The Impact of Dropwindsonde Observations on Typhoon Track Forecasts in DOTSTAR and T-PARC

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 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.

A Long-Term, High-Quality, High-Vertical-Resolution GPS Dropsonde Dataset for Hurricane and Other Studies

“…However, the WSR program has been cancelled recently due to minimal impact in recent years, perhaps because of other improvements in the data assimilation systems (Hamill et al 2013). The impact of dropsonde data on typhoon-track forecasts has been also studied extensively (e.g., Wu et al 2007;Chou et al 2011;Wu et al 2012). For example, the typhoon-track forecast error in four numerical weather prediction models was reduced by 20%-40% consistently as a result of dropsonde data collected during The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC) in 2008 .…”

Improving the spin-up of regional EnKF for typhoon assimilation and forecasting with Typhoon Sinlaku (2008)

“…We note that many studies were made to highlight the impact of the T-PARC dropsondes for typhoon prediction (Aberson, 2003(Aberson, , 2011Wu et al, 2007;Harnisch and Weissmann, 2010;Chou et al, 2011;Weissmann et al, 2011;Kunii et al, 2012). Weissmann et al (2011) compared the results of assimilating the dropsonde data during the T-PARC period with four different models (ECMWF, JMA, NCEP and WRF).…”

“…Weissmann et al (2011) compared the results of assimilating the dropsonde data during the T-PARC period with four different models (ECMWF, JMA, NCEP and WRF). Chou et al (2011) evaluated the improvement on the TC track forecast derived by assimilating the dropsonde data in the DOTSTAR and T-PARC projects with the NCEP global forecast system (GFS). Both studies have shown that after assimilating the dropsonde data, the TC track forecast could be significantly improved.…”

“…Both studies have shown that after assimilating the dropsonde data, the TC track forecast could be significantly improved. Wu et al (2012a) investigated the steering flow from the results of Chou et al (2011). They concluded that the primary benefit from assimilating dropsondes for Sinlaku was to improve the vertical vortex structure that extends further into the troposphere; therefore, the typhoon could be advected by a more representative steering flow in the deep troposphere.…”

“…These valuable observations provide a great opportunity to improve our understanding of typhoons at different stages. As a result, Sinlaku has been widely studied for topics related to typhoon dynamics (Huang et al, 2011(Huang et al, , 2012Wu et al, 2012b), model simulation (Chou et al, 2011;Weissmann et al, 2011), regional data assimilation and observation impact estimation .…”