#### Transcript Four-Dimensional Ensemble-Variational (4DEnVAR)

Implementation and Testing of 3DEnVAR and 4DEnVAR Algorithms within the ARPS Data Assimilation Framework Chengsi Liu, Ming Xue, and Rong Kong Center for Analysis and Prediction of Storms University of Oklahoma Outline • ARPS 4DEnVar framework design • OSSE for a storm case – Only Vr – Vr + reflectivity – Vr + reflectivity + mass continuity constraint • Conclusions • On-going work Hybrid En-4DVar (Lorenc 2003, Clayton et al 2012) Ensemble covariance part Static B part Localization matrix B UU T xs Uv C CC C Alpha control variable α 0 N xe (xbi' C ' αi ) 0 α C i 1 Analysis increment and cost function 12 22 1 N x0 1 x s 2 xe 1Uv 2 (xbi' C ' αi ) I i 1 1 T 1 T 1 J ( v, α) v v α α [Ht L t x0 dt ]T R 1[Ht L t x0 dt ] 2 2 2 t 1 innovation 4DEnVar-NPC (Non-Propagation of alpha Control variable) Two approximations (1) Neglecting temporal propagation of alpha control variable by TLM Lt x e = Lt ( N x i 1 bi m N N Cαi ) Lt ( xbi Cαi ) (Lt xbi ) Cαi i 1 N i 1 αi J (αi ) αi CT (L t xbi ) Ht T R 1[Ht {(L t xbi ) Cαi } dt ] t 1 i 1 AJM avoided !! (2) Use nonlinear model ensemble forecasts to replace the temporal propagation of perturbations by the TLM Lt x Mt (xbi ) Mt (xb ) ' bi TLM avoided !! The hybrid 4DEnVAR DA system based on the ARPS variational DA framework • 4DEnVar-NPC is adopted as ARPS hybrid variational algorithm because: 1. Adjoint and tangent linear model are not good approximations in convective scale DA. 2. High resolution observations, like Radar, are main observation source for convective DA. 3. The alternative observation-spaceperturbation-based 4DEnVar algorithm(Liu et al 2008, 2009) is computationally more expensive. The ARPS 4DEnVar characteristics • 3D-recursive filter is used for ARPS-4DEnVar localization. • The capabilities for convective-scale radar DA ( Vr and reflectivity) • Physical constraint terms (e.g. mass continuity constraint) can be considered in the cost function. Radar data operators RadialVelocity Vr u sin cos v cos cos w sin : elevation angle, : azimuth angle Reflectivity(Lin et al. 1983; Gilmore et al. 2004; Dowell et al. 2011): Z e Z qr Z qs Z qh Z dB 10log10 Z e Z (qr ) 3.63 109 ( qr )1.75 Z (qs ) 9.80 108 ( qs )1.75 (drysnow) Z (qs ) 9.80 108 ( qs )1.75 ( wetsnow) Z (qh ) 4.33 1010 ( qh )1.75 EnKF-En4DVar Hybrid Obs Obs Obs Obs Obs Obs Obs EnKF En4DVar Obs t0 Obs 4D Assimilation Window – 1 cycle t1 OSSE for a storm case • Tested with simulated data from a classic supercell storm of 20 May 1977 near Del City, Oklahoma • Domain : 35 x 35 x 35 grids. 2km horizontal resolution • 70-min length of simulation, 5-min cycle intervene Error specification: Smoothed Gaussian obs. error: rdrstd_vr = 1m/s rdrstd_zhh=3dBZ Perturbations added to the sounding (added to whole field): stdu = stdv = stdw = 2.0 m/s, stdptprt = 2.0K, stdqv = stdqc = stdqr = stdqi = stdqs = stdqh = 0.6g/kg, Assimilation related parameters inflation factor 1.07 (i.e., 7%) localization function (Gaspari and Cohn 1999), cut-off radius = 6km for hori/vert.. hradius = 1.643km for hori. /vert. influence radius for arps3dvar. Experiment Design • ARPS 3DVar and ARPS En3DVar with full ensemble covariance – Only Vr – Vr + Z – Vr + Z + mass continuity constraint – Vr + Z + mass continuity constraint and model error True reflectivity and wind at 850Hpa Reflectivity 3DVar Vr 3DVar Vr+Z Truth En3DVar En3DVar Vr+Z Vr Reflectivity 3DVar Vr 3DVar Vr+Z Truth En3DVar En3DVar Vr Vr+Z Vertical Velocity 3DVar 3DVar Vr+Z Vr Truth En3DVar Vr En3DVar Vr+Z Pot. Temp. Pert 3DVar Vr 3DVar Vr+Z Truth En3DVar En3DVar Vr+Z Vr RMSE for 3DVar-Vr, 3DVar-Vr+Z, En3dVar-Vr, En3DVar-Vr+Z U V 3DVar-Vr 3DVar-Vr+Z En3dVar-Vr En3DVar-Vr+Z W PT RMSE for 3DVar-Vr, 3DVar-Vr+Z, En3dVar-Vr, En3DVar-Vr+Z Qv 3DVar-Vr+Z Qr Qh Qc Qi 3DVar-Vr En3dVar-Vr En3DVar-Vr+Z Qs Mass Continuity Constraint Test Exp 1 : 3DVar Vr + Z Exp 2 : 3DVar Vr + Z + Constraint Exp 3 : En3DVar Vr + Z Exp 4 : En3DVar Vr + Z + Constraint w and T 3DVar 3DVar-CS Truth En3DVar En3DVar-CS RMSE for 3DVar-CS, 3DVar, En3DVar-CS, En3DVar U W V PT RMSE for 3DVar-CS, 3DVar, En3DVar-CS, En3DVar Qv Qs Qr Qh Qc Qi Divergence Constraint term test with model error • Model error is introducing by using a different microphysical scheme in DA – Truth simulation: Ice microphysics (LINICE) – DA: WRF WSM6 scheme (WSM6WR) • Smaller and larger weights for constraint term are tested (CSSW, CSLW) • Both Vr and Z data Vertical Velocity and T Truth En3DVar En3DVar En3DVar CSLW CSSW RMSE for En3DVar, En3DVar-CSSW, En3DVar-CSLW U V W Summary • 3DEnVar/4DEnVar algorithms are being implemented within the ARPS variational DA framework, coupled with the ARPS EnKF system; • The systems can assimilate both radar Vr and Z data; • Cycled DA OSSEs were performed to compare performances of 3DVar and En3DVar, assimilating Vr and both Vr and Z; • Much better state analyses were obtained using En3DVar assimilating both Vr and Z data; • Adding Z in 3DVAR improved very little (hydrometero classification of Gao and Stensrud 2012 should help – not shown); • Mass continuity constraint hurts En3DVar analyses, and improves w analysis in 3DVar with a perfect model; • Analysis errors are much larger in the presence of mirophysics-related model error – adding mass continuity improves results slightly. • 3DEnVar and EnKF results similar for single time analysis; cycled results to be compared (using deterministic background forecasts in EnKF) On-going Research • ARPS 4DEnVar is being tested with OSSEs; • Time localization is being considered on 4DEnVar; • The 4DEnVar results will be compared with 3DEnVar and 4DEnSRF; • Potential benefits of EnVar for assimilating attenuated X-band reflectivity will be evaluated. • Will include other data sources and test with real cases. • The entire system will directly support WRF model also. The EnKF system already does.