OBJECTIVE STRUCTURE ANALYSIS Working Group: J. Knaff (NESDIS, Lead), Michael Bell (U. Hawaii – USA), Johnny C. L. Chan (City Univ. of Hong Kong), Kelvin

EIGHTH INTERNATIONAL WORKSHOP ON TROPICAL CYCLONES

OBJECTIVE STRUCTURE ANALYSISWorking Group: J. Knaff (NESDIS, Lead), Michael Bell (U. Hawaii – USA), Johnny C. L. Chan (City Univ. of Hong Kong), Kelvin T. F. Chan (City Univ. of Hong Kong - Hong Kong), Hung-Chi Kuo (Pacific Science Association), Cheng-Shang Lee (Pacific Science Association), Wen-Chau Lee (NCAR-USA), Christopher M. Rozoff (CIMSS/U. Wisc – USA), Kim Wood (U. Arizona – USA), Buck Sampson (NRLMRY-USA)

5 December 2014


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Let’s keep this interactive…5 December 2014


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The nature of TC structure analysis

5 December 2014

Marine (i.e. remote) environment Limited tools and observations Indirect or incomplete information

Goal: present some of the new techniques that will help better determine tropical cyclone structure given the current state of observations and technology


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Outline5 December 2014

1. Improving use of operational/best track observations

2. Assessing TC structure from model output

3. Estimating TC structure parameters via passive microwave imagery

4. Estimating TC structure from coastal radar

5. Size and wind structure assessment from scatterometer data

6. Size and wind field inferred from infrared satellite-based observations


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Improving use of operational/best track observations 5 December 2014

Common situation when trying to assess TC structure:

Observations are limited Observations are of varying quality Observations contain conflicting information

Problem: How do you make a physically consistent set of TC-Vitals “TC Bogus” for external use?


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Example situation5 December 2014

Wind Radii

Intensity

7 EIGHTH INTERNATIONAL WORKSHOP ON TROPICAL CYCLONES

Example: Summary

Intensity = 155 kt ± 10 kt

ROCI = 300 n. mi ± 75 n. mi

POCI = 1006 hPa ± 2 hPa

R34 = 180 n.mi ± 40 n.mi

Latitude = 21o N

Radius of Maximum Winds

Central Pressure

5 December 2014

Observations Unknowns


Potential Solutions (1)

Based upon a modified Chan and

Williams (1987) tangential wind

profile on an f-plane Environmental P = POCI-2 This method allows for

interpretation of TC-Vital

information that is considered

higher quality to provide the

additional or missing structure

parameters in a consistent

manner.

1. When storm latitude (φ), DP, and

ROCI/POCI/Penv are known, Vm, R34, and

RMW are estimated.

2. When φ, DP, R34, POCI/Penv are known,

Vm, RMW, and ROCI are estimated.

3. Given φ, DP, RMW, POCI/Penv, the Vm,

R34, and ROCI are calculated.

4. Given φ, Vm, ROCI, and POCI/Penv,

estimates for RMW, R34, and DP can be

created

5. For known φ, Vm, and R34, estimates of

RMW, ROCI, and DP are calculated.

6. Finally for φ, Vm, and RMW, the values of

R34, ROCI and DP can be estimated.

5 December 2014

Davidson et al. (2014) Capabilities


Potential Solutions (2)

A Simplified Holland B (SHB)

parameter

Vm = intensity estimate

DP = Pressure Deficit (intensity, latitude, R34, POCI/Penv), estimated from Courtney and Knaff (2009)

Based on a modified Rankine Vortex

Interactive with the forecaster based on the relative size of the system. Allows for a check of consistency vs climatology (Ask “is this a large storm?” in a dialog)

5 December 2014

Knaff et al. (2011) Capabilities

DP

eVmSHB

2

Small

Large


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Examples of SHB5 December 2014

Small

Large


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Bottom Line5 December 2014

Such diagnostics can

1. In operations, help forecasters to quality control and reduce

uncertainty in the TC Vitals and ultimately speed the

forecast process

2. For historical data, improve comparisons and climatological

studies focused on tropical cyclone size, structure, and

intensity, and provide a method to aid comparing basin-to-

basin and interagency best-track information.


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Related Recommendations5 December 2014

We recommend that methods to aid TC vital consistency be used in operational systems to improve the consistency in TC vitals and inter-agency best tracks


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Assessing TC structure from model output

5 December 2014

Models of TCs are improving (see Section 2.3.3)

They assimilate large scale observations They can resolve much of the outer

structure They can accurately depict the synoptic

environment


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Example: GFDL Tracker5 December 2014

1. Overall and quadrant-based

areal coverage of winds

exceeding the same 34-, 50-,

and 64-kt wind thresholds

2. The Integrated Kinetic

Energy (IKE) for TS

threshold (17.5 m/s) and

above.

3. Parameters related to extra-

tropical transition including

mean and maximum values

of vorticity at 850 hPa & 700

hPa centered on the TC,

storm translation

speed/direction and Hart

(2003) cyclone phase space

parameters.


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Example tracker5 December 2014

Fix estimates (PINK)

Forecasts (BLACK)


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That modeling centers be encouraged to either modify existing trackers to provide wind structure and type (sub-tropical, tropical, extra-tropical) information, or that those same centers run the GFDL track in addition to their in-house tracking software


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Estimating TC structure parameters via passive microwave imagery

5 December 2014

Structures related to Rapid Intensification

Satellite data provide a key way to quantify internal TC latent heating structure

85-GHz MI, Harnos and Nesbitt (2011) showed that the azimuthal symmetry of convection about a TC’s center, including the development of a convective ring, often accompanies RI

the 19- and 37-GHz microwave channels indicate microwave emissions from liquid hydrometeors. Kieper and Jiang (2012) found that a ring pattern observed in the 37-GHz composite color product provided in the Naval Research Laboratory TC MI dataset occurs around the center of a TC during many RI events.

These relationships have been quantified by Rozoff et al. (2015)


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5 December 2014

Structures related to Rapid Intensification

Caption: TMI brightness temperatures (K) of Hurricane Dean at 1314 UTC 15 August 2007 at (a)19.35 GHz (vertical polarization), (b) 37 GHz (vertical polarization), (c) 37 GHz (polarization corrected temperature; PCT), and (d) 85.5 GHz (PCT).

Image Time


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5 December 2014

Detecting secondary eyewall formation

The observational studies of Kuo et al. (2009), Yang et al. (2013), and Yang et al. (2014) provide in-depth documentation of CE characteristics over many years of TCs observed by passive microwave satellites.

Using the antenna gain function associated with the sampled radiometer data, the NRL MI are reprocessed to create high-resolution (1-2 km) products that can aid in defining inner-storm structural details (Hawkins and Helveston 2004; Hawkins et al. 2006).

An objective method is developed using MI to identify CEs in western North Pacific Ocean typhoons (Yang et al. 2013).

Three CE types are identified in Yang et al. (2013): a CE with an eyewall replacement cycle (ERC; 37 cases, 53%), a CE with no replacement cycle (NRC; 17 cases, 24%), and a CE that is maintained for an extended period (CEM; 16 cases, 23%).


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5 December 2014


Caption: Color-enhanced microwave CE imageries of Typhoons (a) Oliwa (1997) and (b) Vamco (2009). The averaged TB profiles of eight radial directions for Typhoon Oliwa are conformed to the CE-determined criteria.The secondary TB minimum for Typhoon Vamco only identified spiral outer rainband. (solid green: WNW, solid yellow:WSW, solid red: SSW, solid blue: NNW, dash green: ENE, dash yellow: ESE, dash red: SSE, and dash blue:NNE).


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5 December 2014


Caption. The concentric eyewall duration time in the western North Pacific and Atlantic.


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5 December 2014

Diagnosis of TC inner-core wind structure

Using a two-dimensional (2D) aircraft reconnaissance wind field analysis dataset described in Knaff et al. (2014) a multiple linear regression model has been developed to relate the azimuthal wavenumber 0-2 amplitudes and phases of the 2D wind fields to a variety of parameters, including the storm’s current intensity, position, and motion and the principle components of the 2D empirical orthogonal functions (EOFs) describing the structures in the MI dataset

This model thereby estimates the inner-core wind structure from MI imagery.


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5 December 2014

Example results

Intensity = 95 kt

Caption. (left) TMI polarization corrected temperatures (K) (37 GHz) of Hurricane Katrina (2005) at 0400 UTC 27 August. Missing data are seen in this image where there is land and in the northeast due to the region falling outside of the satellite swath. (right) Model diagnosed flight-level tangential winds (kt) associated with the 37-GHz MI at left.

246@7 kt


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Research studies dedicated to using MI to diagnose and predict TC intensity and structure should continue to be supported

Research to operations should be facilitated in a timely manner to fully exploit current state-of-the-art forecasting techniques that benefit from the usage of MI

Maintenance of a robust fleet of low-earth orbiting satellites with passive microwave sensors should be continued.


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Estimating TC structure from coastal radar

5 December 2014

Improve and automate objective estimate of TC circulation center

Important for tangential wind estimates/vortex characterization

Caption. Comparison of Hurricane Danny’s circulation centers. (a)–(b) Centers from the original GBVTD-simplex algorithm at heights 2–5 km, from top to bottom, respectively. (e)–(f) Centers from the objective statistical center-finding method. Solid black line indicates track derived from KLIX radar, and dashed gray line indicates track from KMOB radar. (Bell and Lee 2012)


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5 December 2014

Improve cross-vortex mean wind estimate

Harasti (2014) assumed the TC primary circulation follows a Rankine-combined vortex a hurricane VVP (HVVP) algorithm estimates the

full cross-vortex mean wind vector within 2 m/s accuracy while the radar is located beyond a distance of 2.5 times the RMW

Chen et al. (2013) proposed a Modified Ground-Based Velocity Track Display (GBVTD) or MGBVTD technique that combined the GBVTD and HVVP to further improve the accuracy of the retrieved TC primary circulation. The MGBVTD-retrieved mean wind vector and

primary circulation were within 2 m/s compared with those derived from airborne pseudo-dual-Doppler analysis in Hurricane Bret (1999)


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5 December 2014

Improve cross-vortex mean wind estimate

Caption. Ground-relative wind speed for Hurricane Bret at 2-km altitude MSL calculated from the (a),(c) KCRP CAPPI map and (b),(d)KBRO CAPPI map, by (top)GBVTD and (middle)MGBVTD using KCRP-retrieved mean wind as well as (e) MGBVTD using KBRO-retrieved mean wind.Duel


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5 December 2014

Problem: velocity folding occurs in Dopplar wind estimates De-aillising is time consuming but typically necessary Wang et al. (2012) proposed the Gradient Velocity Track

Display (GrVTD) technique that reformulates the GBVTD algorithm to directly ingest the aliased Doppler velocity field.

By dealing with Doppler velocity gradient, GrVTD is more sensitive to Data artifacts Data voids

The GrVTD produces good results when there is adequate data coverage, but input contains multiple velocity folds.


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Support research to 1) overcome geometric distortion issues and 2) better estimate the asymmetric radial circulation in radar wind retrievals

Support research to better use polarimetric radar and biologic target information in radar wind retrievals

Continue to support efforts to assimilate radar data into models


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Size and wind structure assessment from scatterometer data

5 December 2014

Several studies (Lee et al. 2010, Chen et al. 2011,Chan and Chan 2013) have used QuikSCAT wind vectors to study the evolution and behavior of TC wind structure, particularly 17m/s (34-kt) winds


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5 December 2014

Method for data inclusion: I. TC must be at tropical storm intensity or above

(maximum sustained wind ≥ 17 m s-1) II. The TC center must be covered by the swath III. The distance between the TC center and the edge of the

swaths must be > 1° latitude IV. More than 50% of the TC circulation is covered by the

swath V. The TC circulation should have no extensive wind-

discontinuity problem VI. Azimuthally-averaged wind speed profile must reach 17

m s-1 or above after filtering all rain-flagged data VII. R17 is not close to any landmass VIII. Rain-flagged data are excluded


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5 December 2014

Method for estimating TC size: The number of available (not rain-flagged) data points

in each ring belt must be > 5 without considering the wind directions.

The fraction of available data points to total data points in each ring belt must be ≥ 0.5.

It is assumed that a TC behaves like a Rankine vortex outside the radius of maximum winds, that is,

Solve for r, where v=17m/s

𝑣𝜃ሺ𝑟ሻ= 𝐶𝑟−𝑏


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Results of these structure studies

5 December 2014

Correlation between size and intensity is weak TCs typically deviate from a size that is determined during

formation and synoptic origin often determines initial sizes Early intensification stages favor smaller storms and smaller

storms are more likely to undergo rapid intensification Composite IR brightness temperature shows that compact

tropical cyclones have highly axisymmetric convective structures with strong convection concentrated in a small region near the center

Changes in angular momentum (AM) transport in the upper and lower troposphere appear to be important factors that affect TC intensity and size Developing/strengthening (weakening) low-level environmental

anticyclones favors growth (decay) Poleward movement leads to growth


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Importance of angular momentum5 December 2014

(a) Composite of the changes in lower-tropospheric (850-hPa) synoptic flow of the growing TCs within 24 hours. (b) As in (a) but for the shrinking TCs. (c) As in (a) but for the recurving and growing TCs. (d) As in (a) but for the recurving and shrinking TCs. Regions with dots denote that the changes in tangential or radial wind different from 0 have a confidence level higher than 90%.

(a) (b)

(c) (d)


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Support of research to continue our understanding of TC size changes

Support of investments in future technology to estimate surface winds in the TC environment

Support efforts to automate the quantification TC size from scatterometry data and other near surface winds (i.e. fixes for operational centers).


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Size and wind field inferred from infrared satellite-based observations

5 December 2014

In the last four years there have been renewed efforts to make improved use of routine IR satellite imagery. New techniques have been developed that estimate intensity, wind structure, and size.


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5 December 2014

Deviation Angle Variance Technique (DAV-T,Piñeros et al. 2011; Ritchie et al. 2012)

The calculation of the DAVfor an idealized infrared image of a perfectly axisymmetric tropicalcyclone: (a) IR image. (b) The calculated vector gradient field for the box indicated in (a) assuming thecenter pixel of the image is used as the reference pixel (filled circle, d). (c) Histogram of all deviationangles within a 350-km radius of the center pixel in (b). Because the image in (a) is perfectly axisymmetricand the pixel at the very center of the vortex is chosen as the reference pixel, all deviation angles are equal to zero. (d)–(f) As in (a)–(c), but using a different reference pixel.


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5 December 2014

From

Ritchie et al. (2014), top two

Ritchie et al (2012), bottom


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5 December 2014

Knaff et al. (2014)

Uses:

30 years of data

1-D IR principle components related to the tangential winds at 500 km radius

• 1-D IR EOFs

• Largest and smallest Hurricanes

Abe (1990) Kay (1998)

SmallestLargest

Non-majorhurricanes

Majorhurricanes


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5 December 2014

Dolling et al. (2014)

Using DAVT to estimate TC wind radii


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5 December 2014

Knaff et al. (2015)

Uses

IR 2-D priniciple components

Operational intensity and motion

An illustration of the steps taken to estimate the wind field. The progression is from left to right and then top to bottom. Imagery are mapped to a polar grid (1) and then rotated with respect to direction (2). Rotated imagery (via principle components), translation speed, latitude and intensity are then used to estimate the normalized wind field (3). The observed intensity is then applied to create a wind speed field (4). Finally the wind field is rotated back to its earth relative directional component (5). This case is from Hurricane Ike (2008) on September 12 at 1145 UTC


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Support research (new and existing) efforts to use routine IR imagery to objectively diagnose TC structure, including intensity

Support routine best tracking of wind radii and other size parameters by operational and climatological centers

Support research and operational validation of these TC size/structure/wind radii estimation methods.


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Thank you for your attention!!!

5 December 2014

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Questions/Comments

5 December 2014

Documents

OBJECTIVE STRUCTURE ANALYSIS Working Group: J. Knaff (NESDIS, Lead), Michael Bell (U. Hawaii – USA), Johnny C. L. Chan (City Univ. of Hong Kong), Kelvin