experiments of hurricane initialization with wrf variational data assimilation system

Mesoscale and Microscale Meteorological Division 09/22/2008

Experiments of Hurricane Initialization with WRF Variational Data Assimilation System

Qingnong Xiao

NCAR/MMM, Boulder, CO 80307-3000

_________________________________

Acknowledgment: Xiaoyan Zhang, James Done, Zhiquan Liu, Wei Wang, Chris Davis, Jimy Dudhia, and Greg Holland


• 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


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


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.


WRF-Var data assimilation system

J(x) (xb x)T B 1(xb x) y H (x) TO 1 y H (x)

Background constraint (Jb) Observation constraint (Jo)

• xb : 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. 9h 12h 15h

Assimilation window

JbJo

Jo

Jo

obs

obs

obs

Analysis

xa

Background

corrected forecast

former forecast

With hypotheses, the analysis estimates the true state of the atmosphere (in terms of max likelihood).


NCEPAnalysis

WRF-Var(3/4D-Var or

En-Var)

WRF-Var(3/4D-Var or

En-Var)

ObservationPreprocessorObservationPreprocessor

BackgroundError

Calculation

BackgroundError

CalculationB

Forecast

xb

xayo

WRF-Var Flow Chart

WPS WRFREAL

TC VortexRelocationTC VortexRelocation

RegularObs

RegularObs

SatelliteObs

SatelliteObs

RadarObs

RadarObs

TC BogusObs

TC BogusObs

Verificationand

Statistics

Cycling


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


Case studies with BDA:

BDA - Bogus data assimilationBDA - Bogus data assimilation BDA is a technique we proposed for hurricane BDA is a technique we proposed for hurricane

initialization when I worked at FSU. It combines initialization when I worked at FSU. It combines traditional vortex bogussing with data assimilation. Its traditional vortex bogussing with data assimilation. Its initial application was with MM5 4DVAR (Xiao et al. initial application was with MM5 4DVAR (Xiao et al. 2000 (Mon. Wea. Rev.); Zou and Xiao 2000 (J. 2000 (Mon. Wea. Rev.); Zou and Xiao 2000 (J. Atmos. Sci.)Atmos. Sci.)

With the WRF data assimilation development, I With the WRF data assimilation development, I includes the capability in WRF-Var includes the capability in WRF-Var


Hurricane Katrina track


Hurricane Katrina intensity


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.


Case studies with airborne Doppler radar data assimilation

Hurricane Jeanne Hurricane Jeanne (2004)(2004)

Flight at around 1800 Flight at around 1800 UTC 24 September UTC 24 September 20042004

Data include wind Data include wind and reflectivityand reflectivity

Airborne Doppler winds and reflectivity at 2.5 km AMSL


Hurricane initialization

ADR-DA

GTS-DA NO-DA


Hurricane forecast (reflectivity)

24-hr

36-hr

GTS plus radar wind plus reflectivity


Hurricane track

Black: ObservationRed: NO-DABlue: GTS-DAGreen: GTS + ADR wind DACyan: GTS _ ADR wind and reflectivity DA


Hurricane intensity

Black: ObservationRed: NO-DABlue: GTS-DA

Green: GTS + ADR wind DACyan: GTS _ ADR wind and reflectivity DA


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


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 nestMoving nest


Track Forecasts for Hurricane Dean (2007)

IC: 3D-Var using GFS analysis as first-guessInitialization time: 0000 UTC, each dayForecast time: 3 days


• 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


3-day forecast of Humberto (2007) by WRF initialized with GFDL analysis at 1200 UTC 12 September 2007


3-day forecast of Humberto (2007) by WRF initialized with 3D-Var analysis at 1200 UTC 12 September 2007


Best track till 2100 UTC 14 September 2007

3-day forecast of Humberto (2007) by WRF initialized with 3D-Var analysis at 1200 UTC 12 September 2007


3-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.


Verification of hurricane forecasts in 2007 season (3DVAR HI ~ GFDL)

Black: HI with 3DVAR Red: WPS using GFDL


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.


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


Thank you!

experiments of hurricane initialization with wrf variational data assimilation system

Documents