Hurricane Wind Structure and Secondary Eyewall Formation

1. Hurricane Wind Structure and Secondary Eyewall Formation CIMSS Project Lead: Christopher Rozoff NOAA Collaborator: James Kossin CIMSS Support Scientist: Matthew Sitkowski

2. Outline • Purpose of this project • Brief review of FY08 and FY09 milestones • FY10 accomplishments • Applications of synthetic ABI data from MSG-SEVIRI imagery of an Atlantic tropical cyclone • Applications of synthetic ABI data derived from a tropical cyclone simulation • New theoretical insights on secondary eyewall formation • Future work

3. Project Purpose • Motivation: • Secondary eyewall formation (SEF) presents a difficult challenge for tropical cyclone intensity and structure forecasting. We seek ways in which (current and future) GOES imagery can improve forecasting and our physical understanding of SEF. • General approach: • Develop algorithms utilizing GOES imagery to improve the climatology and forecasting of SEF. • Explore the utility of increased spectral and spatial resolution of GOES-R ABI in detecting important cloud structures related to tropical cyclone structure and intensity change. Specifically, we are interested in the connections between latent heating distributions, particularly those associated with SEF, and their role in radial wind structure changes.

4. Brief review of previous progress • Major Accomplishments in FYs 08/09: • A naïve Bayes classifier scheme incorporating environmental and GOES-IR satellite data characterizing storm internal structure was designed to predict secondary eyewall formation (SEF). A climatology of eastern Pacific and North Atlantic SEF cases resulted and the naïve Bayes classifier was shown to be skillful in predicting SEF (Kossin and Sitkowski 2009). As a result of this accomplishment, a JHT grant was awarded to implement this scheme into real-time operations at the NHC. Through GIMPAP funding, this scheme and other similar schemes were adapted to predict rapid intensification as well (Rozoff and Kossin 2010). Publications resulting from this forecasting framework: • Kossin, J. P., and M. Sitkowski, 2009: An objective model for identifying secondary eyewall formation in hurricanes. Mon. Wea. Rev., 137, 876—892. • Rozoff, C. M., and J. P. Kossin, 2010: New probabilistic forecast schemes for the prediction of tropical cyclone rapid intensification. Wea. Forecasting, to be submitted.

5. Brief review of previous progress • Major Accomplishments in FYs 08/09: • 3.9 mm brightness temperatures (Tb) from Meteosat and GOES imagery (in the HURSAT dataset) were examined for the years 1997-2006. Azimuthally averaged radial profiles of Tb for all Atlantic storms over this time period were created. For daylight hours, predictors derived from a principle component analysis of the radial Tb profiles added substantial skill to the SEF scheme of Kossin and Sitkowski (2009). A particularly important aspect was the moat warming that begins to show up outside the primary eyewall as SEF is occurring, reflected in the principle component analysis of azimuthal average Tb. The “shortwave” IR results suggest visible imagery may also greatly improve early detection of SEF in daylight hours.

6. Synthetic ABI data from MSG-SEVIRI • Storm-centered, synthetic data for ABI Channels 7 – 16 covering an eyewall replacement cycle (ERC) in North Atlantic Hurricane Helene (2006) were kindly provided by John Knaff at CIRA. • The overall goal is to investigate how the additional spectral channels of ABI can be utilized to better understand how diabatic heating and subsidence evolve during and influence SEF and ERCs.

7. Brief Helene (2006) overview Helene’s Track Time period of synthetic ABI dataset TMI 85 GHz (H) 9/19 1400 UTC SSMIS 85 GHz (H) 9/18 1205 UTC AMSRE 85 GHz (H) 9/18 1641 UTC SSMIS 85 GHz (H) 9/18 2322 UTC Syn. ABI 7.3 mm 9/18 1200 UTC Syn. ABI 7.3 mm 9/18 2315 UTC Syn. ABI 7.3 mm 9/19 1400 UTC Syn. ABI 7.3 mm 9/18 1645 UTC

8. Synthetic ABI data from MSG-SEVIRI • Olander and Velden (2009; Wea. Forecasting) used a type of enhanced difference field of GOES water vapor (6.5 mm) and infrared window (10.7 mm) brightness temperatures to detect intense convection in TCs. Some of the imagery depicted precipitating structures that can be found in passive microwave imagery but difficult to find in individual GOES channels. • The increased spectral resolution of the ABI has been exploited to create similar difference fields to explore detailed internal structure of tropical cyclones.

9. Synthetic ABI data from MSG-SEVIRI • Synthetic ABI data are analyzed using McIDAS-V • Using the difference formula defined in Olander and Velden [i.e., Tb,high – Tb,low – exp(1 – Tb,high + Tb,low)] (no data smoothing after this formula is applied), it was found that differences between the 10.35 mm IR channeland any of the following water vapor bands isolated some of the most intense convection related to the spiral bands and eyewalls during SEF and the ERC of Helene (2006): 6.19, 6.95, or 7.34 mm. Hereafter, we refer to these 3 differences as IRWV1, IRWV2, and IRWV3. Differences using longer wave IR channels also provided similar information, but 10.35 mm channel provides the strongest signal for isolating active convection from other cirrus clouds, at least in this particular storm. ABI weighting functions for a standard tropical atmosphere (from http://cimss.ssec.wisc.edu/goes/wf/ABI/)

10. Synthetic ABI data from MSG-SEVIRI • As in Olander and Velden (2009), all 3 IR-WV difference fields can distinguish vigorous convection. This is particularly true in rainband regions, which is an important area for the genesis of outer eyewalls and wind field expansion (see results later in this presentation). • IRWV2 provides the best relative emphasis in active convection outside of the primary eyewall. Nonetheless, IRWV1 and IRWV3 still depict nearly the same structural details. • A major limitation of the enhanced difference fields for all channels: • Much like single IR and WV channels, thick outflow cirrus clouds can still obscure some of the inner core structure in IRWV imagery. Helene’s double eyewall structure cannot be fully resolved in any of the ABI difference fields, at least as defined in this application. As a speculation, this may be partially due to the relative outflow heights of the primary and secondary eyewalls being offset, with higher outflow from the primary eyewall.

11. ABI 7.3 mm 9/18 1200 UTC ABI 7.3 mm 9/19 1400 UTC Example 1:Early SEF9/18 ~12 utc Example 2: Double Eyewalls9/19 ~14 utc IRWV1 10.35 mm / 6.19 mm IRWV1 10.35 mm / 6.19 mm SSMIS 85 GHz (H) 9/18 1205 UTC TMI 85 GHz (H) 9/18 1400 UTC IRWV2 10.35 mm / 6.95 mm IRWV2 10.35 mm / 6.95 mm IRWV2 10.35 mm / 7.34 mm IRWV3 10.35 mm / 7.34 mm Note: In difference channels, only negative values are contoured.

12. Synthetic ABI data from a WRF simulation of tropical cyclone SEF • This simulation is from a collaboration with David Nolan (U. Miami), Fuqing Zhang (Penn. State Univ.), and Juan Fang (Nanjing Univ.) • Initialization: • A weak, balanced baroclinic vortex • Representative mean sounding for thermal and moisture stratification. • Sea surface temperature held constant at 29.15oC • Simulation Domain Configuration: • 2 nested grids (D x = 9 – 10800 x 10800 km and1 km – 1000 x 1000 km) • Vertical grid contains 35 points, equally spaced in the hcoordinate (i.e. stretched in phys. space) • b-plane • Physics packages • One moment microphysics, no radiation, YSUboundary layer scheme

13. Synthetic ABI data from a WRF simulation of tropical cyclone SEF • CIMSS forward radiative transfer model applied to WRF output to obtain synthetic ABI data (infrared channels 8-16) • Justin Sieglaff (CIMSS) and Jason Otkin (CIMSS) are gratefully acknowledged for providing their algorithms and for their help with this process! Also, Louis Grasso (CIRA) is thanked for sharing an earlier hurricane simulation with synthetic ABI data (not presented here). • Statistics of brightness temperature were derived that demonstrate a substantial decrease in most channel brightness temperatures around the beginning stages of SEF and substantial moat warming in almost all channels as the ERC ensues.

14. Example of WRF output:6.19 mm ABI / radar reflectivity Synthetic ABI Synthetic radar reflectivity at z = 0.5 km (zoomed In)

15. Synthetic ABI data from a WRF simulation of tropical cyclone SEF • In WRF, the IRWV1, IRWV2, and IRWV3 fields show far greater (and thus more promising) SEF and ERC details than the MSG-SEVIRI data, although this might be an artifact related to the model’s microphysics. • It is found just using the exponential enhancement of IR and WV brightness temperature differences provides significant structural details that are verified in other model fields, such as low-level rain rate.

16. Early secondary eyewall formation example

17. Mature double eyewall example

18. Theoretical progress on secondary eyewall formation • The beginning stages of this work occurred under an ONR grant ending in FY09. • Major accomplishments in FY10: • Potential vorticity budgets were carried out • They show that the majority of PV in the region of SEF is generated by sustained latent heating associated with rainbands. • Absolute angular momentum budgets were carried out. Example follows below and on next slide. The following is an AAM budget for t = 42-43 h. <x> = az. ave. of x x‘ = asym. pert. of x

19. Azimuthal average AAM budget example t = 42-43 h a. ‘observed’ change b. Sum of f and g e. friction/diffusion d. Mean vort. flux e. Mean vert. adv. f. Sum of c, d, e h. Pert. vert. adv. i. Sum of g and h g. Pert. vort. flux

20. Theoretical progress on secondary eyewall formation • Major accomplishments in FY10 Continued: • Assessment of balanced dynamics and response of the vortex to latent heating outside of the primary eyewall. • Used the Eliassen transverse circulation equation version of the linearized, nonhydrostatic, anelastic model of Nolan and Grasso (2003; JAS) to assess the degree to which the axisymmetric mean WRF vortex response to diabatic heating was balanced. The tangential winds (i.e., inertial stability) and latent heating from the WRF model output were prescribed to the idealized model. • Except in the boundary layer, a substantial degree of the WRF transverse circulation during SEF can be adequately described by balanced vortex dynamics. Even the early stages of secondary eyewall formation are strongly driven by latent heating. Inertial stability spreads out due to rainband heating and provides a positive feedback between heating and the spin-up of an outer wind maximum.

21. Evolution of WRF INERTIAL STABILITY and Potential temperature during SEF Inertial stability (10-3 s-1) at t = 25 h Pot. temp. (K) at t = 25 h Change in inertial stability (10-3 s-1) t = 25 h to t = 40 h Change in pot. temp. (K) from t = 25 h to t = 40 h Change in inertial stability (10-3 s-1) t = 25 h to t = 55 h Change in pot. temp. (K) from t = 25 h to t = 55 h

22. COMParison of idealized and WRF model vertical motion fields Diabatic heating (x 10-2 K s-1) Balanced w (m s-1) WRF w (m s-1) t = 25 h t = 25 h t = 25 h t = 40 h t = 40 h t = 40 h t = 55 h t = 55 h t = 55 h

23. Theoretical progress on secondary eyewall formation • Why these idealized results are relevant to this ABI study: Capturing the structural details of latent heating is expected to provide significant information into secondary eyewall formation and wind field expansion. Thus, predictability should benefit from further incorporation of existing and future satellite products that accurately depict latent heating outside of the primary eyewall. • Publication resulting from this work: Rozoff, C. M., J. P. Kossin, D. S. Nolan, F. Zhang, and J. Fang, 2010: Dynamical mechanisms for secondary eyewall formation in a high-resolution simulation of an intense tropical cyclone. Mon. Wea. Rev., to be submitted soon.

24. Future research currently underway • Using the Helene (2006) data, we will continue to investigate derived ABI fields (e.g., difference fields) to search for strong indicators of SEF. A quantitative analysis will be completed for the best derived products. • While we have investigated bi-spectral fields, further investigation of multi-spectral fields is needed. • Derive predictors from existing IR and WV GOES and Meteosat (e.g., HURSAT) imagery to be tested in statistical forecasts of SEF (e.g., new satellite predictors in the naïve Bayes classifier). • IRWV-type enhanced difference fields. • Visibile imagery-based predictors