How are tropical cyclones represented in operational model initial conditions? - PowerPoint Presentation

Slide1

How are tropical cyclones represented in operational model initial conditions? And why does it matter?

Gary LackmannNorth Carolina State University5 July 2012Contributions from Daryl Kleist (EMC), Mike Brennan (NHC), and John Brown (ESRL) and Briana Gordon (STI) are gratefully acknowledged

18 UTC Sunday 20 May 2012, GFS 95-km SLP + GOES Visible

“Alberto”, 1000

mbSlide2

Outline

Background and Motivation1.) Challenges of TC prediction and initialization2.) Data Assimilation background and TC DA3.) Hybrid DA in GFS and TC ICB. Operational Models and TC IC1.) GFS2.) HWRF

3.) GFDL

4.) RAP5.) NAM (briefly)

C. Conclusions and Questions

Some acronyms:

TC = Tropical Cyclone

DA = Data Assimilation

IC = Initial Conditions

EnKF

= Ensemble

Kalman

Filter

BV = Bogus VortexSlide3

Atlantic TC

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

Intensity prediction: Slower improvement, if any

Intensity related to interaction of

multi-scale processes

Source:

www.nhc.noaa.govSlide4

Why are intensity forecasts slow to improve?What are challenges for numerical TC prediction?

Difficulty with initial conditions Need to represent complex process interactions across spatial scales (e.g., eyewall replacements; resolution)Difficulty representing physical TC processes (e.g., convection and swirling PBL over complex surface)Incomplete understanding of physical processes

TC Intensity Forecasting

“Dynamically, the tropical cyclone is a mesoscale power plant with a synoptic-scale supportive system.” (

Ooyama

1982)

Slide5

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

Observations (+/- 3 h)

Background or first guess

Global analysis (statistical interpolation and balancing, Quality Control [QC])

Global forecast model

Operational forecasts

6-h forecast

Initial Conditions

Approach: Use

ALL

available information for best possible analysis

Observations + short-term forecast (“background”) + information about error + dynamical and physical relations, etc.Slide6

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 recon2.) Much available information not used, esp. near TC

Obs, background can differ greatly near TC, QC eliminates

obs

Data density issues (hurricane hunter radar coverage, drops)

Model resolution insufficient to capture inner core structure, observational representativeness challenge

TC Data AssimilationSlide7

Critical aspect: Relative weighting of observations & background (short-term model forecast) in analysisAccurate knowledge of error associated with background and observations determines weighting

Static 3DVAR: Assume constant error statisticsEnsemble Kalman Filter (EnKF): Use ensemble to provide flow-dependent background error information

Data AssimilationSlide8

TC Initialization: GFS

GFS links to NAM, RAP, to some extent GFDL, HWRFStarting with 12Z run, 22 May 2012, new GFS hybrid DA system implementedHybrid: Blend of short-term ensemble and old (constant) information to define background errorSlide9

Change to Analysis from Single Observation

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

All ensemble error

(bf

-1

=0.0)

Hybrid, 50%

ens

, 50% static

(b

f-1=0.5)850-mb Tv ensemble spread, 00Z 9/12/2008Tv observation

All static background error

Background T (contours), and change to analysis from assimilation of ob (shaded)

Slide compliments of Daryl Kleist, EMCSlide10

GFS TC InitializationInformation from: Daryl Kleist (personal communication 2012) and Kleist et al. 2011a,b

(3) NHC storm information written to “TCvitals” file; system reads location, central pressure, used in DA process regardless of 2a or 2b(1) NHC declares storm (TD strength or greater)

(2a) Vortex relocated to NHC position (in background field prior to DA)

Does GFS 6-h forecast represent system?

Yes

No

(2b) Synthetic (bogus) wind “observations” generated for use in DASlide11

F06 (from 18 UTC), Valid 00 UTC 21 May 2012

Note weak representation of Bud; Tracker unable to “find” coherent systemGFSP ANALYSIS 00 UTC 21 May 2012: Note radical change to Bud due to assimilation of synthetic wind observations (no relocation done in this case, since tracker didn’t find storm)Example with new GFS hybrid (parallel) DA system for TS “Bud” (now operational)

Slide compliments of Daryl Kleist, EMC

GFS Parallel 5/21/12 00 UTC 850

, wind (

6-h

fcst

)

GFS Parallel 5/21/12 00 UTC 850

mb

, wind (Analys)Slide12

GFS: Vortex Relocation

4-step process:1.) Locate hurricane vortex in background2.) Separate TC from environmental field (filtering- from GFDL)3.) Move hurricane vortex to NHC official position4.) Data assimilation step includes MinSLP ob from NHC

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

See Liu et al. (2000) for more info on this processSlide13

GFS TC Initialization

Does 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 old DA system, representativeness issues of inner-core obs, so these are flagged and most dropsonde data not assimilatedGFS assimilation of NHC central pressure ob helps some (implemented in 2009- Kleist et al. 2011, WAF)Slide14

Ike (956

obs)Hanna (989 obs)Operational GFS (T382) analysis

Control GFS (T574)

Control with MinSLP (T574)

Operational GFS (T382) F72

Kleist et al. (2011 WAF)Slide15

GFS TC InitializationInformation from: Daryl Kleist (personal communication 2012) and Kleist et al. 2011a,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 representedNew GFS Hybrid DA system, by using ensemble to measure background error, offers potentially major improvement, allows assimilated observation information to distribute in flow-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)Slide16

GFS: Single Observation

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

Slide compliments of Daryl Kleist, EMC

All ensemble error

(b

f

-1

=0.0)

Hybrid, 50%

ens

, 50% static (bf-1=0.5)All static background errorSlide17

GFS: Single Observation

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

Slide compliments of Daryl Kleist, EMC

All ensemble error

(b

f

-1

=0.0)

Hybrid, 50%

ens

, 50% static (bf-1=0.5)Slide18

Sandy, 00 UTC/ 1:30 UTC 29 Oct 2012

ftp://ftp.aoml.noaa.gov/hrd/pub/hwind/Operational/2012/AL182012/1029/0130/AL182012_1029_0130_contour08.pngGFS SLP, 10-m wind, 00 UTC 29 October 2012 (1 degree)Slide19

GFS TC Initialization

New hybrid DA system (5/2012), and assimilation of MinSLP (2009) have improved TC IC for GFSAdditional work is needed to better utilize observational information in/near TC coreResolution limitations remain an obstacle for full-strength initialization; larger, weaker storms better represented2012: Preliminary Atlantic track error results (NHC) indicate GFS better than ECMWF at hours 24-96

Any questions on GFS TC IC?Slide20

HWRF

Became operational in 2007High-resolution (27/9/3 km domains) with moving inner domains for high-resolution TC predictionUtilizes high-resolution data assimilationCoupled with Princeton Ocean Model for air-sea feedbacksSlide modified from Mike Brennan (NHC)Slide21

HWRF TC InitializationInformation taken from:

http://www.emc.ncep.noaa.gov/HWRF/HWRFScientificDocumentation2011.pdf1.) Define HWRF domain based on observed TC position2.) Interpolate GFS analysis to HWRF grid3.) Remove GFS vortex from analysis

4.) Insert high-resolution vortex: - For 1

st 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: Vortex from previous cycle 6-h forecast extractedStorm location, size, and intensity corrected using TCVitals dataIf first-guess vortex does not match the initial intensity specified by NHC, then portions 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 domain

Slide modified from Mike Brennan (NHC)Slide22

HWRF Bogus VortexOnly used for “cold start” situations; ~once per storm

Bogus vortex created from 2D axisymmetric vortex from past model forecast of small, near-axisymmetric system2D 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 valuesStorm size and intensity are corrected following a procedure similar to that for cycled systemVortex in shallow storms undergoes 2 final corrections: Vortex top set to 700

hPa, warm core structure removed

Slide modified from Mike Brennan (NHC)Slide23

HWRF Data AssimilationUses GSI DA system on outer domain and

special 20°x20° “ghost” domain to assimilate conventional and satellite radiancesHowever, conventional data within 150 km of storm center not assimilated due to their negative impact on forecastLargely due to static isotropic background error covariancesTesting 4DVAR and hybrid EnKF-Variational schemes with P3 tail Doppler radar data

Slide modified from Mike Brennan (NHC)Slide24

GFDLOperational since 1995

Triple nest, ~30, 10, and 5-km grid lengthCoupled to Princeton ocean modelUses “bogus vortex” plus asymmetries from previous 12-h forecast Slide modified from Mike Brennan (NHC)Slide25

GFDL InitializationTaken from Bender et al. (2007)

Filters remove vortex from previous 12-h forecastAzimuthal means computed for all prognostic variables, subtracted to get 3-D asymmetries, which are added to the initial axisymmetric vortexDepth of storm adjusted based on NHC intensity analysis (depth of the storm increases as a function of NHC assigned intensity)

In 2002, filtering in upper-levels reduced to retain more of GFS analysis there

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

Slide modified from Mike Brennan (NHC)Slide26

GFDL* Bogus Vortex Specification

Symmetric component (shown)

Created

from axisymmetric

version

of

model

Asymmetric component (not shown)

Added from 12-hr forecast of previous GFDL model run BV specified from observed location/intensity

*Geophysical

Fluid Dynamics Lab (GFDL)

Source:

Kurihara

et.

al

., 1993Slide27

Bogus Vortex with TC Erika: PV on 2 Sept ’09, 00 UTC Analysis

GFS OnlyPV ~ 2 PVUGFDL+GFSPV ~ 7.5 PVUSlide28

RAP

12Z run, 1 May 2012: RAP replaced RUCRAP is WRF ARW model, with RUC-similar physicsImportant changes in DA and some physics from RUCRUC uses previous GFS GSI DA system (not hybrid)Slide29

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

Similar to NAM, GFS information “injected” with “partial cycling” strategyRAP: 03 and 15 UTC, 1-h partial cycle of RAP where GFS 3-h forecast used for backgroundAfter 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 RAPSlide30

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

Bottom line: RAP makes no unique provision for TC initializationUtilizes information from GFS via partial cycling strategy (similar for NAM, with good results)RAP system should improve on RUC, which would not have a TC unless one crossed in from lateral BC, formed in the RUC (rare), or “drawn for”Slide31

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

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

An advantage of RAP is radar-derived diabatic

initialization; 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-type hybrid?Slide32

NAM InitializationInformation from:

http://www.emc.ncsp.noaa.gov/mmb/research/FAQ-eta.html (TC part: 25 May 2012)TCVitals generated from NHC/FNMOC/JTWCGFS first-guess with relocated storm also used as background to NDAS analysisFor 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 in DA

Mass observations near storm flagged and omitted, ditto

dropsondes

See http://www.emc.ncep.noaa.gov/mmb/research/FAQ-eta.html#namgfs_tciniSlide33

NAM InitializationInformation from:

Uses 3-D Var, nothing special for TCs, but partial GFS cycling helpsGraphics courtesy NHC

See http://www.emc.ncep.noaa.gov/mmb/research/FAQ-eta.html#namgfs_tciniSlide34

Conclusions

New hybrid GFS DA system cause for optimismFor NCEP operational models, GFS TC IC most importantGFS cycled in to NAM, RAPGFS large-scale and BC data used in HWRF, GFDLHWRF, GFDL have resolution advantage, but not fully available in AWIPS

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

DA systems in HWRF, NAM don’t use hybrid En-KF strategy of GFSSlide35

Model TC Initial Conditions

Storm initial intensity?Weak storm? Better initializedStrong storm? Model IC too weakStorm size?

Larger storms better represented

Storm age?

Newly declared storms handled differently than “mature” storms in modelsSlide36

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)Brian

Etherton (NOAA/ESRL)

NOAA CSTAR Grant #NA10NWS4680007Jonathan

Blaes

(NWS RAH)

COMET program for graphics and Operational Model Matrix Slide37

Questions?