Dust-cloud interactions remain a major source of uncertainty in weather and climate predictions, particularly within large-scale synoptic systems. This study provides a multi-year perspective on these interactions by examining a decade (2016-2025) of Mongolian cyclones, which are the primary drivers of East Asian dust-infused baroclinic storms (DIBS). Using automated tracking, satellite observations, and reanalysis data, we quantify the impacts of dust on clouds by comparing cloud properties under the lowest and highest thirds of dust loading during DIBS events. We find that at temperatures between and degrees C, high dust loading significantly reduces the supercooled liquid cloud fraction () while dramatically increasing the ice cloud fraction () and decreasing ice effective radius (), consistent with dust promoting immersion freezing and liquid-to-ice conversion. At temperatures below degrees C, high dust loading increases the ice cloud fraction () and decreases the ice effective radius (), indicating that heterogeneous nucleation, rather than homogeneous freezing, dominates ice formation in ice clouds within DIBS. A focused case comparison further corroborates that dust enhances ice-phase clouds and reduces ice particle size. These results offer a robust observational evidence that dust actively modulates the cloud phase and microphysics within extratropical cyclones, highlighting its critical and previously underappreciated role in the evolution of large-scale storm systems.

The optimal interpolation method is widely used in meteorological applications worldwide. However, due to the different precipitation climate characteristics and the spatial distribution of gauges, the implementation of the optimal interpolation method involves many empirical models and key parameters. There is still some uncertainty on how to derive a set of optimised parameters for the application of the optimal interpolation method in a local region. This study analyses and optimises the key parameters of the optimal interpolation method for the calibration of weather radar quantitative precipitation estimation using radar and gauge observations from May to September 2020 in Qingdao, Shandong. The search radius of gauges from a grid point is determined by analysing the spatial distribution of all gauges relative to the analysis point. The optimal number of gauges used to calibrate the precipitation estimation of a grid point is determined by analysing the change of relative analysis error with respect to the number of gauges. Eight groups of sensitivity experiments with different correlation functions are compared by random sampling and cross-validation to find the best set of parameters for the Qingdao radar. The verification of calibration results produced by the best parameters shows that the calibration significantly improves the accuracy of the quantitative precipitation estimation. The median values of MAE, RMAE, and BIAS are 1.5 mm, 1.0, and 0.03 mm respectively, and the CORR is higher than 0.9. Comparing the quantitative precipitation estimation at different levels of precipitation before and after calibration produced by the best parameters shows that the MAE and RMAE of light rain are reduced by 90%, and the CORR is about 0.87. The MAE and RMAE in moderate to heavy rain are decreased by 89%, and the CORR is higher than 0.9. The MAE and RMAE of rainstorm are decreased by more than 83.9%, and the CORR is about 0.77. Based on the verification of a widespread rainfall case on 26 August 2020, the intensity and spatial distribution of the quantitative precipitation estimation after calibration are closer to the gauge observations. The original quantitative precipitation estimation is relatively smooth and lacks small-scale variations. The calibrated results can reflect the characteristics of the local change that is consistent with the observation of gauges. The results of this paper suggest that the optimal interpolation method with optimised local parameters can significantly improve the accuracy of the quantitative precipitation estimation, which has important application value for rainstorm warning and flood disaster prevention.

Large-area soil moisture (SM) data with high resolution and precision are the foundation for the research and application of hydrological and meteorological models, water resource evaluation, agricultural management, and warning of geological disasters. It is still challenging to downscale SM products in complex terrains that require fine spatial details. In this study, SM data from the Soil Moisture Active and Passive (SMAP) satellite were downscaled from 36 to 1 km in the summer and autumn of 2017 in Sichuan Province, China. Genetic-algorithm-optimized backpropagation (GABP) neural network, random forest, and convolutional neural network were applied. A fusion model between SM and longitude, latitude, elevation, slope, aspect, land-cover type, land surface temperature, normalized difference vegetation index, enhanced vegetation index, evapotranspiration, day sequence, and AM/PM was established. After downscaling, the in situ information was fused through a geographical analysis combined with a spatial interpolation to improve the quality of the downscaled SM. The comparative results show that in complex terrains, the GABP neural network better captures the soil moisture variations in both time and space domains. The GDA_Kriging method is able to merge in situ information in the downscaled SM while simultaneously maintaining the dynamic range and spatial details.

This study investigates the impact of assimilating conventional weather observations on the wind forecast over the nearshore region of the East China Sea. Multi-level wind measurements in the boundary layer from five masts near the coast were used to verify the numerical model forecasts. Four numerical experiments with a rapid update cycle were performed to forecast the wind field over the masts. The observation shows that the characteristics of the wind field are distinct between the onshore and offshore masts. The numerical forecasts were able to reproduce the main features of the observed wind field both onshore and offshore. However, the wind forecasts of the offshore masts showed larger BIAS and MAE than those onshore. The forecast skill was shown to be sensitive to different weather events and the choice of control variables in the assimilation. The use of new momentum control variables allows a smaller observation-minus-analysis field compared with the traditional control variables, and the resultant wind forecast showed significant improvements. Further tuning of the new control variable scheme showed little improvement of the wind forecast which demonstrates the importance of maintaining the balance between large-scale and small-scale fields in the analysis. The larger forecast error at the offshore masts was likely due to the distribution of conventional observations and the uncertainties in representing the marine boundary layer in numerical models.

Three machine learning methods, namely support vector machine, decision tree and Naive Bayesian, were applied to model and predict hail events occurred in Weining, Guizhou Province from 2017 to 2019. A variety of hail-related features were extracted from the C-band weather radar and an improved objective hail sample labelling procedure was proposed. The verification results showed that the machine learning methods were able to effectively identify the hail events. Both the support vector machine and decision tree methods had a high POD of 88.9% and 90.5% respectively, CSI of 73.1% and 70.3%, and FAR of 19.6% and 24.1% respectively. The POD and CSI of Naive Bayesian method were lower than the other two methods at 67.8% and 62.8% respectively, but its FAR was the lowest at 10.6%.

The interaction between a squall line and a supercell and its impact on the genesis of a tornado that occurred in Gaoyou, Jiangsu Province, China on 12 June 2020 were analyzed using multi-source observations. The tornado formed as the result of an intensified meso-γ supercell in a favorable large-scale environment. The supercell developed in front of a squall line and slowly intensified after its formation. Due to its small size and weak intensity, the supercell did not produce any severe weather before the approaching of the squall line. As the squall line entered its mature stage with the formation of a well-organized bow echo, the supercell in front of the bow echo began to rapidly intensify and finally led to the tornado touchdown. The analysis of mesoscale and storm-scale wind fields indicated that the bow echo of the approaching squall line modified the kinematic fields near the supercell in such a way that was favorable for the intensification of the supercell. The interaction between the squall line and the supercell may have played a critical role in the occurrence of this tornado.

Precipitation nowcasting is an important tool for nowcasting weather. In recent years, progress has been achieved in some models based on deep learning for precipitation nowcasting. However, these models do not consider the contextual relationships between the input data and the output of a network and their deficiency in capturing the information of prediction objects. To overcome these shortcomings, in this study, we propose a model that performs convolution operation on input data and the output of a Long short-term memory (LSTM) networks. Second, a self-attention operation is added to capture the local and global dependencies of the hidden state of LSTM. The proposed network structure is inserted in an encoding–forecasting network framework and applied to spatiotemporal sequence forecasting. Third, the outputs of the precede sequence are also regarded as the inputs of according LSTM layer and this operation effectively captures temporal feature of sequence data. Comprehensive experiments are conducted on the KTH action dataset and Hong Kong observation 07 radar echo maps dataset. The visual and quantitative prediction results demonstrate the accuracy and efficacy of the proposed model.

利用多普勒天气雷达和探空站等高分辨率多源观测资料,分析了多尺度母体风暴演变特征及其相互作用对2020年6月12日形成于江苏高邮地区的一次龙卷天气过程的影响.此次龙卷过程形成于有利的大尺度环境,龙卷母体风暴由中β尺度的飑线和中γ尺度的超级单体两个不同尺度的对流系统组成.中γ尺度超级单体形成于中β尺度飑线的前侧,形成之后一直维持缓慢增强的趋势.在中β尺度飑线发展成熟并形成显著的弓状回波之后,超级单体的中气旋开始迅速增强.单雷达和多雷达风场反演结果表明,与中β尺度飑线相关的中尺度切变线和对流尺度切变区增强了中γ尺度超级单体附近的垂直涡度,促使中γ尺度超级单体快速增强.快速增强的超级单体最终导致本次龙卷过程的发生.

Using station and reanalysis datasets, the application of C-C scaling in understanding the relation between precipitation extremes and temperature in eastern China has been examined. The results sh...

Qualitative and quantitative assessments of the impact of track forecast error on tropical cyclone (TC) quantitative precipitation forecasts (QPFs) are presented in this study. The original and track-error-corrected QPFs extracted from the Global Forecast System (GFS) of 52 TCs over the coastal region of China during 2015-2017 were compared with the Global Precipitation Measurement (GPM) observations. The track forecast error was corrected by shifting the QPF according to the difference between the forecasted and observed TC centers. Qualitative evaluations revealed that large track forecast error tends to cause severe under-forecast of total rainfall volume over a region. Quantitative assessments showed that the impact of track forecast error generally increases with track forecast error up to 500 km. The rapid decline of QPF skill with lead time is mainly attributed to the fast-growing track forecast error. Track forecast error has large impact on moderate to heavy rainfall and little impact on extremely heavy rainfall. Based on the assessments, a new operational post-processing method was developed to reduce the impact of track forecast error on QPFs from numerical models. The new technique was able to improve the TC QPFs in general and especially at longer lead times.

对美国近20年龙卷探测雷达技术和观测研究进行了文献调研,文献表明美国龙卷探测雷达研制目标主要为实现快速扫描、获取高时空分辨率和高精度的资料,目的基本达到,雷达体扫时间达到10秒级更新,在雷达近距离范围内分辨率可达到10 m,甚至更低,这些都使得采集龙卷精细化结构和细致连续演变过程成为可能.美国雷达发展采用多种技术体制和扫描策略并进,双极化、相控阵、大气成像等技术方面不断改进与应用,效果良好.基于先进雷达,美国进行了大量的龙卷观测试验,取得了丰富成果,对龙卷的结构和演变规律刻画得越来越精细和准确.许多成果已经转化为概念模型,形成重要的龙卷监测与预报指标、预警信号,并在业务中实施,提高了龙卷预警能力.高频次高精度的龙卷雷达探测资料的数值模式应用也明显缩短了资料同化周期,同时提高了对龙卷等对流尺度系统的模拟能力.参照美国的成功经验,我国应汲取其技术成果,尽快选定雷达发展技术体制,加快业务雷达技术升级和高性能龙卷探测雷达研制,组织龙卷科学观测试验,采集龙卷精细数据,开展科学研究,探索中国的龙卷结构和活动规律,为构建龙卷监测预警业务提供基础的科学支撑.

本文对基于径向速度的光流法在实际应用中存在的误差进行了分析,并提出了有效的改进方案.合成数据试验结果表明:基于径向速度的光流法在反演低仰角(小于2.5°)数据和包含非线性变化的实际风场时存在较大的误差.在低仰角情况下,由于径向速度在垂直方向的分量非常小,导致垂直速度的反演包含较大的计算误差并影响水平速度的反演精度.而当实际风场存在较大非线性变化时(如锋面、切变线等),算法中的局部约束条件被破坏,导致切变区域反演精度降低或出现奇异值.针对这两种情况,本文提出了有效的改进算法.在低仰角情况下引入了简化的约束方程,并加入了径向速度一致性的约束条件来克服实际风场非线性带来的误差.通过理想数据试验和实际个例的检验发现,改进后的算法可以有效地提高风场反演的精度和应用范围.

This study examines the impact of a large-scale constraint (LSC) on the large-scale analysis and precipitation forecast of convective weather systems in a regional rapid-update-cycle system. The LSC is imposed by assimilating Global Forecast System forecast fields as bogus observations with a scale selection scheme. The scale selection is achieved by skipping data points of Global Forecast System forecast fields in the horizontal and vertical directions. It is shown that the LSC is able to modify the large-scale component of the analysis fields while leaving the small-scale component mostly intact compared with a control experiment without the constraint. The effects of the LSC on precipitation forecast are verified and analyzed using nine convective cases in the Rocky Mountain Front Range and its east plains. The results show that the LSC is effective in improving the precipitation forecast of different cases. However, the cases with weak large-scale forcing show greater improvements than those with strong large-scale forcing. Further analyses on the dynamic and thermodynamic variables indicate that the use of the LSC is able to construct a favorable environment for the initiation and development of convection in the case of weak large-scale forcing, which leads to significant improvement of convective precipitation forecasting when radar observations are assimilated.

Governed by the Clausius-Clapeyron (CC) equation, daily mean temperature (T-m) and precipitation extremes would be theoretically linked by atmospheric moisture. However, precipitation extremes cannot systematically follow the CC rate of 7% per warming degree, due to moisture limitations. In this study, the observational tri-pole relation among T-m, atmospheric moisture and precipitation intensity over China have been investigated. The results indicate that atmospheric moisture (specific humidity and dew-point temperature) is positively correlated with T-m, (precipitation) across four seasons at the interannual timescale. Particularly, the increase in precipitation extremes is accompanied by high atmospheric moisture, but is different in four seasons. In comparison, the year-to-year relation between T-m and atmospheric moisture is stronger than that between atmospheric moisture and precipitation. The atmospheric moisture has acted as a bridge linking T-m and precipitation extremes. T-m is highly correlated with precipitation extremes, while the relation also shows seasonal difference. The difference may be attributed to the negative scaling of daily precipitation extremes and precipitation efficiency (defined as the percentage of moisture in the air converting into precipitation) with T-m, when T-m exceeds similar to 25 degrees C.

The principal rainband in tropical cyclones is currently depicted as a solitary and continuous precipitation region. However, the airborne radar observations of the principal rainband in Typhoon Hagupit (2008) reveal multiple subrainband structures. These subbands possess many characteristics of the squall lines with trailing stratiform in the midlatitudes and are different from those documented in previous principal rainband studies. The updraft and reflectivity cores are upright and elevated. The updraft is fed by a low-level radial outflow from the inner side. The tangential wind speed shows a clear midlevel jet on the inner side of the reflectivity core. Except for the structural similarities, the dynamics of the subbands is also similar to the squall lines. The local environment near the subbands shows little convective inhibition, modest instability, and vertical wind shear. The temperature retrieval shows a cold pool structure in the stratiform precipitation region. The estimated vertical wind shear induced by the cold pool is close to that of the local environment. The structural and dynamic similarities to the squall lines imply that the variation of principal rainbands is subjected to convective-scale dynamics related to the local environment in addition to storm-scale dynamics. The subbands show positive impacts to the vortex intensity in terms of potential vorticity redistribution and absolute angular momentum advection. The positive impacts are closely related to specific structural characteristics of the subbands, which suggests the importance of understanding the convective-scale structure and dynamics of the principal rainband.

Uncertainties in aircraft inertial navigation system and radar-pointing angles can have a large impact on the accuracy of airborne dual-Doppler analyses. The Testud et al. (THL) method has been routinely applied to data collected by airborne tail Doppler radars over flat and nonmoving terrain. The navigation correction method proposed in Georgis et al. (GRH) extended the THL method over complex terrain and moving ocean surfaces by using a variational formulation but its capability over ocean has yet to be tested. Recognizing the limitations of the THL method, Bosart et al. (BLW) proposed to derive ground speed, tilt, and drift errors by statistically comparing aircraft in situ wind with dual-Doppler wind at the flight level. When combined with the THL method, the BLW method can retrieve all navigation errors accurately; however, it can be applied only to flat surfaces, and it is rather difficult to automate. This paper presents a generalized navigation correction method (GNCM) based on the GRH method that will serve as a single algorithm for airborne tail Doppler radar navigation correction for all possible surface conditions. The GNCM includes all possible corrections in the cost function and implements a new closure assumption by taking advantage of an accurate aircraft ground speed derived from GPS technology. The GNCM is tested extensively using synthetic airborne Doppler radar data with known navigation errors and published datasets from previous field campaigns. Both tests show the GNCM is able to correct the navigation errors associated with airborne tail Doppler radar data with adequate accuracy.

系统地回顾了近10年来国内外针对龙卷形成过程,以及相应母体风暴结构和演变特征的研究成果.从龙卷涡旋垂直方向上的涡度演变、龙卷低层涡旋的产生方式、以及与龙卷形成相关的母体风暴对流尺度结构的极化雷达特征三个方面,重点关注了超级单体风暴中龙卷形成研究的最新进展.通过回顾可以发现,目前龙卷形成研究的最前沿围绕着超级单体内部对流尺度结构的精细化特征和机理而进行,这为中国即将开展的龙卷观测和业务预报试验提供了重要的参考.

In this paper, we propose to reconstruct the vertical plane of a rainfall field using rain attenuation of satellite signal links. With received signals at multiple ground stations form multiple satellites, linear least squares is employed to reconstruct the the rain attenuation, thereby the rainfall maps. We use a synthetic super-cell event as our reconstruction target and firstly propose a simple model to simulate satellite link attenuation caused by the rain event. Simulation results suggest that the reconstruction is successful when two satellite-receiver pairs are employed.

This study examines two strategies for improving the analysis of an hourly update three-dimensional variational data assimilation (3DVAR) system and the subsequent quantitative precipitation forecast (QPF). The first strategy is to assimilate synoptic and radar observations in different steps. This strategy aims to extract both large-scale and convective-scale information from observations typically representing different scales. The second strategy is to add a divergence constraint to the momentum variables in the 3DVAR system. This technique aims at improving the dynamic balance and suppressing noise introduced during the assimilation process. A detailed analysis on how the new techniques impact convective-scale QPF was conducted using a severe storm case over Colorado and Kansas during 8 and 9 August 2008. First, it is demonstrated that, without the new strategies, the QPF initialized with an hourly update analysis performs worse than its 3-hourly counterpart. The implementation of the two-step assimilation and divergence constraint in the hourly update system results in improved QPF throughout most of the 12-h forecast period. The diagnoses of the analysis fields show that the two-step assimilation is able to preserve key convective-scale as well as large-scale structures that are consistent with the development of the real weather system. The divergence constraint is effective in improving the balance between the momentum control variables in the analysis, which leads to less spurious convection and improved QPF scores. The improvements of the new techniques were further verified by eight convective cases in 2014 and shown to be statistically significant.