Minding the Weather

How Expert Forecasters Think
 
 
MIT Press
  • 1. Auflage
  • |
  • erschienen am 4. August 2017
  • |
  • 488 Seiten
 
E-Book | ePUB mit Adobe-DRM | Systemvoraussetzungen
978-0-262-33940-7 (ISBN)
 
This book argues that the human cognition system is the least understood, yet probably most important, component of forecasting accuracy. Minding the Weather investigates how people acquire massive and highly organized knowledge and develop the reasoning skills and strategies that enable them to achieve the highest levels of performance.The authors consider such topics as the forecasting workplace, atmospheric scientists' descriptions of their reasoning strategies, the nature of expertise, forecaster knowledge, perceptual skills, and reasoning, and expert systems designed to imitate forecaster reasoning. Drawing on research in cognitive science, meteorology, and computer science, the authors argue that forecasting involves an interdependence of humans and technologies. Human expertise will always be necessary.
  • Englisch
  • Cambridge
  • |
  • USA
978-0-262-33940-7 (9780262339407)
weitere Ausgaben werden ermittelt
  • Intro
  • Contents
  • About the Authors
  • Preface
  • Acknowledgments
  • Personal Acknowledgment
  • 1 Introduction
  • Motivation for the Study of Forecaster Reasoning
  • The Data Overload Problem
  • GOES Satellite Products
  • NEXRAD Radar Products
  • So, How Much Data Are There?
  • Coping with Data Overload
  • Some Key Terms
  • Sources of Information Concerning Forecaster Cognition
  • 2 What Is the Forecasting Workspace Like?
  • Evolution of the Modern Workstation-based Workplace
  • The Rise of the Workstation: AFOS, McIDAS, and PROFS
  • How Many Displays?
  • Visualization Design
  • PRAVDA and Met.3D
  • Met.3D
  • Conclusions
  • A Day in the Life: A Cautionary Tale about Work System Design
  • 3 How Do People Come to Be Forecasters?
  • Historical Background
  • Forecasting Training within Meteorological Education
  • College-Level Education
  • The Interdependence of Meteorology Education and Forecaster Training
  • Forecaster Training
  • An Ethnographic Study of How People Get to Be Forecasters
  • New Learning Venues
  • COMET
  • INNOVATIVE WEATHER
  • Some Not-so-Formal Learning Venues
  • Conclusions
  • 4 How Do Forecasters Describe How They Reason?
  • Teamwork
  • Knowledge
  • Perception and Recognition
  • Conceptual Models
  • General Forecasting Principles
  • Reasoning Process
  • Forecasters' Descriptions of Forecaster Reasoning
  • Conclusions
  • 5 How Well Do Forecasters (and Forecasts) Perform?
  • Challenges in Measuring the Goodness of Forecasts
  • "Goodness" and the Context of Use
  • The Challenges of Measurement
  • Forecast Verification Research
  • How Good Are Those Probability Forecasts?
  • Predictability versus Understandability
  • Linear Models: Can the Human Forecaster Outperform a Simple Linear Model?
  • Forecast Quality Is More Than Just "Hit Rate"
  • A Cautionary Tale
  • Conclusions
  • 6 What Characterizes Expertise?
  • The History of Expertise Studies
  • Methods for Peering Into the Black Box
  • Expert Knowledge
  • Expert Reasoning
  • Learning and Cognitive Flexibility
  • Learning
  • Cognitive Flexibility and Cognitive Transformation
  • Expert Perceptual Skill
  • Integrated Models of Expert "Macrocognition"
  • Conclusions
  • 7 How Do Forecasters Get to Be Expert Forecasters?
  • Expertise versus Intelligence
  • Forecasting Competitions
  • Recognition of the Need for Training to High Proficiency
  • A General Proficiency Scale
  • The Proficiency Scaling Process
  • U.S. Navy Weather Forecasting Case Study
  • U.S. Air Force Weather Forecasting Case Study
  • Integration of the Findings
  • The Forecasting Process
  • The "Quick Size-Up"
  • Use of Computer Models
  • Sensemaking
  • Conclusion
  • 8 What Does Research on Forecaster Knowledge Tell Us?
  • Preparation
  • Critical Decision Method
  • Concept Mapping
  • It Is Not All Just in the Head
  • Conclusion
  • 9 What Does Research on Forecaster Perception Tell Us?
  • How and Why Diagrams and Visualizations Aid Sensemaking
  • Display Design Principles: Some Work, Some Don't
  • Interpretation of Weather Maps by Forecasters and Non-Forecasters
  • Forecasters' Understanding of Weather Charts: Filling in the Gaps
  • From Cues to Patterns to Dynamics
  • Perceptual Operations during the Forecasting Process
  • Conclusions
  • 10 What Does Research on Forecaster Reasoning Tell Us?
  • Mental Models
  • Modeling Forecaster Reasoning
  • Modeling Forecaster Macrocognition
  • Generic versus Specific Models
  • Flexecution in Forecasting
  • Conclusions
  • 11 Can a Machine Imitate the Human?
  • Can Computers Be Made to Think Like Forecasters?
  • The Structure of Forecasting KBSs
  • So How Well Did the Expert Systems Perform?
  • Peering into the Black Box
  • Metamorphosis from Expert Systems to Intelligent Systems
  • 12 Can a Machine Replace the Human?
  • Computational Weather Prediction Models
  • How Well Do the Computer Models Perform?
  • Computer Models Do Not Generate Forecasts
  • Computer Models can Be Misused
  • The Unintended Negative Impact of Computer Models
  • The Bosart-Doswell Lament
  • The Bastardi Lament
  • Since 2000 .
  • Pitting Man against Machine
  • All Hail the Computer!
  • Just Imagine .
  • Conclusions
  • 13 Forecaster-Computer Interdependence
  • Best Use of Computer Models
  • Defining Forecaster-Computer Interdependence
  • The Norman Oklahoma Area Severe Weather Outbreak of 3 May 1999
  • A View of the Future
  • 14 Conclusions and Prospects
  • Major Conclusion
  • Prospects
  • Human Factors of Workstation Design
  • Perceptual Learning
  • Accelerated Expertise
  • Climate Change
  • Codicil: Funding for the Future of Forecasting
  • Appendix A: List of Acronyms
  • Appendix B: Extended Narratives of Two Cases of Forecasting Severe Weather
  • Case Number 1: Storms Associated with a Stalled Front in the Gulf Coast Region (5 PM - 5 AM shift)
  • Case Number 2: Hurricane Track Forecasting (5 PM - 5 AM shift)
  • Appendix C: Example Synoptic Analyses of GOES Visible Images
  • Appendix D: A Case Study in Human-Machine Interdependence
  • The March 1993 Winter Superstorm
  • Appendix E: References on Visualization Design and Display Technology
  • Visualizations for Climate Research
  • Visualization of Atmospheric Dynamics and Severe Weather
  • Visualizations of the Outputs of Computer Models and Ensembles
  • Visualization of Radar, Satellite, and Remote Sensing Data
  • Visualizations of Uncertainty. Probability, and Risk
  • 3-D and 4-D and Perspectival Visualizations
  • Visualizations Using Open Source and GIS
  • Visualization Using Stereographic and Virtual Reality Techniques
  • Workstation Designs
  • Reviews
  • References
  • Index
  • Color Plates

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