Studies on Tropical Cyclone Forecasting using TIGGE 11th Session of THORPEX GIFS -TIGGE WG Meeting 12-14 June 2013 Met Office Exeter Munehiko Yamaguchi Meteorological Research Institute of Japan Meteorological Agency

Outline of the talk Summary of tropical cyclone related papers using TIGGE Introduction of recent studies on tropical cyclones using TIGGE Status of Cyclone XML data exchange Summary

Tropical Cyclone Related Papers using TIGGE -1- Application (2 papers) Intercomparison (including multi-center grand ensemble) (6 papers) Dynamics and Predictability (6 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) Halperin D. J. and co-authors, 2013: An evaluation of tropical cyclone genesis forecasts from global numerical models. Weather and Forecasting. (In Press) 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. 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. 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 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 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 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-11-00083.1 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-11-00002.1 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-11-00307.1 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/abstract 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/10.1175/2011JAS3727.1 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 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) 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- Others (Sensitivity analysis, ET, etc., 4 papers) Track (8 papers) Genesis (2 papers) Few studies on TC intensity. Few studies on TC intensity

Evaluation of forecasts of Hurricane Sandy 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. Landfall near Brigantine, New Jersey 9 days before the landfall Magnusson et al. (2013, ECMWF Tech Memo)

Intercomparison of TC track predictions in the western North Pacific The ensemble mean has better performance than the control prediction in general and the improvement rate is relatively large for the longer prediction times. 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. Yamaguchi et al. (2012, QJRMS)

Reliability Diagram -Verification for ECMWF EPS- 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). 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 Reliability is improved, especially in the high-probability range. Combine 3 SMEs 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 Typhoon Megi initiated at 1200 UTC 25th Oct. 2010 Typhoon Conson initiated at 1200 UTC 12th Jul. 2010 Observed track 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.

Global TC track predictions initialized with an EKF TIGGE is used as benchmark. Hamill et la. (2011a, 2011b, MWR)

Evaluation of TC activity in the North Indian Ocean using ECMWF ensemble BSS is negative in Week-2. 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 There are prediction cases where the genesis events are predicted well in advance. But …

Evaluation of TC activity in the east of Philippians Verified area: 120E-140E and 10N-25N Verified period is July – October in 2011 and 2012 Storm track procedure: Vitart et al. (2010, MWR) 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 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. Looks no skill in Week-2. Numbers in red are for forecasts better than climatology

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)

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. Pre-Igor Pre-Fiona Courtesy of Will Komaromi (RSMAS, UM)

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 Consistent with those of the western North Pacific.

Selective Ensemble Mean Technique for Tropical Cyclone Track Forecast An example of application study. 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. Another example of application study. Courtesy of Russell Elsberry (NPS)

Evaluation of TC track prediction in bifurcation situations using ECMWF EPS –western North Pacific- In bifurcation situations, the ensemble mean of a cluster with larger ensemble size has less prediction errors than the ensemble mean of the whole ensemble member. 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.

Pre-storm Tracking (TD 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 + location JMA MSC NCEP UKMO All TCs

Experimental product: Tropical cyclone activity The ECMWF monthly forecasting system Experimental product: Tropical cyclone activity If CXML includes tracking information on TCs that do not necessarily exist at the initial time of forecasts, a product of this kind is possible and would be useful for SWFDP for example. 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.

MCGE-3 (ECMWF+JMA+UKMO) MCGE-6 (CMA+CMC+ECMWF+JMA+NCEP+UKMO) Benefit of MCGE over SME -2- MCGE-3 (ECMWF+JMA+UKMO) Best SME (ECMWF) 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. MCGE-6 (CMA+CMC+ECMWF+JMA+NCEP+UKMO) MCGE-9 (All 9 SMEs)

Verification of ensemble spread y axis: position error of ensemble mean track prediction Verification at 3 day predictions x axis: ensemble spread

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