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Chun-Chieh Wu
,
Tzu-Hsiung Yen
,
Ying-Hwa Kuo
, and
Wei Wang

Abstract

In this study, a series of numerical experiments are performed to examine the ability of a high-resolution mesoscale model to predict the track, intensity change, and detailed mesoscale precipitation distributions associated with Typhoon Herb (1996), which made landfall over Taiwan. The fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), with a 2.2-km horizontal grid spacing, successfully simulates the mesoscale rainfall distribution associated with Herb, and the predicted maximum 24-h rainfall of 1199 mm accounts for about 70% of the observed amount of 1736 mm at Mount A-Li. It is shown that, with an accurate track simulation, the ability of the model to simulate successfully the observed rainfall is dependent on two factors: the model's horizontal grid spacing and its ability to describe the Taiwan terrain. The existence of the Central Mountain Range has only a minor impact on the storm track, but it plays a key role in substantially increasing the total rainfall amounts over Taiwan. The analysis presented here shows that the model and terrain resolutions play a nearly equivalent role in the heavy precipitation over Mount A-Li. The presence of maximum vertical motion and heating rate in the lower troposphere, above the upslope mountainous region, is a significant feature of forced lifting associated with the interaction of the typhoon's circulation and Taiwan's mountainous terrain. Overall, Typhoon Herb is a case in point to indicate the intimate relation between Taiwan's topography and the rainfall distribution associated with a typhoon at landfall.

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Chun-Chieh Wu
,
Kun-Hsuan Chou
,
Po-Hsiung Lin
,
Sim D. Aberson
,
Melinda S. Peng
, and
Tetsuo Nakazawa

Abstract

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.

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Eric A. Hendricks
,
Yi Jin
,
Jonathan R. Moskaitis
,
James D. Doyle
,
Melinda S. Peng
,
Chun-Chieh Wu
, and
Hung-Chi Kuo

Abstract

High-impact Typhoon Morakot (2009) was investigated using a multiply nested regional tropical cyclone prediction model. In the numerical simulations, the horizontal grid spacing, cumulus parameterizations, and microphysical parameterizations were varied, and the sensitivity of the track, intensity, and quantitative precipitation forecasts (QPFs) was examined. With regard to horizontal grid spacing, it is found that convective-permitting (5 km) resolution is necessary for a reasonably accurate QPF, while little benefit is gained through the use of a fourth domain at 1.67-km horizontal resolution. Significant sensitivity of the track forecast was found to the cumulus parameterization, which impacted the model QPFs. In particular, the simplified Arakawa–Schubert parameterization tended to erroneously regenerate the remnants of Tropical Storm Goni to the southwest of Morakot, affecting the large-scale steering flow and the track of Morakot. Strong sensitivity of the QPFs to the microphysical parameterization was found, with the track and intensity showing little sensitivity. It is also found that Morakot’s accumulated precipitation was reasonably predictable, with the control simulation producing an equitable threat score of 0.56 for the 3-day accumulated precipitation using a threshold of 500 mm. This high predictability of precipitation is due in part to more predictable large-scale and topographic forcing.

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