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Göber, Pardowitz, Kox: Linguistic uncertainy of warning uncertainty 1 Verification of the linguistic uncertainty of warning uncertainty Martin Göber 1,3,

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Presentation on theme: "Göber, Pardowitz, Kox: Linguistic uncertainy of warning uncertainty 1 Verification of the linguistic uncertainty of warning uncertainty Martin Göber 1,3,"— Presentation transcript:

1 Göber, Pardowitz, Kox: Linguistic uncertainy of warning uncertainty 1 Verification of the linguistic uncertainty of warning uncertainty Martin Göber 1,3, Tobias Pardowitz 2,3, Thomas Kox 2,3 1 Deutscher Wetterdienst, Offenbach, Germany 2 Institut für Meteorologie Freie Universität, Berlin, Germany, 3 Hans-Ertel-Centre for Weather Research, Germany

2 Göber, Pardowitz, Kox: Linguistic uncertainy of warning uncertainty 2 Operational praxis of probabilistic warning information at DWD 1.7-day forecast of weather hazards compulsary use of „possible, likely, very likely“, non-quantitativ 2.Report on the regional warning situation (next 36 hours) a variety of terms used („can not be excluded“, „might occur“, „are expected“, …) 3.County based warnings (now, up to next hours/day) No uncertainty used apart fron spatial and temporal restrictions („exposed areas, temporal“…)

3 Göber, Pardowitz, Kox: Linguistic uncertainy of warning uncertainty 3 Question: “Imagine, the DWD states the advent of a storm for your area for the next day as ‘possible’, ‘likely’ or ‘very likely’. Which of the following probability terms would you associate with this prediction?” In: Kox, T., Gerhold, L. and Ulbrich, U. (2014) : Perception and use of uncertainty in severe weather warnings by emergency responders in Germany. In Atmospheric Research.

4 Göber, Pardowitz, Kox: Linguistic uncertainy of warning uncertainty 4 Source: 19 counties within Berlin+Brandenburg: km^2 Berlin: 900 km^2

5 Göber, Pardowitz, Kox: Linguistic uncertainy of warning uncertainty 5 1.Can human forecasters estimate warning uncertainty ? 2.How well is this done in verbal terms ? probabilistic short range (T+36h, in 6h-intervals) forecast for warning events (gusts, thunderstorms) for Berlin (900 km2) or larger area of Berlin+Brandenburg ( km2) since February different forecasts: human forecaster: from regional office in Potsdam 1) numerical estimate (deliberately produced for our project) 2) textual (operational text issued 4 times a day) 3) WarnMOS: Warning Model Output Statistics based on the global models GME and IFS includes latest observations

6 Göber, Pardowitz, Kox: Linguistic uncertainy of warning uncertainty 6 Wind guststhunderstorms N=1924

7 Göber, Pardowitz, Kox: Linguistic uncertainy of warning uncertainty 7 wind gusts man vs machine: reliability similar Brier Skill Score = 16 %

8 Göber, Pardowitz, Kox: Linguistic uncertainy of warning uncertainty 8 thunderstorms man vs machine: reliability similar Brier Skill Score =6 %

9 Göber, Pardowitz, Kox: Linguistic uncertainy of warning uncertainty 9 P(usage) P(obs given terms)

10 Göber, Pardowitz, Kox: Linguistic uncertainy of warning uncertainty 10 P(usage) P(obs given terms)

11 Göber, Pardowitz, Kox: Linguistic uncertainy of warning uncertainty 11 thunderstorms gusts

12 Göber, Pardowitz, Kox: Linguistic uncertainy of warning uncertainty 12 Summary and Conclusions 1. human forecasters can estimate warning uncertainty reliably (and tend to have higher resolution than statistical estimates) 2. minimise overlap and vagueness by restricting oneself to only a few terms

13 Göber, Pardowitz, Kox: Linguistic uncertainy of warning uncertainty 13 India warnings

14 Göber, Pardowitz, Kox: Linguistic uncertainy of warning uncertainty 14 Summary and Conclusions 1. human forecasters can estimate warning uncertainty reliably (and tend to have higher resolution than statistical estimates) 2. minimise overlap and vagueness by restricting oneself to only a few terms 3. reduce underspecificity by sharply delineate categories and specification of relationship between words and numbers

15 Göber, Pardowitz, Kox: Linguistic uncertainy of warning uncertainty 15 USA NWS

16 Göber, Pardowitz, Kox: Linguistic uncertainy of warning uncertainty 16 Summary and Conclusions 1. human forecasters can estimate warning uncertainty reliably (and tend to have higher resolution than statistical estimates) 2. minimise overlap and vagueness by restricting oneself to only a few terms 3. reduce underspecificity by sharply delineate categories and specification of relationship between words and numbers 4. “stronger” words for thunderstorms  points to forecasting of risk rather than uncertainty alone

17 Göber, Pardowitz, Kox: Linguistic uncertainy of warning uncertainty 17 UK weather warnings

18 Göber, Pardowitz, Kox: Linguistic uncertainy of warning uncertainty 18 Summary and Conclusions 1. human forecasters can estimate warning uncertainty reliably (and tend to have higher resolution than statistical estimates) 2. minimise overlap and vagueness by restricting oneself to only a few terms 3. reduce underspecificity by sharply delineate categories and specification of relationship between words and numbers 4. “stronger” words for thunderstorms  points to forecasting or risk rather than uncertainty alone The possibility of a potentially not too bad conference might possibly has to be accounted for !


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