Transcript My Background, Previous Work, and Future Plan

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
Mesoscale and Microscale Meteorological Division
09/22/2008
Introduction
•
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
Mesoscale and Microscale Meteorological Division
09/22/2008
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
J (x )  (x b  x ) B (x b  x )   y  H (x)  O
T
T
1
1
y 
H (x ) 
Background constraint (Jb) Observation constraint (Jo)
•
obs
xb : model background
Jo
(former information)
former
forecast
Analysis
•
H(x) : observation operator (simulating
observations from model)
•
Jo
Background
[y – H(x)] : innovation vector
obs
(new information)
corrected
forecast
Jb
•
Minimum of the cost function J(x),
(analysis) updates the background with
new information from observations.
xa obs
9h
Jo
12h
15h
Assimilation window
With hypotheses, the analysis estimates the true state of the atmosphere (in terms of max likelihood).
Mesoscale and Microscale Meteorological Division
09/22/2008
WRF-Var Flow Chart
NCEP
Analysis
WPS
WRF
REAL
xb
Cycling
TC Vortex
Relocation
Regular
Obs
Satellite
Obs
Radar
Obs
Observation
Preprocessor
yo
WRF-Var
(3/4D-Var or
En-Var)
xa
Forecast
TC Bogus
Obs
Background
Error
Calculation
B
Mesoscale and Microscale Meteorological Division
Verification
and
Statistics
09/22/2008
WRF-Var Hurricane Initialization
•
Vortex relocation in background fields

•
Synthetic vortex (bogussing/relocation) in observation data

•
The algorithm is described in Xiao et al. (2006)
Satellite data assimilation


•
WMO GTS
Dropsonde data from reconnaissance
Bogus data assimilation

•
Similar to JMA’s scheme, see Xiao et al. (2006)
Assimilation of regular observations


•
If cycling, vortex relocation in background fields is important.
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.
2000 (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
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)
 Flight at around 1800
UTC 24 September
2004

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
NO-DA
GTS-DA
Mesoscale and Microscale Meteorological Division
09/22/2008
Hurricane forecast (reflectivity)
GTS
plus radar wind
plus reflectivity
24-hr
36-hr
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
Mesoscale and Microscale Meteorological Division
09/22/2008
Mesoscale and Microscale Meteorological Division
09/22/2008
Mesoscale and Microscale Meteorological Division
09/22/2008
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
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
3-day forecasts for Hurricane Dean (2007) from 0000 UTC daily
•
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 underpredicts it when Dean
becomes strong
•
3D-Var analyses are
not well balanced with
model, so there is
initial adjustment
Mesoscale and Microscale Meteorological Division
09/22/2008
3-day forecast of Humberto (2007) by WRF initialized
with GFDL analysis at 1200 UTC 12 September 2007
Mesoscale and Microscale Meteorological Division
09/22/2008
3-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
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
Mesoscale and Microscale Meteorological Division
09/22/2008
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 overpredicts its strength
inland.
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!
Mesoscale and Microscale Meteorological Division
09/22/2008