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User experience with extended range forecasts -- climatic aspects of ECMWF products

User experience with extended range forecasts -- climatic aspects of ECMWF products. Christof Appenzeller Wolfgang Müller Heike Kunz Mark Liniger ERA-40 compared to Swiss observations Verification of seas. forecasts monthly forecasts in electricity market seas. forecasts in media.

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User experience with extended range forecasts -- climatic aspects of ECMWF products

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  1. User experience with extended range forecasts-- climatic aspects of ECMWF products Christof Appenzeller Wolfgang Müller Heike Kunz Mark Liniger ERA-40 compared to Swiss observations Verification of seas. forecasts monthly forecasts in electricity market seas. forecasts in media

  2. ERA-40 T2 Upscaling ZERA-40 - ZSchw.Stat. ~ 40m

  3. Temperature: ERA-40, Swiss Stations Temperature difference: ERA-40 – Swiss Stations 1.2 16 14 0.8 12 10 0.4 DT[°C] Temperature[°C] 8 6 0.0 4 2 -0.4 0 - 2 -0.8 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC ERA-40 T2 seasonal Cycle • ERA-40 in Summer 0.8°C too high • ERA-40 in Winter 0.4°C too low

  4. ERA-40 & GPS IWV: Correlation Morland et al., submitted

  5. IWV: Height dependent bias Morland et al., submitted

  6. System 2: Climate variability DJF North Atlantic Oscillation (NAO)EOF Z500 ERA-401959 - 2001 System 2Nov. Fcsts.1987 - 2001 Müller et al. (2005), Clim. Dyn.

  7. Predictability of NAO regression of Z500 fields Müller et al. (2005), Clim. Dyn.

  8. Impact of climate variability DJF North Atlantic Oscillation (NAO)SVD Z500 & T2m ERA-401959 - 2001 System 2Nov. Fcsts.1987 - 2001 Müller et al. (2005), Clim. Dyn.

  9. Predictability of NAO Impact regression of T2m fields Müller et al. (2005), Clim. Dyn.

  10. Verification grid point against ERA-40 Perfect model approach RPSSdT2m, 1987 – 2002, fc234, 3 cat. Schwierz et al., subm.

  11. extended range forecast products at MeteoSwiss Klimagramm • seasonal forecasts • T2m, Precip. • Klimagramms • probability maps • monthly forecasts • T2m, Precip, Geopot. • tercile maps

  12. temperature anomaly months Probabilistic Downscaling Weather risk application Seasonal Forecast local weather risk

  13. Electricity Market • Electricity company in Southern Switzerland • Monthly forecasts

  14. T2 Zurich Contracts for peak load in week 12 (21.-27.3.2005) EEX Eu/MWh lowermost tercile % Lead time Using monthly forecasts • Contracts at EEX (European Energy Exchange) • Lowermost tercile for S-Germany (spatial avg). • persistence? • other forecasts? • ?

  15. Klimagramm for Swiss-Temperature Summer 2005 in Switzerland ProbabilityT2m > norm

  16. Media Basis • MeteoSwiss does not intend to publish seasonal forecasts for Switzerland. • Public presentation of Swiss climate research programme. Interview • Probability of Temperature in JJA > norm. Signal: 40-50%. • Norm value: 17 oC. (JJA daily avg). • Hit rate around 55 to 60%. • Ocean information is important. Published • Headline: “MeteoSwiss: Summer will be cool” • 40% used as signal. • Hit rate compared to skill of 84% for next day forecast. • Map of current buoys as basis for forecasts.

  17. same and next days: • prime time news on national television (French and German). • several radio interviews. • copied by several news paper (without feedback). • scientific facts and context somewhat distorted and wrongly quoted. main message: cool summer

  18. Competing Sunday Press Journal • Front page“Summer 05: Weather-forecast destroys tourism!Tourism directors criticize meteorologists” • Headline“Weather forecasters drive away tourists” • Short street interviews → people would use seasonal forecasts for holiday planning. • Tourism industry: • “Other countries would never have the idea to talk badly about their own products” • Forecasts are reason for low earnings in tourism industry ?! • “Would the forecasts predict a nice summer, we would be happy”

  19. Conclusions for handling press • JJA average of 17 oC is cool. • Give clear numbers, no ranges/choices → extremes will be chosen • Available actual forecasts will be published and used. A “scientific” context or “experimental” label can get lost. • Observations are preferred to models as technological basis for forecasts. • Should seasonal forecasts be published for the public? if yes, how? • Bulletin, press release, selected interviews, individually on request • Tourism industry: stronger fluctuations due to weather and climate forecasts? • Tourists (would) use forecasts for booking, industry seems not to.

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