Aircraft Observation and Simulation of the Supercooled Liquid Water Layer in a Warm Conveyor Belt over North China

This paper studied a snow event over North China on 21 February 2017,using aircraft in-situ data,a Lagrangian analysis tool,and WRF simulations with different microphysical schemes to investigate the supercooled layer of warm conveyor belts(WCBs).Based on the aircraft data,we found a fine vertical structure within clouds in the WCB and highlighted a 1-2 km thin supercooled liquid water layer with a maximum Liquid Water Content(LWC) exceeding0.5 g kg^(-1) during the vertical aircraft observation.Although the main features of thermodynamic profiles were essentially captured by both modeling schemes,the microphysical quantities exhibited large diversity with different microphysics schemes.The conventional Morrison two-moment scheme showed remarkable agreement with in-situ observations,both in terms of the thermodynamic structure and the supercooled liquid water layer.However,the microphysical structure of the WCB clouds,in terms of LWC and IWC,was not apparent in HUJI fast bin scheme.To reduce such uncertainty,future work may focus on improving the representation of microphysics in bin schemes with in-situ data and using similar assumptions for all schemes to isolate the impact of physics.

Quantitative Precipitation Estimation Using X-band Phased Array Weather Radar

This study utilized data from an X-band phased array weather radar and ground-based rain gauge observations to conduct a quantitative precipitation estimation(QPE)analysis of a heavy rainfall event in Xiong an New Area from 20:00 on August 21 to 07:00 on August 22,2022.The analysis applied the Z-R relationship method for radar-based precipitation estimation and evaluated the QPE algorithm s performance using scatter density plots and binary classification scores.The results indicated that the QPE algorithm accurately estimates light to moderate rainfall but significantly underestimates heavy rainfall.The study identified disparities in the predictive accuracy of the QPE algorithm across various precipitation intensity ranges,offering essential insights for the further refinement of QPE techniques.