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Introduction to ensemble forecasting

Introduction to ensemble forecasting. The meteorological science in the service of Mankind. …is investigated and explored by. Atmosphere. Scientists. …who summarize their finding into mathematical. Scientists. Computer models.

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Introduction to ensemble forecasting

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  1. Introduction to ensemble forecasting WMO SWFDP Macau 8 April 2013 Anders Persson

  2. The meteorological science in the service of Mankind …is investigated and explored by Atmosphere Scientists …who summarize their finding into mathematical Scientists Computer models In 1966 I was told that “in 5-10 years time there will be no need for human weather forecasters” Computer models …which are used as an important tools by Forecasters But are they still needed? Forecaster …whose final work is used as a basis for decision making by Customers/Public Customer/Public 2014-08-29 WMO SWFDP Macau 8 April 2013 Anders Persson

  3. The progress of weather forecasting The arrival of the computer meant increasing forecast skill and efficiency but also new educational needs. Irony:In agriculture nobody said:“ -With the introduction of the tractor in 5-10 years there will be no need of farmers” The human weather forecaster before the scientific age: simple rules and no complicated machinery The arrival of the scientific method meant increasing forecast skill and efficiency but also an increased burden with thousands of observations, complex rules and more stressful work WMO SWFDP Macau 8 April 2013 Anders Persson

  4. On the contrary: There are perhaps more weather forecasters today than ever, even in – or particularly in – the commercial sector Training Course at Meteo Group, Wageningen, NL But what are they doing? WMO SWFDP Macau 8 April 2013 Anders Persson 29/08/2014 29/08/2014 4

  5. How can human weather forecasters compete with the super computers? • Humans should not try to compete with them • Instead they should play an entirely other “game”! • The key word is not “skilful”, but “useful” • – How to best serve the people! WMO SWFDP Macau 8 April 2013 Anders Persson

  6. Some don’t and engage in the Blame Game The atmosphere is ”chaotic”! Atmosphere Erroneous observations misled the NWP! Scientists Computer models The NWP misled me! Forecaster The forecaster misled me! Customer/Public 2014-08-29 WMO SWFDP Macau 8 April 2013 Anders Persson

  7. Most meteorologists surely do this! The atmosphere is chaotic! Atmosphere Erroneous observations may mislead the NWP! Scientists Computer models I will take the uncertainty into account! Forecaster Now I make better decisions! Customer/Public 2014-08-29 WMO SWFDP Macau 8 April 2013 Anders Persson

  8. The main reason why we need ensemble forecasting:We want to estimate the uncertainties, in particular the risks of extreme or high-impact weather -But I do not want any risks, or probabilities or uncertainties – I want to KNOW! OK, let’s take your words seriously WMO SWFDP Macau 8 April 2013 Anders Persson

  9. Come with me to nice friendly Scandinavia . . and who might turn up there? You venture out in the forest Although the risk of meeting a wolf is small you would have liked to be warned WMO SWFDP Macau 8 April 2013 Anders Persson

  10. Computer based “accurate-looking” forecast ψ Dangerous threshold Computer made weather forecast (NWP) -12 0 12 24 36 48 60 72 84 h No risk? No problems? Should we go ahead? WMO SWFDP Macau 8 April 2013 Anders Persson

  11. Computer based “accurate-looking” forecasts are far from perfect ● ● ψ Dangerous threshold ● Computer made weather forecast (NWP) ● ● ● ● ● ● ● ● ● ● ● ● obs 3. Forecast out of phase -12 0 12 24 36 48 60 72 84 h 2. Good timing but systematically too low 1. Forecast doesn’t verify “now” WMO SWFDP Macau 8 April 2013 Anders Persson

  12. 1st problem: -Is the forecast correct “now”? WMO SWFDP Macau 8 April 2013 Anders Persson

  13. The problem with very short computer forecasts ψ It did not even verify at initial time (t=0) Most NWP models do not analyse the weather! ● ● ● obs The forecasters nudge the forecast towards the observation 0 12 24 36 48 60 72 84 96 h The forecast does not verify “now” 29/08/2014 WMO SWFDP Macau 8 April 2013 Anders Persson 13

  14. Most state-of-the-art NWP models do not assimilate weather observations, only: • Upper air temperature, wind, relative humidity and winds from radio sondes • Radiances from satellites to be converted to temperature and humidity • Upper air winds from drifting clouds • Surface winds from satellites, ocean based ships and buoys • Surface or MSL pressure from land and sea platforms • They do NOT assimilate 10 m winds, 2 m temperatures or dew points, clouds and weather • These are (pretty well) calculated from the other parameters! WMO SWFDP Macau 8 April 2013 Anders Persson

  15. 2nd problem: -Are the NWP systematically wrong? WMO SWFDP Macau 8 April 2013 Anders Persson

  16. Statistical interpretation (archived data) ● obs - Ψ= corr ● corr = AΨ + B ● ● ● ● ● ● ● ● ● ● ● ● ψ ● WMO SWFDP Macau 8 April 2013 Anders Persson

  17. The solution to the problem of systematically misleading computer forecasts ψ ● ● ● ● ● ● Statistical correction or “calibration” ● ● 0 12 24 36 48 60 72 84 96 h From experience (verification or statistical interpretation) we know that the NWP model underestimates high forecast values, which can be corrected for 29/08/2014 WMO SWFDP Macau 8 April 2013 Anders Persson 17

  18. 3rd problem: -Is the forecast “jumpy”? WMO SWFDP Macau 8 April 2013 Anders Persson

  19. Computer based “accurate” forecast can not only be wrong but also “jumpy” ψ Dangerous threshold Today’s forecast yesterday’s forecast Tomorrow’s forecast -12 0 12 24 36 48 60 72 84 h WMO SWFDP Macau 8 April 2013 Anders Persson

  20. Downstream development of influence Day 0 L L L H L H Energy propagation Day 2 L L H H L H Day 4 L L H L H H WMO SWFDP Macau 8 April 2013 Anders Persson

  21. Extra-tropical influence → Tropics L But the influence can also be in the opposite direction Persson-Petersen WMO workshop 1996 WMO SWFDP Macau 8 April 2013 Anders Persson

  22. At the same time as we try to improve the initial analysis by • Increasing the number of observations • Improving their quality • Improving our analysis methods • …. we also do the opposite: • We “tickle” the analysis by imposing perturbations (possible errors) to fins out how it affects the NWP WMO SWFDP Macau 8 April 2013 Anders Persson

  23. Where and how are the atmospheric analyses perturbed? WMO SWFDP Macau 8 April 2013 Anders Persson

  24. Stochastic physics everywhere EDA Singular vectors EDA Singular vectors Singular vectors Tropical singular vectors (when a cyclonic feature is formed) Singular vectors WMO SWFDP Macau 8 April 2013 Anders Persson

  25. EDA in action – typhoon Aere over northern Philippines The first guess is fairly reliable to the SW of the typhoon, but not to the NE of the typhoon WMO SWFDP Macau 8 April 2013 Anders Persson

  26. 10 (from June this year 25) EDA short range forecasts are constantly running in parallel randomly perturbed by stochastic physics and varying SST Surface pressure 00 UTC 03 UTC 06 UTC 09 UTC 12 UTC 15 UTC 18 UTC 21 UTC Time Now we want to make a new analysis for the 12 UTC forecast WMO SWFDP Macau 8 April 2013 Anders Persson

  27. To arrive at the best possible analysis for 12 UTC we consider all the forecasts 09-21 UTC as 12-hour first guesses in anew assimilation cycle Surface pressure To launch a 10 day forecast from here 00 UTC 03 UTC 06 UTC 09 UTC 12 UTC 15 UTC 18 UTC 21 UTC Time Assimilation window WMO SWFDP Macau 8 April 2013 Anders Persson

  28. These 10 forecasts, 12-hour first guesses, are confronted with observation, perturbed to account for observation errors and representativeness Surface pressure 10 forecasts (first guesses) ● ● ● Observations perturbed within their error estimates 09 UTC 12 UTC 15 UTC 18 UTC 21 UTC Time Assimilation window WMO SWFDP Macau 8 April 2013 Anders Persson

  29. Influenced by these observations the 10 first guesses are modified 4 DVAR trajectories Surface pressure ● ● ● 09 UTC 12 UTC 15 UTC 18 UTC 21 UTC Time Assimilation window WMO SWFDP Macau 8 April 2013 Anders Persson

  30. Influenced by these observations the 10 first guesses are modified Odd member 3 is perturbed by SV 6 times to produce members 3, 4, 23, 24, 43 and 44 4 DVAR trajectories Surface pressure Even member 8 is perturbed by SV 4 times to produce members 17, 18, 37 and 38 09 UTC 12 UTC 15 UTC 18 UTC 21 UTC Time Assimilation window WMO SWFDP Macau 8 April 2013 Anders Persson

  31. EDA member Corresponding EPS members 1-50 WMO SWFDP Macau 8 April 2013 Anders Persson

  32. EDA forecast 10 members perturbed by stochastic physics, varying SST and perturbed observations Ensemble forecast50 members perturbed by singular vectors and stochastic physics Surface pressure Formally starting from 12 UTC 09 UTC 12 UTC 15 UTC 18 UTC 21 UTC Time Assimilation window Forecast WMO SWFDP Macau 8 April 2013 Anders Persson

  33. Exchanging the “accurate” forecast with a more “honest” one ψ Dangerous threshold Today’s NWP forecast Today’s EPS forecast -12 0 +12 +24 +36 +48 +60 +72 +84 h WMO SWFDP Macau 8 April 2013 Anders Persson

  34. Correction for systematic errors ψ -12 0 +12 +24 +36 +48 +60 +72 +84 h 29/08/2014 WMO SWFDP Macau 8 April 2013 Anders Persson 34

  35. The final ensemble forecast – with verification ψ ● ● 70% 50% ● ● ● ● ● ● ● ● ● ● ● ● ● obs -12 0 +12 +24 +36 +48 +60 +72 +84 h 29/08/2014 WMO SWFDP Macau 8 April 2013 Anders Persson 35

  36. Prob(> 15 m/s) 20 March 2013 12 UTC + 156h Prob(> 15 m/s) 22 March 2013 12 UTC + 108h Prob(> 15 m/s) 24 March 2013 12 UTC + 60h Probability maps of the 10 m wind exceeding 15 m/s +12 h forecast (verification) → WMO SWFDP Macau 8 April 2013 Anders Persson

  37. Probability maps of more than 20 mm rain in 24r h Prob(> 20 mm/d) 21 March 2013 00 UTC + 144h Prob(> 20 mm/d) 22 March 2013 12 UTC + 108h Prob(> 20 mm/d) 24 March 2013 12 UTC + 60h Prob(> 20 mm/d) 26 March 2013 00 UTC + 24h WMO SWFDP Macau 8 April 2013 Anders Persson

  38. Tropical storms genesis map 2 March 12 UTC VT 3-5 March Storm WMO SWFDP Macau 8 April 2013 Anders Persson

  39. Tropical cyclones genesis map 2 March 12 UTC VT 3-5 March WMO SWFDP Macau 8 April 2013 Anders Persson

  40. Tropical cyclones genesis map 3 March 00 UTC VT 4-6 March WMO SWFDP Macau 8 April 2013 Anders Persson

  41. The TC was born on the 6 March! 6 March 00 UTC ensemble plume 7 March 12 UTC ensemble plume WMO SWFDP Macau 8 April 2013 Anders Persson

  42. 9 March 12 UTC ensemble plume 11 March 12 UTC ensemble plume WMO SWFDP Macau 8 April 2013 Anders Persson

  43. Summary: • Ensemble forecasts help us • To judge the (un)certainty of the weather situation • To acquire probability estimates of anomalous events (extreme or high impact) • To get the most accurate and least “jumpy” deterministic forecast value WMO SWFDP Macau 8 April 2013 Anders Persson

  44. Other advantages with ensemble forecasting WMO SWFDP Macau 8 April 2013 Anders Persson

  45. 24 hour ”jumpiness” of 2 m temperature forecasts ECMWF Southern Sweden Ens Mean ECMWF Central Sweden Ens Mean The jumpiness is decreased by 50%-75% WMO SWFDP Macau 8 April 2013 Anders Persson

  46. Lagging reduced the MA error by 20% but the jumpiness by 70% Error decreased after lagging Jumpiness decreased even more WMO SWFDP Macau 8 April 2013 Anders Persson

  47. Why does an ensemble technique affect the jumpiness more than the error?? Look at a small ensemble of consecutive forecasts a 1.0 f h g error = f-a g-a Averaging will decrease error by 13% f g 0.50 …and jumpiness by 50% Mean of g and h error ● ● h-a a h h ● g WMO SWFDP Macau 8 April 2013 Anders Persson

  48. The perturbations on average make the analysis worse On average 35% of the perturbed analyses are better 29/08/2014 WMO SWFDP Macau 8 April 2013 Anders Persson 48

  49. The perturbed forecasts are individually on average 1-1½ day worse than the unperturbed forecast 29/08/2014 WMO SWFDP Macau 8 April 2013 Anders Persson 49

  50. Downstream development of influence Day 0 L Analysis perturbed L L H L H Response Day 2 L L H H L H Response Day 4 L L H L H H WMO SWFDP Macau 8 April 2013 Anders Persson

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