Initialization Schemes in the Naval Research Laboratory’s Tropical Cyclone Prediction Model (COAMPS-TC) Eric A. Hendricks 1 Melinda S. Peng 1 Tim Li 2 Xuyang Ge 3 1 Naval Research Laboratory (NRL), Monterey, CA, USA 2 University of Hawaii and IRPC, Honolulu, HI 3 Pennsylvania State University, State College, PA USA Acknowledgements: Jim Doyle (NRL), Rich Hodur (SAIC), COAMPS-TC group CMOS 2012 Congress / AMS 21st NWP and 25th WAF Conferences Montreal, Canada, 29 May-1 June 2012

Introduction A crucial part of TC intensity predictions is an accurate and balanced TC vortex initially 3DVAR data assimilation systems usually lack proper balance constraints suitable for multi-scale TC; rapid adjustment often occurs after initialization A 4D data assimilation system would alleviate the initial imbalance problem to some degree Lack of observational data for TC intensity and structure remains What do we do in the mean time? Hybrid 3DVAR/Dynamic Initialization Schemes have the possibility of improving the initial balance and storm intensity/structure, while allowing model physics spin-up, potentially leading to improved intensity and track forecasts

Dynamic Initialization Schemes: TCDI, DI, TCDI/DI Application to TC Prediction Using COAMPS-TC NOGAPS/NCEP analysis 3DVAR data assimilation Remove TC vortex Generate vortex from TCDI (nudge MSLP) Insert vortex Run forecast model Warm Start Cold Start 12-h forward DI CNTL DI TCDI TCDI/DI CNTL: Standard 3DVAR Initialization DI: 3D Dynamic Initialization to analysis momentum u a (12-h relaxation) after 3DVAR TCDI: Tropical Cyclone Dynamic Initialization (TC component is dynamic) after 3DVAR TCDI/DI: Run TCDI, then run DI Synthetic TC obs, Liou and Sashegy (2011) TCDI: Hendricks et al. (2011) WAF, Zhang et al. (2012) WAF

COAMPS-TC Overview Current and Future Capabilities Complex Data Quality Control Relocation of TC in background Synthetic Observations: TC vortex NAVDAS 3DVAR: u, v, T, q, TC option Initialization: Digital Filter Option TC Balance Step: (underway) Navy Coupled Ocean Data Assimilation (NCODA) System 2D OI: SST 3D MVOI, 3DVAR: T, S, SSH, Ice, Currents Complex Data Quality Control Initialization: Stability check Numerics: Nonhydrostatic, Scheme C, Moving Nests, Sigma-z, Flexible Lateral BCs Physics: PBL, Convection, Explicit Moist Physics, Radiation, Surface Layer TC Tools: Moving nests, dissipative heating, spray parameterization, shallow convection NRL Coastal Ocean Model (NCOM) Numerics: Hydrostatic, Scheme C, Nested Grids, Hybrid Sigma/z Physics: Mellor-Yamada 2.5 Wave Models (WWIII and SWAN) Generalized Coupling Layer (ESMF) Ocean Analysis Ocean Models Atmospheric Model Atmospheric Analysis Atmospheric Ensembles Initial Cond. Perturbation: ET, EnKF Physics Perturbations: PBL, Convection… Lateral BCs: Global ensemble (NOGAPS) Probabilistic Products: Intensity, track… Ocean Ensembles Initial Cond. Perturbation: ET Physics Perturbations: PBL, Fluxes… Lateral BCs: NCOM Probabilistic Products: Mixed layer, OHC.. The Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS®) is a registered trademark of NRL

COAMPS-TC Control (CNTL) Setup Dynamics:Non-hydrostatic, compressible, C-grid (Klemp and Wilhemson 1978) Vertical Discretization:Sigma-z vertical coordinate (40 levels, higher resolution near sfc) Grids:3 nests, 45/15/5-km resolution (2-way nesting), 15/5-km meshes move with the TC PBL:Mellor-Yamada (1.5-order turbulence closure), dissipative heating (Jin et al. 2007) Cumulus:Kain-Fritsch on 45/15-km, shallow convection, explicit convection on 5-km Microphysics:NRL scheme, 6 species, based from Rutledge and Hobbs 1984 & Lin et al Radiation:Fu-Liou scheme Initialization/DA:3DVAR scheme (NAVDAS), synthetic observations added that match observed TC structure and intensity

COAMPS-TC Nest Setup 3 Domains: 45/15/5 km 45 km grid fixed Inner 2 grids (15/5-km) move with the TC

DI Case Study: 2011 Hurricane Irene (09L) , Cold Start (Domain 3) During DI, the winds are held quasi-constant 3DVAR is not able to produce gradient balanced vortex, rapid adjustment to winds during DI 10-m Winds (kt) Sea Level Pressure (hPa)

CNTL TCDI TCDI/DI DI 2011 IRENE (09L)

Wind Structure Verification (t=0 h) H*WIND Hurricane Irene (09L), COAMPS-TC using CNTL COAMPS-TC using TCDI/DI H*WIND courtesy NOAA/AOML/ HRD Powell et. al (2010) COAMPS-TC using DI 10-m Winds (kt)

Case Study: 08W (2011) Ma-On Significant intensity error reductions for Ma-On by using TCDI/DI 15 cases 10 kt CNTL TCDI/DI JTWC Best Track in black COAMPS-TC in color

Case Study: 07L (2010) Earl CNTL TCDI/DI Significant intensity error reductions for Earl by using TCDI/DI 13 cases 10 hPa NHC Best Track in black COAMPS-TC in color

Case Study: 12L (2011) Katia TCDI/DICNTL TCDI/DI does not over-intensify Katia as much as CNTL earlier, and gets rapid deepening better NHC Best Track in black COAMPS-TC in color

Track Error: Homogenous Large Sample TCDI/DI (blue curve) has lower track error for ALL cases and < 990 hPa ALL cases Initial intensity < 990 hPa Years: Atlantic Storms: Danielle, Earl, Igor, Irene, Katia, Maria, Rina, Julia Western North Pacific storms: Chaba, Fanapi, Ma-On Cases: 120

Intensity Error: Homogenous Large Sample ALL cases Initial intensity < 990 hPa TCDI/DI (blue curve) has lowest intensity error for ALL cases and < 990 hPa cases with more statistical significance, and further reduced errors Years: Atlantic Storms: Danielle, Earl, Igor, Irene, Katia, Maria, Rina, Julia Western North Pacific storms: Chaba, Fanapi, Ma-On Cases: 120

Summary Three different TC initialization schemes have been developed, tested with COAMPS-TC – TCDI: tropical cyclone vortex spun-up – DI: Full 3D dynamic initialization to analyses winds – TCDI/DI: Run TCDI, then run DI TCDI/DI is shown to have superior performance – Average intensity errors reduced by 3-5 hPa and 2-3 kts over all lead times – Average track errors reduced by nm – Better for intense initializations (< 990 hPa) The dynamic initialization procedures allow model physics spin-up and “less shock” Future work – DI to satellite observed heating profiles