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Ensemble Prediction Systems and Probabilistic Forecasting

Ensemble Prediction Systems and Probabilistic Forecasting. Prepared by C. Tubbs, P. Davies, Met Office UK Revised, delivered by R.Jones, WMO Consultant SWFDP-Eastern Africa Training Workshop Bujumbura, Burundi, 11-22 November 2013. ”The Blame Game” or ”The Passing of The Buck”.

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Ensemble Prediction Systems and Probabilistic Forecasting

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  1. Ensemble Prediction Systems and Probabilistic Forecasting Prepared by C. Tubbs, P. Davies, Met Office UK Revised, delivered by R.Jones, WMO Consultant SWFDP-Eastern Africa Training Workshop Bujumbura, Burundi, 11-22 November 2013

  2. ”The Blame Game” or ”The Passing of The Buck” The atmosphere is ”chaotic” Atmosphere Errorneous observations misled the NWP Scientists Computer models The NWP misled me Forecaster The forecaster misled me Customer/Public

  3. Understanding chaos

  4. Forecast distribution Analysis Error Data Assimilation Diagnostic Tools and Products Numerical Weather Prediction The Atmosphere Forecast Model System

  5. Quantifying uncertainty with ensembles Deterministic Forecast Forecast uncertainty Initial Condition Uncertainty X CHAOS Analysis Climatology time

  6. The Effect of Chaos • We can usually forecast the general pattern of the weather up to about 3 days ahead. • Chaos then becomes a major factor • Tiny errors in our analysis of the current state of the atmosphere lead to large errors in the forecast – these are both equally valid 4-day forecasts. • Fine details (eg rainfall) have shorter predictability

  7. Ensembles • In an ensembleforecast we run the model many times from slightly different initial conditions • This provides a range of likelyforecast solutions which allows forecasters to: • assess possible outcomes; • estimate risks • gauge confidence.

  8. Reminder on scales and predictability

  9. 1000km 100km 10km 1km Tropical Cyclone Space Scale MCS Front Thunderstorm Hail shaft Lifetime Predictability? 10mins 1 hr 12hrs 3 days 30mins 3 hrs 36hrs 9 days Temporal Resolution

  10. MOGREPS

  11. ECMWF EPS has transformed the way we do Medium-Range Forecasting Uncertainty also in short-range: Rapid Cyclogenesis often poorly forecast deterministically Uncertainty of sub-synoptic systems (eg thunderstorms) Many customers most interested in short-range Assess ability to estimate uncertainty in local weather QPF Cloud Ceiling, Fog Winds etc Short-range Ensembles

  12. Initial conditions perturbations • Perturbations centred around 4D-Var analysis • Transforms calculated using same set of observations as used in 4D-Var (including all satellite obs) within +/- 3 hours of data time • Ensemble uses 12 hour cycle (data assimilation uses 6 hour cycle)

  13. Initial conditions perturbations Differences with ECWMF Singular Vectors: • It focuses on errors growing during the assimilation period, not growing period: - Suitable for Short-range! • Calculated using the same resolution than the forecast • ETKF includes moist processes • Running in conjunction with stochastic physics to propagate effect

  14. Model error: parameterisations • QUMP (Murphy et al., 2004) • Initial stoch. Phys. Scheme for the UM (Arribas, 2004) Random parameters

  15. MOGREPS products

  16. Using probabilities

  17. Using probabilities • Recipients of forecasts & warnings are sensitive to different levels of risk: reflecting cost of mitigation vs expected loss • An intelligent response to forecasts & warnings depends on risk analysis, requiring knowledge of impact probability • Use of ensembles to estimate probability at longer lead times is well established in meteorology

  18. Ensemble forecast products

  19. Stamp maps and clusters

  20. Products: Stamp maps High resol EPS control All 50 EPS member

  21. Probability maps

  22. EC Total ppn prob > 20mm 12z Tue – 12z Wed

  23. EPSgrammes

  24. max 90% median (50%) Total cloud cover Deterministic 75% 6 hourly precipitation 25% 10m wind speed EPS control 10% 2m temperature min

  25. Pretoria

  26. The Extreme Forecast Index (EFI)

  27. Extreme forecast index (EFI) • EFI measures the distance between the EPS cumulative distribution and the model climate distribution • Takes values from –1 (all members break climate minimum records) and +1 (all beyond model climate records) • The main idea is to have an index that can be conveniently mapped – removing the effect from different climatologies – to use as an “alarm bell”

  28. Advantages with probability density functions Means and asymmetric variances are easily spotted 100% Clim. mean EPS mean Climate distribution EPS distribution 0% Temperature

  29. Advantages with probability density functions Means and asymmetric variances are easily spotted 100% Climate distribution EPS distribution 0% EPS mean Temperature Clim. mean

  30. EFI ~ 70% The EFI generally does not take the probability into account EFI ~ 70%

  31. EFI ~ 50% For temperature the EFI can take values < 0 EFI ~ -50%

  32. Product under development

  33. Product under development

  34. Working with the EPS -Ensemble mean acts as a dynamic filter and removes normally unpredictable features -The removed features are put back in a consistent way as probabilities

  35. Would you guide the ships to go steer into Newcastle harbour?? 50% ? 50%

  36. Questions & Answers

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