from vortex initialization to assimilation

From vortex initialization to assimilation

Sharan MajumdarRSMAS / University of Miami

JCSDA/HFIP Workshop on Satellite DA in hurricanes, Dec 2-3 2010

To provide accurate initial conditions

• Structure of vortex should be dynamically and thermodynamically consistent with mechanisms occurring in TC

• Specified vortex should be compatible with resolution and physics of prediction model

• Need accurate representation of mesoscale structures, not always balanced

Initialization to Assimilation

“Pure” Initialization

Initialization plus

assimilation

“Pure”Assimilation

GFDL HWRF,NRL (COAMPS-TC)

Assimilation of

synthetic / bogus dataUKMet, NOGAPS, JMA, operational COAMPS-TC, research

GFS (after relocation), ECMWF, EnKF research (Torn, Zhang, AOML, NRL…), 4d-Var

The case for assimilation

• Direct computation of initial fields based on real data.

• Firm theoretical basis.• Advancements in 4d-Var and EnKF (and hybrid

techniques).• Initialization requires assumptions on structure via

parametric fits to observations or best track; in contrast, assimilation lets full model handle this.

• Initialization can be particularly poor for INVESTs / TDs / sheared storms.

The case for initialization

• 4d-Var and EnKF at <4 km resolution and high temporal frequency (at least every 3 hours?) are very expensive computationally.

• Unclear about dynamical consistency between analyzed vortex in DA and actual TC.– large increments violate theoretical assumptions?– Incorrect balance constraints?

• Inaccurate covariance information in DA.• Initialization is relatively cheap and offers some

level of dynamical consistency.

Initialization methods

a. GFDL and HWRFb. NRL COAMPS-TC Dynamic Initializationc. RSMAS Direct Insertiond. Satellite data: initialization or assimilation?

ENVIRONENTAL FIELD = BASIC FIELD + DISTURBANCE FIELD – GLOBAL VORTEX

a. Remove vortex from global analysis.

b. Compute environmental field.

c. Spin-up axisymmetric integration of GFDL model, force tangential wind towards observed storm structure.

d. Compute storm asymmetries determined from vortex fields in previous forecast cycle.

e. Insert symmetric and asymmetric vortex into environmental field at observed position; rebalance mass field in vortex GFDL

EMC

*Vortex:•Currently created by hydrostatic version of

TCM3 model (future plans will use ideal version of COAMPS-TC)

•Idealized simulation•SLP field nudged in over 48 h spin-up run•TCM3 spin-up fields inserted into COAMPS-

TC initial conditions within specified radius, followed by blending zone into the environment

•For automation and efficiency, master script chooses vortex closest to observed intensity (using pre-existing lookup table of vortices spun-up to different intensities from 900-1010 mb)

NOGAPS/NCEP analysis

3DVAR data assimilation

Remove TC vortex

Insert vortex*

Run forecast model

WarmStart

Cold Start

NOGAPS/NCEP analysis

3DVAR data assimilation

Cold Start

NOGAPS/NCEP analysis

3DVAR data assimilation

NOGAPS/NCEP analysis

Cold Start

NOGAPS/NCEP analysis

3DVAR data assimilation

Cold Start

NOGAPS/NCEP analysis

Remove TC vortex

3DVAR data assimilation

Cold Start

NOGAPS/NCEP analysis

Insert vortex*

Remove TC vortex

3DVAR data assimilation

Cold Start

NOGAPS/NCEP analysis

Run forecast model

Insert vortex*

Remove TC vortex

3DVAR data assimilation

Cold Start

NOGAPS/NCEP analysis

WarmStart

Run forecast model

Insert vortex*

Remove TC vortex

3DVAR data assimilation

Cold Start

NOGAPS/NCEP analysis

Tropical Cyclone Dynamic Initialization (TCDI)

NRL

The use of TCDI produces a better initial intensity (in both SLP and winds) resulting in a better intensity forecast for Hurricane Bill (2009)

3DVAR + Synthetics TCDI

NRL

Improved structure using TCDI in COAMPS-TC

Example: Choi-Wan (09W)

2009091600Z

Control tau=48 h TCDI, tau=48 h Satellite OBS

Max wind Minimum SLP

Black: JTWC Best Track, Blue: Control, Red: With TCDI

12Z 13 Sep 2009Cold Start

NRL

Potential community TC initialization code (RSMAS)

• Parameterized vortex profiles.• Portable code, model-independent, flexible.• Acts as a ‘benchmark’ for initialization and DA

methods to surpass• Method:

– Vortex removal and relocation similar to GFDL. – Direct insertion of synthetic vortex in gradient +

hydrostatic balance. – Fit idealized profile to RMW, VMax, wind decay.– Options for vertical wind profile and water vapor. – Add secondary circulation: mimic boundary layer

convergence and upper-level outflow.Rappin et al.

NATURE NATURE NATURE NATURE

BOGUS BOGUS BOGUS BOGUS

Tropical Storm Category-4 typhoon

•Bogus = parameterized profile that best fits corresponding parameters sampled from nature run.•Dynamics in initial conditions here are consistent with dynamics in model and our synthetic ‘nature’•In reality: initial conditions, model and nature are inconsistent Rappin et al.

Rappin et al.

Potential use of satellite wind data

• Use directly in initialization – e.g. fit of spun-up idealized vortex to satellite winds?

• Use to initialize upper-level divergence field?– Derive secondary circulation

• Or simply assimilate?– Will DA provide adequate secondary circulation or

would the increment be confined to upper levels?

Operational Hourly AMVs Rapid-Scan AMVs

250 hPa divergence and upper-level AMVs

CIMSS

NOPP/HFIP project (PI: Velden, CIMSS, collaborators: U.Miami, NCAR, NRL, NESDIS, AOML)

• Use multiple and integrated satellite data sets at their highest resolution (minimize thinning, maximize information content), in a high-resolution analysis/forecast system for tropical cyclones.

• With current satellite data spatial resolutions, a 27/9 km nested WRF framework may be adequate.

• Explore first using EnKF within NCAR WRF/DART framework. Then explore Navy COAMPS-TC system.

• Seek an optimal assimilation strategy for integrated satellite data, not just one data type.

DA experiments in Year 1

• “Control” dataset prepared, all data excluding satellite radiances.

• Special data listed below processed at CIMSS.

• Using WRF/EnKF, examining impact of assimilating– Hourly satellite cloud winds– Rapid-scan satellite cloud winds– AIRS T and Q soundings

Hui Liu, NCAR

Final thoughts• It has not yet been demonstrated quantitatively that

assimilation is superior to initialization.• Lack of peer-reviewed publications on

– Critical review of initialization methods– Honest appraisal of DA (Torn 2010 in press)– Relative forecast sensitivity to initial conditions in the

storm versus the environment• Require improved understanding of how initialization

and DA project analysis onto mechanisms captured in forward model.– Need to understand how mechanisms in forward model

project onto reality!• Explore combinations of initialization and DA – exploit

their respective strengths in blended product?