How are tropical cyclones represented in …...How are tropical cyclones represented in operational model initial conditions? And why does it matter? Gary Lackmann North Carolina State

How are tropical cyclones represented in operational model initial conditions?

And why does it matter?y

Gary LackmannNorth Carolina State University

5 July 201218 UTC Sunday 20 May 2012,

“Alberto”, 1000 mb

5 July 2012

Contributions from Daryl Kleist (EMC), Mike Brennan (NHC), and John Brown (ESRL) and Briana Gordon (STI) are gratefully acknowledged

GFS 95‐km SLP + GOES Visible

OutlineA. Background and Motivation

1.) Challenges of TC prediction and initialization2.) Data Assimilation background and TC DA3.) Hybrid DA in GFS and TC IC

B. Operational Models and TC IC1.) GFS2.) HWRF3.) GFDL

Some acronyms:4.) RAP5.) NAM (briefly)

Some acronyms:TC = Tropical CycloneDA = Data AssimilationIC = Initial Conditions

C. Conclusions and Questions EnKF = Ensemble Kalman FilterBV = Bogus Vortex

Atlantic TC track prediction: Improving

Track related to large-scale steering flow; improvements in satellite data assimilation (DA)satellite data assimilation (DA), environmental recon sampling, NWP, human forecasting skill

Source: www.nhc.noaa.gov

Intensity prediction: Slower improvement, if any

Intensity related to interaction of multi-scale processes

TC Intensity Forecasting

Why are intensity forecasts slow to improve?

What are challenges for numerical TC prediction?What are challenges for numerical TC prediction?– Difficulty with initial conditions

N d t t l i t ti– Need to represent complex process interactions across spatial scales (e.g., eyewall replacements; resolution)

Diffi lt ti h i l TC (– Difficulty representing physical TC processes (e.g., convection and swirling PBL over complex surface)

Incomplete understanding of physical processes– Incomplete understanding of physical processes

“Dynamically, the tropical cyclone is a mesoscale power plant with a synoptic‐scale supportive system.” (Ooyama 1982)

Data Assimilation (DA) Overview (after Kalnay Fig. 5.1.2a)

Ob iObservations (+/- 3 h)

Background or first guess

Global analysis (statistical i t l ti d

Approach: Use ALL available information for b t ibl l iinterpolation and

balancing)

Initial Conditions

best possible analysis

Observations + short

Global forecast model

Initial Conditions Observations + short-term forecast

(“background”) +

6-h forecastinformation about error

+ dynamical and physical relations, etc.

Operational forecasts

p y ,

TC Data Assimilation

Specific TC DA Challenges:1.) Sometimes not enough information, esp. inner core

- Rain contamination of some satellite-borne sensors- Few in-situ observations other than recon

2.) Much available information not used, esp. near TCObs background can differ greatly near TC QC eliminates obs- Obs, background can differ greatly near TC, QC eliminates obs

- Data density issues- Model resolution insufficient to capture inner core structure,

observational representativeness challenge

Data Assimilation

Critical aspect: Relative weighting of observations & background (short-term model forecast) in analysis

Accurate knowledge of error associated with background and observations determines weightingbackground and observations determines weighting

Static 3DVAR: Assume constant error statistics

Ensemble Kalman Filter (EnKF): Use ensemble to provide flow-dependent background error information p p g

TC Initialization: GFSStart ith GFS model hich links to NAM RAP (andStart with GFS model, which links to NAM, RAP (and

to some extent GFDL and HWRF)

Starting with 12Z run, 22 May 2012, new GFS hybrid DA system implemented

Hybrid: Blend of short-term ensemble and old (constant) information to define background error( ) g

Single Observation: GFS850‐mb Tv ensemble spread 00Z 9/12/2008 Background T (contours), and change to 850 mb Tv ensemble spread, 00Z 9/12/2008 g ( ), g

analysis from assimilation of ob (shaded)

Tv observation

All static background error

Single 850mb Tv observation (1K O-F, 1K error)

All ensemble error (bf-1=0.0) Hybrid, 50% ens, 50% static (bf

-1=0.5)

Slide compliments of Daryl Kleist, EMC

GFS TC InitializationInformation from: Daryl Kleist (personal communication 2012) and Kleist et al. 2011a,bo at o o a y e st (pe so a co u cat o 0 ) a d e st et a 0 a,b

When NHC declares a storm of TD strength or greater:

1a: If GFS 6-h forecast represents system, vortex relocated to NHC position (in background field) prior to DA

f G S f1b: If storm not represented in GFS 6-h forecast, then synthetic (bogus) wind observations generated

2: Declared NHC storm information written to “TCvitals” file; system reads location, central pressure, used in DA process regardless of 1a or 1bprocess regardless of 1a or 1b

GFS PARA F06 (from 18 UTC) Valid at

Example with new GFS hybrid (parallel) DA system for TS “Bud”

GFS PARA F06 (from 18 UTC), Valid at 00 UTC on 21 May 2012. Note weak representation of Bud….the tracker was unable to “find” a coherent storm.

GFS PARA ANALYSIS at 00 UTC on 21 May 2012. Note radical change to Bud due to assimilation of synthetic wind observations (no relocation was done in this case, since tracker could not “find”

)storm).

Slide compliments of Daryl Kleist, EMC

GFS: Vortex Relocation4-step process:

1 ) L t h i t i b k d1.) Locate hurricane vortex in background

2.) Separate TC from environmental field (filtering- from GFDL)

3.) Move hurricane vortex to NHC official position

4.) Data assimilation step includes MinSLP ob from NHC) p

No relocation if storm center over major land mass, or if terrain j ,elevation > 500 m

See Liu et al. 2000 for more info on this process

GFS TC InitializationDoes GFS utilize recon data in Data Assimilation system?

GFS uses some G IV and P3 data, but DA system makes limited use of in-situ observations in/near storm. With

fold DA system, representativeness issues of inner-core obs, so these are flagged and most dropsonde data not assimilated

GFS assimilation of NHC central pressure ob helps some (implemented in 2009- Kleist et al. 2011, WAF)

Operational GFS (T382) analysis Operational GFS (T382) F72

Ike (956 obs)

Hanna (989 obs) (956 obs)

Control GFS (T574)

Control with MinSLP (T574)

Kleist et al. (2011 WAF)

GFS TC InitializationInformation from: Daryl Kleist (personal communication 2012) and Kleist et al. 2011a,bo at o o a y e st (pe so a co u cat o 0 ) a d e st et a 0 a,b

Due to coarse GFS resolution (effectively 27-km grid length), small and strong TCs will still be weaker in model IC than in reality; larger, weaker storms better represented

New GFS Hybrid DA system by using ensemble to measureNew GFS Hybrid DA system, by using ensemble to measure background error potential major improvement, allows assimilated observation information to distribute in flow-dependent fashion (see following slides)dependent fashion (see following slides)

Due to coarseness of ensemble, the former static part of error covariance is needed to represent small scales (static part of hybrid system uses higher-resolution background)

GFS: Single ObservationSlide compliments of Daryl Kleist, EMC


Single ps observation (-2mb O-F, 1mb error) near center of Hurricane Ike


-1=0.5)

GFS: Single ObservationSlide compliments of Daryl Kleist, EMC


Single 850mb zonal wind observation (3 m/s O-F, 1m/s error) in Hurricane Ike circulation


-1=0.5)

GFS TC InitializationNew hybrid DA system (5/2012), and assimilation of

MinSLP (2009) have improved TC IC for GFS( ) p

Additional work is needed to better utilize observational f / Cinformation in/near TC core

Resolution limitations remain an obstacle for full strengthResolution limitations remain an obstacle for full-strength initialization; larger, weaker storms better represented

Any questions on GFS TC IC?

HWRFBecame operational in 2007

High-resolution (27/9/3 km domains) with moving inner domains for high-resolution TC predictioninner domains for high resolution TC prediction

Utilizes high-resolution data assimilationUtilizes high-resolution data assimilation

Coupled with Princeton Ocean Model for air seaCoupled with Princeton Ocean Model for air-sea feedbacks

Slide modified from Mike Brennan (NHC)

HWRF TC InitializationInformation taken from: http://www.emc.ncep.noaa.gov/HWRF/HWRFScientificDocumentation2011.pdf

1.) Define HWRF domain based on observed TC position2.) Interpolate GFS analysis to HWRF grid3.) Remove GFS vortex from analysis4.) Insert high-resolution vortex:

F 1st t th < 25 kt it b t- For 1st run or strength < 25 kt, composite bogus vortex:- Used for initial HWRF run of any system of any intensity- Used for any HWRF run for systems of initial intensity < 25 kt

Subsequent runs with initial intensity ≥ 25 kt:- Subsequent runs with initial intensity ≥ 25 kt: - Vortex from previous cycle 6-h forecast extracted- Storm location, size, and intensity corrected using TCVitals data- If first-guess vortex does not match the initial intensity specified by NHC, then

portions of composite vortex addedportions of composite vortex added

5.) Run GSI (previous GFS DA system) with obs and vortex in DA cycle; GSI run separately for each domain

For 2012, vortex constructed on 3-km inner domainSlide modified from Mike Brennan (NHC)

HWRF Bogus Vortex• Only used for “cold start” situations; ~once per storm• Bogus vortex created from 2D axisymmetric vortex from

t d l f t f ll i t i tpast model forecast of small, near-axisymmetric system– 2D vortex includes perturbations of horizontal wind component,

temperature, specific humidity and sea-level pressure

• To create the bogus storm:– Wind profile of 2D vortex smoothed until its RMW / maximum

wind speed matches observed values– Storm size and intensity are corrected following a procedure

similar to that for cycled system– Vortex in shallow storms undergoes 2 final corrections: Vortex

top set to 700 hPa, warm core structure removed


HWRF Data Assimilation• Uses GSI DA system on outer domain and

special 20°x20° “ghost” domain to assimilatespecial 20 x20 ghost domain to assimilate conventional and satellite radiances

• However, conventional data within 150 km of storm center not assimilated due to their

ti i t f tnegative impact on forecast– Largely due to static isotropic background error

covariancescovariances– Testing 4DVAR and hybrid EnKF-Variational schemes

with P3 tail Doppler radar data


GFDL• Operational since 1995

• Triple nest, ~30, 10, and 5-km grid length

• Coupled to Princeton ocean modelp

• Uses “bogus vortex” plus asymmetries• Uses bogus vortex plus asymmetries from previous 12-h forecast


GFDL InitializationTaken from Bender et al. (2007)( )

• Filters remove vortex from previous 12-h forecast

• Azimuthal means computed for all prognostic variablesAzimuthal means computed for all prognostic variables, subtracted to get 3-D asymmetries, which are added to the initial axisymmetric vortex

• Depth of storm adjusted based on NHC intensity analysis (depth of the storm increases as a function of NHC i d i t it )NHC assigned intensity)

• In 2002, filtering in upper-levels reduced to retain more f GFS l i thof GFS analysis there

• GFDL bogus vortex is available, can be used for local model initialization


GFDL* Bogus Vortex Specification• Symmetric component (shown)y p ( )

Created from axisymmetric version of model• Asymmetric component (not shown)

Added from 12‐hr forecast of previous GFDL model runmodel run

• BV specified from observed location/intensity

Source: Kurihara et. al., 1993

*Geophysical Fluid Dynamics Lab (GFDL)

Former student Briana Gordon: Tested Bogus

GFDL Bogus Vortex (BV) greatly reduces initial intensity error

Why not just download GFDL BV and add to GFS for local TC modeling?local TC modeling?

GFDL Hurricane Model:Inner 11° x 11° domain with 1/12° grid spacing

Bob Hart’s (FSU) method (Hart 2008):Bob Hart s (FSU) method (Hart 2008):Merge GFDL inner grid with GFS 1/2° analysis

Tropical Cyclone Cases

1. Category 1 Hurricane Ike (2008)( )• GFS vs. GFDL Bogus Vortex (BV)

2. Tropical Storm Erika (2009) • GFS vs. GFDL BV • Initialized 0000 UTC 2 Sept 2009

weak

3. Category 3 Hurricane Earl (2010) • GFS vs. GFDL BV strong

• Initialized 0000 UTC 1 Sept 2010

Example: Erika (2009)

0000 UTC 2 September 20090000 UTC 2 September 2009

Source: www.nhc.noaa.gov

Example: Erika (2009)Initialized 06Z 3 September, 126‐h forecasts valid 1200 UTC 8 September 2009:

HWRF* Model932 mb | 98 kt

GFDL** Hurricane Model957 mb | 108 kt

Category 3 Hurricane Erika?

| |

Source: http://moe.met.fsu.edu/tcgengifs/*Hurricane Weather Research and Forecasting**Geophysical Fluid Dynamics Lab

HURNC IC: Tropical Storm Erika0000 UTC 2 September 2009

Merged BVGFS

SLP (mb, contoured) and 10 i d (kt h d d)10‐m winds (kt, shaded)

1003 mb, 65 kt1010 mb, 35 kt

Motivation Background GFDL Bogus Vortex Hybrid Data Assimilation Conclusions

1004 mb, 45 kt

Potential Vorticity: 2 Sept 2009 00 UTC Analysis

GFS OnlyyPV ~ 2 PVU

G G SGFDL+GFSPV ~ 7.5 PVU

Sea Level Pressure and Wind Shear: 2 Sept 2009 00 UTC Analysis2 Sept 2009 00 UTC Analysis

GFS Only GFDL+GFS

1009 mb 1001 mb

Example: Erika

HURNC Forecast: Tropical Storm ErikaInitialized 0000 UTC 2 September 2009

1015

1020

Minimum Central Sea Level Pressure

1000

1005

1010

SLP (m

b)

GFS SLP

BV SLP

980

985

990

995S BV SLP

Best Track 54‐hour Storm Track

0 12 24 36 48Forecast Hour

GFS RMSE*: 3.2 mb

BV RMSE: 5.2 mb

Source: www.nhc.noaa.gov*Root Mean Squared Error

48-hour Forecast: Tropical Storm ErikaInitialized 0000 UTC 2 September 2009

Simulated Radar Reflectivity (dBz) and SLP (mb)GFS Merged BV

1009 mb 1004 mb

Best Track: Erika almost dissipated at 1009 mb

Tropical Rainfall MeasuringMission (TRMM) MicrowaveImager (TMI) and GOES 12 IRat 1009 mb Imager (TMI) and GOES‐12 IR Satellite

Source: www.nrlmry.navy.mil

General Bogus Vortex Conclusions Intensity

• Reduces initial condition intensity error• Does not always improve forecast skill‐ positive intensity biasDoes not always improve forecast skill positive intensity bias

Structure• Tall, narrow, symmetric inner core• Strong PV maximum at mid‐ to high‐levels• BV overly robust in high‐shear environment

f lUsefulness• Might be adequate for mature, strong hurricanes• Overly robust for weaker TCs• BV IC not “sticking” in the model

• Possibly due to lack of precip/clouds at initialization – need “hot start” (clouds, hydrometeors in IC)?( , y )

RAP

• 12Z run, 1 May 2012: RAP replaced RUC

• RAP is WRF ARW model, with RUC-similar physics

• Important changes in DA and some physics from RUC

• RUC uses previous GFS GSI DA system (not hybrid)

RAP InitializationInformation from: John Brown (NOAA ESRL personal communication)Information from: John Brown (NOAA ESRL, personal communication)

• Similar to NAM, GFS information “injected” with “ ”“partial cycling” strategy

• RAP: 03 and 15 UTC, 1-h partial cycle of RAP , p ywhere GFS 3-h forecast used for background

• After 3Z 15Z analyses DFI radar initialization• After 3Z, 15Z analyses, DFI radar initialization applied, and IC for next 1-h forecast generated

• Process repeated hourly until 09 and 21Z, when 1-h RAP forecast substituted into ongoing RAP


• Bottom line: RAP makes no unique provision for CTC initialization

• Utilizes information from GFS via partial cycling strategy (similar for NAM, with good results)

• RAP system should improve on RUC, which would t h TC l d i f l t lnot have a TC unless one crossed in from lateral

BC, formed in the RUC (rare), or “drawn for”


• Does RAP draw in recon or special TC obs? - If special in-situ obs in NAM then attempt to use in RAP- Radar and wind from P3 not used at this time

• An advantage of RAP is radar-derived diabatici iti li ti ff h i TC thi d t linitialization; offshore in TC, this advantage less, but lightning used as proxy to help (GSD version)

• Basic RAP DA system is based on previous GFS GSI 3DVar system In future use GFS typeGSI 3DVar system. In future, use GFS-type hybrid?

NAM InitializationInformation from: http://www emc ncsp noaa gov/mmb/research/FAQ-eta html (TC part: 25 May 2012)Information from: http://www.emc.ncsp.noaa.gov/mmb/research/FAQ-eta.html (TC part: 25 May 2012)

TCVitals generated from NHC/FNMOC/JTWC

GFS first-guess with relocated storm also used as background to NDAS analysis

• For all storms, NDAS process mimics GFS process for weak storms where vortex not found in background

• TCVitals used for synthetic (bogus) wind profile obs for use• TCVitals used for synthetic (bogus) wind profile obs for use in DA

• Mass observations near storm flagged and omitted, ditto ass obse at o s ea sto agged a d o tted, d ttodropsondes

See http://www.emc.ncep.noaa.gov/mmb/research/FAQ‐eta.html#namgfs_tcini

NAM InitializationInformation from:Information from:

• Uses 3-D Var, nothing special for TCs, but partial GFS cycling helpsGFS cycling helps

Graphics courtesy NHC

See http://www.emc.ncep.noaa.gov/mmb/research/FAQ‐eta.html#namgfs_tcini

ExamplesExamples

TD 2-E (later Hurricane Bud)AL94 (later TS Beryl)AL94 (later TS Beryl)


TD 2‐E Initialization – 00Z 21 May 2012GFS HWRF GFDL


GFS HWRF GFDLNote symmetric vortex in HWRF, stronger than GFS

or GFDL

Winds

or GFDL

Vorticity

TD 2‐E Initialization – 06Z 21 May 2012GFS HWRF GFDLEven when cycling begins


GFS HWRF GFDLEven when cycling begins, this symmetric vortex structure persists for a

couple of cycles

Winds

Vorticity

TD 2‐E Initialization – 12Z 21 May 2012GFS HWRF GFDLEven when cycling begins


GFS HWRF GFDLEven when cycling begins, this symmetric vortex structure persists for a

couple of cycles, especially in the wind

fil

Winds

profile

Vorticity

Invest AL94 Initialization – 00Z 23 May 2012GFS HWRF GFDL


GFS HWRF GFDLStronger, deeper vortex in the HWRF for this

case too

Winds

Vorticity

case too

y



GFS HWRF GFDLVortex has more symmetry and

structure in the wind and MSLP fields

Surface Winds

and MSLP

SurfaceSurface Winds

and MSLP zoom



GFS HWRF GFDL

Winds

Vorticity



GFS HWRF GFDL

Surface Winds

and MSLP

SurfaceSurface Winds

and MSLP zoom

Conclusions

• New hybrid GFS DA system cause for optimism

• For NCEP operational models, GFS TC IC most importantimportant• GFS cycled in to NAM, RAP• GFS large-scale and BC data used in HWRF, GFDLg

• HWRF, GFDL have resolution advantage, but not f ll il bl i AWIPSfully available in AWIPS

High resolution TC DA in HWRF has promise but• High-resolution TC DA in HWRF has promise, but more computer power needed

Model TC Initial Conditions

– Storm initial intensity?y– Weak storm? Better initialized– Strong storm? Model IC too weak

– Storm size?– Larger storms better represented

Storm age?– Storm age? – Newly declared storms handled differently

than “mature” storms in models

Acknowledgements• Daryl Kleist (NOAA/NCEP/EMC)• Mike Brennan (NOAA/NCEP/NHC)( )• John Brown (NOAA/ESRL)• Briana Gordon (Sonoma Technology, Inc)• Wallace Hogsett (TSB NHC)• Stan Benjamin (NOAA/ESRL)

B i Eth t (NOAA/ESRL)• Brian Etherton (NOAA/ESRL)• NOAA CSTAR Grant #NA10NWS4680007• Jonathan Blaes (NWS RAH)• Jonathan Blaes (NWS RAH) • COMET program for graphics and Operational Model

Matrix

Strategies and ProcessesHelpful to define some terms and show examples:

– Vortex relocation (GFS) – TC location corrected in short‐term model forecast prior to data assimilation

– Bogus Vortex (GFDL) – Synthetic vortex added to model initial conditionsconditions

– Synthetic obs (GFS) – Fictitious observations created, used in data assimilation

– MinSLP assimilation (GFS) – Use NHC SLP minimum (and location) in assimilation

– Data Assimilation + Bogus (HWRF) – Use previous vortex as DA input

– Ensemble Kalman Filter (used in new GFS Hybrid DA system)( y y )

References• Aberson, S. D., 2008: Large forecast degradations due to synoptic surveillance during the 2004 and 2005 hurricane seasons. , , g g y p g

Mon. Wea. Rev., 136, 3138‐3150.• Bender, M. A., I. Ginis, R. Tuleya, B. Thomas, and T. Marchok, 2007: The Operational GFDL coupled hurricane–ocean prediction

system and a summary of its performance. Mon. Wea. Rev., 135, 3965–3989. • Davis, C., W. Wang, S.S. Chen, Y. Chen, K. Corbosiero, M. Demaria, J. Dudhia, G. Holland, J. Klemp, J. Michalakes, H. Reeves, R.

Rotunno, C. Snyder, and Q. Xiao, 2008: Prediction of landfalling hurricanes with the advanced hurricane WRF model. Mon. Wea. , y , Q , gRev., 136, 1990.

• Hamill, T. M., J. S. Whitaker, M. Fiorino, and S. G. Benjamin, 2011: Global Ensemble Predictions of 2009’s Tropical Cyclones Initialized with an Ensemble Kalman Filter. Mon. Wea. Rev., 139, 668–688

• Hamill, T. M., J. S. Whitaker, D. T. Kleist, M. Fiorino, and S. G. Benjamin, 2011: Predictions of 2010’s Tropical Cyclones Using the GFS and Ensemble‐Based Data Assimilation Methods.Mon. Wea. Rev., 139, 3243–3247.GFS and Ensemble Based Data Assimilation Methods. Mon. Wea. Rev., 139, 3243 3247.

• Hart, R.E., 2008: Merging GFDL and GFS Analyses for MM5 Initialization of Hurricanes. 28th Conference on Hurricanes and Tropical Meteorology, Orlando, FL, Amer. Meteor. Soc., P1F.14.

• Kalman, R. E., 1960: A new approach to linear filtering and prediction problems. J. Basic Eng., 82D, 35–45.• Kleist, D. T., 2011: Assimilation of Tropical Cyclone Advisory Minimum Sea Level Pressure in the NCEP Global Data Assimilation

System Wea Forecasting 26 1085 1091System. Wea. Forecasting, 26, 1085–1091. • Kurihara, Y., M. Bender, and R. Ross, 1993: An initialization scheme of hurricane models by vortex specification. Mon. Wea. Rev.,

121, 2030–2045. • Liu, Q., T. Marchok, H‐L. Pan, M. Bender, and S. Lord, 2000: Improvements in hurricane initialization and forecasting at NCEP with

global and regional (GFDL) models. Tech. Procedures Bull. 472, NCEP/EMC Tech. Rep., 7 pp.O K V 1982 C l E l i f h Th d M d li f h T i l C l J M S J 60 369 379• Ooyama, K.V., 1982: Conceptual Evolution of the Theory and Modeling of the Tropical Cyclone. J. Meteor. Soc. Japan, 60, 369‐379.

• Skamarock, W.C., J.B. Klemp, J. Dudhia, D.O. Gill, D.M. Barker, M.G. Duda, X. Huang, W. Wang, and J.G. Powers, 2008: A description of the Advanced Research WRF version 3. NCAR Tech.

• Torn, R. D., 2010: Performance of a mesoscale EnKF during the NOAA high‐resolution hurricane test. Mon. Wea. Rev., 138, 4375‐4392.

• Torn, R. D., and G. J. Hakim, 2009: Ensemble data assimilation applied to RAINEX observations of Hurricane Katrina (2005). Mon. Wea. Rev., 137, 2817–2829.

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How are tropical cyclones represented in …...How are tropical cyclones represented in operational model initial conditions? And why does it matter? Gary Lackmann North Carolina State