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Hurricane center-fixing with the Automated Rotational Center Hurricane Eye Retrieval (ARCHER) method Tony Wimmers, Chris Velden University of Wisconsin.

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Presentation on theme: "Hurricane center-fixing with the Automated Rotational Center Hurricane Eye Retrieval (ARCHER) method Tony Wimmers, Chris Velden University of Wisconsin."— Presentation transcript:

1 Hurricane center-fixing with the Automated Rotational Center Hurricane Eye Retrieval (ARCHER) method Tony Wimmers, Chris Velden University of Wisconsin - Cooperative Institute for Meteorological Satellite Studies (CIMSS) Sponsored by The Oceanography of the Navy through the PEO C4I PMW-150 program office and the Naval Research Laboratory

2 Motivation Forecasting (track) Forecasting (track) Input into automated retrievals (intensity, eye diameter, ERC…) Input into automated retrievals (intensity, eye diameter, ERC…)  Real time and reanalysis

3 Objective Automated, robust location of TC rotational centers in individual microwave or IR images Automated, robust location of TC rotational centers in individual microwave or IR images Must be resilient to false eyes (moats), obstructions in the eye, partial eyes, partial scan coverage Must be resilient to false eyes (moats), obstructions in the eye, partial eyes, partial scan coverage Must rely only loosely on a first- guess (forecast) position estimate Must rely only loosely on a first- guess (forecast) position estimate Must apply to microwave and IR imagery Must apply to microwave and IR imagery “First guess” Center fix 85 GHz (H) TMI retrieval Additional information can be found in Wimmers, A. and C. Velden, 2010: Additional information can be found in Wimmers, A. and C. Velden, 2010: Objectively Determining the Rotational Center of Tropical Cyclones in Passive Microwave Satellite Imagery, J. Appl. Meteor., 49, 2013–2034, 2010.

4 ARCHER overview 1) Produce a 2D field (contoured) that expresses how well a location registers as the center of the large-scale spiral pattern “Spiral Score” 2) Produce a separate 2D field that rates how well a location is centered inside a circular ring of convection “Ring Score” 3) Combine the two 2D fields as a weighted sum into a single score field “Combined Score”

5 ARCHER: Additional procedures Images are pre-processed to compensate for a ~12 km parallax shift Images are pre-processed to compensate for a ~12 km parallax shift If the Combined Score at the center fix does not exceed a certain threshold value, then the algorithm defaults back to the first guess position If the Combined Score at the center fix does not exceed a certain threshold value, then the algorithm defaults back to the first guess position

6 Example: Unresolved eye (Dennis 2005) 1) Wider domain

7 Example: Unresolved eye (Dennis 2005) 2) Zoomed view

8 Example: Unresolved eye (Dennis 2005) 3) With best track position

9 Example: Unresolved eye (Dennis 2005) 4) With simulated forecast position (first guess)

10 Example: Unresolved eye (Dennis 2005) 5) Spiral score

11 Example: Unresolved eye (Dennis 2005) 6) Ring score

12 Example: Unresolved eye (Dennis 2005) 7) Combined score

13 Example: Unresolved eye (Dennis 2005) 8) Compare to best track position “First guess” Center fix Best track

14 Example: Asymmetric eye (Chaba 2010) 1. Strong, complete eyewall Center-fix

15 Example: Asymmetric eye (Chaba 2010) 2. Shearing leads to asymmetric eyewall pattern Center-fix

16 Example: Asymmetric eye (Chaba 2010) 3. Eyewall only evident on the western side Center-fix

17 Example: Asymmetric eye (Chaba 2010) 4. Sub-pixel eye and banding only on the west Center-fix

18 Example: Asymmetric eye (Chaba 2010) 5. Sub-pixel eye and a developing secondary eyewall Center-fix

19 Example: Asymmetric eye (Chaba 2010) 6. Completed eyewall replacement cycle Center-fix

20 Validation: 2005 season, North Atlantic Independent from calibration sample Independent from calibration sample Uses the NHC best track as “truth” Uses the NHC best track as “truth” Only uses cases that are < 3 hours from an aircraft position fix Only uses cases that are < 3 hours from an aircraft position fix

21 Effect of vertical wind shear The algorithm performance will degrade in cases of high vertical shear, because of displacement between the centers of rotation at the surface and the image height, and also loss of symmetry The algorithm performance will degrade in cases of high vertical shear, because of displacement between the centers of rotation at the surface and the image height, and also loss of symmetry Because of this we separate the test sample into Group A (low/moderate shear) and Group B (high shear) Because of this we separate the test sample into Group A (low/moderate shear) and Group B (high shear) Group B is <10% of the sample Group B is <10% of the sample The ARCHER error for Group B averaged to be about double that of Group A The ARCHER error for Group B averaged to be about double that of Group A

22 Results: GHz (H) Group A RMS (w/o defaults) 0.06  RMS (all) 0.15  0.06  0.15 

23 Results: GHz (H) Group A RMS (w/o defaults) 0.06  RMS (all) 0.15 

24 Results: GHz (H) Group A RMS (w/o defaults) 0.06  RMS (all) 0.15  Default rate37%

25 Results: GHz (H) Default rate Trop. storm83% (*) Category 115% Category % Group A RMS (w/o defaults) 0.06  RMS (all) 0.15  Default rate37%

26 Tropical Storm example Center fix Best track (.25  away)

27 ARCHER: Adapting to IR imagery Calibrated and validated to a probability density function (PDF) Calibrated and validated to a probability density function (PDF) Error, degrees (Low organization) Organization score = 0.91 (Medium organization) Organization score = 1.55 (High organization) Organization score = 2.60

28 ARCHER: Output for IR imagery Numerical output: Numerical output: Forecast position (lon, lat): , Center-fix position (lon, lat): , Eye confidence (%) = 31 Error distribution parameter (alpha) = 6.39 Probability of error < 0.2° (%) = 36.5 Probability of error < 0.4° (%) = 72.4 Probability of error < 0.6° (%) = 90.0 Probability of error < 1.0° (%) = 98.8 Graphical output: Graphical output: Center-fix Spiral grid Combined grid Often ~95% for more organized TCs

29 Final remarks Distribution A free, licensed version of ARCHER is available for distribution (Matlab code). A free, licensed version of ARCHER is available for distribution (Matlab code).HURSAT ARCHER does work with HURSAT and yields good results, although the imagery becomes twice-interpolated (once from HURSAT and then again by ARCHER), which means the result can often be improved by using original data. ARCHER does work with HURSAT and yields good results, although the imagery becomes twice-interpolated (once from HURSAT and then again by ARCHER), which means the result can often be improved by using original data. Ongoing work Current work involves a cross-comparison of ARCHER accuracy for microwave and IR imagery, and also finding the best way of combining microwave/IR results into a single storm track. Current work involves a cross-comparison of ARCHER accuracy for microwave and IR imagery, and also finding the best way of combining microwave/IR results into a single storm track.

30 ARCHER: Adapting to IR imagery Calibrated and validated to a probability density function (PDF) Calibrated and validated to a probability density function (PDF) (Low organization)(High organization)(Medium organization) Error, degrees Organization score = 0.91Organization score = 1.55Organization score = 2.60

31 Extras

32 Forecast position error North Atlantic West Pacific

33 ARCHER: “Spiral Score” component High values occur where the vector field lines up with the image gradients High values occur where the vector field lines up with the image gradients

34 ARCHER: “Spiral Score” component High values occur where the vector field lines up with the image gradients High values occur where the vector field lines up with the image gradients

35 Summary ARCHER has several unique innovations: ARCHER has several unique innovations: – Balances the evidence from large-scale spiral edges with small-scale eyewall patterns – Has an optimized default to the first guess as a backup option – Validated with a large, independent sample of images The center-fix accuracy is ~16 km in all cases with low-to-moderate shear and ~6 km for non-default cases only. The center-fix accuracy is ~16 km in all cases with low-to-moderate shear and ~6 km for non-default cases only. Current applications include TC visualization, TC diameter size retrieval, intensity estimation and prediction of rapid intensification. Current applications include TC visualization, TC diameter size retrieval, intensity estimation and prediction of rapid intensification.

36 Applications (1 of 4): MIMIC (MIMIC: Morphed Integrated Microwave Imagery at CIMSS) using multi-satellite GHz retrievals. Finding the center of rotation of each image is critical to blending them together properly. (MIMIC: Morphed Integrated Microwave Imagery at CIMSS) using multi-satellite GHz retrievals. Finding the center of rotation of each image is critical to blending them together properly.

37 Applications (2 of 4): Microwave-based Intensity est. Eye and eyewall statistics in 85 GHz images add important TC intensity information to the MW-ADT at CIMSS in the kt range, when eyes are often obscured by central dense overcast Eye and eyewall statistics in 85 GHz images add important TC intensity information to the MW-ADT at CIMSS in the kt range, when eyes are often obscured by central dense overcast Diagnostic image for TC 26W (2009), leading to a MW-ADT estimate of Vmax = 73 kts. JTWC estimate was at 65 kts. [24 Nov 1052 UTC]

38 Applications (3 of 4): TC diameter information ARCHER RMW is significantly lower than the value produced by JT. A lower RMW would contribute to a better SATCON (Satellite Consensus) estimate for this TC, indicating that it is probably more accurate (see Herndon and Velden: SATCON Evaluation and Recent changes, Poster 33). ARCHER RMW is significantly lower than the value produced by JT. A lower RMW would contribute to a better SATCON (Satellite Consensus) estimate for this TC, indicating that it is probably more accurate (see Herndon and Velden: SATCON Evaluation and Recent changes, Poster 33).

39 Applications (4 of 4): Rapid intensification Certain characteristics of the eyewall in 37 GHz imagery can indicate rapid intensification, but previously this has only been shown manually*. Certain characteristics of the eyewall in 37 GHz imagery can indicate rapid intensification, but previously this has only been shown manually*. The ARCHER algorithm can automate this method by identifying the eye and eyewall The ARCHER algorithm can automate this method by identifying the eye and eyewall This is described in the talk, Improvements in the Statistical Prediction of TC Rapid Intensification [Rozoff et al. Thursday am session] This is described in the talk, Improvements in the Statistical Prediction of TC Rapid Intensification [Rozoff et al. Thursday am session] * Kieper (2008; 28 th AMS Conf. on Hurr. and Trop. Meteor.)


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