is consistent with prior research on a technique called the Systematic Approach Forecast Aid (SAFA) used at the JTWC to allow a forecaster to form a selective consensus of model guidance, or SCON (Sampson, Knaff, and Fukada, 2006; also see Carr, Elsberry & Peak, 2001). Experience with SAFA was mixed.…”

Section: Figure 2-3 a Case In Which Guidance Provided By All But Onesupporting

confidence: 60%

Human Factors In the Joint Typhoon Warning Center Watch Floor

Regnier¹,

Kirlik²

2012

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYY) 30-11-2012 REPORT TYPE Technical Report DATES COVERED PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) AND ADDRESS(ES)Naval Postgraduate School Monterey, CA 93943 PERFORMING ORGANIZATION REPORT NUMBERNPS-OR-11-003Rev. SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES)Joint Typhoon Warning Center 425 Luapele Road Pearl Harbor, HI 96860 SPONSOR/MONITOR'S ACRONYM(S)NMFC/JTWC SPONSOR/MONITOR'S REPORT NUMBER(S) DISTRIBUTION / AVAILABILITY STATEMENTApproved for public release; distribution is unlimited SUPPLEMENTARY NOTESThe views expressed in this thesis are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government. ABSTRACTThis study evaluates the task and support environments associated with the Joint Typhoon Warning Center (JTWC) watch floor and provides recommendations to improve forecast accuracy. The principal findings indicate that, at this time, factors in the task environment are very likely limiting forecast accuracy. In particular, system-induced practical and cognitive limits on the forecaster's repeatability, i.e., the ability to reproduce an identical forecast given identical information, limits forecast accuracy. Thus, forecasters' performance and forecast accuracy could be enhanced by an information integration system with recommended features, by more precise standard operating procedures, and by training and feedback better matched to the task. Further studies are also recommended.This version rescinds and replaces prior versions. The appendix has been corrected.

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Section: Figure 2-3 a Case In Which Guidance Provided By All But Onesupporting

confidence: 60%

Human Factors In the Joint Typhoon Warning Center Watch Floor

Regnier¹,

Kirlik²

2012

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“…This module supports the forecasters with information management, visualization and proactive investigation of frequently occurring forecasting errors (Carr et al, 2001).…”

Section: Other Cyclone Forecasting Techniquesmentioning

confidence: 99%

Tropical cyclone track forecasting techniques ― A review

Roy

Kovordanyi

2012

Atmospheric Research

128

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Delivering accurate cyclone forecasts in time is of key importance when it comes to saving human lives and reducing economic loss. Difficulties arise because the geographical and climatological characteristics of the various cyclone formation basins are not similar, which entails that a single forecasting technique cannot yield reliable performance in all ocean basins. For this reason, global forecasting techniques need to be applied together with basin-specific techniques to increase the forecast accuracy. As cyclone track is governed by a range of factors variations in weather conditions, wind pressure, sea surface temperature, air temperature, ocean currents, and the earth' rotational force-the coriolis force, it is a formidable task to combine these parameters and produce reliable and accurate forecasts. In recent years, the availability of suitable data has increased and more advanced forecasting techniques have been developed, in addition to old techniques having been modified. In particular, artificial neural network based techniques are now being considered at meteorological offices. This new technique uses freely available satellite images as input, can be run on standard PCs, and can produce forecasts with good accuracy. For these reasons, artificial neural network based techniques seem especially suited for developing countries which have limited capacity to forecast cyclones and where human casualties are the highest.

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“…A critical extension of this work was to demonstrate that such erroneous track error characteristics could be recognized, and associated error mechanism conceptual models could be applied, in a (simulated) real-time forecast scenario. As described by Carr et al (2000), such a successful demonstration of the real-time detection of large track errors by dynamical models was accomplished for storms 19W through 30W in the western North Pacific during 1999. In the terminology introduced above, Carr et al (2000) were able to form a selective consensus track after elimination of a likely erroneous model track(s) that had smaller errors than a nonselective consensus track of all the models.…”

Section: Cmentioning

confidence: 99%

“…As described by Carr et al (2000), such a successful demonstration of the real-time detection of large track errors by dynamical models was accomplished for storms 19W through 30W in the western North Pacific during 1999. In the terminology introduced above, Carr et al (2000) were able to form a selective consensus track after elimination of a likely erroneous model track(s) that had smaller errors than a nonselective consensus track of all the models. This demonstration is another important motivation for developing a corresponding set of error mechanism conceptual models for Southern Hemisphere TCs.…”

Section: Cmentioning

confidence: 99%

See 1 more Smart Citation

Southern Hemisphere Application of the Systematic Approach to Tropical Cyclone Forecasting Part 4: Sources of Large Track Errors by Dynamical Models

Boothe¹,

Elsberry²,

Carr³

et al. 2000

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Public reporting burden for this collection of information is estimated to average I hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 222024302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188), Washington, DC 20503.

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Beta Test of the Systematic Approach Expert System Prototype as a Tropical Cyclone Track Forecasting Aid

Cited by 35 publications

References 8 publications

Human Factors In the Joint Typhoon Warning Center Watch Floor

Human Factors In the Joint Typhoon Warning Center Watch Floor

Tropical cyclone track forecasting techniques ― A review

Southern Hemisphere Application of the Systematic Approach to Tropical Cyclone Forecasting Part 4: Sources of Large Track Errors by Dynamical Models

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