Since 2003, a field program has been conducted under the name of Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR). As the name DOTSTAR suggests, targeted observation is one of its key objectives. The prerequisite for designing the observing strategy is to identify the sensitive areas, which would exert great influence on the results of numerical forecast or the extent of the forecast error.In addition to various sensitivity products already adopted in DOTSTAR, a new way to identify the sensitive area for the targeted observation of tropical cyclones based on the fifth-generation Pennsylvania State University-National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) is proposed in this paper. By appropriately defining the response functions to represent the steering flow at the verifying time, a simple vector, adjoint-derived sensitivity steering vector (ADSSV), has been designed to demonstrate the sensitivity locations and the critical direction of typhoon steering flow at the observing time. Typhoons Meari and Mindulle of 2004 have been selected to show the use of ADSSV. In general, unique sensitive areas 36 h after the observing time are obtained.The proposed ADSSV method is also used to demonstrate the signal of the binary interaction between Typhoons Fungwong and Fengshen (2002). The ADSSV is implemented and examined in the field project, DOTSTAR, in 2005 as well as in the surveillance mission for Atlantic hurricanes conducted by the Hurricane Research Division. Further analysis of the results will be vital to validate the use of ADSSV.

The South China Sea Monsoon Experiment (SCSMEX) is an international field experiment with the objective to better understand the key physical processes for the onset and evolution of the summer monsoon over Southeast Asia and southern China aiming at improving monsoon predictions. In this article, a description of the major meteorological observation platforms during the intensive observing periods of SCSMEX is presented. In addition, highlights of early results and discussions of the role of SCSMEX in providing valuable in situ data for calibration of satellite rainfall estimates from the Tropical Rainfall Measuring Mission are provided. Preliminary results indicate that there are distinctive stages in the onset of the South China Sea monsoon including possibly strong influences from extratropical systems as well as from convection over the Indian Ocean and the Bay of Bengal. There is some tantalizing evidence of complex interactions between the supercloud cluster development over the Indian Ocean, advancing southwest monsoon flow over the South China Sea, midlatitude disturbances, and the western Pacific subtropical high, possibly contributing to the disastrous flood of the Yangtze River Basin in China during June 1998.

A unique dataset of targeted dropsonde observations was collected during The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC) in the autumn of 2008. The campaign was supplemented by an enhancement of the operational Dropsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR) program. For the first time, up to four different aircraft were available for typhoon observations and over 1500 additional soundings were collected.This study investigates the influence of assimilating additional observations during the two major typhoon events of T-PARC on the typhoon track forecast by the global models of the European Centre for MediumRange Weather Forecasts (ECMWF), the Japan Meteorological Agency (JMA), the National Centers for Environmental Prediction (NCEP), and the limited-area Weather Research and Forecasting (WRF) model. Additionally, the influence of T-PARC observations on ECMWF midlatitude forecasts is investigated.All models show an improving tendency of typhoon track forecasts, but the degree of improvement varied from about 20% to 40% in NCEP and WRF to a comparably low influence in ECMWF and JMA. This is likely related to lower track forecast errors without dropsondes in the latter two models, presumably caused by a more extensive use of satellite data and four-dimensional variational data assimilation (4D-Var) of ECMWF and JMA compared to three-dimensional variational data assimilation (3D-Var) of NCEP and WRF. The different behavior of the models emphasizes that the benefit gained strongly depends on the quality of the first-guess field and the assimilation system.

The typhoon surveillance program Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR) has been conducted since 2003 to obtain dropwindsonde observations around tropical cyclones near Taiwan. In addition, an international field project The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC) in which dropwindsonde observations were obtained by both surveillance and reconnaissance flights was conducted in summer 2008 in the same region. In this study, the impact of the dropwindsonde data on track forecasts is investigated for DOTSTAR and T-PARC (2008) experiments. Two operational global models from NCEP and ECMWF are used to evaluate the impact of dropwindsonde data. In addition, the impact on the two-model mean is assessed.The impact of dropwindsonde data on track forecasts is different in the NCEP and ECMWF model systems. Using the NCEP system, the assimilation of dropwindsonde data leads to improvements in 1-to 5-day track forecasts in about 60% of the cases. The differences between track forecasts with and without the dropwindsonde data are generally larger for cases in which the data improved the forecasts than in cases in which the forecasts were degraded. Overall, the mean 1-to 5-day track forecast error is reduced by about 10%-20% for both DOTSTAR and T-PARC cases in the NCEP system. In the ECMWF system, the impact is not as beneficial as in the NCEP system, likely because of more extensive use of satellite data and more complex data assimilation used in the former, leading to better performance even without dropwindsonde data. The stronger impacts of the dropwindsonde data are revealed for the 3-to 5-day forecast in the two-model mean of the NCEP and ECMWF systems than for each individual model.

Starting from 2003, a new typhoon surveillance program, Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR), was launched. During 2004, 10 missions for eight typhoons were conducted successfully with 155 dropwindsondes deployed. In this study, the impact of these dropwindsonde data on tropical cyclone track forecasts has been evaluated with five models (four operational and one research models). All models, except the Geophysical Fluid Dynamics Laboratory (GFDL) hurricane model, show the positive impact that the dropwindsonde data have on tropical cyclone track forecasts. During the first 72 h, the mean track error reductions in the National Centers for Environmental Prediction's (NCEP) Global Forecast System (GFS), the Navy Operational Global Atmospheric Prediction System (NOGAPS) of the Fleet Numerical Meteorology and Oceanography Center (FNMOC), and the Japanese Meteorological Agency (JMA) Global Spectral Model (GSM) are 14%, 14%, and 19%, respectively. The track error reduction in the Weather Research and Forecasting (WRF) model, in which the initial conditions are directly interpolated from the operational GFS forecast, is 16%. However, the mean track improvement in the GFDL model is a statistically insignificant 3%. The 72-h-average track error reduction from the ensemble mean of the above three global models is 22%, which is consistent with the track forecast improvement in Atlantic tropical cyclones from surveillance missions. In all, despite the fact that the impact of the dropwindsonde data is not statistically significant due to the limited number of DOTSTAR cases in 2004, the overall added value of the dropwindsonde data in improving typhoon track forecasts over the western North Pacific is encouraging. Further progress in the targeted observations of the dropwindsonde surveillances and satellite data, and in the modeling and data assimilation system, is expected to lead to even greater improvement in tropical cyclone track forecasts.