Mesoscale and Microscale Meteorological Division 09/22/2008 Experiments of Hurricane Initialization with WRF Variational Data Assimilation System Qingnong Xiao NCAR/MMM, Boulder, CO _________________________________ Acknowledgment: Xiaoyan Zhang, James Done, Zhiquan Liu, Wei Wang, Chris Davis, Jimy Dudhia, and Greg Holland

Mesoscale and Microscale Meteorological Division 09/22/2008 WRF: Weather Research and Forecasting (WRF) Model Developed by NCAR, NCEP, and several US universities and DOD labs. Two cores: ARW - Advanced Research WRF, led by NCAR and the university community NMM - Nonhydrostatic Mesoscale model, led by NCEP and in operational application WRF-Var: WRF Variational (WRF-Var) Data Assimilation System Introduction

Mesoscale and Microscale Meteorological Division 09/22/2008

Why WRF hurricane initialization? WRF ARW improved track and intensity over official forecast beyond 36 h. Short-term forecasts (< 2 days) show a rather poor skills in WRF ARW, due to model spin-up problem. An improved hurricane initialization, using advanced data assimilation technique, can augment the skills of short-term forecasts. WRF hurricane forecast in 2005 (Orange), Davis et al. 2008

Mesoscale and Microscale Meteorological Division 09/22/2008 Why WRF-Var for hurricane initialization? WRF-Var is an advanced data assimilation system based on the variational technique. It includes WRF 3D-Var, 4D-Var, and ensemble/variational hybrid (En3D-Var, En4D-Var). It can assimilate all observational data, including satellite and radar data. It is robust, and facilitates research and real-time applications.

Mesoscale and Microscale Meteorological Division 09/22/2008 WRF-Var data assimilation system Background constraint (J b ) Observation constraint (J o ) x b : model background (former information) H(x) : observation operator (simulating observations from model) [y – H(x)] : innovation vector (new information) Minimum of the cost function J(x), (analysis) updates the background with new information from observations. 9h12h15h Assimilation window JbJb JoJo JoJo JoJo obs Analysis xaxa Background corrected forecast former forecast With hypotheses, the analysis estimates the true state of the atmosphere (in terms of max likelihood).

Mesoscale and Microscale Meteorological Division 09/22/2008 Theoretically, the gradient of cost funbction should be zero at the minimum: WRF-Var data assimilation system

Mesoscale and Microscale Meteorological Division 09/22/2008 WRF-Var data assimilation system However, it is very difficult to calculate the gradient of the cost function B matrix is usually huge, B -1 is nonexistent or difficult to calculate. ( ▽ x H) T, adjoint of observation operators and adjoint model (in 4D-Var), is difficult to develop and needs significant computation time.

Mesoscale and Microscale Meteorological Division 09/22/2008 Technically, the analysis x a, is iteratively calculated with a pre-defined minimum criterion. WRF-Var data assimilation system

Mesoscale and Microscale Meteorological Division 09/22/2008 NCEP Analysis WRF-Var (3/4D-Var or En-Var) WRF-Var (3/4D-Var or En-Var) Observation Preprocessor Observation Preprocessor Background Error Calculation Background Error Calculation B Forecast xbxb xaxa yoyo WRF-Var Flow Chart WPS WRF REAL TC Vortex Relocation TC Vortex Relocation Regular Obs Regular Obs Satellite Obs Satellite Obs Radar Obs Radar Obs TC Bogus Obs TC Bogus Obs Verification and Statistics Cycling

Mesoscale and Microscale Meteorological Division 09/22/2008 WRF-Var Hurricane Initialization Vortex relocation in background fields If cycling, vortex relocation in background fields is important. Synthetic vortex (bogussing/relocation) in observation data Similar to JMA’s scheme, see Xiao et al. (2006) Assimilation of regular observations WMO GTS Dropsonde data from reconnaissance Bogus data assimilation The algorithm is described in Xiao et al. (2006) Satellite data assimilation Raw data - brightness temperatures Retrieved data Radar data assimilation Ground-based Doppler radar data Airborne Doppler radar data

Mesoscale and Microscale Meteorological Division 09/22/2008 Case studies with BDA:  BDA - Bogus data assimilation  BDA is a technique we proposed for hurricane initialization when I worked at FSU. It combines traditional vortex bogussing with data assimilation. Its initial application was with MM5 4DVAR (Xiao et al (Mon. Wea. Rev.); Zou and Xiao 2000 (J. Atmos. Sci.)  With the WRF data assimilation development, I includes the capability in WRF-Var

Mesoscale and Microscale Meteorological Division 09/22/2008

Hurricane Katrina track

Mesoscale and Microscale Meteorological Division 09/22/2008 Hurricane Katrina intensity

Mesoscale and Microscale Meteorological Division 09/22/2008 Comparison with GFS ICs Green: without BDA, Red: with BDA (statistics from 21 cases in 2004 and 2005 seasons, Xiao et al. 2008) It is clearly shown that BDA improves hurricane track and intensity. More improvements are seen in the forecast of intensity than track.

Mesoscale and Microscale Meteorological Division 09/22/2008 Case studies with airborne Doppler radar data assimilation Hurricane Jeanne (2004) Hurricane Jeanne (2004) Flight at around 1800 UTC 24 September 2004 Flight at around 1800 UTC 24 September 2004 Data include wind and reflectivity Data include wind and reflectivity Airborne Doppler winds and reflectivity at 2.5 km AMSL

Mesoscale and Microscale Meteorological Division 09/22/2008 Hurricane initialization ADR-DA GTS-DA NO-DA

Mesoscale and Microscale Meteorological Division 09/22/2008 Hurricane forecast (reflectivity) 24-hr 36-hr GTS plus radar wind plus reflectivity

Mesoscale and Microscale Meteorological Division 09/22/2008 Hurricane track Black: Observation Red: NO-DA Blue: GTS-DA Green: GTS + ADR wind DA Cyan: GTS _ ADR wind and reflectivity DA

Mesoscale and Microscale Meteorological Division 09/22/2008 Hurricane intensity Black: Observation Red: NO-DA Blue: GTS-DA Green: GTS + ADR wind DA Cyan: GTS _ ADR wind and reflectivity DA

Mesoscale and Microscale Meteorological Division 09/22/2008 Real-time hurricane forecasts in 2007 Initialization: 3D-Var analysis  Observations: All conventional data: TEMP, SYNOP, METAR, PILOT, AIREP, SHIPS, BUOY, etc. Satellite-retrievals: QUIKSCAT and GOES WINDS, GPS PW and REFRACTIVITY Satellite radiances: AMSU-A and AMSU-B from NOAA-15, 16, and 17 Synthetic observations: CSLP and winds (bogus observations)  First-guess: GFS analysis

Mesoscale and Microscale Meteorological Division 09/22/2008

Real-time hurricane forecasts in 2007 Model: WRF V2.2  Domain Configuration: 3 domains, 2-way moving nest of domain 2 and 3, 35 vertical layers, dimensions of 424X325 (domain1), 202X202 (domain 2), 241X241 (domain 3), grid-spacings of 12, 4, and 1.333km.  Physics: WSM5 microphysics, YSU PBL, Kain-Fritsch cumulus for Domain 1,  Forecast: 3 days Moving nest

Mesoscale and Microscale Meteorological Division 09/22/2008 Real-time hurricane forecasts in 2007 Visualization Track and intensity display, Animation of SLP and surface temperature, Surfacde wind, Accumulated rainfall, Column max reflectivity, 700 hPa vertical velocity, Wind and temperature at different levels, km shear, km shear, hPa thickness, Cloud-top temperature, etc.

Mesoscale and Microscale Meteorological Division 09/22/2008 Track Forecasts for Hurricane Dean (2007) IC: 3D-Var using GFS analysis as first-guess Initialization time: 0000 UTC, each day Forecast time: 3 days

Mesoscale and Microscale Meteorological Division 09/22/2008 The general intensifying and decaying trend of the forecasts is good The landfall time and location is pretty good It over-predicts the intensity when Dean is weak, and under- predicts it when Dean becomes strong 3D-Var analyses are not well balanced with model, so there is initial adjustment 3-day forecasts for Hurricane Dean (2007) from 0000 UTC daily

Mesoscale and Microscale Meteorological Division 09/22/ day forecast of Humberto (2007) by WRF initialized with GFDL analysis at 1200 UTC 12 September 2007

Mesoscale and Microscale Meteorological Division 09/22/ day forecast of Humberto (2007) by WRF initialized with 3D-Var analysis at 1200 UTC 12 September 2007

Mesoscale and Microscale Meteorological Division 09/22/2008 Best track till 2100 UTC 14 September day forecast of Humberto (2007) by WRF initialized with 3D-Var analysis at 1200 UTC 12 September 2007

Mesoscale and Microscale Meteorological Division 09/22/ day forecasts for Humberto from 1200 UTC September 2007 The intensification from tropical storm to category I hurricane just before landfall is predicted well The landfall time and location is pretty good The trend of weakening after landfall is predicted. However, it over- predicts its strength inland.

Mesoscale and Microscale Meteorological Division 09/22/2008 Track verification of Hurricane (2007) forecasts (3DVAR HI ~ GFDL) Black: HI with 3DVAR; Red: WPS using GFDL

Mesoscale and Microscale Meteorological Division 09/22/2008 CSLP verification of Hurricane (2007) forecasts (3DVAR HI ~ GFDL) Black: HI with 3DVAR; Red: WPS using GFDL

Mesoscale and Microscale Meteorological Division 09/22/2008 MWS verification of Hurricane (2007) forecasts (3DVAR HI ~ GFDL) Black: HI with 3DVAR; Red: WPS using GFDL

Mesoscale and Microscale Meteorological Division 09/22/2008 Verification of hurricane forecasts in 2007 season (3DVAR HI ~ GFDL) Black: HI with 3DVAR Red: WPS using GFDL

Mesoscale and Microscale Meteorological Division 09/22/2008 Conclusions  The hurricane initialization program using WRF-Var is designed. It includes assimilation of all available observations (in-situ and remote-sensing) and BDA (bogus data assimilation).  Case studies demonstrate positive impact of the hurricane initialization scheme on the hurricane forecasts (track and intensity).  Statistics from 21 cases in 2004 and 2005 hurricane seasons indicates that hurricane track and intensity forecasts are improved compared with the forecasts using the NCEP/GFS-interpolated initial conditions.  Airborne Doppler radar data assimilation has great potential to improve hurricane vortex initialization and forecasts of hurricane structure and intensity.  The WRF-Var hurricane initialization scheme was implemented in real time runs in the 2007 hurricane season. It ran smoothly and robustly. The results are comparable with the runs from GFDL initial conditions.

Mesoscale and Microscale Meteorological Division 09/22/2008 Future Plan Develop a regional coupled ocean-atmosphere model –Atmosphere model: WRF ARW –Ocean model: ROMS or HYCOM Develop a data assimilation system for the regional coupled ocean-atmosphere model –3D-Var (initially) –4D-Var (after 3D-Var works properly) –En3/4D-Var (hybrid with EnKF technique) Hurricane initialization and modeling –Assimilate atmospheric data (especially satellite data and radar data) –Assimilate ocean data –Research and real-time applications

Mesoscale and Microscale Meteorological Division 09/22/2008 Thank you!