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

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

3 WMO SWFDP Macau 8 April 2013 Anders Persson 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 progress of weather forecasting 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

4 WMO SWFDP Macau 8 April 2013 Anders Persson 09/12/ 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?

5 WMO SWFDP Macau 8 April 2013 Anders Persson 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!

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

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

8 WMO SWFDP Macau 8 April 2013 Anders Persson 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

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

10 WMO SWFDP Macau 8 April 2013 Anders Persson h ψ Computer based “accurate-looking” forecast Dangerous threshold No risk? No problems? Should we go ahead? Computer made weather forecast (NWP)

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

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

13 WMO SWFDP Macau 8 April 2013 Anders Persson 09/12/ h ψ obs ● ● ● The forecast does not verify “now” It did not even verify at initial time (t=0) Most NWP models do not analyse the weather! The forecasters nudge the forecast towards the observation The problem with very short computer forecasts

14 WMO SWFDP Macau 8 April 2013 Anders Persson Most state-of-the-art NWP models do not assimilate weather observations, only: 1.Upper air temperature, wind, relative humidity and winds from radio sondes 2.Radiances from satellites to be converted to temperature and humidity 3.Upper air winds from drifting clouds 4.Surface winds from satellites, ocean based ships and buoys 5. 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!

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

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

17 WMO SWFDP Macau 8 April 2013 Anders Persson 09/12/ h ● ● Statistical correction or “calibration” ● ● ● ●● ● ψ From experience (verification or statistical interpretation) we know that the NWP model underestimates high forecast values, which can be corrected for The solution to the problem of systematically misleading computer forecasts

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

19 WMO SWFDP Macau 8 April 2013 Anders Persson h ψ Computer based “accurate” forecast can not only be wrong but also “jumpy” Dangerous threshold Today’s forecast yesterday’s forecast Tomorrow’s forecast

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

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

22 WMO SWFDP Macau 8 April 2013 Anders Persson At the same time as we try to improve the initial analysis by 1.Increasing the number of observations 2.Improving their quality 3.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

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

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

25 WMO SWFDP Macau 8 April 2013 Anders Persson 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

26 WMO SWFDP Macau 8 April 2013 Anders Persson 00 UTC03 UTC06 UTC09 UTC12 UTC15 UTC18 UTC21 UTCTime Surface pressure 10 (from June this year 25) EDA short range forecasts are constantly running in parallel randomly perturbed by stochastic physics and varying SST Now we want to make a new analysis for the 12 UTC forecast

27 WMO SWFDP Macau 8 April 2013 Anders Persson 00 UTC03 UTC06 UTC09 UTC12 UTC15 UTC18 UTC21 UTCTime Surface pressure To arrive at the best possible analysis for 12 UTC we consider all the forecasts UTC as 12-hour first guesses in anew assimilation cycle Assimilation window To launch a 10 day forecast from here

28 WMO SWFDP Macau 8 April 2013 Anders Persson 09 UTC12 UTC15 UTC18 UTC21 UTCTime Assimilation window ● ● ● 10 forecasts (first guesses) Observations perturbed within their error estimates Surface pressure These 10 forecasts, 12-hour first guesses, are confronted with observation, perturbed to account for observation errors and representativeness

29 WMO SWFDP Macau 8 April 2013 Anders Persson 09 UTC12 UTC15 UTC18 UTC21 UTCTime Assimilation window ● ● ● Surface pressure 4 DVAR trajectories Influenced by these observations the 10 first guesses are modified

30 WMO SWFDP Macau 8 April 2013 Anders Persson 09 UTC12 UTC15 UTC18 UTC21 UTCTime Assimilation window Surface pressure 4 DVAR trajectories 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 Even member 8 is perturbed by SV 4 times to produce members 17, 18, 37 and 38

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

32 WMO SWFDP Macau 8 April 2013 Anders Persson 09 UTC12 UTC15 UTC18 UTC21 UTCTime Assimilation windowForecast Surface pressure Ensemble forecast 50 members perturbed by singular vectors and stochastic physics EDA forecast 10 members perturbed by stochastic physics, varying SST and perturbed observations Formally starting from 12 UTC

33 WMO SWFDP Macau 8 April 2013 Anders Persson h ψ Exchanging the “accurate” forecast with a more “honest” one Dangerous threshold Today’s NWP forecast Today’s EPS forecast

34 WMO SWFDP Macau 8 April 2013 Anders Persson 09/12/ ψ Correction for systematic errors h

35 WMO SWFDP Macau 8 April 2013 Anders Persson 09/12/ ψ ● ● ● ● ● ● ● ● ● ● ● ● obs ●● ● The final ensemble forecast – with verification 70%50% h

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

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

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

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

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

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

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

43 WMO SWFDP Macau 8 April 2013 Anders Persson Summary: Ensemble forecasts help us 1.To judge the (un)certainty of the weather situation 2.To acquire probability estimates of anomalous events (extreme or high impact) 3.To get the most accurate and least “jumpy” deterministic forecast value

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

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

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

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

48 WMO SWFDP Macau 8 April 2013 Anders Persson 09/12/ The perturbations on average make the analysis worse On average 35% of the perturbed analyses are better

49 WMO SWFDP Macau 8 April 2013 Anders Persson 09/12/ The perturbed forecasts are individually on average 1-1½ day worse than the unperturbed forecast

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

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