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14 th Annual WRF Users’ Workshop. June 24-28, 2013 Improved Initialization and Prediction of Clouds with Satellite Observations Tom Auligné Gael Descombes,

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Presentation on theme: "14 th Annual WRF Users’ Workshop. June 24-28, 2013 Improved Initialization and Prediction of Clouds with Satellite Observations Tom Auligné Gael Descombes,"— Presentation transcript:

1 14 th Annual WRF Users’ Workshop. June 24-28, 2013 Improved Initialization and Prediction of Clouds with Satellite Observations Tom Auligné Gael Descombes, Francois Vandenberghe National Center for Atmospheric Research Acknowledgments: Greg Thompson, Dongmei Xu, Thomas Nehrkorn, Brian Woods. Funding provided by the Air Force Weather Agency

2 Introduction Approaches for the initialization of clouds with satellite radiances Nowcasting COP + advection 3D cloud fraction + WRF dynamical transport Oklahoma U. / GSI-RR cloud analysis Expansion of analysis control variable Total water + linearized physics Microphysical variables

3 Outline Control Variable Transform Processing of Cloudy Satellite Observations Experimental Demonstration

4 Pseudo Single Observation Test Multivariate covariances for qc, qr, qi, qsn Binning option: dynamical cloud mask Vertical and Horizontal autocorrelations via Recursive Filters 3D Variance Poster Descombes et al.

5 NEW ALGORITHM: update of the ensemble perturbations inside the Variational analysis through Lanczos minimization (without external EnKF) NEW ALGORITHM: update of the ensemble perturbations inside the Variational analysis through Lanczos minimization (without external EnKF) Ensemble covariance included in 3D/4DVar through state augmentation (Lorenc 2003, Wang et al. 2008, Fairbairn et al., 2012) Ensemble/Variational Integrated Localized (EVIL) with

6 Forecast Calibration & Alignment Hurricane Katrina OSSE Synthetic observations (TCPW) Balanced displacement Nehrkorn et al., MWR 2013 (submitted)

7 Unified processing for Radiances and COP Retrievals VarBC: Variational Bias Correction (unchanged predictors) Revisited QC: extended Gross and First-Guess check (to conserve cloudy data) Huber Norm: robust definition of observation error Land Surface: T skin introduced as a sink variable Field of View: advanced interpolation scheme CRTM Jacobians: rescaled base state (floor and ceiling values for cloud parameters) Middle Loop: Multiple re-linearizations of obs. operator Unified Processing of Cloud-affected Satellite data

8 First Guess Second Guess Third Guess Observation Update of q cloud, q ice in WRF Observations AIRS Window Channel #787

9 WRF-ARW model, CONUS 15km, Thompson microphysics First Guess = Mean of 50-memb ens. from EnKF expt (Romine) Single analysis at 20012/06/03 (12UTC), 5 middle-loops GOES-Imager: Radiances (Thompson) & COD (Uwisc.) CTRL no DA COD Cloud Optical Depth (3DVar) FCACloud Optical Depth (Displacement) 3DVARRadiances (3DVar) EVIL Radiances (Hybrid) Experimental Framework

10 q cloud Level 10 Analysis increments CODFCA 3DVAREVIL

11 q cloud Level 10 Analysis increments CODFCA 3DVAREVIL

12 0.12° 0.25° 0.5° 1°2°3° Multi-scale verification

13 Multi-scale verification AnalysisForecast EVIL 3DVAR CTRL Impact of all-sky radiances on forecast up to 3-4h Best forecast with EVIL system Impact of all-sky radiances on forecast up to 3-4h Best forecast with EVIL system EVIL 3DVAR CTRL

14 Conclusion Expansion of analysis vector to clouds Multivariate, flow-dependent background errors Displacement pre-processing Updated processing of cloud-affected satellite data (bias correction, QC, interpolation, RTM, middle-loop) Sustained impact in short-term forecast More work required…

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