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

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

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

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.

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.

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)

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

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

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

Predictability error Model error 850 hPa T 10 m wind speed

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

Initial condition error are “flow-dependant”!

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

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.

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. 2012. ISSN 1600-0870. With manually determined PV perturbations Reliability diagrams +Fresnay PhD, (UPS Toulouse)

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

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

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

Amplitude error

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

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

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

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