Observation System Experiments for Typhoon Jangmi (200815) Observed During T-PARC

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1 Asia-Pacific J. Atmos. Sci, 46(3), , 2010 DOI: /s y Observation System Experiments for Typhoon Jangmi (200815) Observed During T-PARC Byoung-Joo Jung 1, Hyun Mee Kim 1, Yeon-Hee Kim 2, Eun-Hee Jeon 2 and Ki-Hoon Kim 2 1 Atmospheric Predictability and Data Assimilation Laboratory, Department of Atmospheric Sciences, Yonsei University, Seoul, Korea 2 Forecast Research Laboratory, National Institute of Meteorological research/kma, Seoul, Korea (Manuscript received 4 Jan 2010; revised 18 May 2010; accepted 25 May 2010) The Korean Meteorological Society and Springer 2010 Abstract: In this study, the impact of various types of observations on the track forecast of Tropical Cyclone (TC) Jangmi (200815) is examined by using the Weather Research and Forecasting (WRF) model and the corresponding three-dimensional variational (3DVAR) data assimilation system. TC Jangmi is a recurving typhoon that is observed as part of the THORPEX Pacific Asian Regional Campaign (T-PARC). Conventional observations from the Korea Meteorological Administration (KMA) and targeted dropsonde observations from the Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR) were used for a series of observation system experiments (OSEs). We found that the assimilation of observations in oceanic areas is important to analyze environmental flows (such as the North Pacific high) and to predict the recurvature of TC Jangmi. The assimilation of targeted dropsonde observations (DROP) results in a significant impact on the track forecast. Observations of ocean surface winds (QSCAT) and satellite temperature soundings (SATEM) also contribute positively to the track forecast, especially two- to three-day forecasts. The impact of sensitivity guidance such as real-time singular vectors (SVs) was evaluated in additional experiments. Key words: OSE, THORPEX, T-PARC, adaptive observation guidance, singular vectors, tropical cyclone 1. Introduction Tropical cyclones (TCs) are characterized by strong vortices (winds) and severe precipitation. When TCs approach coastal regions or make landfall, they cause a great deal of socioeconomic damage. However, it is difficult to predict the movement and development of TCs, because during their lifetimes these cyclones are mostly located in oceanic regions where observational networks are sparse. Nevertheless, there have been steady improvements in the performance of TC track forecasts achieved primarily by using bogus techniques and new observational systems. Bogus techniques insert a vortex structure into analysis fields based on the TC report and the empirical formula (Kurihara et al., 1995; Kwon et al., 2002; Chou and Wu, 2008). Recently, the bogus data assimilation (BDA) techniques were developed that use Corresponding Author: Hyun Mee Kim, Department of Atmospheric Sciences, Yonsei University, Shinchon-dong 134, Seodaemun-ku, Seoul, , Korea khm@yonsei.ac.kr bogus observations in the framework of variational data assimilation systems (Zou and Xiao, 2000; Pu and Braun, 2001; Wu et al., 2006; Xiao et al., 2006, 2008). More balanced analysis fields can be obtained in model space by constraints of variational data assimilation systems in BDA. The use of new observational systems also increases the forecast skill of TC track. Due to the sparse observational networks in ocean areas, the use of satellite-derived observations can improve forecast skill of TCs. The atmospheric motion vectors (AMVs; Velden et al., 2005; Pu et al., 2008) that are retrieved from tracking clouds and moisture by various satellite channels provide dense wind observations for whole levels of the atmosphere. Langland et al. (2009) showed that rapid scan AMV from Geostationary Operational Environmental Satellites (GOES) improved the track forecast for hurricane Katrina (200512), especially for the 84- to 120-hour forecast time. Global Posi-tioning System (GPS) Radio-Occultation (RO) observations are also applied to various meteorological applications (e.g., weather, climate, and space weather; Anthes et al., 2008). Huang et al. (2005) showed that the assimilation of GPS refractivity sounding improved the prediction of track and precipitation for Typhoon Nari (200116). Since the early 1980s, dropsonde observations have been used to enhance the core and environment prediction of TCs (Burpee et al., 1996). Atmospheric soundings inside TCs can be observed by GPS dropsondes that are released directly into TCs from airplanes. Dropsonde observations from synoptic flow experiments produced significant improvements (16% - 30% error reduction for hour forecasts) in 15 cases observed from 1982 to 1995 (Burpee et al., 1996). For the five TC cases observed during 1997, dropsonde observations improved the mean track forecast by 32% and the intensity forecast by 20% within 48 hours before landfall by using the Geophysical Fluid Dynamics Laboratory (GFDL) hurricane model (Aberson and Franklin, 1999). Aberson (2002) summarized the impact of dropsonde observations for 24 missions during 1997 and It was found that the improvements seen in two-year samples are not promising. The additional dropsonde data provided statistically significant improvements with the GFDL model only at the 12 hour forecasts. It was demonstrated that accurate synthetic data and more widespread coverage of dropsonde data are necessary to improve track forecasts. Wu et al. (2007) evaluated 10 missions of Dropwindsonde Observations for

2 306 ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES Typhoon Surveillance near the Taiwan Region (DOTSTAR) 2004 using four operational models and one research model. They showed that the 72-hour average track error is reduced by 22%, which is consistent with the forecast improvement in Atlantic tropical cyclones studies. Aberson (2008) found that problems with the quality control, issues with the complex data assimilation itself, and inner-core assimilation can lead to the large forecast degradations in several cases. Recently, sensitivity guidance (Majumdar et al., 2006; Wu et al., 2009) has been incorporated in the assimilation of dropsonde observations. Assimilation of only a subset of data from subjectively sampled target regions produced a statistically significant reduction of track error by 25% (Aberson, 2003). Aberson and Etherton (2006) found that additional improvement of track forecasts can be achieved by the use of Ensemble Transform Kalman Filter (ETKF) for assimilating dropsonde data. Yamaguchi et al. (2009) also used sensitivity guidance in the assimilation of dropsonde observations with the Japan Meteorological Agency (JMA) global model for the track forecast of TC Conson (200404). In this study, the impacts of various types of observations on the track forecast of TC Jangmi (200815) were examined by the use of the Weather Research and Forecasting (WRF) model and corresponding three-dimensional variational (3DVAR) data assimilation system. The impacts of observations in regions indicated by sensitivity guidance are also examined in an additional experiment. A short review of THe Observing System Research and Predictability EXperiment (THORPEX) Pacific Asian Regional Campaign (T-PARC) and case descriptions for TC Jangmi are given in section 2. The model configuration and experimental design are summarized in section 3. The observations and sensitivity guidance used in this study are also presented in section 3. Results of a series of observation system experiments are presented in section 4, and the summary and discussion are presented in section T-PARC 2008 and case description a. T-PARC 2008 THORPEX is an international research program designed to accelerate improvements in the accuracy of one-day to two-week high-impact weather forecasts (WMO, 2002). Its objectives include: i) global-to-regional influences on the evolution and predictability of weather systems, ii) global observing-system design and demonstration, iii) targeting and assimilation of observations, and iv) societal, economic, and environmental benefits of improved forecasts (Shapiro and Thorpe, 2004). These objectives are established by international collaborations of academic institutions, operational forecast centers, and users of forecast products. The core initiatives include international field campaigns focused on regional forecast problems facing Africa, Asia, Europe, North America, and the Southern Hemisphere. During August and September 2008, the T-PARC was implemented in the Western North Pacific to investigate formation, structures, targeting, extratropical transition, and downstream impacts of tropical cyclones. Four aircraft were used and many new observations (buoy drop, driftsonde, and airborne radar) were examined for the first time in western Pacific regions. Additional scientific background that also related to the Tropical Cyclone Structure (TCS08 1 ), can be found in the Elsberry and Harr (2008). b. TC JANGMI (200815) Figure 1 shows the best track and minimum sea level pressures for TC Jangmi from the Regional Specialized Meteorological Center (RSMC) Tokyo. After its formation on 1200 UTC 24 September 2008, TC Jangmi intensified, moving northwestward (Fig. 1). Figure 2a shows the mean sea level pressure and geopotential height on 500 hpa at 1200 UTC 26 September TC Jangmi was moving northwestward along the side of the North Pacific High (denoted by 5880 gpm contour line and shading). It reached its lowest central pressure of 905 hpa at 1200 UTC 27 September 2008 (Fig. 2b). It made landfall on Taiwan about 0900 UTC 28 September 2008 (Fig. 2c). After its landfall, its intensity weakened and it recurved to the northeast (Fig. 2d). Afterward, TC Jangmi lost its symmetric structure and disappeared at 0000 UTC 1 October 2008 in the southern oceanic area of Kyushu Island. 3. Model configuration and experimental framework a. Model configuration This study uses the Weather Research and Forecasting (WRF) model version 2.2 (Skamarock et al., 2005) and the WRF 3DVAR version 2.2 beta (Barker et al., 2003; 2004). The model domain for this study is horizontal grids (centered at 33 o N in latitude and 133 o E in longitude), with a 30 km horizontal resolution focused on the East Asia region and 31 vertical levels. The model domain is shown in Fig. 2a. The model's initial and lateral boundary condition is the National Centers for Environmental Prediction (NCEP) final analysis (FNL; 1 o 1 o global grid). Physical parameterizations used in the simulation include New Kain-Fritsch scheme (Kain, 2004) for cumulus parameterization, WRF Single Momentum 6 class scheme (Hong and Lim, 2006) for microphysics parameterization, Dudhia scheme (Dudhia, 1989) for shortwave radiation parameterization, Rapid Radiative Transfer Model (RRTM) scheme (Mlawer et al., 1997) for longwave radiation parameterization, Yonsei University (YSU) scheme (Hong et al., 2006) for planetary boundary layer parameterization, and Noah Land Surface model (Chen and Dudhia, 2001) for land surface parameterization. The Background Error Statistics (BES) provide error infor- 1 The TCS08 is one of the cooperating field program of T-PARC.

3 31 August 2010 Byoung-Joo Jung et al. 307 the true background error, respectively. X f represents the forecast state at T + 12 or T + 24 hours verified at the same time. In this study, four-month statistics from July to October 2007 are calculated and used to construct the BES using gen_be utilities in the WRF 3DVAR system described in Barker et al. (2003, 2004). b. Observations Fig. 1. (a) Best track and (b) minimum sea level pressure of TC Jangmi (200815) from Regional Specialized Meteorological Center (RSMC) Tokyo. The corresponding date (month/day) of best track is denoted in (a), and the times (0000 UTC 27 and 0000 UTC 28 September 2008) of targeted dropsonde observation are denoted by dashed line in (b). mation of background fields and work as a transformation operator to convert model space to analysis space, and vice versa. The most typical method used to calculate the BES is the National Meteorological Center (NMC) method (Parrish and Derber, 1992). The NMC method provides the climatological estimate of BES by longtime average of forecast differences verified at the same time: B = ( X b X t )( X b X t ) T = ε b ε T b [ X f ( T + 24) X f ( T + 12) ][ X f ( T + 24) X f ( T + 12) ] T. (1) X t, X b, and ε b represent the true state, background state, and As observations for data assimilation, conventional observations through the Global Telecommunication System (GTS) from the Korea Meteorological Administration (KMA) were used. These include SYNOP (surface observation from weather station), AWS (Automatic weather system), SHIP (surface observation from ship), BUOY (surface observation from buoy), TEMP (upper-air observation from radiosonde), AMDAR (aircraft meteorological data relay), AIREP (aircraft report), PILOT (wind observations from tracking balloons), SATEM, QSCAT, and PROFL (wind profiler). The targeted dropsonde observations were made at 0000 UTC 27 and 0000 UTC 28 September 2008 by Astra aircraft of the DOTSTAR project (Wu et al., 2005) as parts of T-PARC Figure 3 shows the observational distributions at 0000 UTC 27 September The targeted dropsonde observation (DROP) is around TC Jangmi (Fig. 3a). The ocean surface wind measured by scatterometer (QSCAT 2 ) is distributed along the path of a polar orbit satellite (Fig. 3b). QSCAT in the center of TC Jangmi is missing on account of strong convective rains. SATEM is a temperature sounding retrieved from the radiances measured by polar orbit satellites. SATEM is distributed in both land and oceanic area and is relatively sparse (Fig. 3c). The distributions of the other observations are shown in Fig. 3d. Most of the observations are located in land areas, especially in Korea and Japan. There are very dense observing networks in those areas. There are additional observations in the oceanic area, mostly from aircraft. However, the distribution of these observations is limited by the predetermined routes of the aircraft, and the availability of these observations is not good, compared to the North American region. c. Experimental design Three sets of experiments were conducted according to the distributions and types of observations that were assimilated into the 3DVAR system. All experiments were initiated at 0600 UTC 26 September 2008 with National Centers for Environmental Prediction (NCEP) Final analysis (FNL) data as an initial field. WRF 3DVAR data assimilation is applied in cycling mode with six-hour windows to the 0000 UTC 28 September The first set of experiments (OSE set-1) is composed of ALL, LAND, and SEA (Figs. 4a, b, and c). The experiment called ALL assimilated all available observations. The experiments called LAND and SEA only assimilated observations on the land and oceanic areas, respectively. The relative importance 2 The validation of this observation type in the typhoon environment is studied in Chou et al. (2010).

4 308 ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES Fig. 2. Mean sea level pressure (thin lines with 4 hpa interval) and geopotential height (thick lines with 60 gpm interval) on 500 hpa of the analysis at (a) 1200 UTC 26, (b) 1200 UTC 27, and (d) 0000 UTC 29 September The radar reflectivity (shaded with 5 dbz interval) from Central Weather Bureau (CWB) of Taiwan at 0900 UTC 28 September 2008 is shown in (c). The subtropical ridge is denoted by shaded area (over 5880 gpm) and the translational velocity of TC Jangmi is denoted by thick arrows. of observations in each area on the typhoon track forecast was tested in these experiments. The second set of experiments (OSE set-2) was a data denial experiment. As mentioned in section 2b, DROP, QSCAT, and SATEM were the main observational types over the oceanic area. To identify the relative importance of those oceanic observations, experiments called ALL-DROP, ALL-QSCAT, and ALL-SATEM were conducted. Each experiment assimilated all available observations 3 except DROP, QSCAT, and SATEM observations, respectively. The third set of experiment was configured to identify the impact of observations located in the regions indicated by a sensitivity guidance3. Here, observations that distributed in both land and the regions denoted by the sensitivity guidance were assimilated, and this experiment was denoted as LAND + SV (Fig. 4d) similar to the experiment of Buizza et al. (2007). More detailed information about the sensitivity guidance used is given In this study, total energy singular vectors based on MM5 adjoint modeling system (Kim et al., 2008) calculated in real-time are used as the sensitivity guidance.

5 31 August 2010 Byoung-Joo Jung et al. 309 Fig. 3. Distributions of observations for (a) targeted dropsonde (DROP), (b) ocean surface wind (QSCAT), (c) satellite temperature sounding (SATEM), and (d) the others at 0000 UTC 27 September The mean sea level pressure (solid lines with 4 hpa intervals) of the analysis is superimposed. in section 3d. For all experiments, three-day forecasts were performed initiated at 0000 UTC 27, 1200 UTC 27, and 0000 UTC 28. All forecast typhoon tracks were verified with RSMC Tokyo best track. d. Singular vectors (SVs) As sensitivity guidance in the LAND + SV experiment, realtime fifth-generation mesoscale model (MM5) singular vectors (SVs) were used 4. The MM5 SV were provided in real time to the European Centre for Medium-Range Weather Forecasts (ECMWF) data targeting system (DTS) and the Japan Meteorological Agency (JMA) T-PARC website during the period of T-PARC field experiment (1 August - 4 October 2008). The SVs are the fastest growing perturbations given basic state and norms (Kim and Morgan, 2002). The MM5 SVs are calculated by MM5 Adjoint Modeling System (Zou et al., 1997) and Lanczos algorithm. The domain for MM5 SVs is horizontal grids (centered at 25 N in latitude and 125 E in longitude), with a 120 km horizontal resolution (Fig. 5f). In the calculation of realtime MM5 SVs, the dry total energy (TE) norms were used at 4 A different model (i.e. MM5) is used for sensitivity guidance because it is not feasible to calculate the SVs in the WRF system. The MM5 SVs are only used to sort the observations in the LAND + SV experiment.

6 310 ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES moisture processes, both Grell convective parameterization (Grell, 1993) and explicit moisture schemes (Dudhia, 1989) were used for the nonlinear model integrations, but only largescale condensation parameterization was used for the tangent linear and adjoint model integrations. To identify the sensitive regions objectively, the composite SV was constructed by singular value weighted summation of first to third SVs in energy units as in Kim and Jung (2009a, b). The composite SV was interpolated to 0.5 degree by 0.5 degree grid, and then 10% of grids sorted from the largest values were selected as the sensitive regions. The objectively selected sensitivity guidance and the distribution of observations for LAND + SV experiment are shown in Fig Results Fig. 4. Schematics for the (a) ALL, (b) LAND, (c) SEA, and (d) LAND + SV experiments. The observations in the shaded regions are assimilated for each experiment. both initial and final times (Zou et al., 1997; Palmer et al., 1998; Kim et al., 2008; Kim and Jung, 2009a, b). Because the sensitivity guidance for fixed target regions should be provided in advance of a targeting time, the two- to four-day forecasts of NCEP Global Forecast System (GFS) were used as the initial and boundary conditions for the calculation of MM5 SVs. For a. OSE set-1 Figure 6 shows the forecast track and track error of the first set of experiments, verified with the RSMC best track. For three-day track forecasts, the SEA experiment showed similar performance to the ALL experiment, and even better performance than the ALL experiment in the forecast initiated at 0000 UTC 28 September The 12-hour forecast difference of 500 hpa geopotential height between the ALL and the LAND experiments initiated at 0000 UTC 27 September 2008 shows a dipole structure near the center of TC Jangmi (Fig. 7a). Fig. 5. Distribution of real-time sensitivity guidance (J kg 1, shaded) and observations for LAND + SV experiment at (a) 0000 UTC 27, (b) 0600 UTC 27, (c) 1200 UTC 27, (d) 1800 UTC 27, and (e) 0000 UTC 28 September The model domain for MM5 SV calculation is show as a shaded area in (f).

7 31 August 2010 Byoung-Joo Jung et al. 311 Fig. 6. The left panel shows the tracks of three-day forecast for OSE set-1 and LAND + SV experiment initiated at (a) 0000 UTC 27, (c) 1200 UTC 27, and (e) 0000 UTC 28 September The RSMC best track is denoted by black thick line. The right panels show the track error (km) of threeday forecasts initiated at (b) 0000 UTC 27, (d) 1200 UTC 27, and (f) 0000 UTC 28 September 2008, verified with RSMC best track. At this time, the LAND experiment predicts the geopotential height more southwestward than the ALL experiment. Therefore, the LAND experiment that did not assimilate the observations in the oceanic area predicts the typhoon track toward the southern part of China, and the recurvature of TC Jangmi occurred closer to China for the LAND experiment compared

8 312 ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES Fig. 7. (a) The 12 hour forecast difference (shaded, gpm) of 500 hpa geopotential height between the ALL and the LAND experiments and (b) the 48 hour forecast (shaded, gpm) of 500 hpa geopotential height for the LAND experiment initiated at 0000 UTC 27 September The solid lines denote the 500 hpa geopotential height (60 gpm interval) for the ALL experiment at the corresponding forecast time. to other experiments (Figs. 6a and 7b). b. OSE set-2 In the results of the first set of experiments (Fig. 6 and section 4a), it was found that the observations in the oceanic area are more important to the track forecast of TC Jangmi. To investigate the effects of DROP, QSCAT, and SATEM, which are the main observation types in the oceanic area, data denial experiments were conducted. The impact of each observation type was evaluated by the degree of relative degradations in track forecasts. In the analysis time, the difference between the ALL and other data denial experiments was co-located with the distribution of examined observations (compare Figs. 3a-c and 8a-c). The largest analysis differences were observed at the right half-circle near the center of TC Jangmi by DROP (Figs. 8a and 8d). SATEM makes smaller but broader analysis differences than DROP (Fig. 8c). In the forecast initiated at 0000 UTC 27 September 2008, DROP is the most important. QSCAT and SATEM also have positive impacts to the track forecast. In the forecast initiated at 1200 UTC 27 September 2008, DROP, QSCAT, and SATEM have positive impacts on the track forecast (not shown). While DROP has a positive impact in one- to three-day forecasts initiated at 0000 UTC 27 (Fig. 9b), the dropsonde observations have the biggest impact in one-day forecast initiated at 1200 UTC 27 (Fig. 9d). In the two- to three-day forecasts initiated at 1200 UTC 27, QSCAT and SATEM are more important than DROP (Fig. 9d). This is caused by the fact that there are no deopsonde observations at 1200 UTC 27 September Therefore, for one-day forecasts, DROP has more impact due to the dropsonde observations assimilated earlier (i.e., 0000 UTC 27 September) and QSCAT and SATEM have more impact for two- to three-day forecasts due to the assimilation of the latest information (1200 UTC 27 September). The positive impact of dropsonde observations on typhoon track forecasts in four operational models during T-PARC is also reported in Weissmann et al. (2010). Even though Aberson (2008) reported that the inner-core dropsonde assimilation may degrade the forecast due to the representation error and assimilation limitation, the forecast degradation is not occurred in this study because the dropsonde observations used in this study are circularly distributed at least 120 km distant from the typhoon center. c. LAND + SV experiment Finally, the results of the LAND + SV experiment are shown in Fig. 6. In this experiment, the impact of assimilating observations in the area denoted by the sensitivity guidance was investigated. The sensitive regions are defined as described in the section 3d. The impact of observations in the sensitive regions was evaluated by comparing the performance of the LAND + SV experiment to that of the LAND experiment. As the result, the additional use of observations denoted by the sensitivity guidance has a positive impact on the typhoon track forecast, compared to the LAND experiment, especially at 0000 UTC 27 and 0000 UTC 28 September These are the times when the targeted dropsonde observations were available. At 1200 UTC 27, the impact of observations in the sensitive regions is small for a one-day forecast, but the impact increased for twoto three-day forecasts (Fig. 6d), which is related to the fact that most of additional observations denoted by the sensitivity guid-

9 31 August 2010 Byoung-Joo Jung et al. 313 Fig. 8. The analysis difference (shaded, gpm) of 850 hpa geopotential height between the ALL and (a) ALL-DROP, (b) ALL-QSCAT, and (c) ALLSATEM experiments at 0000 UTC 27 and (d) ALL-DROP at 0000 UTC 28 September The 850 hpa geopotential height (30 gpm interval) of the ALL experiment at the corresponding time is superposed with solid lines, and the thick solid line denotes the 1500 gpm lines. ance were composed of QSCAT and SATEM at this time. QSCAT and SATEM at 1200 UTC 27 had more impact for twoto three-day forecasts in the data denial experiment (Fig. 9d and section 4b). 5. Summary and discussion To investigate the impacts of observations on the track forecast of TC Jangmi (200815), a series of observation system experiments were conducted with WRF model and corresponding 3DVAR data assimilation system. TC Jangmi was a recurving typhoon occurred during the T-PARC period that made landfall on Taiwan. The conventional observations from KMA GTS system and the targeted dropsonde observations from DOTSTAR are used. In the first set of experiments (ALL, LAND, and SEA), the ALL and SEA experiments show similar performance on the track forecasts. The LAND experiment was unable to forecast the recurving feature at the proper time. From this, it was found that the assimilation of observations in the oceanic area was important on the track forecast of TC Jangmi. To investigate the relative importance of observation types in the oceanic area, data denial experiments were also conducted (i.e., ALL, ALLDROP, ALL-QSCAT, and ALL-SATEM). DROP, QSCAT, and

10 314 ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES Fig. 9. The left panel shows the tracks of three-day forecast for OSE-2 initiated at (a) 0000 UTC 27, (c) 1200 UTC 27, and (e) 0000 UTC 28 September The RSMC best track is denoted by black thick line. The right panels show the track error (km) of three-day forecasts initiated at (b) 0000 UTC 27, (d) 1200 UTC 27, and (f) 0000 UTC 28 September 2008, verified with RSMC best track. SATEM had positive impacts in the track forecast of TC Jangmi. DROP was the most important at 0000 UTC 27 and 0000 UTC 28 September 2008 when the dropsonde observations were available. However, at 1200 UTC 27 September 2008 QSCAT and SATEM were more important for two- to threeday forecasts because the impact of dropsonde observations assimilated 12-hour earlier decreased as the forecast time becomes longer. In addition to these two sets of experiments, the

11 31 August 2010 Byoung-Joo Jung et al. 315 LAND + SV experiment was performed to investigate the impact of observations in the regions indicated by the sensitivity guidance. The objectively selected composite total energy SVs that were provided to ECMWF DTS and JMA during T-PARC were used as the sensitivity guidance. The assimilation of additional observations located at the area denoted by the sensitivity guidance reduced the track error in LAND + SV experiment, and also improved the recurving features of TC Jangmi. From a series of observation system experiments in this study, it was found that the assimilation of observations near the center of TCs (e.g., dropsonde observations) as well as the environmental large-scale regions is important on the track forecast. Acknowledgements. The authors wish to thank two anonymous reviewers for their valuable comments, and Professor Chun-Chieh Wu of National Taiwan University for providing DOTSTAR observational data used in this study. 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