How are tropical cyclones represented in operational model initial conditions? And why does it matter?
“Alberto”, 1000 mb Gary Lackmann North Carolina State University 18 UTC Sunday 20 May 2012, GFS 95‐km SLP + GOES Visible5 July 2012
Contributions from Daryl Kleist (EMC), Mike Brennan (NHC), and John Brown (ESRL) and Briana Gordon (STI) are gratefully acknowledged Outline A. Background and Motivation 1.) Challenges of TC prediction and initialization 2.) Data Assimilation background and TC DA 3.) Hybrid DA in GFS and TC IC B. Operational Models and TC IC 1.) GFS 2.) HWRF 3.) GFDL Some acronyms: 4.) RAP TC = Tropical Cyclone 5.) NAM (briefly) DA = Data Assimilation IC = Initial Conditions EnKF = Ensemble Kalman Filter C. Conclusions and Questions BV = Bogus Vortex Atlantic TC track prediction: Improving
Track related to large-scale steering flow; improvements in 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? – Difficulty with initial conditions – NdtNeed to represen t comp lex process itinterac tions across spatial scales (e.g., eyewall replacements; resolution) – Difficu lty represen ting p hys ica l TC processes (e.g., convection and swirling PBL over complex surface) – 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)
Observat ions Background or (+/- 3 h) first guess
Approach: Use ALL Global analysis (statistical available information for itinterpol ltiation and btbest poss ible ana lilysis balancing)
Initial Conditions Observations + short- term forecast Global forecast model (“background”) + information about error 6-h forecast + dynamical and ppyhysical relations , etc.
Operational forecasts 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 TC - 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 weighting
Static 3DVAR: Assume constant error statistics
Ensemble Kalman Filter (EnKF): Use ensemble to provide flow-dependent background error information TC Initialization: GFS Start 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 back ground error Single Observation: GFS 850‐mb Tv ensemble spread, 00Z 9/12/2008 Background T ((),contours), and change to analysis from assimilation of ob (shaded)
Tv observation
All static background error
All ensemble error (b -1=0.0) -1 f Hybrid, 50% ens, 50% static (bf =0.5)
Single 850mb Tv observation (1K O-F, 1K error) Slide compliments of Daryl Kleist, EMC GFS TC Initialization Inform ati on fr om: D ar yl Kl ei st (per son al comm uni cati on 2 012 ) an d Kl ei st et al. 2 011 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 1b: IfGSf 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 1b 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 Relocation
4-step process:
1)L1.) Loca te hurr icane vor tex ibin bac kgroun d
2.) Separate TC from environmental field (filtering- from GFDL)
3.) Move hurricane vortex to NHC official position
4.))p Data assimilation step includes MinSLP ob from NHC
No relocation if storm center over majj,or land mass, or if terrain elevation > 500 m
See Liu et al. 2000 for more info on this process 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 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
Hanna Ike (989 obs) (956 obs)
Control GFS (T574)
Control with MinSLP (T574)
Kleist et al. (2011 WAF) GFS TC Initialization Inform ati on fr om: D ar yl Kl ei st (per son al comm uni cati on 2 012 ) an d Kl ei st et al. 2 011 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 measure background error potential 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) GFS: Single Observation Slide compliments of Daryl Kleist, EMC
All static background error
-1 -1 All ensemble error (bf =0.0) Hybrid, 50% ens, 50% static (bf =0.5)
Single ps observation (-2mb O-F, 1mb error) near center of Hurricane Ike GFS: Single Observation Slide compliments of Daryl Kleist, EMC
All static background error
-1 -1 All ensemble error (bf =0.0) Hybrid, 50% ens, 50% static (bf =0.5)
Single 850mb zonal wind observation (3 m/s O-F, 1m/s error) in Hurricane Ike circulation GFS TC Initialization
New hybrid DA system (5/2012), and assimilation of MinSLP (()2009) have im proved TC IC for GFS
Additional work is needed to better utilize observational inf/Cformation in/near TC core
Resolution limitations remain an obstacle for full-strength initialization; larger, weaker storms better represented
Any questions on GFS TC IC? HWRF Became operational in 2007
High-resolution (27/9/3 km domains) with moving inner domains for high-resolution TC prediction
Utilizes high-resolution data assimilation
Coupled with Princeton Ocean Model for air-sea feedbacks
Slide modified from Mike Brennan (NHC) HWRF TC Initialization Information taken from: http://www.emc.ncep.noaa.gov/HWRF/HWRFScientificDocumentation2011.pdf 1.) Define HWRF domain based on observed TC position 2.) Interpolate GFS analysis to HWRF grid 3.) Remove GFS vortex from analysis 4.) Insert high-resolution vortex: - F1For 1st run or stth<25trength < 25 kt, composite b ogus vor tex: - 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 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 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) HWRF Bogus Vortex • Only used for “cold start” situations; ~once per storm • Bogus vortex created from 2D axisymmetric vortex from pastdlftfllt model forecast of small, near-axitiisymmetric 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
Slide modified from Mike Brennan (NHC) HWRF Data Assimilation
• Uses GSI DA system on outer domain and special 20°x20°“ghost” domain to assimilate conventional and satellite radiances
• However, conventional data within 150 km of storm center not assimilated due to their negative impac t on forecast – Largely due to static isotropic background error covariances – Testing 4DVAR and hybrid EnKF-Variational schemes with P3 tail Doppler radar data
Slide modified from Mike Brennan (NHC) GFDL • Operational since 1995
• Triple nest, ~30, 10, and 5-km grid length
•Coupled to Princeton ocean model
• Uses “bogus vortex ” plus asymmetries from previous 12-h forecast
Slide modified from Mike Brennan (NHC) GFDL Initialization Taken from Bender et al. (()2007) • Filters remove vortex from previous 12-h forecast • Azimuthal 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 ass igne d in tens ity )
• In 2002, filtering in upper-levels reduced to retain more ofGFSf GFS anal ysi s th ere
• GFDL bogus vortex is available, can be used for local model initialization
Slide modified from Mike Brennan (NHC) 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
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?
GFDL Hurricane Model: Inner 11° x 11° domain with 1/12° grid spacing
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) weak • GFS vs. GFDL BV • Initialized 0000 UTC 2 Sept 2009 3. Category 3 Hurricane Earl (2010) strong • GFS vs. GFDL BV • Initialized 0000 UTC 1 Sept 2010 Example: Erika (2009)
0000 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* Model GFDL** Hurricane Model 932 mb | 98 kt 957 mb | 108 kt Category 3 Hurricane Erika?
*Hurricane Weather Research and Forecasting Source: http://moe.met.fsu.edu/tcgengifs/ **Geophysical Fluid Dynamics Lab HURNC IC: Tropical Storm Erika 0000 UTC 2 September 2009 GFS Merged BV
SLP (mb, contoured) and 10‐m widinds (kt, shdd)haded)
1010 mb, 35 kt 1003 mb, 65 kt
1004 mb, 45 kt
Motivation Background GFDL Bogus Vortex Hybrid Data Assimilation Conclusions Potential Vorticity: 2 Sept 2009 00 UTC Analysis
GFS Only PV ~ 2 PVU
GGSGFDL+GFS PV ~ 7.5 PVU Sea Level Pressure and Wind Shear: 2 Sept 2009 00 UTC Analysis
GFS Only GFDL+GFS
1009 mb 1001 mb Example: Erika HURNC Forecast: Tropical Storm Erika Initialized 0000 UTC 2 September 2009
Minimum Central Sea Level Pressure 1020
1015
1010
1005 (mb)
1000 GFS SLP LP SS 995 BV SLP Best Track 990 54‐hour Storm Track 985
980 0 12243648 Forecast Hour
GFS RMSE*: 3.2 mb BV RMSE: 5.2 mb
* Root Mean Squared Error Source: www.nhc.noaa.gov 48-hour Forecast: Tropical Storm Erika Initialized 0000 UTC 2 September 2009 GFSSimulated Radar Reflectivity (dBz) and SLP (mb) Merged BV
1009 mb 1004 mb
Best Track: Tropical Rainfall Measuring Erika almost dissipated Mission (TRMM) Microwave at 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 bias
Structure • Tall, narrow, symmetric inner core • Strong PV maximum at mid‐ to high‐levels • BV overly robust in high‐shear environment
Useflfulness • 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)? 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 Initialization 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 ppyartial cycle of RAP where GFS 3-h forecast used for background • 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 RAP Initialization Information from: John Brown (NOAA ESRL, personal communication)
• Bottom line: RAP makes no unique provision for TC initialization
• Utilizes information from GFS via partial cycling strategy (similar for NAM, with good results)
• RAP system should improve on RUC, which would notht have a TC un less one crosse did in from la tera l BC, formed in the RUC (rare), or “drawn for” RAP Initialization Information 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 RAP - Radar and wind from P3 not used at this time
• An advantage of RAP is radar-derived diabatic in itia liza tion; o ffs hore in TC, thi s ad vant age l ess, 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? NAM Initialization 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 in DA •Mass observ ati on s n ear storm fl agged an d omi tted, di tto dropsondes
See http://www.emc.ncep.noaa.gov/mmb/research/FAQ‐eta.html#namgfs_tcini NAM Initialization Information from:
• Uses 3-D Var, nothing special for TCs, but partial GFS cycling helps
See http://www.emc.ncep.noaa.gov/mmb/research/FAQ‐eta.html#namgfs_tcini
Graphics courtesy NHC Examples
TD 2-E (later Hurricane Bud) AL94 (later TS Beryl)
Slide modified from Mike Brennan (NHC) Slide modified from Mike Brennan (NHC) TD 2‐E Initialization – 00Z 21 May 2012 GFS HWRF GFDL Note symmetric vortex in HWRF, stronger than GFS or GFDL
Winds
Vorticity Slide modified from Mike Brennan (NHC) TD 2‐E Initialization – 06Z 21 May 2012 Even when cycling begins, GFS HWRF GFDL this symmetric vortex structure persists for a couple of cycles
Winds
Vorticity Slide modified from Mike Brennan (NHC) TD 2‐E Initialization – 12Z 21 May 2012 Even when cycling begins, GFS HWRF GFDL this symmetric vortex structure persists for a couple of cycles, especially in the wind profile
Winds
Vorticity Slide modified from Mike Brennan (NHC) Invest AL94 Initialization – 00Z 23 May 2012 GFS HWRF GFDL Stronger, deeper vortex in the HWRF for this case too
Winds Vorticity Slide modified from Mike Brennan (NHC) Invest AL94 Initialization – 00Z 23 May 2012
Vortex has more GFS HWRF GFDL symmetry and structure in the wind and MSLP fields
Surface Winds and MSLP
Surface Winds and MSLP zoom Slide modified from Mike Brennan (NHC) Invest AL94 Initialization – 00Z 24 May 2012 GFS HWRF GFDL
Winds
Vorticity Slide modified from Mike Brennan (NHC) Invest AL94 Initialization – 00Z 24 May 2012 GFS HWRF GFDL
Surface Winds and MSLP
Surface Winds and MSLP zoom Conclusions
• New hybrid GFS DA system cause for optimism
• For NCEP operational models, GFS TC IC most important • GFS cycled in to NAM, RAP •GFS large-scale and BC data used in HWRF, GFDL
• HWRF, GFDL have resolution advantage, but not fllfully ava ilbliilable in AWIPS
• High-resolution TC DA in HWRF has promise, but more computer power needed Model TC Initial Conditions
– Storm initial intensity? – Weak storm? Better initialized – Strong storm? Model IC too weak
– Storm size? – Larger storms better represented
– 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) • BiBrian Ether ton (NOAA/ESRL) • NOAA CSTAR Grant #NA10NWS4680007 • Jonathan Blaes (NWS RAH) • COMET program for graphics and Operational Model Matrix Strategies and Processes
Helpful 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 conditions
– 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 syy)stem) References • Aberson, S. D., 2008: Large forecast degradations due to syypnoptic surveillance during the 2004 and 2005 hurricane seasons. 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. Snyy,der, and Q. Xiao, 2008: Prediction of landfalling hurricanes with the advanced hurricane WRF model. Mon. Wea. Rev., 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. • 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, 1085–1091. • 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. • Ooyama, KVK.V., 1982: ClConceptual EliEvolution of the Theory and MMdliodeling of the TilTropical ClCyclone. 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. Questions?