A consensus approach to operationally estimate and forecast tropical cyclone wind radii John Knaff (John.Knaff@noaa.gov) 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

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

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

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

Facts 11/15/2016 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

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?

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.

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)

Microwave-Sounder-based Methods 11/15/2016 Algorithm (Demuth et al. 2004, 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 R34 - 177, 231, 169, 135 - Statistical retrieval

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 105 85 75 100 50-kt 45 40 35 45 64-kt 20 15 15 20 SAB (102 kt) NE SE SW NW 34-kt 125 105 100 125 50-kt 60 50 50 60 64-kt 30 25 20 30

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

Multi-satellite-platform TC Sfc wind Analysis 11/15/2016 Algorithm (Knaff et al. 2011) Analysis of Typhoon Haima (wp252012, 20 Oct. 18 UTC) Inputs: TC location TC intensity IR-based flight-level proxy winds at 700 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

6-hour Forecasts from NWP 11/15/2016 Marchok Tracker Analysis of Typhoon Soudelor 2015 7 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)

Creating the Consensus (OBTK) 11/15/2016 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

Errors/Biases of the Members 11/15/2016 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 2014-2015 best tracks coincident with scatterometer passes in the Atlantic and eastern North Pacific, and (bottom) for entire western North Pacific 2014-2015 JTWC best track data set. Standard error is shown as black bars on means, and the number of cases is shown in parentheses.

Availability of the Members 11/15/2016 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 2014-2015 seasons.

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

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

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

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

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

The “Radii Analysis Button” 11/15/2016 International Workshop on High Winds Over the Ocean R50= -54.1 + 0.50*vmax + 0.63 * ( R34 +58.5 -0.71*vmax) R64= -32.9 + 0.29*vmax + 0.35 * ( R34 +58.5 -0.71*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.

Why Objective Radii Estimates Are Needed? 11/15/2016 R50= -54.1 + 0.50*vmax + 0.63 * ( R34 +58.5 -0.71*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.

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.

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.

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)

The “ Radii Forecast Button” 11/15/2016 International Workshop on High Winds Over the Ocean

Justification for Interpolation/Phase out 11/15/2016 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.

Performance in the Atlantic 11/15/2016 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.

Example RVCN for Typhoon Haima (WP252016) 11/15/2016 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

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. 50- 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

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

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 http://rammb.cira.colostate.edu/products/tc_realtime/index.asp

Extra slides 11/15/2016 International Workshop on High Winds Over the Ocean

References used: 11/15/2016 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, 282-296. 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. 3249-3272. 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), 2149-2166. 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, 1233-1247. Sampson C. R, and J. A. Knaff, 2015: A consensus forecast for tropical cyclone gale wind radii. Wea. Forecasting, 30, 1397-1403. 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

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

Multi-satellite-platform approach 11/15/2016 Algorithm (Knaff et al. 2011) Analysis of Typhoon Bolaven (2012) Inputs: TC location TC intensity IR-based flight-level proxy winds at 700 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

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

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

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

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

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

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