1. A consensus approach to operationally estimate and forecast tropical cyclone wind radii
John Knaff
NOAA Center for Satellite Applications and Research, Fort Collins, CO
Buck Sampson – Naval Research Laboratory, Monterey, CA
Brian Strahl – Joint Typhoon Warning Center, Pearl Harbor, HI

2. A special thanks to:
The European Space Agency for providing travel funding (airfare + accommodations)
11/15/2016
Outline
What are tropical cyclone wind radii?
Why we should care?
Facts about how they are created
An objective method to estimate tropical cyclone wind radii at t = 0
Why getting good initial estimates is important?
A method to improve forecasts of wind radii, particularly gale-force winds
International Workshop on High Winds Over the Ocean

3. What are wind radii?
11/15/2016
The maximum extent of 34-, 50-, and 64-kt winds from the center of the storm
Recorded in geographic quadrants from the storm (NE, SE, SW, NW).
These quantities are estimated for each tropical cyclone advisory
50-kt
34-kt
International Workshop on High Winds Over the Ocean

4. Operational wind Radii: Why do We Care?
11/15/2016
Wind radii are the only operational estimate of the surface wind structure
Are provided to NWP via TC vitals
Are input to the wind speed probability product
They are the basis for values in the best tracks and all the validation that uses them
They are important in determining the size/length of watches, warnings, and MSLPs
They are important for determining waves, wave setup and storm surge
International Workshop on High Winds Over the Ocean

5. Facts
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Wind radii estimates and forecasts are often the last things determined in the forecast cycle
Wind radii determination is often difficult or time consuming due to data availability and quality resulting in obvious stair-stepping pattern
Forecasters often feel that they have no tools to do the wind radii part of the forecast
There are lots of methods out there that make wind radii estimates and they are pretty noisy/lousy.
JTWC’s wind radii have been often close to the CLIPER Model (Knaff et al. 2007), which is a poor (too small) West Pacific climatology - these habitually end up in the best track files
International Workshop on High Winds Over the Ocean

6. Motivations for an Objective Method
11/15/2016
R34
Few tools for forecasters
Stair-stepping
Waiting for the ASCAT
Persistence during intensification
Generally too small
Leads to poor initial estimates
Bad for NWP
Bad for warning tools
Leads to Bad things.
International Workshop on High Winds Over the Ocean
Real-time
R50
R50 for CHAN-HOM (WP092015). Real-time estimates (lower left) rely on scat passes and observations, but what if there aren’t any?

7. Motivations for an Objective Method
11/15/2016
R34
9 July, 06 UTC
Kadena, AB in TC-COR 4
10 July, 06 UTC
Kadena, AB in TC- COR 1
Storm Warning
International Workshop on High Winds Over the Ocean
Verification
38KT/G66
Real-time
R50
R50 for CHAN-HOM (WP092015). Real-time estimates (lower left) rely on scat passes and observations, but what if there aren’t any? Objective radii would rely on estimates from AMSU, Dvorak, multi-platform wind analyses and NWP model analyses.

8. Solution 1: Initial Estimates
A evenly-weighted consensus 34-kt wind radii
Satellite estimates
Microwave-sounder-based methods (NOAA-15,18, 18,19, Metop-A, B (two retrievals statistical, MIRS)
Infrared method [Dvorak fixes (JTWC, SAB)]
Multi-satellite-platform approach (NESDIS)
6-h model forecasts
GFS/AVN (NOAA Global Forecast System)
HWRF (Hurricane Weather Research and Forecast Model)
GFDL (Geophysical Fluid Dynamics Laboratory Hurricane Model)
COAMPS-TC (Navy’s Coupled Ocean and Atmosphere Mesoscale Prediction System)
50-kt and 64-kt are estimated (statistically) based on the 34-kt consensus estimates
Forecaster dialog that fills in the initial wind radii estimates “THE RADII ANALYSIS BUTTON” – EASY!!!
11/15/2016
Tropics / Subtropics
Tropics / Subtropics/ Mid-lat
International Workshop on High Winds Over the Ocean
(Sampson et al. 2017, submitted)

9. Microwave-Sounder-based Methods
11/15/2016
Algorithm (Demuth et al , 2006)
Azimuthally averaged (Ta, Vt) Typhoon Lionrock
Inputs:
Retrieve T(x,y,z) from all AMSU-A channels (statistical or via MIRS)
Hydrostatic integration for P(x,y,z)
Estimate Gradient wind for V(r,z)
Statistical Prediction
Independent Variables:
Parameters from retrieved T, P, V (right)
Max Wind (Vmax, latest advisory)
Dependent Variables:
Azimuthally averaged (non-zero) wind radii
(r34, r50, r64)
Procedure:
Fit parametric wind model to with predicted r34, r50, r64
Use storm speed/direction get asymmetric radii
Output to ATCF fix format
Estimate non-linear balanced winds at standard pressure levels (other uses)
International Workshop on High Winds Over the Ocean
R , 231, 169, Statistical retrieval

10. Infrared Method via Dvorak Fixes
11/15/2016
Algorithm (Knaff et al. 2016)
Hurricane Amanda (2014) at 0000 UTC 25 May
From the working best track (90 kt)
Inputs:
TC location (Dvorak)
TC intensity (Dvorak)
TC latitude (Dvorak)
TC heading (Dvorak)
TC speed (Dvorak)
Procedure:
Center IR image
Calculated azimuthally averaged PCs
Calculate TC size (R5)
Normalize R5
Estimate azimuthally average r34, r50, r64
Estimated RMW (vmax, lat)
Calculate asymmetries and phase for a Rankine vortex
Estimated r34, r50, r64
Output to ATCF fix format
International Workshop on High Winds Over the Ocean
R5 obtained following Knaff et al. 2014
From the Dvorak Fixes:
TAFB (77 kt) NE SE SW NW
34-kt
50-kt
64-kt
SAB (102 kt) NE SE SW NW
34-kt
50-kt
64-kt

11. Details of TC Size via R5 R5 or TC size R5 Climatology (R5c)
11/15/2016
R5 or TC size
Create a multiple linear regression that estimates TC circulation (V500, based on GFS) based on
IR principle components ( first 3)
Storm latitude
Estimates TC size by scaling TC circulation to a radius where the TCs influence vanishes at 850 hPa (R5) using a climatological vortex decay rate
R5 has units of degrees latitude and explains ~ 30% of the variance.
R5 Climatology (R5c)
International Workshop on High Winds Over the Ocean
𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 𝑅5−𝐹 𝑅5 = 𝑅5 𝑅5 𝑐

12. Multi-satellite-platform TC Sfc wind Analysis
11/15/2016
Algorithm (Knaff et al )
Analysis of Typhoon Haima (wp252012, 20 Oct. 18 UTC)
Inputs:
TC location
TC intensity
IR-based flight-level proxy winds at hPa based on Mueller et al. (2006) uses observed intensity
Cloud Drift/Feature track winds below 600 hPa
MW-Sounder-based non-linear-balanced winds at 700 hPa
Scatterometry (A-Scat, two satellites)
Procedure:
Compile winds in a storm motion relative framework (9 h window)
Adjust winds to a common level (700 hPa)
Perform a variational analysis on a polar grid
Adjust winds from flight-level to the surface
Estimate MSLP and wind radii
Output to ATCF fix format
International Workshop on High Winds Over the Ocean

13. 6-hour Forecasts from NWP
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Marchok Tracker
Analysis of Typhoon Soudelor Aug 00UTC
NWP model output is typically not available during the forecast cycle
Six-hour forecast of several NWP models are used as psuedo-fixes in the fdeck
Positional errors are small (similar to wind radii fixes) an are neglected
These wind radii psuedo- fixes are used as members of the consensus
GFS
GFDL
International Workshop on High Winds Over the Ocean
HWRF
Vitals (-6)

14. Creating the Consensus (OBTK)
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Requires 4 fixes
Initially looks in a three hour window for 4 fixes
Window expands until 4 fixes are found
Consensus is produced by the fixes contained in the time window
As with any consensus more is better!
For objective best tracks a centered binomial filter is passed over each quadrant 10 times.
Satellite Based Estimates
Cyclone Fantala (sh192016)
International Workshop on High Winds Over the Ocean

15. Errors/Biases of the Members
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North Atlantic / East Pacific
With Scatterometry
Key:
AMSU (AMSU)
Multi-platform (CIRW)
Dvorak-based (DVRK)
GFDL Model 6h fcst (GFDT)
HWRF Model 6h fcst (HWRF)
GFS Model 6h fcst (AVNO)
International Workshop on High Winds Over the Ocean
Western North Pacific
Special wind radii best track
Caption: 34-kt wind radii fix mean errors (brown) and biases (blue) relative (top) to NHC best tracks coincident with scatterometer passes in the Atlantic and eastern North Pacific, and (bottom) for entire western North Pacific JTWC best track data set. Standard error is shown as black bars on means, and the number of cases is shown in parentheses.

16. Availability of the Members
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International Workshop on High Winds Over the Ocean
Key:
AMSU (AMSU)
Multi-platform (CIRW)
Dvorak-based (DVRK)
GFDL Model 6h fcst (GFDT)
HWRF Model 6h fcst (HWRF)
GFS Model 6h fcst (AVNO)
Caption: Wind radii availability based on 4251 best track wind radii estimates for the JTWC western North Pacific seasons.

17. WP252015 Consensus Operational 11/15/2016
International Workshop on High Winds Over the Ocean
Consensus
Operational

18. WP162016 Consensus Operational 11/15/2016
International Workshop on High Winds Over the Ocean
Consensus
Operational

19. SH192016 Consensus Operational 11/15/2016
International Workshop on High Winds Over the Ocean
Consensus
Operational

20. SH192016 Consensus Operational 11/15/2016
International Workshop on High Winds Over the Ocean
Consensus
Operational

21. IO042015 Consensus Operational 11/15/2016
International Workshop on High Winds Over the Ocean
Consensus
Operational

22. The “Radii Analysis Button”
11/15/2016
International Workshop on High Winds Over the Ocean
R50= *vmax * ( R *vmax)
R64= *vmax * ( R *vmax)
Equally weighted average of R34 estimates (AMSU, Dvorak wind radii, NWP model 6-h forecasts, possibly scatterometer) to get dashed line. From R34 estimates we compute R50 and R64 using regression, then fill in all with button.

23. Why Objective Radii Estimates Are Needed?
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R50= *vmax * ( R *vmax)
R34
Objective R50
International Workshop on High Winds Over the Ocean
Verification
38KT/G66
Real-time
R50
R50 for CHAN-HOM (WP092015). Real-time estimates (lower left) rely on scat passes and observations, but what if there aren’t any? Objective radii rely on estimates from AMSU, Dvorak (Knaff et al. 2016), multi-platform wind analyses (Knaff et al. 2011) and NWP model analyses.

24. Reduced “Stair Stepping”
Why Objective Radii Estimates Are Needed?
Reduced “Stair Stepping”
11/15/2016
International Workshop on High Winds Over the Ocean
Four panel shows the objective gradually expanding the R34, as opposed to the stair stepping real-time JTWC estimates. The "theory" behind the stair step is that there are no observations, and then there are. The objective algorithm smooths that out.

25. Why Objective Radii Estimates Are Needed?
11/15/2016
Intensity
Errors
Track
Errors
International Workshop on High Winds Over the Ocean
Intensity
Bias
Bender et al. 2017, in preparation
Two years of west Pacific forecasts were rerun using the current GFDL hurricane model. The control used the real-time wind radii estimates and the experiment used the objectively determined wind radii. Results suggest 1) intensity improvements and 2) no degradation in track forecasts.

26. Solution 2: Wind Radii Forecasts
A evenly weighted consensus of radii of 34-kt winds (RVCN)
Numerical Weather Prediction Models
NCEP Global Forecast System (AHNI)
European Center for Medium Range Forecasts (EMXI)
Hurricane Weather Research and Forecast (HHFI)
Geophysical Fluid Dynamics Laboratory - Hurricane (GHTI)
Coupled Ocean Atmosphere Mesoscale Prediction System –TC (COTC) – Added in West Pacific following publication.
* 6-h old forecast interpolated (bias corrected) to the initial wind radii estimates, phased out after 36h
* Bias correction never phased out
Forecasts of 34-kt wind radii (50-, 64- kt remain experimental)
Forecaster dialog that fills in the initial and forecast wind radii estimates the “RADII FORECAST BUTTON” – EASY!!!
Near future capability (2017) Statistical-dynamical method based on large-scale environmental diagnostics.
11/15/2016
International Workshop on High Winds Over the Ocean
(Sampson and Knaff 2015)

27. The “ Radii Forecast Button”
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International Workshop on High Winds Over the Ocean

28. Justification for Interpolation/Phase out
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International Workshop on High Winds Over the Ocean
Caption: (top) Forecast MAE increase from removing R34 bias correction from the interpolator. (bottom) Mean forecast bias for aids with (solid) and without (dotted) R34 bias correction. The sample is homogeneous from the Atlantic during 2012–14 and the numbers of forecasts are 2696, 2532, 2268, 2008, 1732, 1272, 932, and 668 at 0, 12, 24, 36, 48, 72, 96, and 120 h, respectively.

29. Performance in the Atlantic
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International Workshop on High Winds Over the Ocean
Caption: For the Atlantic basin during 2012–14, the (top left) R34 MAE of individual NWP model aids and OFCL, (top right) R34 mean bias, (bottom left) hit rate, and (bottom right) FAR. The homogeneous set is considered to be dependent. The numbers of forecasts are 2628, 2436, 2172, 1892, 1644, 1212, 904, and 636 at 0, 12, 24, 36, 48, 72, 96, and 120 h, respectively.

30. Example RVCN for Typhoon Haima (WP252016)
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RVCN
Important: Forecaster can start by using RVCN as the initial 34-kt wind radii forecast using a dialog. Once in use it can be modified
In this case track and intensity are also provided by consensus approaches.
International Workshop on High Winds Over the Ocean
Objective
Subjective

31. Summary (Solution #1, Initial Wind Radii)
A consensus method has been developed to estimate initial wind radii for the operational forecaster
This method makes use of satellite-based and model- forecast-based 34-knot wind radii and 64- knot winds are estimated via statistical relationships with 34- knot wind radii
The method 1) has been automated, 2) compares well with NHC best tracks and A-SCAT observations, 3) provides a wind radii estimate tool for JTWC forecasters, and 4) saves forecasters time
The method results in a consistent and stable estimate that is more ideally suited for model initialization
This capability should result in dramatic improvements of JTWC’s wind radii estimates
More quality techniques/fix types will only improve wind radii estimates using this technique (ideas from this workshop?)
11/15/2016
International Workshop on High Winds Over the Ocean

32. Summary (Solution #2, Forecasting Wind Radii)
A consensus method has been used to forecast wind radii, 34-kt winds, for the operational JTWC forecaster
This method makes use of existing model-forecast guidance 34-knot wind radii
The six-hour old forecast is interpolated (bias corrected) for most models, with the bias correction phasing out after 36h.
The method 1) has been automated 2) compares well with NHC best tracks, and NHC forecasts 3) provides a wind radii estimate tool for JTWC forecasters, and 4) saves forecasters time
This capability should result in dramatic improvements of JTWC’s wind radii forecasts and has led to JTWC now issuing 34-kt wind radii forecasts through 120 h
More quality forecast techniques/models will only improve this technique
11/15/2016
International Workshop on High Winds Over the Ocean

33. Thank ESA for getting me here!
I’m interested in new methods to estimate and predict TC structure, especially the surface winds
I would very much like to collaborate on these topics:
Surface wind fields without aircraft reconnaisance
Estimating the radius of maximum winds without aircraft
What processes lead to growth during intensification or growth during steady or slowly weakening storms
11/15/2016
International Workshop on High Winds Over the Ocean
Check out

34. Extra slides
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International Workshop on High Winds Over the Ocean

35. References used:
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Demuth, J. L., M. DeMaria, J. A. Knaff, and T.H. Vonder Haar, 2004: Evaluation of advanced microwave sounder unit (AMSU) tropical cyclone intensity and size estimation algorithm, J. App. Met., 43, Demuth, J., M. DeMaria, and J. A. Knaff, 2006: Improvement of Advanced Microwave Sounding Unit Tropical Cyclone Intensity and Size Estimation Algorithms, Journal of Applied Meteorology and Climatology, 45(11), 1573–1581. Mueller, K. J., M. DeMaria, J. A. Knaff, J. P. Kossin, T. H. Vonder Haar:, 2006: Objective Estimation of Tropical Cyclone Wind Structure from Infrared Satellite Data. Wea Forecasting, 21(6), 990–1005. Knaff, J. A., C. R. Sampson, M. DeMaria, T. P. Marchok, J. M. Gross, and C. J. McAdie, 2007: Statistical Tropical Cyclone Wind Radii Prediction Using Climatology and Persistence, Wea. Forecasting, 22(4), 781–791. Boukabara, S. A. and Co-authors 2011: MiRS: An All-Weather 1DVAR Satellite Data Assimilation and Retrieval System. IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 9, pp Knaff, J. A., M. DeMaria, D. A. Molenar, C. R. Sampson and M. G. Seybold, 2011: An automated, objective, multi-satellite platform tropical cyclone surface wind analysis. J. of Applied Meteorology and Climatology. 50(10), Knaff, J. A., C. J. Slocum, K. D. Musgrave, C. R. Sampson, and B. R. Strahl, 2016: Using routinely available information to estimate tropical cyclone wind structure. Mon. Wea. Rev., 144:4, Sampson C. R, and J. A. Knaff, 2015: A consensus forecast for tropical cyclone gale wind radii. Wea. Forecasting, 30, Sampson C. R., E. M. Fukada, J. A. Knaff, B. R. Strahl, M. J. Brennan, and T. Marchok, 2017: Tropical cyclone gale wind radii estimates for the western North Pacific. Submitted Wea. Forecasting.
International Workshop on High Winds Over the Ocean

36. Microwave-Sounder-based Methods
11/15/2016
Algorithm
Azimuthally averaged (Ta, Vt)
Inputs:
Retrieve T(x,y,z) from all AMSU-A channels (statistical or via MIRS)
Hydrostatic integration for P(x,y,z)
Estimate Gradient wind for V(r,z)
Statistical Prediction
Independent Variables:
Parameters from retrieved T, P, V (right)
Max Wind (Vmax, latest advisory)
Dependent Variables:
Azimuthally averaged (non-zero) wind radii
(r34, r50, r64)
Proceedure:
Fit parametric wind model to with predicted r34, r50, r64
storm speed/direction get asymmetric radii
Output to ATCF fix format
Estimate non-linear balanced winds at standard pressure levels (other uses)
International Workshop on High Winds Over the Ocean

37. Multi-satellite-platform approach
11/15/2016
Algorithm (Knaff et al )
Analysis of Typhoon Bolaven (2012)
Inputs:
TC location
TC intensity
IR-based flight-level proxy winds at hPa based on Mueller et al. (2006) uses observed intensity
Cloud Drift/Feature track winds below 600 hPa
MI-Sounder-based non-linear-balanced winds at 700 hPa
Scatterometry (A-Scat, two satellites)
Procedure:
Compile winds in a storm motion relative framework (9 h window)
Adjust winds to a common level (700 hPa)
Perform a variational analysis on a polar grid
Adjust winds from flight-level to the surface
Estimated , MSLP and wind radii
Output to ATCF fix format
International Workshop on High Winds Over the Ocean

38. Hurricane Danny (2015) Objective Best Track NHC Best Track 11/15/2016
International Workshop on High Winds Over the Ocean
Objective Best Track
NHC Best Track

39. Hurricane Patricia (2015) Objective Best Track NHC Best Track
11/15/2016
International Workshop on High Winds Over the Ocean
Objective Best Track
NHC Best Track

40. CP032015 Consensus Operational 11/15/2016
International Workshop on High Winds Over the Ocean
Consensus
Operational

41. AL112015 Consensus Operational 11/15/2016
International Workshop on High Winds Over the Ocean
Consensus
Operational

42. AL152016 Consensus Operational 11/15/2016
International Workshop on High Winds Over the Ocean
Consensus
Operational

43. Typhoon Chan-Hom Objective Best Track JTWC Best Track 11/15/2016
International Workshop on High Winds Over the Ocean