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 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. Singular vectors Perfect score  Good spread-error relationship
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. With manually determined PV perturbations
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 ISSN
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
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
TKE
ECUME
NO-
TKE
KFB
G87
CAPE
CHARNOCK 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