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WRF 4D-Var Hans Huang, MMM/NCAR

WRF 4D-Var Hans Huang, MMM/NCAR. A short introduction to WRF 4D-Var The current status: The basic system The structure function: single ob exp Results from cold-start experiments Results from cycling experiments Our first radar data assimilation experiments Summary. WRF 4D-Var developers.

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WRF 4D-Var Hans Huang, MMM/NCAR

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  1. WRF 4D-VarHans Huang, MMM/NCAR A short introduction to WRF 4D-Var The current status: The basic system The structure function: single ob exp Results from cold-start experiments Results from cycling experiments Our first radar data assimilation experiments Summary

  2. WRF 4D-Var developers Xiang-Yu Huang, Qingnong Xiao, Dale Barker, Xin Zhang, John Michalakes, Wei Huang, John Bray, Zaizhong Ma, Tom Henderson, Jimy Dudhia, Xiaoyan Zhang, Duk-Jin Won, Yongsheng Chen, Yongrun Guo, Juanzhen Sun, Hui-Chuan Lin, Ying-Hwa Kuo Acknowledgments. The WRF 4D-Var development has been primarily supported by the Air Force Weather Agency (AFWA). The Korean Meteorological Administration (KMA) also funded some 4D-Var tasks.

  3. 4D-Var: 4-Dimensional Variation data assimilation (new) (initial condition for NWP) (old forecast)

  4. WRF 4D-Var: J=Jo +Jb +Jc

  5. Why 4D-Var? • Use observations over a time interval, which suits most asynoptic data and use tendency information from observations. • Use a forecast model as a constraint, which enhances the dynamic balance of the analysis. • Implicitly use flow-dependent background errors, which ensures the analysis quality for fast developing weather systems. • NOT easy to build and maintain!

  6. A short 4D-Var review The idea: Le Dimet and Talagrand (1986); Lewis and Derber (1985) Implementation examples: Courtier and Talagrand (1990); a shallow water model Thepaut and Courtier (1991); a multi-level primitive equation model Navon, et al. (1992); the NMC global model Zupanski M (1993); the Eta model Zou, et al. (1995); the MM5 model Sun and Crook (1998); a cloud model Rabier, et al. (2000); the ECMWF model Huang, et al. (2002); the HIRLAM model Zupanski M, et al. (2005); the RAMS model Ishikawa, et al. (2005); the JMA mesoscale model Huang, et al. (2005); the WRF model Xu, et al. (2005); NAVDAS-AR Gauthier, et al. (2007); MSC Operation: ECMWF, Meteo France, JMA, UKMO, MSC. Pre-operation: HIRLAM, NAVDAS-AR

  7. Necessary components of 4D-Var H observation operator, including the tangent linear operator H and the adjoint operator HT. M forecast model, including the tangent linear model M and adjoint model MT. B background error covariance (N*N matrix). R observation error covariance, which includes the representative error (K*K matrix).

  8. WRF 4D-Var milestones 2003: WRF 4D-Var project. ?? FTE 2004: WRF SN (simplified nonlinear model). 1.5 FTE Modifications to WRF 3D-Var. 2005: TL and AD of WRF dynamics. 1.5 FTE WRF TL and AD framework. WRF 4D-Var framework. 2006: The WRF 4D-Var prototype. 2.5 FTE Single ob and real data experiments. Parallelization of WRF TL and AD. Simple physics TL and AD. JcDF 2007: The WRF 4D-Var basic system. 2.5 FTE

  9. TL00 TLDF WRFINPUT WRF WRF_NL WRF_TL WRF_AD VAR Outerloop Mk dk Innerloop U UT call AF(K),…,AF(1) Basic system: 3 exes, disk I/O, parallel, full dyn, simple phys, JcDF I/O xb B WRFBDY call NL(1),…,NL(K) R y1 … yK WRF+ BS(0),…,BS(N) TL(1),…,TL(K) call xn AD00

  10. Single observation experiment The idea behind single ob tests: The solution of 3D-Var should be Single observation 3D-Var  4D-Var: HHM; HHM; HTMTHT The solution of 4D-Var should be Single observation, solution at observation time

  11. Analysis increments of 500mb q from 3D-Var at 00h and from 4D-Var at 06h due to a 500mb T observation at 06h + + FGAT(3D-Var) 4D-Var

  12. + OBS 500mb q increments at 00,01,02,03,04,05,06h to a 500mb T ob at 06h

  13. + OBS 500mb q difference at 00,01,02,03,04,05,06h from two nonlinear runs (one from background; one from 4D-Var)

  14. + OBS 500mb q difference at 00,01,02,03,04,05,06h from two nonlinear runs (one from background; one from FGAT)

  15. Real Case: Typhoon HaitangExperimental Design (Cold-Start) • Domain configuration: 91x73x17, 45km • Observations from Taiwan CWB operational database. • 5 experiments are conducted before Haitang’s landfall at 0000 UTC 18 July 2005. • FGS – forecast from the background [The background fields are 6-h WRF forecasts from National Center for Environment Prediction (NCEP) GFS analysis.] • AVN- forecast from the NCEP AVN analysis • 3DVAR – forecast from WRF-Var3d using FGS as background • FGAT - forecast from WRF-Var3dFGAT using FGS as background • 4DVAR – forecastfrom WRF-Var4d using FGS as background

  16. Observations used in a 4D-Var experiment

  17. Typhoon Haitang 20052005.07.16.00Z

  18. Typhoon Haitang 2005

  19. A KMA Heavy Rain Case Period: 12 UTC 4 May - 00 UTC 7 May, 2006 Assimilation window: 6 hours Cycling (6h forecast from previous cycle as background for analysis) All KMA operational data Grid : 60x54x31 Resolution : 30km Domain size: the same as the KMA operational 10km domain.

  20. Observations used in 3D-Var

  21. Observations used in 4D-Var

  22. Observations Verification

  23. Precipitation Verification

  24. Observation Verification: Precipitation, CSI

  25. Observation Verification: Precipitation, BIAS

  26. First radar data assimilation experiment using WRF 4D-VarYong-Run Guo and Juanzhen Sun

  27. 061301Z, 4VAR Exp. Initial time 061212Z 061300Z 061312Z Convection period The OSSE setup 061212Z 061300Z 061312Z 4DVAR time window 12/4-km WRFV2.2 run Initial and boundary conditions: Eta 3-hly analysis starting at 2002061212Z Domain size: 271x241x31 (12-km) and 325x280x31 (4km) The control experiment with the domain2 (325x280x31):

  28. Domain settings height landuse

  29. Hourly rainfall forecast from the control run starting from 2002061212Z

  30. WRF 4D-Var Experiment design • Physics option as the control run (truth) • Initial time for Experiments at 2002061301Z (the 13-h forecast starting from 2002061212Z) • Domain size: 151x118x31, covered the convective cells. • Grid size: 4-km, Time step: 20 seconds • First guess from NCEP GFS analysis • Time window for WRF 4DVar: 0.25-h, 45 steps 0.5-h, 90 steps (?) • Forecast length: 11-h. • BES interpolated from 12km IHOP BES (Hongli Wang)

  31. Control run 4-km domain 118 280 151 WRF 4DVar Exp 4-km Domain 325

  32. OSSE Radar data generation • Gaussian random perturbation (0,1), Xpert, added to truth • The truth is obtained by using iowrf utility to extract a box-domain <x121to 271, y94to211> from the 4-km control run <325x280> domain. • Soichiro Sugimoto’s code as the reference • The Radius of Radar OBS: 200km • When Rain water mixing ratio > 1.e-7, the reflectivity truth will be computed as Xtdbz • It is regarded as clear air when Xodbz< 5, no clear air radar obs considered. • When the bean angle > 20o, no radar obs • The radial velocity data are generated in the same way as reflectivity

  33. Rain water mixing ratio every 5 minutes from 130100 to 130130 130100 130105 130110 130115 130120 130125 130130

  34. OSSE Radar data coverage 10 NEXRAD Radar sites over the Exp. Domain OSSE Radar data coverage at 0100 UTC 13 June 20

  35. Experiment design • TRUTH ----- Initial condition from TRUTH (13-h forecast initialized at 2002061212Z from AWIPS 3-h analysis) run cutted by ndown, boundary condition from NCEP GFS data. • NODA ----- Both initial condition and boudary condition from NCEP GFS data. • 3DVAR ----- 3DVAR analysis at 2002061301Z used as the initial condition, and boundary condition from NCEP GFS. Only Radar radial velocity at 2002061301Z assimilated (total # of data points = 65,195). • 4DVAR ----- 4DVAR analysis at 2002061301Z used as initial condition, and boundary condition from NCEP GFS. The radar radial velocity at 4 times: 200206130100, 05, 10, and 15, are assimilated (total # of data points = 262,445).

  36. (Truth-FG) Temperature/wind at lowest h level (4DVAR15m-FG) Temperature/wind at lowest h level INCREMENTS (A-B):B is the NCEP GFS analysis at 2002061301Z Truth Temperature/wind at lowest h level (3DVAR-FG) Temperature/wind at lowest h level

  37. TRUTH NODA 4DVAR 3DVAR Hourly precipitation ending at 01-h forecast

  38. TRUTH NODA 4DVAR 3DVAR Hourly precipitation ending at 03-h forecast

  39. NODA 3DVAR 4DVAR Hourly precipitation ending at 06-h forecast TRUTH

  40. Summary A short introduction to WRF 4D-Var The current status: The basic system The structure function: single ob exp A cold-start experiment A cycling experiment First radar data assimilation experiment

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