In February 2024, Hunan encountered two regional rainfall/snowfall and freezing events with low temperatures, one in northern Hunan from February 2 to 6 (hereinafter referred to as the“2.02”event), and the other across the whole province from February 21 to 26 (hereinafter referred to as the“2.21”event). Using conventional upper-air and surface weather observations, regional automatic weather station data, and NCEP reanalysis data, we conducted a comparative analysis of the circulation background, cooling mechanism, and water vapor transport characteristics of the two events. The results are as follows. (1) There are obvious differences in the circulation background between the two events. The“2.02”event occurred in the background with eastward movement of both the southern branch trough and westerly trough overlapping in phase. However, the“2.21”event occurred under the cold wave caused by the collapse of the blocking high, with the transition of rain and snow phases appearing multiple times due to the small influence on the southern branch trough. (2) There were the strong low-level frontal zone in both events. A rare north-south temperature gradient greater than 26 ℃ was observed in the“2.21”event, which may be an important indication of this extreme cold wave weather. (3) Temperature advection played an important role in the transition of rain and snow phases. In the“2.02”event, the strong cold advection intrusion in the middle level was driven by the westerly trough, causing a temperature decrease across the whole layer. In the“2.21”event, the continuous input of the ultra-low-level strong cold advection was one of the important cooling mechanisms. In addition, the strongest transport layer of warm advection corresponded to the negative center of vertical velocity, and the stronger vertical wind shear in the middle and lower troposphere resulted in stronger rain and snow intensity. (4) The Arabian Sea was the main source of water vapor for both events. Southwest warm and wet airflow in the middle troposphere moved with the low trough at 500 hPa or the small trough from it, with the highest contribution of specific humidity and water vapor flux to both events. The low troughs provided both favorable dynamical conditions and abundant water vapor for the occurrence of rain and snow weather. In the “2.21”event, the warm and wet air carried by three combined channels from the South China Sea channel, the Arabian Sea channel and the low latitude inland regions provided water vapor and warm layer conditions for the freezing area in the southern Hunan, which was an important reason for the severe freezing in this area.
High-quality three-dimensional wind field retrieval products from Doppler weather radars are essential data for studying and forecasting mesoscale weather systems. The existing 3D variational wind field retrieval method (3DVAR) mainly uses data from single or multiple radars to conduct the wind field retrieval of small regions. However, for wind field retrieval of large regions using multiple radars, challenges exist, such as high computational requirements and slow processing speeds, which obstruct operational applications. In this study, anan improved method (D-3DVAR) that can obtain high-quality large-scale wind field results is proposed by combining the dual-radar wind field retrieval method, based on the 3DVAR method, using 11 S-band Doppler weather radars and 27 X-band phased array weather radars in Guangdong Province. The optimization process consists of three steps. First, determine the wind field retrieval area based on the location of the weather system to reduce the number of radars; Second, reduce the number of radars by calculating the contribution ratio of the radar network in the dual-radar wind field retrieval; Third, during the dual-radar wind field retrieval process, only select the optimal two radial velocities for each grid point to conduct the wind field retrieval, while the remaining radial velocities do not participate in subsequent wind field retrieval. The results of wind field retrieval after each optimization step are recorded as Majorization 1-3. Taking a squall line process along the coastal area of Guangdong Province on 13 May 2022, as an example, wind field retrieval was performed on the radar network using D-3DVAR, and the retrieval results were compared and verified. The results show that the three-step optimized wind field retrieval method can obtain dual-Doppler radar wind field retrieval results for a large area of the Greater Bay Area, which can better cover the land area of the Greater Bay Area at an altitude of 2 km and most areas within the coastline at an altitude of 4 km. Compared to Majorization 1, the wind field retrieval program execution speed of Majorization 2 and 3 is increased by 3 times. The basic characteristics of Majorization 3 and Majorization 1 are consistent, with wind speed errors less than 1.7 m·s-1 and wind direction errors within 10°. However, the wind field retrieval result of Majorization 3 can better highlight the characteristics of strong updraft and vertical vortex in the wind field of mesoscale weather systems.
To gain an in-depth understanding of the asymmetric structure of the Southwest Vortex producing rainstorms, the rainstorm process in the Sichuan Basin on 30 June 2013 was investigated in this study. This rainstorm was caused by the slowly moving Southwest Vortex, and had the most significant impact in the past decade. Based on WRF numerical simulation and other data, the asymmetric structure characteristics and causes in the mature stage of the Southwest Vortex rainstorm were analyzed by using a dynamic composite method that followed the vortex center, combined with vorticity equation diagnosis, divergence equation diagnosis, and wind field decomposition. The results are as follows. (1) The Southwest Vortex exhibited convergent airflows which rotated into the center and gradually weakened at low altitudes, while divergent airflows rotated at high altitudes. The precipitation, positive vorticity, low-level convergence, and vertical ascending motion of the Southwest Vortex exhibited significant asymmetric distribution characteristics. The front side of the vortex exhibited significant positive vorticity, and prominent convergence ascending motion, leading to heavy rainfall. While on the rear side of the vortex, the positive and negative vorticity coexisted, with weaker convergence and ascending motion, accompanied by weaker rainfall. (2) The asymmetric distribution of the Southwest Vortex was related to the impact of subtropical high in front of the vortex. The maintenance of subtropical high favors the formation of the asymmetric structure of the Southwest Vortex, which was conducive to the formation of the gradually increasing potential height gradient and the enhancement of the low-level jet in front of the southeastward vortex. The spatial distribution led to negative evolutions of the Laplace term of potential height and the Laplace term of kinetic energy, which produced a sustained low-level negative non-equilibrium dynamic forcing effect. Then the negative non-equilibrium dynamic forcing promoted the development of low-level convergence, and vertical ascending motion, and led to heavy rainfall. In addition, the positive vorticity was generated through low-level convergence and vertical ascending motion in front of the vortex, forming asymmetric distribution characteristics of rainfall, positive vorticity, low-level convergence, and vertical ascending motion associated with the Southwest Vortex.
The Tropical Cyclone (TC) Rainfall Climatology and Persistence Model (R-CLIPER) is a statistical parametric model for TC precipitation based on observations of North Atlantic TCs or global TCs. It boasts simple inputs and quick calculations, enabling climate-scale TC precipitation simulations, thus providing technical support for TC precipitation forecasting and risk assessments. This study focuses on TCs in the Western North Pacific region and performs localization of the R-CLIPER model. Firstly, based on precipitation data from Tropical Rainfall Measuring Mission (TRMM) satellite and Fengyun satellite (FY2C/2E) precipitation data, the radial average profiles of precipitation from TCs of 6 different intensity levels (i.e., super typhoon, strong typhoon, typhoon, severe tropical storm, tropical storm, tropical depression) are extracted. Secondly, combined with the R-CLIPER model framework, a globally optimal parameterization fitting method for TC precipitation profiles is developed. Two models are constructed, including the R-CLIPER for the Western North Pacific Ocean based on TRMM data/FY data, namely TRMM-R-CLIPER-WNP (Model 1) and FY-R-CLIPER-WNP (Model 2). Finally, based on the 62 tropical cyclones in the Northwest Pacific from 2012 to 2013 and the 26 cyclones that affected Zhejiang from 2009 to 2021 the models are evaluated in terms of fitting error and precipitation falling area. The results are as follows. (1) The TC rainfall profiles derived from TRMM data are relatively sharp near the maximum rainfall, while those from FY2C/2E satellite data are smoother and more detailed due to its higher spatial resolution. (2) The RMSE of TRMM parameterized profiles is 0.28 mm·h-1, and that of FY2C/2E profiles is 0.51 mm·h-1. (3) Based on the ground-based station data evaluation, when the rainfall threshold is less than 3 mm·h-1, the ETS score of Model 1 is better than that of Model 2. But when the threshold is greater than 3 mm·h-1, Model 2 is better. Both models perform better when the TC is centered over the sea than over land, indicating higher accuracy of TC central rainfall intensity prediction over the ocean than over land. In conclusion, using the parameterized fitting method, the accuracy of the R-CLIPER model in simulating TC precipitation in the Northwest Pacific region can be improved by 5.5%. Additionally, it was revealed that the error in TC precipitation simulations is influenced by factors such as model data sources, model framework, and parameterization schemes.
Strong convective weather dominated by thunderstorm strong winds appeared in Liaoning Province in the evening of 25th June 2022. Eighty-two automatic weather stations across the province recorded thunderstorm strong winds of magnitude 8 or above. Among them, the Ma Sanjia Station in the northern part of Shenyang City, Liaoning Province, recorded wind speeds of 13 on the Beaufort scale (39.1 m·s-1), resulting in severe damage. By utilizing the minute-level observational data from automatic weather stations in Liaoning Province on June 25 from 17:00 BT to 22:00 BT, as well as conventional radiosonde, dual-polarization radar, and data from the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis, an analysis and study were conducted on the formation mechanism of this thunderstorm strong wind event. The results are as follows. (1) Liaoning is located at the southwest quadrant of the northeast cold vortex, where the cold dry air on the western side of the vortex converged with warm moist air in the northern part of Liaoning Province on the evening of the 25th. (2) The Shenyang sounding curve exhibits a low-level warm and dry layer, a mid-low-level moist layer, and a mid-high-level dry and cold layer with an "X" shaped pattern. Additionally, it features strong vertical wind shear, which creates favorable conditions for the occurrence of severe convective weather. (3) The evolution of radar echoes in this process is consistent with the evolution of classic bow echoes; isolated thunderstorms in the initial stage lead to locally strong winds. Subsequently, multiple individual storms gradually merge into a bow echo. The interaction of negative buoyancy, momentum transport, and cold pool density currents collectively results in widespread thunderstorm winds in the Shenyang urban area. (4) Along the leading edge of the bow echo, shallow low-level gamma scale cyclones are present. The rotation of low-level cyclones forms perturbation low pressure near the ground, enhancing the downdraft, while the cyclones interact with the strong inflow jet on the rear side, leading to locally strong thunderstorm winds.
