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Cognitive Psychology & Military Weather Forecasting

Cognitive Psychology & Military Weather Forecasting. Earl Hunt Susan Joslyn. Larger context. Briefings vs. forecasting Communicating forecast to customer Time pressure/time sharing Weather analysis in conjunction with other duties Automate portions of briefing process. Time Pressure.

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Cognitive Psychology & Military Weather Forecasting

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  1. Cognitive Psychology & Military Weather Forecasting Earl Hunt Susan Joslyn

  2. Larger context • Briefings vs. forecasting • Communicating forecast to customer • Time pressure/time sharing • Weather analysis in conjunction with other duties • Automate portions of briefing process

  3. Time Pressure People: filter information • Salient or available (Sieber, 1974) • Negative (Svenson, Edland, & Slovic, 1990; Wright, 1974) Forecasters: "problem of the day" (Pilske et al, 1997) Pattern matching (Klein, & Calderwood, 1991) co-occurrences of atmospheric events

  4. Information overload • Facilitate encoding and integration of decision-relevant information • Forecasters mental model (Hoffman, 1991, Trafton et al., 2000, Pilske et al, 1997) Hypotheses Forecasting funnel Qualitative model from quantitative information Numerical models to check qualitative model Spatial, Temporal dimensions Cause & effect Model model

  5. Questions Step 1 1) What are the currently used sources of information? For what tasks are they used? 2) What are the components and structure of the mental model? How do information sources inform the model? 3) What are the procedures or steps for common forecasting tasks.

  6. Related work Miyamoto & Jones (2001), Preliminary results from human systems Miyamoto, (1999), Human Systems Study on use of meteorological and oceanographic data to support naval Air Strike Trafton, Kirschenbaum, Tsui, Miyamoto, Ballas, Raymond (2000), Turning pictures into numbers: Extracting and generating information from complex visualizations

  7. Pilot Study • Expert Forecaster • Forecast: temperature, winds, cloud cover, precipitation and thunderstorm • 4 cities, • the next morning • 40 minutes • Tape recorded verbalized thoughts

  8. Process • Orderly, routine • Set forecast of parameter as goal • Evaluated evidence • Made decision • Moved on to next parameter • Classic expert foreword reasoning

  9. Analysis • Individual numbered statements • Coded • statement type • decision • pre-decision change in the mental model • post decision verification (confirmatory or disconfirm) • change decision • mental model • causal • temporal element • model • information source • station report, • satellite and radar imagery, • numerical model information, • prior knowledge ( general principles or local climate

  10. First Parameter Forecast • OKC: Temperature 53% (17/32) of statements • Pittsburgh:Precipitation 49% (22/45) of statements • Fargo :Precipitation 42% (19/45) of statements

  11. Precipitation & Thunderstorms 29% (48/165) of statements Qualitative 4-D mental model Source # of statements Satellite 3 Radar 8 Model: Eta 8 Model: Aviation 4 Model: MM5 2 Station Report 4 Station Report: Surrogate 4 Local weather knowledge 3 General weather knowledge 2 Climate chart 1 Previous forecast 1

  12. Cloud cover 12% (20/165) the statements Qualitative from qualitative & quantitative Source # of statements Satellite 4 Model: Eta 2 Model: Aviation 1 Station Report 1 Local weather knowledge 2

  13. Temperature 31% (51/165) of statements Source # of statements Satellite 1 Model: ETA 12 Model: Aviation 3 Model: NGM 1 Model: MM5 5 Station Report 6 Station Report: Surrogate 5 Local weather knowledge 3 General weather knowledge 2

  14. Sequential Quantitative Reasoning

  15. Winds 20% (33/165) of the statements Numerical models consulted early (reliable) Sequential quantitative reasoning Source # of statements Model: Eta 6 Model: Aviation 5 Model: MM5 5 Station Report 6 Local weather knowledge 2 General weather knowledge 1

  16. Take Home Message • Forecasting process/mental model may vary with task • Compatible display format will facilitate encoding and synthesis & decrease information overload

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