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
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 Qualitative from qualitative & quantitative
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
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 Numerical models consulted early (reliable)
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