Topic 1: Motion. Recent Advances. Chairs: Grant Elliott 1 and Munehiko Yamaguchi 2

Transcription

1 Topic 1: Motion Recent Advances WMO 8 th International Workshop on Tropical Cyclones (IWTC-VIII) Jeju, Republic of Korea 2 December 2014 (Thu) Chairs: Grant Elliott 1 and Munehiko Yamaguchi 2 1: Australian Bureau of Meteorology 2: Japan Meteorological Agency

2 Working Group Members (co-authors) Thomas Birchard (RSMC Honolulu), Johnny C. L. Chan (City Univ. of Hong Kong), Thierry Dupont (Meteo-France), Russell L. Elsberry (NPS), T C Lee (HKO), Xiaotu Lei (STI), Sharanya J. Majumdar (UM), Mrutyunjay Mohapatra (IMD), Li Ying (CMA). (reviewers) Dong-Hyun Cha (Ulsan National Institute of Science and Technology), Im-Yong Oh (KMA), Do-Scick Shin (KMA), OP Singh (IMD), Hsiao-Chung Tsai (NPS).

3 Other Contributors Edward Fukada (JTWC), Julian Heming (UKMO), Piers Buchanan (UKMO), Christopher Short (UKMO), Helen Titley (UKMO) Olivier Bousquet (Meteo-France), Wai-Kin Wong (HKO), Nam-Young Kang (KMA), Linus Magnusson (ECMWF), Chris Landsea (US NHC), Billy Lynch (Australian Bureau of Meteorology), Peter Otto (Australian Bureau of Meteorology), Vijay Tallapragada (NOAA), Andy Brown (UKMO), Jean-Noël Thépaut (ECMWF) Penny Jones (NPS)

4 Introduction At the last IWTC (La Reunion 2010) TC motion was not discussed specifically in a separate topic, perhaps reflecting that the subject matter was considered to be relatively well understood and that forecast processes had matured accordingly. The refocusing on motion in IWTC-VIII reflects an expansion of forecast service specifications, with requirements for extensions in forecast duration (out 5 to 7 days ahead), a desire for forecasts to start earlier in the development life cycle (up 3 to 5 days prior to formation), and a need for accurate definitions of forecast uncertainty to refine response strategies. In addition, the increased availability of relatively high resolution ensemble guidance has created opportunities for the development of new forecast techniques, whilst the ever expanding observational network has demanded improved data assimilation techniques to facilitate improvements in initial analysis.

5 Outline of the talk 1. Current status of the accuracy of operational TC track forecasting 2. Improvement in NWP systems in TC track forecasting 3. Research and development behind the improvement 4. Forecast uncertainty consideration in operational TC track forecasting 5. Research and development to estimate the forecast uncertainty 6. Theoretical and analytical studies to further improve TC track forecasting 7. Research or forecasting demonstration projects on TC track forecasting 8. Challenges and recommendations

6 1. Current status of the accuracy of operational TC track forecasting

7 SIO AUS CPC EPC ATL NIO WNP Time Series of the Annual Average Position Errors (km or n mi) of Operational TC track Forecasts by RSMCs and TCWCs 2006 (IWTC-VI in Costa Rica where we had a session on TC motion) Note that scales of ordinates are different

8 CMA: WNP KMA: WNP JTWC: SHEM HKO: WNP JTWC: NIO JTWC: WMP Time Series of the Annual Average Position Errors (km or n mi) of Operational TC track Forecasts by Other Weather Centers In general, the errors have been decreasing globally since the time of IWTC-VI in However year-to year variations exist that are due mainly to differences in the annual number of TCs and difficulty in forecasting TC track over the TC basins.

9 Forecast Length of TC Track by RSMC and TCWCs Basin Center Fcst Length Note North Indian Ocean RSMC New Delhi 5 days Extended from 3 to 5 days in 2013 Western North Pacific RSMC Tokyo 5 days Extended from 3 to 5 days in 2009 Central Pacific RSMC Honolulu 5 days Extended from 3 to 5 days in 2003 Eastern Pacific RSMC Miami 5 days Extended from 3 to 5 days in 2003 Planned to extend from 5 to 7 days Atlantic RSMC Miami 5 days Extended from 3 to 5 days in 2003 Planned to extend from 5 to 7 days South Indian Ocean RSMC La Reunion 5 days Extended from 3 to 5 days in 2010 Australian basin TCWCs 5 days Extended from 3 to 5 days in 2013 South Pacific RSMC Nadi 3 days (estimated from online graphics) Issuing TC positions 5 days ahead has become a global standard.

10 2. Improvement in NWP systems in TC track forecasting

11 Courtesy of WGNE and its co-chairs, Andy Brown at the UK Met Office and Jean-Noël Thépaut at ECMWF Performance in TC Track Forecasting by Global NWP Systems Annual average 3-day TC track forecast errors (km) from 1991 to 2012 The Inter-comparison NIO has been WNP conducted since Eastern 1991 and under Central the Pacific Working Group on Numerical Experimentation (WGNE). Eleven global models were included in the latest verification. The errors are in general on a decreasing trend globally though year-to-year fluctuations are seen in some basins, especially where the ATL annual number of TCs SIO is relatively small. SHEM w/o SIO It is notable that the ECMWF model performs well throughout the globe and that the differences in errors between the ECMWF and other centers are relatively large in the North Indian Ocean and the Southern Hemisphere.

12 WMO Lead Centre for Deterministic Forecast Verification (LCDFV) It is also noted that improvements have been achieved in global predictions of the synoptic variables such as Z500, which is highly related to the steering flows of TCs. Thépaut (2014, WGNE-29:

13 Performance in TC Track Forecasting by Regional NWP Systems An Example: HWRF Recent advancements to the HWRF model have shown, for the first time, that the regional model forecast performance is comparable or better than host model (GFS) forecasts. Courtesy of Vijay Tallapragada at NCEP

14 3. Research and development behind the improvement

15 Research and Development behind the Improvement Three factors may be attributed to this improvement in TC track forecasts by the NWP systems: 1. Data assimilation Assimilation of satellite-based observations (2 slides) Assimilation of aircraft-based observations (3 slides) TC initialization Data assimilation methods (1 slide) 2. NWP model and EPS (3 slides) 3. Application Consensus (1 slide) Ensemble mean (1 slide)

16 Increasing Number of Satellite Data Assimilated Number of satellite data products monitored at ECMWF The number of satellite instruments used in NWP models has been increasing (e.g., from 12 to over 50 during the last 12 years at ECMWF), and this trend is expected to continue. Enhanced use of satellite observations has improved the initial conditions for the NWP models along with the recent advances in data assimilation, which then contributes to a general decrease in forecast errors. English et al. (2013, ECMWF Tech. Memo.)

17 Atmospheric Motion Vector (AMV) and Rapid Scan Rapid Scan: Case for Typhoon MA-ON (T1106) Assimilation of cloud-tracked Atmospheric Motion Vectors (AMVs, Velden AMV et based al. 2005) on images leads taken to a noticeable AMV improvement based on images to TC taken track forecasts (Langland every 30 mins et al. 2009, Yamashita et every al. 2010; 5 mins Berger et al. 2011, Osuri et al. 2012). A rapid-scan mode can be activated for the United States and Japan geostationary satellites in order to provide a temporally and spatially higher density of AMVs in the vicinity of TCs. These special AMV data have been shown to influence track forecasts (Berger et al 2011; Wu et al 2014; Yamashita 2014). Kotaro Bessho will talk more about rapid-scan on Friday afternoon. Courtesy of Ryo Oyama at MRI/JMA

18 Current status of operational airborne observations of TCs AIMMS-20 (Hong Kong) Aircraft: J41 DOTSTAR (NTU, CWB, TTFRI etc) Aircraft: ASTRA SPX Hurricane Hunters (US Air Force and NOAA) Aircraft: WC130, P-3, G-IV P-3 Wu et al. (2005, BAMS) WC130 G-IV NOAA Hurricane hunter association

19 Aberson (2010, MWR) The impact of the synoptic surveillance missions on TC track forecasts is reported in Aberson (2010). From , the assimilation of the supplementary dropwindsondes improved NCEP GFS track forecasts by 10-15% during the first 60 h of the forecasts.

20 Chou et al. (2010, MWR) In the western North Pacific basin, the DOTSTAR campaign has been in operation for over a decade, with average improvements of % to 1-5 day track forecasts being accomplished due to the assimilation of the extra dropwindsonde data (Chou et al 2010).

21 Landsea and Franklin (2013, MWR)

22 Majumdar et al. (2011,WMO/THORPEX review) Regarding the impact of targeted observations, a general, comprehensive overview is reported in Majumdar et al. (2011). Sharan will provide the details on Wednesday morning. It concluded that in the extratropics, the value of targeted data has been found to be positive but small on average when evaluated over continental or hemispheric areas. Meanwhile, for forecasts of the track of TCs, targeted observations have mostly proven to be beneficial statistically. However, in common with the extratropics, the quantitative benefit differs from model to model (Weissmann et al. 2011), and the range of aircraft is a limiting constraint given that the sensitive areas are often of synoptic scale (e.g., Majumdar et al. 2006; Wu et al. 2009).

23 ECMWF NWP System -1- The superior performance in TC track forecasts by the ECMWF may be attributed to their state-of-the-art NWP system, including the data assimilation, model dynamics and physics, and applications. The ECMWF Ensemble of Data Assimilation (EDA, Isaksen et al. 2010) was implemented June The EDA consists of an ensemble of ten independent lower-resolution 4-Dimensional Variational Data Assimilation (4D-VAR) that differs by perturbing observations, sea-surface temperature fields, and model physics. The EDA quantifies analysis uncertainty by taking model dynamics into account and estimating flow-dependent background errors in the deterministic 4D-VAR, which also improves the initial uncertainty for the EPS (Buizza et al. 2010). A further strength of ECMWF is in the number of assimilated observations including satellite-based all-sky radiances and their quality control (e.g., Bauer et al. 2010, Geer and Bauer 2011).

24 ECMWF NWP System -2- It is also noted that the ECMWF ensemble was changed in 2013 to use a coupled model from the initial time rather than at Day 10 (Janssen et al. 2013). Evaluation of TC track forecasts with the coupled and un-coupled ensemble models is under way. ECMWF uses no special method to initialize TCs; rather it relies on the data assimilation scheme to provide an accurate position and structure. Other operational models have a vortex relocation technique, or assimilation of synthetic observations, or the insertion of a bogus vortex.

25 NCEP s Improvement NCEP introduced a hybrid 3D-VAR / Ensemble Kalman Filter scheme into operations in 2012 (Wang et al. 2013), and tests against the previous 3D-VAR scheme had significant improvements to GFS forecasts of TC tracks. The improvement was confirmed also in the WGNE intercomparison study (Brown and Thepaut 2014, Ota et al. 2014). Annual average 3-day TC track forecast errors (km) Western North Pacific North Atlantic

26 Selective Ensemble using Observations Qi et al. (2013, QJRMS) Qi et al. (2013) developed a selective ensemble mean technique in which larger weights are given to ensemble members with short-term track forecasts close to the observed motion. The delayed time availability of ensemble forecasts (typically 9-12 h after synoptic time) makes it possible to utilize the short-term observed motion. Qi et al. (2013) compared these ensemble track forecasts with the ECMWF high-resolution global model to demonstrate the effectiveness of the technique, with improvements in skill demonstrated up to 24 hours ahead.

27 Consensus and Ensemble Mean Consensus A mean of the forecast TC positions from multiple deterministic models, which is known as a consensus forecast, was extensively discussed in IWTC-V (Jeffries and Fukada 2002) and IWTC-VI (Elsberry 2006 and 2007). This consensus approach is now widely used for operational TC track forecasting. Either simple (e.g., Goerss et al. 2000), weighted (e.g., Krishnamurti et al. 2000), or selective (e.g., Elsberry and Carr 2000; Sampson et al. 2007) mean TC track forecasts have statistically smaller position errors than the single-model based forecasts, especially at long lead times (e.g., 5 days). Ensemble Mean Global ensemble predictions systems from several NWP centers provide TC track forecasts Relatively from each late ensemble availability member. The time ensemble of ensemble mean TC track forecasts forecast tends should to have smaller be taken errors into at long account lead times when than the comparing control (unperturbed) their ensemble performance member (Yamaguchi with the et high al. 2012), resolution but not deterministic necessarily the track forecasts by the mother higher-resolution deterministic model. In addition, the ensemble forecasts mean of (Elsberry multiple ensembles 2010, also APJAS) has a limited benefit when compared to track forecasts by the ECMWF high-resolution global model.

28 4. Forecast uncertainty consideration in operational TC track forecasting

29 Graphical Products of Operational TC track Forecasts by RSMCs and TCWCs NIO WNP CNP ESP/ATL SIO AUS SPC

30 Graphical Products of Operational TC track Forecasts by Other Weather Centers JTWC: WNP HKO: WNP CMA: WNP KMA: WNP

31 Forecast Uncertainty Considered in Operational TC Track Forecasts by RSMCs and TCWCs Basin Center Method Definition North Indian Ocean Western North Pacific RSMC New Delhi Cone 67 % of TCs fall within the circle RSMC Tokyo Circle 70 % of TCs fall within the circle Central Pacific RSMC Honolulu Cone The entire track of TCs can be expected to remain within the cone roughly 60-70% of the time Eastern Pacific RSMC Miami Cone Same as above Atlantic RSMC Miami Cone Same as above South Indian Ocean RSMC La Reunion Cone 75 % of TCs falls within the cone Australian basin TCWCs Cone 70 % of TCs falls within the cone South Pacific RSMC Nadi No information received

32 Forecast Uncertainty Considered in Operational TC Track Forecasts by Other Weather Centers Basin Center Method Definition North Indian Ocean Western North Pacific Southern Hemisphere JTWC Error Swath Five-year running mean track forecast error added to forecast 34kt wind radii. Since JTWC does not forecast wind radii for the 96- and 120-hour forecast times, the 96 hr and 120hr area of uncertainty is calculated by adding the 72-hour 34 knot radii to the forecast track error at those times. JTWC Error Swath Same as above JTWC Error Swath Same as above Western North Pacific HKO Cone Region within which the TC is more likely to fall and move. Western North Pacific Western North Pacific CMA Cone 70 % of TCs falls within the circle KMA Cone 70 % of TCs falls within the circle

33 How to decide the size Most of the centers use historical forecast error statistics to determine the size of either cone, circle or error swath. Goerss (2000) and Elsberry and Carr (2000) have Ensemble forecasts (i.e. spread) is not used much, except at suggested the benefit of the track spread among RSMC Tokyo, La Reunion and TCWCs. Basin Center Method Way to decide the size Western North Pacific South Indian Ocean Australian basin multiple single-model forecasts as a measure of confidence in TC track forecasts. That is, a small RSMC spread of the tracks is often an indication of a small Tokyo error of the consensus forecast. RSMC La Reunion Circle Ensemble spread of JMA Typhoon EPS is used for 4 and 5 day forecasts. Cone TC forecasters on duty can choose either calibrated ECMWF ensemble-based uncertainty or climatological uncertainty. The ensemble-based uncertainty is selected more frequently. However, the Australian TCWCs are the only operational TC forecast centers to use consensus spread of multiple single-model forecasts to estimate their track forecast uncertainty at this moment. TCWCs Cone Default uncertainty is based on 5 year Australian climatology using 70th percentile uncertainty error statistics. TC forecasters can vary this climatological uncertainty to match expectations based on confidence in synoptic steering mechanisms, consensus spread and ensemble spread.

34 5. Research and development to estimate the forecast uncertainty

35 Ensemble spread vs error (3-day Fcst, WNP ) The relationship is improved in Multi- Center Grand Ensemble (MCGE) Yamaguchi et al. (2012, QJRMS)

36 6. Theoretical and analytical studies to further improve TC track forecasting

37 Theoretical and analytical studies to further improve track forecasts 1. Local scale influence Topography (Chambers and Lee 2011; Huang et al. 2011; Tang and Chang 2014a,b) Roughness discontinuity (Wong and Chan 2006; Szeto and Chan 2010; Au- Yeung and Chan 2010) SST distribution (e.g. gradient) (Choi et al. 2013; Yun et al. 2012) 2. Broad scale influence TUTT (Patla et al. 2009; Ying et al. 2012) Monsoon (Chen et al. 2010) MJO&QBW (Wu et al. 2011, 2013; Li et al. 2012) 3. Predictability and dynamics Predictability and error diagnosis (Plu 2010; Galarneau and Davis 2013) Perturbation growth (Yamaguchi and Majumdar 2010; Yamaguchi et al. 2011) Long-term prediction (Elsberry et al. 2010, 2011, 2014; Tsai et al. 2013)

38 7. Research or forecasting demonstration projects on TC track forecasting

39 Research and Forecasting Demonstration Projects on TC Track Forecasting 1. WMO North Western Pacific Tropical Cyclone Ensemble Forecast Project (NWP-TCEFP) 2. WMO Typhoon Landfall Forecasting Demonstration Project (TLFDP) 3. NOAA Hurricane Forecast Improvement Project (HFIP) 4. Forecast Demonstration Project on Landfalling Cyclones over the Bay of Bengal. 5. The Observing System Research and Predictability Experiment (THORPEX) 6. THORPEX Interactive Grand Global Ensemble (TIGGE)

40 TIGGE The TIGGE archive consists of ensemble forecast data from 10 global NWP centers, starting from October 2006, which has been made available for scientific research with about a 2-day delay. Application (2 papers) Dynamics and Predictability (11 papers) Tropical Cyclone Related Papers using TIGGE Intercomparison (including multi-center grand ensemble) (9 papers) Others (Sensitivity analysis, ET, etc.) (7 papers) Genesis (4 papers) Track (11 papers) Statistics based on the TIGGE article website etc:

41 Cyclone XML (CXML) WMO GIFS-TIGGE WG took the initiative to calculate and exchange TC track forecasts by global EPSs in real-time, following a recommendation at IWTC-VI. This exchange required the development and implementation of the CXML. TIGGE (started 2006) Cyclone XML (started 2008) EPS at KMA MCGE EPS at NCEP EPS at JMA EPS at ECMWF EPS at CMA MCGE is an ensemble of ensembles of various NWP centers. Exchange of TIGGE and CXML dataset makes it possible to construct a new ensemble, that is Multi-Center Grand Ensemble (MCGE).

42 Track Prediction for Typhoon SOULIK (2013) Blue portion of the tracks is the Day 1 forecast and the green, orange, and red portions are the Day 2, Day 3, and Day 4 forecasts.

43

44

45 Ensemble Size = 207

46 Black line is the observed track. The number on the black line indicates day(s) from the initial date.

47 Track Prediction for Typhoon FITOW (2013)

48 Ensemble Size = 207

49 What is the benefit of using MCGE? Typhoon SOULIK Init.: UTC Typhoon FITOW Init.: UTC MCGE products provide forecasters with additional information on the forecast uncertainty and increase the level of confidence in the forecast.

50 8. Challenges and recommendations

51 Consensus Forecasts Steadiness vs Accuracy Official Forecasts Optimizing the use of numerical track forecasts to minimize the track variability without sacrificing accuracy or timeliness still remains to be developed and studied.

52 Relatively poor performance in forecasting TC track in its weak stage NHC official track forecast error binned by initial intensity for the Atlantic basin. The track errors averaged over the past 5 years are largest for the weakest initial intensity TCs. Model improvements are still required with an emphasis to increase numerical track forecast skill for the weak or initial stages of TCs.

53 Observed track Forecast bust including ensembles According to Chan (2010), since Chan et al. (2002) paper, research on the physics of general TC motion has been almost non-existent, which suggests that most scientists are quite content with the current theories of TC motion. In reality, however, significant prediction errors still exist and there are prediction cases where the position error can exceed 1000 km over 3 days. Although the theories of TC motion might have reached a satisfactory level, our knowledge on the causes of prediction errors is still poor. Identifying the causes of large prediction errors and modifying NWP systems accordingly are of great importance to further improve our ability in forecasting TCs. MCGE-9 (BOM, CMA, CMC, CPTEC, ECMWF, JMA, KMA, NCEP, UKMO) Typhoon Megi initiated at 1200 UTC 25 th Oct Typhoon Conson initiated at 1200 UTC 12 th Jul. 2010

54 Petential Recommendations 1/3 Although the position errors of TC track have been on a decreasing trend globally since the time of IWTC-VI in 2006, TC track forecasting remains a high priority. It is recommended that efforts (observations, data assimilation, both global and regional NWP models, and application) should continue to further reduce the errors. Special emphasis should be given to investigation of the causes of large forecast errors and more accurate TC track forecasts in its weak stage and in the landfalling events. Research and development to create optimal deterministic TC tracks from multiple numerical forecasts (i.e. consensus) and to further extend the forecast length (e.g. 7 days) should be encouraged. Systematic verification such as the WGNE intercomparison study in regards to NWP and its TC forecasting should continue to monitor the recent performance as well as the historical progress.

55 Potential Recommendations 2/3 The enhanced use of ensembles in forecasting and research communities should be further encouraged. However more studies are needed in order to develop a more systematic and optimized approach for estimating forecast uncertainty based on ensembles. The development of best practice use of ensemble guidance focusing on calibrated risk estimation across a range of time scales and forecast periods should be supported. The exchange of CXML and TIGGE dataset should be continuously supported for both operational forecasting and research purposes. However, for the CXML, there is some inconsistency in the tracked TCs and the format among the data providers. Also extension of CXML such as including more weather parameters needs to be discussed in accordance with user demand.

56 Potential Recommendations 3/3 From a perspective of disaster risk reduction, research and development and utilization of ensemble-based probabilistic guidance for severe weather events associated with TC motion and its landfalling such as heavy rainfall, strong wind, flood, storm tides, etc. should be encouraged.

57 Potential Discussion Points Verification continues to be important and underpins user confidence and program sponsor support. Exchange of TIGGE data has been critical in achieving this, as has thorough and comprehensive post event best tracking and reporting. There is a need to improve forecast skill with weak systems, landfalling systems and also at longer time periods (> 7 days) and earlier in the system lifecycle (> 5 days prior to formation). There is a also need to understand why forecasts bust, and then identify how to remediate those forecasts. Ensemble forecasts are adding value to forecasts products. Opportunities exist to extract greater value by calibrating risks and by investing how partitioning and clustering can inform forecasters of alternate forecast scenarios. Whilst having calibrated risk estimates of track guidance will inform forecasters, challenges still remain in communicating those risks.

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