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Forecasting Uncertainty Met Office

Forecasting Uncertainty Met Office. Ewen McCallum Chief Meteorologist. Met Office - Exeter. Forecasting the weather. Worldwide Observations Satellite, land, ship, aircraft, radar, radiosonde, drifting buoy. Sources of Observation. Things you may not know about the Met Office. Each day:

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Forecasting Uncertainty Met Office

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  1. Forecasting Uncertainty Met Office Ewen McCallum Chief Meteorologist

  2. Met Office - Exeter

  3. Forecasting the weather Worldwide Observations Satellite, land, ship, aircraft, radar, radiosonde, drifting buoy

  4. Sources of Observation

  5. Things you may not know about the Met Office • Each day: • 10 million pieces of observation data processed • 100,000 million pieces of information within our forecasting models • 8,500 weather observations, from over 3,000 sites, produced in the UK • 1,500 surface and over 40 upper-air observations produced by our staff

  6. Forecasting the weather Worldwide Observations Satellite, land, ship, aircraft, radar, radiosonde, drifting buoy Numerical Models

  7. What is NWP? • NWP: Numerical Weather Prediction • Create mathematical models of the atmosphere • Analyse global observations • Giant simulator representing physical laws • Run the model to produce a prediction • Use of supercomputers in weather forecasting

  8. Computers NEC SX6

  9. NWP grid • Global 60km, 38L • UK 12km, 38L • Several relocatable Defence Models • 16km, 38L • Global Stratosphere 300km, 42L

  10. Improvements in NWP Mean Sea Level Pressure N Atlantic and W Europe

  11. Forecasting the weather Worldwide Observations Satellite, land, ship, aircraft, radar, radiosonde, drifting buoy Numerical Models Operations Centre

  12. Severe weather warnings

  13. Forecasting the weather Worldwide Observations Satellite, land, ship, aircraft, radar, radiosonde, drifting buoy Numerical Models Operations Centre Forecaster Consultant Media Presenters Local Centre

  14. Media BBC

  15. Forecasting the weather Worldwide Observations Satellite, land, ship, aircraft, radar, radiosonde, drifting buoy Numerical Models Operations Centre Forecaster Consultant Media Presenters Local Centre Customer

  16. Revenue 2003/4 Income vs costs

  17. Managing Risk

  18. The Met Office has a World-leading forecasting system, but nevertheless… • All forecasts are uncertain • High-profile forecast failures are now rare, but do still occur (e.g. Dec 1999 European storms) • Less severe errors are much more common, e.g. • medium-range forecasts • finer details such as timing of rainfall • Ensembles turn weather forecasts into Rick Management tools

  19. The atmosphere is a chaotic system: “… one flap of a seagull’s wing may forever change the future course of the weather”, (Lorenz, 1963) Up to about 3 days ahead we can usually forecast the general pattern of the weather quite accurately Beyond 3 days Chaos becomes a major factor The Effect of Chaos

  20. Ensembles seven-day sequence

  21. Day three

  22. Day seven

  23. Day one

  24. Ensembles... Deterministic Forecast Forecast uncertainty Initial Condition Uncertainty X Analysis Climatology time

  25. Ensembles - • By running the model many times with small differences in initial conditions (and model formulation) we can: • take account of uncertainty • estimate probabilities and risks (eg. 30 members out of 51 = 60%)

  26. The EPS provided a range of possible scenarios Return to mild... Range 22 K Obs 8 deg warmer than fcst Long cold spell...

  27. EPS Meteogram • Plot of ensemble spread • Box shows 25-75% range • Whiskers show 95% confidence range • Central bar shows median – can indicate most probable • Summarises forecast at one location for 10 days ahead • Met Office calibrates ensemble forecasts to improve quality

  28. Short-Range Applications • Allow us to assess uncertainty in short-range forecasts, e.g. • Energy demand (European Open Market) • autoTAF – visibility, low cloud • Transport – road, rail, air, shipping • Aircraft icing • Assess Uncertainty where local detail required, eg. • Sea breezes • River catchment flooding

  29. Forecasting for North Sea Rigs

  30. What decisions are you planning to make?

  31. Traditionally……. • Five day ahead forecasts • Deterministic • Consultative discussion • Subjective assessments • Precedent & Experience • Climatology ……it’s still a risky business!

  32. “I’ve seen the forecast, how confident can I be?” Can we do this job next week? …..to hire five vessels will cost me £100,000 a day….. What’s the chance of a reliable 24 hour weather window during the next week or so?

  33. Giving our clients that confidence • Complement the five day forecast • Measure the uncertainty • Better focus for discussion • Objective not subjective • Dynamic, not historical • Responds to climate shift

  34. TypicalOffshore Outputfrom Met OfficePrevin System

  35. 1.5 M Looking at waves in more detail

  36. Can a Probability Forecast be Wrong? • A single Probability Forecast cannot be right or wrong.Consider: • Probability of X is 30% • If X happens, is this right? Or wrong? • But… out of 100 such forecasts, X should happen 30 times. • Verification must be done over many forecasts

  37. Calibration of Probability Forecasts • Calibration forecasts of a single “event” is straightforward using a reliability diagram: • 70% EPS prob50% issued In practise we use a more flexible approach which can adapt to the requirements of different customers.

  38. Can we use low probabilities? • Most extreme events are inherently improbable - how should we respond to low probabilities? • Event probability must be related to “climatology” for decision-making, eg. • 5% risk that a plane will crash - would you board it? • 5% risk of rain – would you play golf? • Decisions must be based on user’s Cost/Loss ratio • users with low C/L should protect at low probabilities

  39. Making Decisions – Simple Cost/Loss Model With a probability forecast p, the user’s beststrategy is to: protect when p(event)>C/L Averaged over many occasions this will maximise savings Thus we have turned probability forecasts into decision tools

  40. Summary – Ensembles help balance risks in an uncertain World! • We cannot get away from uncertainty • False Alarms and missed events are unavoidable • Ensembles let us turn this to our advantage • Reliable probability forecasts allow users to balance the impact of false alarms and missed events

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