桃園台地都會與海岸帶關鍵區之時空特徵跨域鏈結研究-以新開發通量儀測量近地表大氣通量並探討陸氣交換機制(子計畫三)

According to the development trend of the Critical Zone Observatory (CZO), this project proposes a rural station for atmospheric and hydrological flux observation in Taiwan to gain a better understanding on atmospheric flux variability under different land use and weather variability. The NCU Atmospheric and Hydrological Observatory (NAHO) is one of meteorological sites with long-term (e.g. 1980 to present) observation data in Taoyuan. However, most of the past data are paper records, and need to be documented. With this project, we will also try to use the meteorological data from the Central Weather Bureau near Taoyuan and try to interpolate into NAHO location to recover missing data. It is expected that historical data can be recovered for a longer period of time. This new dataset will be beneficial for the analysis of other projects. In addition, the NAHO already has some basic atmospheric and surface flux observation instruments, but the observation and data collection of high frequencyatmospheric thermal-dynamics fluxes are still insufficient. This project will setup atmospheric flux observations and perform atmospheric flux calculations. We propose a two-year plan. It is hoped that with the establishment of NAHOobservation data, the following two scientific research objectives will be completed: (1) With data recovery technology, the long-term climate database of NAHO is recovered and can be used as a database to explore the long-term climate variability of the metropolis and coastal zone of Taoyuan. (2) Establish a complete surface flux and hydrological observations at NAHO. Through case study, the relationship of air land exchanges in terms of energy and water budgets response to different surface characteristics can be studied. In addition, with the implementation of this project, it is expected that it will also assist in the development of the following important technologies, including atmospheric flux calculation, severe weather event screening, and long-term data trend analysis technology.