[A-AS10] General Meteorology

[AAS10-09] MAX-DOAS Observations of Water Vapor Gradient South of the Baiu Front and Their Application to Data Assimilation

*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, Data assimilation

Heavy rainfall disasters have become more frequent in recent years, particularly during the Baiu season. It was reported that a distinct horizontal gradient in water vapor concentration, referred to as a "water vapor gradient," appeared as part of the so-called water vapor front south of the Baiu front. However, since previous studies have shown that the water vapor front occurs at altitudes of 0.5–1.5 km rather than near the surface, observations of the water vapor gradient have been limited. In this study, we conducted observations of the water vapor concentration in the lower atmosphere in Chiba, Japan, for 10 years from 2015 to 2024 using the 4-different-azimuth-viewing Multi-Axis Differential Optical Absorption Spectroscopy (4AZ-MAXDOAS), which observed water vapor concentration at north, south, east, and west of Chiba. The data for the average concentration at altitudes of 0–1 km revealed that the most pronounced water vapor gradient in 10 years during the Baiu season was observed on July 27, 2020, with a magnitude exceeding approximately 2 g/kg/10 km, as shown in model analysis from previous studies for the same season and altitude. Notably, such a gradient was not detected by ground-based hygrometers. These results indicate that the spatial distribution of water vapor concentration south of the Baiu front is not uniform, supporting previous studies that the water vapor front structure appeared at altitudes of 0.5–1.5 km. In this presentation, we will also discuss the spatial distribution of water vapor concentration south of the Baiu front in detail by assimilating the 4AZ-MAXDOAS observations into the Cloud Resolving Storm Simulator (CReSS) and the three-dimensional variational data assimilation system (CReSS-3DVAR).