Mid-latitude cyclone dynamics and ensemble prediction

1. Mid-latitude cyclone dynamics and ensemble prediction Ph. Arbogast , L. Descamps, M. Boisserie, P. Cébron, C. Labadie, K. Maynard, M. Plu , P. Raynaud Météo-France –CNRM

2. Mid-latitude cyclone predictability • Deterministic perspective; initial conditions improvement leads to forecast error reduction. • Singular vector, PV • Probabilistic perspective: the state of the atmosphere must be known using pdf sampled by ensembles. • Ensembles must account for initial conditions and model uncertainties

3. Initial condition perturbations • Improvement of the initial conditions: • “linear” rationale : if the initial conditions are improved in sensitive areas defined by “targeted” singular vectors the forecast is improved • if additional observations are collected in sensitive areas defined by “targeted” singular vectors the forecast is improved • But • Linear approach • The atmosphere does not resemble a singular vector • Acting on coherent structures / Potential Vorticity anomalies may help…. • A step forward: introducing PV perturbation in ensembles

4. Singular vectors Geopotential perturbations/incipient stage of Lothar Descamps et al., 2007: Is a Real Cyclogenesis Case Explained by Generalized Linear Baroclinic Instability?. J. Atmos. Sci., 64, 4287–4308.

5. Initial condition improvement using PV inversion Arbogast et al, 2012: About the Reliability of Manual Model PV Corrections to Improve Forecasts. Wea. Forecasting, 27, 1554–1567.

7. PV modifications : • Successful case studies of mid-latitude cyclone developments and Heavy Precipitation Events over Mediterranean (Argence et al.) • Attempt of Quantification of the relationship between WV brightness temperature and PV (Michel Y.) • Assimilation of PV without inversion within 4DVAR (Guérin et al, 2005, Michel Y)

8. PCA of 14 modifications (7 forecasters/synopticians) Is this approach reliable ? Yes! Look at the first mode 

9. Mid-latitude cyclone predictability • Deterministic perspective; initial condition improvement leads to forecast error reduction. • Singular vector, PV • Probabilistic perspective: the state of the atmosphere must be known using pdf sampled by ensembles. • Ensembles must account for initial conditions and model uncertainties

10. Model error vs predictability error • What is model error ? Errors due to unresolved processes or subgrid scale • What is predictability error ? Errors due to IC uncertainties • Daley approach to disentangle initial condition and model error: • F~Pp+Pq • (Error variances matrices of forecast, predictability and model) Boisserie, M., Arbogast, P., Descamps, L., Pannekoucke, O. and Raynaud, L. (2014), Estimating and diagnosing model error variances in the Météo-France global NWP model. Q.J.R. Meteorol. Soc., 140: 846–854

12. Predictability error Model error 850 hPa T 10 m wind speed

13. The components required in an Ensemble dedicated to short to medium range Ensemble of 4DVAR samples the initial uncertainties analysis Singular vectors Model error / multi-physics or stochastic physics 35 members

14. Initial condition error are “flow-dependant”!

15. Descamps, L., Labadie, C., Joly, A., Bazile, E., Arbogast, P. and Cébron, P. (2014), PEARP, the Météo-France short-range ensemble prediction system. Q.J.R. Meteorol. Soc. Singular vectors Perfect score  Good spread-error relationship Multi-physics

16. Ensemble building using PV perturbations (2-level QG framework) Reference (2000 members) ~20 members Matthieu Plu and Philippe Arbogast, 2005: A Cyclogenesis Evolving into Two Distinct Scenarios and Its Implications for Short-Term Ensemble Forecasting. Mon. Wea. Rev., 133, 2016–2029.

17. Vich, Maria-del-Mar et al. Perturbing the potential vorticity field in mesoscale forecasts of two Mediterranean heavy precipitation events. Tellus A, [S.l.], v. 64, aug. 2012. ISSN 1600-0870. With manually determined PV perturbations Reliability diagrams +Fresnay PhD, (UPS Toulouse)

18. Better knowledge of model error through the use of reforecast datasets • Calibrated windstorm detection using the reforecast • Systematic error of mid-latitude cyclone development through feature detection Revisiting the predictability of the extreme events that have hit France over the last three decades. Part 1: application to Windstorms M. Boisserie, L. Descamps, P. Arbogast in preparation

19. Skill of windstorm detection Daily ensemble climate threshold q p Extremeness through pdf differences  Extreme Forecast Index (ECMWF approach) +102h / France

23. Climatology of systematic errors regarding mid-latitude cyclones • Mid-latitude cyclones are coherent features with position and amplitude error • Use of a 25 year reforecast based on ERA-Interim analyses • 1 run every 4 days/ September to March/+96h forecast/ 850 hPa vorticity 6400 cyclones detected • 10 different physics • Method: • Feature detection (threshold given by max. entropy method) and counting • “Optical flow” to calculate a displacement error • Forecast advected using the optical flow. Difference with the analysis amplitude error • Summary of both errors cyclone by cyclone

24. Amplitude error

25. 2s Too strong TKE s Moisture conv ECUME KFB CHARNOCK G87 NO-TKE CAPE -s Too weak cyclones -2s TURBULENCE SHALLOW CONV. DEEP CONV. SEA-AIR FLUXES

26. Amplitude error Outside this area the forecast is unable to detect the cyclone 4300  3400 cyclones

27. Northward shift of cyclone locations 2s Eastward shift of cyclone locations v s Moisture conv ECUME TKE NO- TKE KFB G87 CHARNOCK CAPE u -s TURBULENCE SHALLOW CONV. DEEP CONV. SEA-AIR FLUXES -2s

28. Conclusion • Better understanding of systematic error using PV concepts (diabatic heating diabatic PVC …) • Feature-wise probabilistic evaluation of ensemble with reliability and resolution scores