Water vapor lidar observation and data assimilation for a moist low-level jet triggering a mesoscale convective system

Abstract

We conducted field observations using two water vapor Raman lidars (RLs) in Kyushu, Japan, to clarify the characteristics of a moist low-level jet (MLLJ), which plays a fundamental role in the formation and maintenance of mesoscale convective systems (MCSs). The two RLs observed the inside and outside of an MLLJ, providing moisture to an MCS with local heavy precipitation on 9 July 2021. Our observations revealed that the MLLJ contained large amounts of moisture below the convective mixing layer height of 1.6 km. The large amount of the moisture in the MLLJ might be intensified by low-level convergences and/or water vapor buoyancy facilitated by strong horizontal wind. We conducted four data assimilation experiments; CNTL assimilated Japan Meteorological Agency operational observation data, and other three experiments that ingested the lidar-derived vertical moisture profiles as well as the operational observation data. The experiments assimilating lidar-derived vertical moisture profiles caused intensification and southwestward extensions of the low-level convergence zone, resulting in local heavy precipitation at lower latitudes in experiments assimilating lidar-derived moisture profiles than in CNTL. All three experiments ingesting vertical moisture profiles generally produced better nine-hour precipitation forecasts than CNTL, implying that the assimilation of vertical moisture profiles could be well suited for numerical weather prediction of local heavy precipitation. Moreover, experiment assimilating both two RL sites data reproduced better forecast fields than experiments assimilating single RL site data, implying that data assimilation of vertical moisture profiles at multiple RL sites enables us to improve initial conditions compared to single RL site.