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Assimilate NOAA P3 Tail Doppler Radar Data for Hurricane Initialization at NCEP Mingjing Tong 1 Collaborators: David Perish 2, Daryl Kleist 3, Russ Treadon.

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Presentation on theme: "Assimilate NOAA P3 Tail Doppler Radar Data for Hurricane Initialization at NCEP Mingjing Tong 1 Collaborators: David Perish 2, Daryl Kleist 3, Russ Treadon."— Presentation transcript:

1 Assimilate NOAA P3 Tail Doppler Radar Data for Hurricane Initialization at NCEP Mingjing Tong 1 Collaborators: David Perish 2, Daryl Kleist 3, Russ Treadon 2, John Derber 2 Xuyang Ge 1, Fuqing Zhang 4 John Gamache 5 NCEP/EMC HWRF team 1 UCAR (NCEP/EMC), 2 NOAA/NWS/NCEP/EMC, 3 IMSG (NCEP/EMC), 4 PSU, 5 NOAA/AOML/HRD

2 Jorgensen et al., 1983, J. Climate Appl. Meteor, 22, km P-3 regular 400 km P-3 weak storm

3 global forecast environment first guess 126 hr forecast HWRF 6 hr forecast HWRF vortex initialization HWRF modified vortex Relocation, size correction, intensity correction, adjust mass fields GDAS forecast analysis GSI Data assimilation observation Global hybrid EnKF-3DVAR ensemble forecast member 1 global forecast vortex HWRF environment Data thinning, quality control Iterative minimization Global hybrid EnKF-3DVAR ensemble forecast member 2 Global hybrid EnKF-3DVAR ensemble forecast member N HWRF Initialization

4 TC initialization with TDR data assimilation HWRF model * Non-hydrostatic coupled atmosphere-ocean model, WRF-NMM dynamic core, rotated E-grid, two way interactive nesting * Resolution: Parent (Outer) domain 0.18˚ (27 km), inner domain 0.06˚ (9 km), 42 vertical levels Data assimilated * Quality controlled tail Doppler radar radial velocity data within [-3 hr, 3 hr] are assimilated * Rawinsonde, pibal, class sounding, profiler, dropsonde, AIRCFT, AIRCAR, GPSIPW, surface data (marine/land/splash-level/mesonet), satellite wind * satellite radiance data: HIRS, AMSU-A, AMSU-B, MHS, sounding GSI * Analysis variables: streamfunction, unbalanced part of velocity potential, unbalanced part of temperature, unbalanced part of surface pressure, normalized relative humidity, satellite bias correction coefficients * flow-dependent anisotropic background error covariance [Riishøjgaard (1998), Purser 2003b, 2005]. * Background error statistics are estimated in grid space with the NMC method (3DVAR). * Hybrid DA option Four HWRF domains (Fig 4.1 of HWRF USERS’ GUIDE) e/HWRF_V3.3a_UG.pdf

5 conventional obs ps, t, q, uv, TDR vr ghost domain Satellite radiance obs UTC outer domainghost domain

6 experimentTDRPTDRATDRBTDRCNTDR Assimiate TDR Vr data yes no backgroundFull HWRF vortex initialization Relocate vortex only Full HWRF vortex initialization Relocate vortex only Full HWRF vortex initialization GSI FGAT noyesnoyesno Post balance yes no 2011 TDR DA Real Time Parallel

7 Real time 3DVAR TDR DA experiments

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9 Hybrid Ensemble-3DVAR System for HWRF Current hybrid ensemble-3DAV system is one way coupled system Ensemble perturbations come from global EnKF-3DVAR system ensemble forecast interpolated to HWRF analysis domains Ensemble covariance is incorporated as part of the background error covariance though extended control variable method (Lorenc 2003; Buehner 2005; Wang et al. 2007) May not help much with vortex initialization, hopefully can improve storm environment

10 O-A and O-F (Obs - conventional data) analysis6 hr fcst12 hr fcst18 hr fcst24 hr fcst 3DVAR: black, Hybrid: green

11 Pseudo-ensemble hybrid for HWRF Poterjoy and Zhang (2011): Wavenumber 0 storm structures have the largest influence on forecast uncertainty for TCs with category 1 or higher intensity. Back ground error covariance is dominated by wavenumber 0 component. Flow-dependent forecast covariance can be approximated from a sample of near- axisymmetric TC vortices TC vortices are created by running idealized 3 km resolution HWRF simulations and grouped by intensity (Xuyang Ge) TC library vortices are mapped to the background vortex region (300 km from background TC center) in ghost domain and perturbations are calculated Replace global ensemble perturbations with TC vortex library perturbations within 150 km from background TC center TC library perturbations are gradually blended with global ensemble perturbations from 150 km to 300 km from background TC center

12 Analysis increment from single obs test u obs at 0.5 degree north of storm center at 850 level PEDA HYBB PEDA – hybrid DA with pseudo-ensemble HYBB – hybrid DA with pure global ensemble Weight given to ensemble B is 0.8

13 Analysis increment from single obs test u obs at 0.5 degree north of storm center at 850 level PEDA – hybrid DA with pseudo-ensemble HYBB – hybrid DA with pure global ensemble PEDA HYBB

14 PEDA HYBB analysis

15 PEDA HYBB 12 hour forecast

16 TDR real time parallel (TDRP) configurations HWRFTDRP Storm environmentGFS analysisGDAS forecast Vortex initializationFull vortex initializationRelocation only if TDR data are available Use of GSIOnly for deep storms Isotropic static B All storms Use FGAT Anisotropic B (some flow Dependency) in ghost domain Data assimilatedData time window [-1.5,1.5] hr Exclude inner core (150 km from TC center) SYNOP, METAR or sfc marine, ATLAS buoy, Sfc mesonet, synthetic t.c. wind data Data time window [-3,3] hr Assimilate all data Assimilate TDR data All hybrid experiments follow TDRP configuration, except B is a combination of static B and ensemble B

17 First TDR DA cycle of Hurricane Earl

18 Hybrid TDR test with Hurricane EARL

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21 Issues and Discussion Over/under estimate storm intensity * poor background * DA configuration Short term forecast spin up/down * Mass-wind balance * storm structure May need more comprehensive verifications

22 Plans  Model: move on to higher resolution (27km-9km-3km) HWRF.  Assimilation method * Intensive hybrid (1 way coupled) test will be done using ensemble data from the new global EnKF-3DVAR hybrid parallel (prd12q3k). * Further investigate the performance of PEDA. Improve TC library, e.g. including the size variability. * Move on to 2 way coupled hybrid system by collaborating with Jeff Whitaker and AOML/HRD * Analyze cloud variables  DATA * Investigate the impact of assimilating ground based radar data. * The impact of satellite radiance data in regional model * Cloudy radiance data


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