Munehiko Yamaguchi

Meteorological Research Institute of Japan Meteorological Agency

　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　

Studies on Tropical Cyclone Forecasting

using TIGGE

11th Session of THORPEX GIFS -TIGGE WG Meeting

12-14 June 2013

Met Office Exeter

Outline of the talk

1. Summary of tropical cyclone related papers using TIGGE

2. Introduction of recent studies on tropical cyclones using TIGGE

3. Status of Cyclone XML data exchange

4. Summary

Tropical Cyclone Related Papers using TIGGE -1-

Intercomparison (including multi-center grand ensemble) (6 papers)

Dynamics and Predictability (6 papers)

Application (2 papers)

Statistics based on the TIGGE article website: http://tigge.ecmwf.int/references.html

Tropical Cyclone Related Papers using TIGGE -2-

Intercomparison (including multi-center grand ensemble)

1.Halperin D. J. and co-authors, 2013: An evaluation of tropical cyclone genesis forecasts from global numerical models. Weather and Forecasting. (In Press)

2.Magnusson, L., A. Thorpe, M. Bonavita, S. Lang, T. McNally and N. Wedi, 2013: Evaluation of forecasts for hurricane Sandy, ECMWF Technical Memorandum, 699, 1-28.

3.Yamaguchi, M., T. Nakazawa, and S. Hoshino, 2012: On the relative benefits of a multi-centre grand ensemble for tropical cyclone track prediction in the western North Pacific. Q. J. Roy. Meteorol. Soc., doi: 10.1002/qj.1937.

4.Hamill, T.M., J.S. Whitaker, M. Fiorino and S.G. Benjamin, 2011, Global Ensemble redictions of 2009's Tropical Cyclones Initialized with an Ensemble Kalman Filter, Monthly Weather Review, 139, 668-688. doi: http://dx.doi.org/10.1175/2010MWR3456.1

5.Keller, J. H., S. C. Jones, J. L. Evans, and P. A. Harr, 2011: Characteristics of the TIGGE multimodel ensemble prediction system in representing forecast variability associated with extratropical transition, Geophys. Res. Lett., 38, L12802, doi:10.1029/2011GL047275

6.Majumdar, Sharanya J. and Peter M. Finocchio, 2010: On the ability of global Ensemble Prediction Systems to predict tropical cyclone track probabilities. Weather and Forecasting, 25, 2, 659-680. http://journals.ametsoc.org/doi/abs/10.1175/2009WAF2222327.1

Tropical Cyclone Related Papers using TIGGE -3-

Dynamics and Predictability Study

1.Belanger, James I., Peter J. Webster, Judith A. Curry, Mark T. Jelinek, 2012: Extended prediction of north indian ocean tropical cyclones. Wea. Forecasting, 27, 757–769. doi: http://dx.doi.org/10.1175/WAF-D-11-00083.12.Gombos, Daniel, Ross N. Hoffman, James A. Hansen, 2012, Ensemble statistics for diagnosing dynamics: Tropical cyclone track forecast sensitivities revealed by ensemble regression, Monthly Weather Review, e-View. doi: http://dx.doi.org/10.1175/MWR-D-11-00002.13.Schumacher, Russ S., Thomas J. Galarneau, Jr., 2012, Moisture transport into midlatitudes ahead of recurving tropical cyclones and its relevance in two predecessor rain events, Monthly Weather Review, e-View. doi:http://dx.doi.org/10.1175/MWR-D-11-00307.14.Majumdar, S. J., Chen, S.-G. and Wu, C.-C., 2011, Characteristics of Ensemble Transform Kalman Filter adaptive sampling guidance for tropical cyclones. Q.J.R. Meteorol. Soc., 137, 503-520. doi: 10.1002/qj.746 http://onlinelibrary.wiley.com/doi/10.1002/qj.746/abstract5.Yamaguchi, Munehiko, David S. Nolan, Mohamed Iskandarani, Sharanya J. Majumdar, Melinda S. Peng, Carolyn A. Reynolds, 2011, Singular Vectors for Tropical Cyclone–Like Vortices in a Nondivergent Barotropic Framework, Journal of the Atmospheric Sciences, 68 (10), 2273-2291. doi: http://dx.doi.org/10.1175/2011JAS3727.16.Yamaguchi, M. and S. J. Majumdar, 2010: Using TIGGE data to diagnose initial perturbations and their growth for tropical cyclone ensemble forecasts. Mon. Wea. Rev., 138, 9, 3634-3655. http://journals.ametsoc.org/doi/abs/10.1175/2010MWR3176.1

Tropical Cyclone Related Papers using TIGGE -4-

Application

1.Liangbo Qi, Hui Yu, and Peiyan Chen, 2013: Selective Ensemble Mean Technique for Tropical Cyclone Track Forecast by Using Ensemble Prediction Systems. Q. J. Roy. Meteorol. Soc. (Accepted)

2.Hsiao-Chung Tsai, Russell L. Elsberry, 2013: Detection of Tropical Cyclone Track Changes from the ECMWF Ensemble Prediction System. Geophysics Research Letter, doi: 10.1002/grl.50172

Tropical Cyclone Related Papers using TIGGE -5-

Track (8 papers)

Genesis (2 papers)

Others (Sensitivity analysis, ET, etc., 4 papers)

Few studies on TC intensity

Evaluation of forecasts of Hurricane Sandy

Magnusson et al. (2013, ECMWF Tech Memo)

Probability (%) of 850 hPa wind speed greater than 38 m/s somewhere inside a radius of 100 km for New York Harbour between 2012-10-29 12z and 2012-10-30 12z.

Lan

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9 days before the landfall

Intercomparison of TC track predictions in the western North Pacific

Position errors (km) of 1- to 5-day TC track predictions by the unperturbed control member (unfilled bars) and ensemble mean (filled bars) of each SME.

The circle (hyphen) mark means that the difference in the errors between the control member and ensemble mean is (not) statistically significant at the 95 % significance level.

The ensemble mean has better performance than the control prediction in general and the improvement rate is relatively large for the longer prediction times.

The ensemble mean has better performance than the control prediction in general and the improvement rate is relatively large for the longer prediction times.

Yamaguchi et al. (2012, QJRMS)

Verification result of TC strike probability prediction

Strike prob. is computed at every 1 deg. over the responsibility area of RSMC Tokyo - Typhoon Center (0∘-60∘N, 100∘E-180∘) based on the same definition as Van der Grijn (2002). Then the reliability of the probabilistic forecasts is verified.

Reliability Diagram -Verification

for ECMWF EPS-

In an ideal system, the red line is equal to a line with a slope of 1 (black dot line).

In an ideal system, the red line is equal to a line with a slope of 1 (black dot line).

The number of samples (grid points) predicting the event is shown by dashed blue boxes, and the number of samples that the event actually happened is shown by dashed green boxes, corresponding to y axis on the right.

Benefit of MCGE over SME

Combine 3 SMEs

Reliability is improved, especially in the high-probability range.Reliability is improved, especially in the high-probability range.

MCGE reduces the missing area (see green dash box at a probability of 0 %).MCGE reduces the missing area (see green dash box at a probability of 0 %).

Typhoon track prediction by MCGE-9 (BOM, CMA, CMC, CPTEC, ECMWF, JMA, KMA, NCEP, UKMO)

Good example Bad example

There are prediction cases where any SMEs cannot capture the observed track. => It would be of great importance to identify the cause of these events

and modify the NWP systems including the EPSs for better probabilistic forecasts.

There are prediction cases where any SMEs cannot capture the observed track. => It would be of great importance to identify the cause of these events

and modify the NWP systems including the EPSs for better probabilistic forecasts.

Typhoon Megi initiated at 1200 UTC 25th Oct. 2010

Observed track

Typhoon Conson initiated at 1200 UTC 12th Jul. 2010

Global TC track predictions initialized with an EKF

Hamill et la. (2011a, 2011b, MWR)

Evaluation of TC activity in the North Indian Ocean using ECMWF ensemble

Belanger et al. (2012, WAF)

Case Study for Typhoon SON-TINH (2012)

Black: detected ensemble storms, Blue: Tropical Depression, Green: Tropical Storm, Yellow: Severe Tropical Storm, Red: Typhoon

Evaluation of TC activity in the east of Philippians

Prediction window Day1 – Day3

Day3 – Day7

Day7 – Day14 (Week 2)

Climatology (based on the best track data by RSMC Tokyo)

0.057 0.0849 0.120

ECMWF 0.030 0.069 0.124

JMA 0.043 0.0845 N/A

NCEP 0.034 0.074 0.130

UKMO 0.041 0.076 0.127

Numbers in red are for forecasts better than climatology

Verified area: 120E-140E and 10N-25NVerified period is July – October in 2011 and 2012Storm track procedure: Vitart et al. (2010, MWR)

•Probabilities are calculated at each grid point (0.5 x 0.5 degree) in the verified box.

•A threshold distance of 300 km is used to determine whether observed or forecasted TCs affect a grid point.

Verification of TC genesis events in the western North Pacific using ECMWF 1-mont EPS

OBJECTIVE VERIFICATIONS AND FALSE ALARM ANALYSES OF WESTERN NORTH PACIFIC TROPICAL CYCLONE EVENT FORECASTS BY THE ECMWF 32-DAY ENSEMBLE

Tsai et al. (2013, Asia-Pacific JAS)

Pre-Fiona Pre-Igor

Courtesy of Will Komaromi (RSMAS, UM)

How well in advance ECMWF EPS predicts the genesis events of Fiona and Igor.

The number of members with strong vortices (pink) gradually increases as the forecast time gets shorter in the Igor case while it increases rapidly in the Fiona case.

Probabilistic Verification• ECMWF ensemble forecasts, Jun 1 – Nov 30, 2010-

2012– 7-day forecasts, 00 UTC only

• All forecasts up to and including genesis.

• Verification: NHC best track. TC or not TC.

Question: what is the probability that a TC exists at XX h? (with time tolerance of 1 day).

Courtesy of Sharan Majumdar (RSMAS, UM)

Reliability Diagram: 2010-2 Seasons

Selective Ensemble Mean Technique for Tropical Cyclone Track Forecast

Qi et al. (2013, QJRMS)

DETECTION OF TRACK CHANGES FROM ECMWF ENSEMBLE FORECASTS

• Tsai and Elsberry (2013 GRL*) demonstrated that the ECMWF 5-day ensemble track forecasts available on the TIGGE website in near-real time provide information on alternate tracks– Cluster analysis of historical forecast tracks yielded six track

clusters– When the ensemble track spread is large, cluster analysis will

indicate the two or more distinct cluster tracks contributing to that spread

– In bifurcation (two track clusters) situations, the track clusters with percentages greater than 70% can be reliably selected as the better choice

* Tsai, H.-C., R. L. Elsberry, 2013: Detection of tropical cyclone track changes from the ECMWF ensemble prediction system. Geophys. Res. Lett., 40, 797-801, doi: 10.1002/grl.50172.

Courtesy of Russell Elsberry (NPS)

Evaluation of TC track prediction in bifurcation situationsusing ECMWF EPS –western North Pacific-

Tsai and Elsberry (2013, GRL)

Cyclone XML (CXML) Homepage

Producing center: CMC, CMA, ECMWF, JMA, KMA, Meteo-France, STI, UKMO, NCEP (9 centers in total)

Data are used for A WWRP-RDP “North Western Pacific Tropical Cyclone (TC) Ensemble Forecast Project (NWP-TCEFP), Severe Weather Forecast Demonstration Project (SWFDP), etc.

Some issues

• Data from STI seems to be unavailable.• The last date that the TCEFP retrieved the data is October

2010.• Differences in a coverage and pre-storm tracking as follows:

Center Coverage Pre-storm Tracking (TD Min. Pressure

Max. Wind Speed

CMA NWP only Named TCs Yes No

ECMWF Globe All TCs, but need to exist at T+0 Yes Yes + location

JMA NWP only Named TCs Yes Yes

MSC Globe Named TCs Yes Yes

NCEP Globe Named TCs Yes Yes

UKMO Globe All TCs No No

Experimental product: Tropical cyclone activity

The ECMWF monthly forecasting system

80°N

70°N

60°N

50°N

40°N

30°N

20°N

10°N

0°N

10°S

20°S

30°S

40°S

50°S

60°S

70°S

80°S

80°N

70°N

60°N

50°N

40°N

30°N

20°N

10°N

0°N

10°S

20°S

30°S

40°S

50°S

60°S

70°S

80°S

340°E320°E300°E280°E260°E240°E220°E200°E180°E160°E140°E120°E100°E80°E60°E40°E20°E

340°E320°E300°E280°E260°E240°E220°E200°E180°E160°E140°E120°E100°E80°E60°E40°E20°E

< 10% 10.. 20 20.. 30 30.. 40 40.. 50 50.. 60 60.. 70 70.. 80 80.. 90 > 90%

Probability of a TC passing within 300km radiusWeekly Mean Tropical Cyclone Strike Probability. Date: 20100408 0 UTC t+(264-432)

Courtesy of Frederic Vitart (ECMWF)

Summary

• There are 14 tropical cyclone research articles using the TIGGE data (http://tigge.ecmwf.int/references.html). Eight of them are studies on TC track forecasting (intercomparison, benefit of multi-centre grand ensemble, application).

• Studies on predicting TC genesis (activity) seem to be done more recently.

• There are few studies on TC intensity.

• Extension of CXML may be beneficial in order to enhance research on TC genesis and intensity as well as TC track.

(discrepancy of the information included in the CXML limits studies of these kinds)

Verification result of TC strike probability -2-

All SMEs are over-confident (forecasted probability is larger than observed frequency), especially in the high-probability range.

All SMEs are over-confident (forecasted probability is larger than observed frequency), especially in the high-probability range.

Best SME (ECMWF)MCGE-3

(ECMWF+JMA+UKMO)

MCGE-6 (CMA+CMC+ECMWF+JMA+NCEP+UKMO) MCGE-9 (All 9 SMEs)

Benefit of MCGE over SME -2-

MCGEs reduce the missing area! The area is reduced by about 1/10 compared with the best SME. Thus the MCGEs would be more beneficial than the SMEs for those who need to avert missing TCs and/or assume the worst-case scenario.

MCGEs reduce the missing area! The area is reduced by about 1/10 compared with the best SME. Thus the MCGEs would be more beneficial than the SMEs for those who need to avert missing TCs and/or assume the worst-case scenario.

Verification of ensemble spreadV

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Ver

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Reliability Diagram of Day3-Day7 (T+72 – T+168)