[A-AS08] General Meteorology

[AAS08-07] Long-term Continuous Observations of Horizontal Inhomogeneity in Water Vapor Concentration in the Lower Atmosphere using MAX-DOAS

*Shunya Mizobuchi¹, Hitoshi Irie¹, Shimizu Shingo² (1.Center for Environmental Remote Sensing, Chiba University, 2.National Research Institute for Earth Science and Disaster Resilience)

Keywords:Water vapor, Ground-based remote sensing

We utilized the Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) method to conduct long-term continuous observations of water vapor concentration in the lower atmosphere in Tsukuba and Chiba from 2017 to 2022. MAX-DOAS is a passive ground-based remote sensing technique that uses sunlight as a light source, allowing for cost-effective long-term continuous observations. We employed the 4AZ-MAXDOAS system, which directed its observational lines of sight towards four different azimuth directions. Through these observations, we explored the relationship between atmospheric instability and horizontal inhomogeneity in lower atmospheric water vapor concentration. Analysis of the Lifted Index (LI), an indicator of lower atmosphere stability, revealed a correlation between increased atmospheric instability and greater horizontal inhomogeneity in water vapor distribution. We identified 26 cases of particularly unstable atmospheres, with notable horizontal inhomogeneity in water vapor observed in 20 of these cases, coinciding with the presence of a stationary front over Japan. Additionally, the case exhibiting the most significant inhomogeneity underwent Local Analysis (LA) by the Japan Meteorological Agency, confirming the inflow of warm, moist air from the southwest as the cause of pronounced horizontal inhomogeneity in water vapor. Thus, MAX-DOAS observations of horizontal inhomogeneity in lower atmospheric water vapor offer valuable insights beyond traditional methods, complementing technologies like radiosondes. They are expected to contribute to the refinement of data assimilation techniques, enhancing predictive capabilities and facilitating the implementation of early warning systems for localized heavy rainfall.