This study examines the atmospheric boundary layer (ABL) structure and temperature inversion characteristics in the Chiayi region of Taiwan during wintertime, using high vertical and temporal resolution data from a microwave radiometer (MWR). The results show that surface-based inversions (SBIs) exhibit a distinct diurnal pattern, with the frequency of SBIs being much higher at night, reaching nearly 100% between 05:00 and 08:00 LST, primarily due to surface longwave cooling. Temperature inversions are associated with surface conditions of higher relative humidity, lower temperatures, and weaker wind speeds, which contribute to the stability of the inversion layer and inhibit vertical mixing. Interestingly, the mixing ratio remains stable despite the presence of inversions, suggesting that the increase in relative humidity during inversion events is likely due to surface cooling or weak winds, rather than an increase in water vapor. Further flux analysis is needed to confirm the causes of the increase in relative humidity.
The initial and boundary conditions are critical to the numerical weather prediction (NWP) model. It is known that satellite observations can overcome the limitations of the terrain, especially over the oceans where conventional observations are difficult to obtain. Therefore, the use of satellite data will expect to improve those regions where lack of traditional observation. The Advanced Microwave Sounding Unit (AMSU) and Atmospheric InfraRed Sounder (AIRS) onboard NASA’s EOS Aqua satellite, represent microwave and hyperspectral infrared observations, respectively. Both of them may provide atmospheric temperature and moisture soundings with complementary characteristics. For example, AMSU has the advantage to give cloudy retrievals while AIRS may retain the atmospheric gradient due to its finer high spatial resolution. Both data could estimate atmospheric thermodynamic state with substantial accuracy to improve high impact weather forecast In this study, we adopt the Weather Research and Forecasting (WRF) model and the community Gridpoint Statistical Interpolation (GSI) data assimilation system to evaluate the use of AMSU/AIRS retrievals for severe precipitation at Taiwan. The front, UTC 2016/01/05 22Z, is selected to demonstrate the benefit of using sounding data. The preliminary results shows a positive impact on total precipitable water while the time slope may need further investigation.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.