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- Author or Editor: Po-Hsiung Lin x
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Abstract
Two contrasting localized heavy rainfall events during Taiwan’s early summer rainy season with the daily rainfall maximum along the windward mountain range and coast were studied and compared using a combination of observations and numerical simulations. Both events occurred under favorable large-scale settings including the existence of a moisture tongue from the tropics. For the 31 May case, heavy rainfall occurred in the afternoon hours over the southwestern windward slopes after a shallow surface front passed central Taiwan. The orographic lifting of the prevailing warm, moist, west-southwesterly flow aloft, combined with a sea breeze–upslope flow at the surface provided the localized lifting needed for the development of heavy precipitation. On 16 June before sunrise, pronounced orographic blocking of the warm, moist, south-southwesterly flow occurred because of the presence of relatively cold air at low levels as a result of nocturnal and rain evaporative cooling. As a result, convective systems intensified as they moved toward the southwestern coast. During the daytime, the cold pool remained over southwestern Taiwan without the development of onshore/upslope flow. Furthermore, with a south-southwesterly flow aloft parallel to terrain contours, orographic lifting aloft was absent and preexisting rain cells offshore diminished after they moved inland. Over northern Taiwan on the lee side, a sea breeze/onshore flow developed in the afternoon hours, resulting in heavy thundershowers. These results demonstrate the importance of diurnal and local effects on determining the location and timing of the occurrences of localized heavy precipitation during the early summer rainy season over Taiwan.
Abstract
Two contrasting localized heavy rainfall events during Taiwan’s early summer rainy season with the daily rainfall maximum along the windward mountain range and coast were studied and compared using a combination of observations and numerical simulations. Both events occurred under favorable large-scale settings including the existence of a moisture tongue from the tropics. For the 31 May case, heavy rainfall occurred in the afternoon hours over the southwestern windward slopes after a shallow surface front passed central Taiwan. The orographic lifting of the prevailing warm, moist, west-southwesterly flow aloft, combined with a sea breeze–upslope flow at the surface provided the localized lifting needed for the development of heavy precipitation. On 16 June before sunrise, pronounced orographic blocking of the warm, moist, south-southwesterly flow occurred because of the presence of relatively cold air at low levels as a result of nocturnal and rain evaporative cooling. As a result, convective systems intensified as they moved toward the southwestern coast. During the daytime, the cold pool remained over southwestern Taiwan without the development of onshore/upslope flow. Furthermore, with a south-southwesterly flow aloft parallel to terrain contours, orographic lifting aloft was absent and preexisting rain cells offshore diminished after they moved inland. Over northern Taiwan on the lee side, a sea breeze/onshore flow developed in the afternoon hours, resulting in heavy thundershowers. These results demonstrate the importance of diurnal and local effects on determining the location and timing of the occurrences of localized heavy precipitation during the early summer rainy season over Taiwan.
Abstract
In 2008, abundant dropwindsonde data were collected during both reconnaissance and surveillance flights in and around tropical cyclones (TCs) in the western North Pacific basin under the framework of The Observing System Research and Predictability Experiment (THORPEX)–Pacific Asian Regional Campaign (T-PARC). The National Centers for Environmental Prediction Global Forecast System (GFS) showed significant track improvements for Typhoon Sinlaku (2008) after the assimilation of dropwindsonde data. For this particular typhoon, the potential vorticity (PV) diagnosis is adopted to understand the key factors affecting the track. A data denial run initialized at 0000 UTC 10 September is examined to evaluate how the extra data collected during T-PARC improve GFS track forecasts.
A quantitative analysis of the steering flow based on the PV diagnosis indicates that the Pacific subtropical high to the east of Sinlaku is a primary factor that advects Sinlaku northwestward, while the monsoon trough plays a secondary role. The assimilation of dropwindsonde data improves the structure and intensity of the initial vortex and maintains the forecast vortex structure in the vertical. The difference in the vertical extent of the vortices could be regarded as a cause for the discrepancy in steering flow between runs with and without the dropwindsonde data. This paper highlights the importance of improved analyses of the vertical TC structure, and thus of a representative steering flow in the deep troposphere during the forecasts.
Abstract
In 2008, abundant dropwindsonde data were collected during both reconnaissance and surveillance flights in and around tropical cyclones (TCs) in the western North Pacific basin under the framework of The Observing System Research and Predictability Experiment (THORPEX)–Pacific Asian Regional Campaign (T-PARC). The National Centers for Environmental Prediction Global Forecast System (GFS) showed significant track improvements for Typhoon Sinlaku (2008) after the assimilation of dropwindsonde data. For this particular typhoon, the potential vorticity (PV) diagnosis is adopted to understand the key factors affecting the track. A data denial run initialized at 0000 UTC 10 September is examined to evaluate how the extra data collected during T-PARC improve GFS track forecasts.
A quantitative analysis of the steering flow based on the PV diagnosis indicates that the Pacific subtropical high to the east of Sinlaku is a primary factor that advects Sinlaku northwestward, while the monsoon trough plays a secondary role. The assimilation of dropwindsonde data improves the structure and intensity of the initial vortex and maintains the forecast vortex structure in the vertical. The difference in the vertical extent of the vortices could be regarded as a cause for the discrepancy in steering flow between runs with and without the dropwindsonde data. This paper highlights the importance of improved analyses of the vertical TC structure, and thus of a representative steering flow in the deep troposphere during the forecasts.
Abstract
Targeted observation is one of the most important research and forecasting issues for improving tropical cyclone predictability. A new parameter [i.e., the adjoint-derived sensitivity steering vector (ADSSV)] has been proposed and adopted as one of the targeted observing strategies in the Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR). The ADSSV identifies the sensitive areas at the observing time to the steering flow at the verifying time through the adjoint calculation. In this study, the ADSSV is calculated from the nonlinear forecast model of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) and its adjoint to interpret the dynamical processes in the interaction between Typhoon Shanshan (2006) and the midlatitude trough. The ADSSV results imply that high-sensitivity regions affecting the motion of Typhoon Shanshan are located at the edge of the subtropical high and the 500-hPa midlatitude trough over northern central China. These ADSSV signals are in very good agreement with the quantitative evaluation based on the potential vorticity (PV) diagnosis. The vertical structure of the ADSSV is also shown for more physical insights into the typhoon–trough interaction. The maximum ADSSV occurs at 800–500 hPa to the southeast of Shanshan (associated with the subtropical high), while distinct ADSSV signals are located upstream of the storm center at about 500–300 hPa (associated with the mid- to upper-tropospheric midlatitude trough). Overall, it is demonstrated that the ADSSV features can well capture the signal of the large-scale trough feature affecting the motion of Shanshan, which can also be well validated from the PV analysis.
Abstract
Targeted observation is one of the most important research and forecasting issues for improving tropical cyclone predictability. A new parameter [i.e., the adjoint-derived sensitivity steering vector (ADSSV)] has been proposed and adopted as one of the targeted observing strategies in the Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR). The ADSSV identifies the sensitive areas at the observing time to the steering flow at the verifying time through the adjoint calculation. In this study, the ADSSV is calculated from the nonlinear forecast model of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) and its adjoint to interpret the dynamical processes in the interaction between Typhoon Shanshan (2006) and the midlatitude trough. The ADSSV results imply that high-sensitivity regions affecting the motion of Typhoon Shanshan are located at the edge of the subtropical high and the 500-hPa midlatitude trough over northern central China. These ADSSV signals are in very good agreement with the quantitative evaluation based on the potential vorticity (PV) diagnosis. The vertical structure of the ADSSV is also shown for more physical insights into the typhoon–trough interaction. The maximum ADSSV occurs at 800–500 hPa to the southeast of Shanshan (associated with the subtropical high), while distinct ADSSV signals are located upstream of the storm center at about 500–300 hPa (associated with the mid- to upper-tropospheric midlatitude trough). Overall, it is demonstrated that the ADSSV features can well capture the signal of the large-scale trough feature affecting the motion of Shanshan, which can also be well validated from the PV analysis.
Abstract
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 (2003–09) 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.
Abstract
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 (2003–09) 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.
Abstract
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 Medium-Range 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.
Abstract
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 Medium-Range 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.
