Task D4C1: Forecast errors in clouds and precipitation Thibaut Montmerle CNRM-GAME/GMAP

1. Task D4C1: Forecasterrors in clouds and precipitation Thibaut Montmerle CNRM-GAME/GMAP IODA-Med Meeting 16-17th of May, 2014

2. Outlines • Introduction • Modelization of B for specificmeteorologicalphenomena • Applications: - Use of a heterogeneousBfor DA in rain - Assimilation of cloudy IR radiances 4. Conclusions and Perspectives

3. Introduction: DA in AROME • 3h-cycled 3DVar (Seity et. Al 2011) : • The analysis is the solution of the BLUE : • B has a profound impact on the analysis in VARby : • imposing the weight of the background, smoothing and spreading of information from observation points through spatial covariances • distributing information to other variables and imposing balance through multivariate relationships • Practical difficulties: • The “true” state needed to measure error against is unknown • Because of its size ((108)2 for AROME), B can be neither estimated at full rank nor stored explicitly

4. analytical linear balance operator ensuring geostrophical balance Berre (2000) regression operators that adjust couplings with scales Introduction: B modeling In AROME, use of the CVT formulation: Following notation of Derber and Bouttier (1999) : • Kpis the balance operator allowing to output uncorrelated parameters using balance constraints. • BSis the spatial transform in spectral space, giving isotropic and homogeneous increments: Kp and BS are static and are deduced from an ensemble assimilation (EDA, Brousseau et al. 2011a)

5. Introduction: limitations of the operational B B strongly depends on weather regimes Examples for an EDA that gathers anticyclonic and perturbed situations : Time evolutions of Z500, T500 and of different background error standard deviations at different levels Spread of daily forecast error of std deviations for q (Brousseau et al. 2011)

6. Modelization of B for specificmeteorologicalphenomena (Montmerle and Berre 2010) EDA designed for LAM : B Calibration • Few cycles needed to get the full spectra of error variances • High impact phenomena under-represented in the ensemble

7. 1 0 Binarymasks: rain/rain non rainy/non rainy Modelization of B for specificmeteorologicalphenomena Forecast errors are decomposed using features in the background perturbations that correspond to a particular meteorological phenomena. Example for precipitation (G : Gaussian blur)

8. “No Rain” “Rain” Application #1: use of a « rainy » B for DA of radar data (Montmerle, MWR 2012) Use of the heterogeneous formulation: (Montmerle and Berre, 2010) Where F1 and F2 define the geographical areas where B1 and B2 are applied: Vertical Cross section of q increments 4 obs exp: Innovations of – 30% RH At 800 and 500 hPa • This formulation allows to consider simultaneously different BSandKp that are representative of one particular meteorological phenomena

9. Application #1: use of a « rainy » B for DA of radar data • Example for a real case: D is deduced from the reflectivity mosaic Radar mosaic 15th of June 2010 at 06 UTC Resulting gridpoint mask • In addition to conventional observations, DOW and profiles of RH, deduced from radar reflectivities using a 1D Bayesian inversion (Caumont et al., 2010), are also assimilated in precipitating areas.

10. Oper Precip Clear air Application #1: use of a « rainy » B for DA of radar data EXP OPER Here: EXP: B1=rain, B2=OPER , D=radar mosaic Dij >0.5 Humidity increment at 600 hPa (g.kg-1) (zoom over SE France) • B1 has shorter correlation lengths: • increments have higher spatial resolutions in precipitation • Potential increase of the spatial resolution of assimilated radar data

11. z = 800 hPa z = 400 hPa div EXP OPER conv Divergence increments Application #1: use of a « rainy » B for DA of radar data Cov(dq, dhu) OPER Rain • Spin-up reduction correlated with the number of grid points where B1 is applied • Positive forecasts scores up to 24h for precipitation and for T and q in the mid and lower troposphere Vertical cross covariances

12. Application #2: Assimilation of cloudy radiances in a 1DVar Liquid cloud Computation of background error covariances for all hydrometeors in clouds: Analogously to Michel et al. (2011), the mask-based method and an extension of Kphave been used: Ice cloud Vertical covariances between qi , ql and the unbalanced humidity qu % of explained error variances for ql (top) and qi (bottom)

13. Application #2: Assimilation of cloudy radiances in a 1DVar Flow-dependent vertical covariances : Use of mean contents to distort vertically climatological values Mean contents vs. error std dev. “of the day” for rain (left) and ice cld (right) Error variances for rain and ice cloud

14. Application #2: Assimilation of cloudy radiances in a 1DVar Assimilation of IASI cloudy radiances (Martinet et al., 2013) ql and qi have been added to the state vector of a1DVar, along with T and q Reduction of background error variances for selections of high opaque cloud (left) and low liquid cloud (right) • Background errors are reduced for ql and qi (as well as for T and q (not shown)), increments are coherently balanced for all variables.

15. Application #2: Assimilation of cloudy radiances in a 1DVar Evolution of analyzed profiles using AROME 1D Example for low semi-transparent ice clouds: Time evolution of integrated ice cloud contents (min) • Thanks to the multivariate relationships and despite the spin-down, integrated contents keep values greater than those forecasted by the background and by other assimilation methods up to 3h

16. Conclusions & perspectives • High impact weather phenomena (e.g convective precipitations, fog…) are under-represented in ensembles that are used to compute climatological Bc : DA of observations is clearly sub-optimal in these areas • By using geographical masks based on features in background perturbations from an EDA, specific Bc matrices can be computed and used simultaneously in the VAR framework using the heterogeneous formulation • So far, combining radar data and “rainy”Bc leads to spin-up reduction and to positive scores • The formulation of the balance operator has been extended for all hydrometeors that are represented in AROME in order to compute their multivariate background error covariances using cloudy mask. • The latter have been successfully exploited to analyzed cloud contents from DA of cloudy radiances in a 1D framework.

17. Conclusions & perspectives • At CS and for LAM, Bc need to be updated frequently : the set up of a daily ensemble is essential • Problems : • need of perturbed LBCs • the estimation and the representation of model error • sampling noise is severe, especially at CS • the computational cost ! • Solutions : • Cheaper ensembles in the limit of the “grey zone” (providing that explicit convection is activated) : use of perturbations from an AROME 4 km? • Optimal filtering of forecast error parameters : B. Ménétrier’s thesis

18. Conclusions & perspectives Possible evolution of B in operationalNWP systemsat CS EnVarwith more optimal localizations in Be Degree of flow dependency EnVar: use of a spatially localized covariance matrix Be deduced from an ensemble, combined with BC One or several BC updated daily from an ensemble Static BC with covariances modulated by filtered values from an ensemble Static BC with balance relationships, homogeneous and isotropic covariances for unbalanced variables Ensemble size 10 1 100

19. Thank you for your attention…

20. References Brousseau, P.; Berre, L.; Bouttier, F. & Desroziers, G. : 2011. Background-error covariances for a convective-scale data-assimilation system: AROME France 3D-Var. QJRMS., 137, 409-422 Berre, L., 2000: Estimation of synoptic and mesoscale forecast error cavariances in a limited area model. MWR. 128, 644–667. • Martinet et al 2013: Towards the use of microphysical variables for the assimilation of cloud-affected infrared radiance, QJRMS. Ménétrier, B. and T. Montmerle, 2011 : Heterogeneous background error covariances for the analysis of fog events. Quart. J. Roy. Meteor. Soc., 137, 2004–2013. Michel, Y., Auligné T. and T. Montmerle, 2011 : Diagnosis of heterogeneous convectivescale Background Error Covariances with the inclusion of hydrometeor variables. Mon. Wea Rev., 138 (1), 101-120. Montmerle T, Berre L. 2010. Diagnosis and formulation of heterogeneous background-error covariances at themesoscale. QJRMS, 136, 1408–1420. Montmerle T., 2012 : Optimization of the assimilation of radar data at convective scale using specific background error covariances in precipitations. MWR, 140, 3495-3505.