Search Results
You are looking at 1 - 2 of 2 items for :
- Author or Editor: Po-Hsiung Lin x
- Targeted Observations, Data Assimilation, and Tropical Cyclone Predictability x
- Refine by Access: All Content x
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.
