Publications

Alpers, W; Wong, WK; Dagestad, KF; Chan, PW (2012). A northerly winter monsoon surge over the South China Sea studied by remote sensing and a numerical model. INTERNATIONAL JOURNAL OF REMOTE SENSING, 33(23), 7361-7381.

Abstract
A northerly winter monsoon surge, which occurred on 15 December 2009 over the South China Sea (SCS), is studied by using satellite-based and ground-based remote-sensing data and an atmospheric numerical model. The remote-sensing data are from the advanced synthetic aperture radar (ASAR) onboard the Environmental Satellite (Envisat), the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra satellite, the imager onboard the geostationary satellite MTSAT-1R (Multi-Functional Transport Satellite-1R) and the weather radar of the Hong Kong Observatory (HKO). A northerly winter monsoon surge is a cold air outbreak associated with a northerly wind, the passage of a cold front from north to south and a strong drop in air temperature. The analyses of the weather radar and the MTSAR-1R images of 15 December show that the surge of 15 December was associated with a rain band and a cloud front travelling over the SCS in a southeastward direction. Due to the interaction of the cold air (13 degrees C) with the warm water (19 degrees C), they dissolved when they had reached an offshore distance of approximately 160 km. The high-resolution (150 m) ASAR image reveals fine-scale features of the wind field, in particular details of the wind front, such as embedded rain cells and atmospheric gravity waves. Quantitative information on the near-surface wind field is retrieved from the ASAR, and it is shown that the wind field associated with the surge is quite variable and that speeds up to 15 m s(-1) are encountered in coastal wind jets. Finally, the remote-sensing data are compared with the simulation results of the pre-operational version of the Atmospheric Integrated Rapid-cycle (AIR) forecast model of the HKO. It is shown that, in general, the AIR model reproduces quite well the observational data.

DOI:
0143-1161

ISSN:
10.1080/01431161.2012.685983