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    The Cloud Hunter’s Problem: An Automated Decision Algorithm to Improve the Productivity of Scientific Data Collection in Stochastic Environments

    Source: Monthly Weather Review:;2011:;volume( 139 ):;issue: 007::page 2276
    Author:
    Small, Arthur A.
    ,
    Stefik, Jason B.
    ,
    Verlinde, Johannes
    ,
    Johnson, Nathaniel C.
    DOI: 10.1175/2010MWR3576.1
    Publisher: American Meteorological Society
    Abstract: decision algorithm is presented that improves the productivity of data collection activities in stochastic environments. The algorithm was developed in the context of an aircraft field campaign organized to collect data in situ from boundary layer clouds. Required lead times implied that aircraft deployments had to be scheduled in advance, based on imperfect forecasts regarding the presence of conditions meeting specified requirements. Given an overall cap on the number of flights, daily fly/no-fly decisions were taken traditionally using a discussion-intensive process involving heuristic analysis of weather forecasts by a group of skilled human investigators. An alternative automated decision process uses self-organizing maps to convert weather forecasts into quantified probabilities of suitable conditions, together with a dynamic programming procedure to compute the opportunity costs of using up scarce flights from the limited budget. Applied to conditions prevailing during the 2009 Routine ARM Aerial Facility (AAF) Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations (RACORO) campaign of the U.S. Department of Energy?s Atmospheric Radiation Measurement Program, the algorithm shows a 21% increase in data yield and a 66% improvement in skill over the heuristic decision process used traditionally. The algorithmic approach promises to free up investigators? cognitive resources, reduce stress on flight crews, and increase productivity in a range of data collection applications.
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      The Cloud Hunter’s Problem: An Automated Decision Algorithm to Improve the Productivity of Scientific Data Collection in Stochastic Environments

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4213322
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    contributor authorSmall, Arthur A.
    contributor authorStefik, Jason B.
    contributor authorVerlinde, Johannes
    contributor authorJohnson, Nathaniel C.
    date accessioned2017-06-09T16:38:29Z
    date available2017-06-09T16:38:29Z
    date copyright2011/07/01
    date issued2011
    identifier issn0027-0644
    identifier otherams-71431.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4213322
    description abstractdecision algorithm is presented that improves the productivity of data collection activities in stochastic environments. The algorithm was developed in the context of an aircraft field campaign organized to collect data in situ from boundary layer clouds. Required lead times implied that aircraft deployments had to be scheduled in advance, based on imperfect forecasts regarding the presence of conditions meeting specified requirements. Given an overall cap on the number of flights, daily fly/no-fly decisions were taken traditionally using a discussion-intensive process involving heuristic analysis of weather forecasts by a group of skilled human investigators. An alternative automated decision process uses self-organizing maps to convert weather forecasts into quantified probabilities of suitable conditions, together with a dynamic programming procedure to compute the opportunity costs of using up scarce flights from the limited budget. Applied to conditions prevailing during the 2009 Routine ARM Aerial Facility (AAF) Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations (RACORO) campaign of the U.S. Department of Energy?s Atmospheric Radiation Measurement Program, the algorithm shows a 21% increase in data yield and a 66% improvement in skill over the heuristic decision process used traditionally. The algorithmic approach promises to free up investigators? cognitive resources, reduce stress on flight crews, and increase productivity in a range of data collection applications.
    publisherAmerican Meteorological Society
    titleThe Cloud Hunter’s Problem: An Automated Decision Algorithm to Improve the Productivity of Scientific Data Collection in Stochastic Environments
    typeJournal Paper
    journal volume139
    journal issue7
    journal titleMonthly Weather Review
    identifier doi10.1175/2010MWR3576.1
    journal fristpage2276
    journal lastpage2289
    treeMonthly Weather Review:;2011:;volume( 139 ):;issue: 007
    contenttypeFulltext
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