A Method to Update Model Kinematic States by Assimilating Satellite‐Observed Total Lightning Data to Improve Convective Analysis and Forecasting

Chen, Zhixiong; Sun, Juanzhen; Qie, Xiushu; Zhang, Ying; Ying, Zhuming; Xian, Xuefu; Cao, Dongjie

doi:10.1029/2020jd033330

Cited by 17 publications

(14 citation statements)

References 70 publications

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“…The quality control method about w max references to the study of Chen, Sun, et al. (2020), and the upper and lower limits for w max are set to 15 and 5 m/s, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…Chen, Sun, et al. (2020) proposed a method to update model kinematic states by assimilating satellite‐observed total lightning data to improve the convective forecasting. Xiao et al.…”

Section: Introductionmentioning

confidence: 99%

“…Current studies focus more on assimilating the water vapor and hydrometeors. Chen, Sun, et al (2020) proposed a method to update model kinematic states by assimilating satellite-observed total lightning data to improve the convective forecasting. Xiao et al (2021) assimilated pseudo vertical velocity fields derived from observation total rates and statistically computed vertical velocity profile.…”

mentioning

confidence: 99%

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Assimilation of the Maximum Vertical Velocity Converted From Total Lightning Data Through the EnSRF Method

et al. 2021

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A total lightning data assimilation (LDA) scheme is developed at the cloud‐resolving scale in this study. The LDA scheme assimilates total lightning data through the ensemble square root filter (EnSRF) method based on the relationship between the maximum vertical velocity and the instantaneous flash rate. To verify the effect of LDA, three assimilation experiments based on a convective activity on July 6, 2019 are performed. The results show that all LDA experiments can improve the forecast. LDA improves the water vapor field and provides a warm and moist environment in which convection can develop. LDA can also increase the convergence at low levels and the divergence at upper levels, which leads to intense convection development. However, the LDA experiment that does not consider vertical localization (LDA_no) shows excessive adjustments of the model variables, which leads to overestimation of precipitation. The LDA experiment which adjusts the state variables in the charge zone (LDA_CZ) underestimates the forecast. The LDA experiment using a global group filter (GGF) localization function (LDA_GGF) is optimal in theory and produces a better forecast. The results of LDA_GGF show that the interaction between the cold pool and the strong low‐level vertical wind shear in front of the cold pool is beneficial for triggering new convection in front of the convection, which leads to a rapid shifting in convective activity. In addition, the LDA_GGF scheme is evaluated further for a rainfall process on August 1, 2020 and obtains similar forecast improvements for composite reflectivity and accumulated precipitation.

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“…The quality control method about w max references to the study of Chen, Sun, et al. (2020), and the upper and lower limits for w max are set to 15 and 5 m/s, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…Chen, Sun, et al. (2020) proposed a method to update model kinematic states by assimilating satellite‐observed total lightning data to improve the convective forecasting. Xiao et al.…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Assimilation of the Maximum Vertical Velocity Converted From Total Lightning Data Through the EnSRF Method

et al. 2021

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“…The LMI events represent all the lightningilluminated pixels, and the flash and group products are collections of lightning events satisfying some prespecified temporal and spatial thresholds. From this perspective, the LMI event products offer more information on the lightning spatial propagation and the storm location and coverage, which are more suitable for convective-scale data assimilation concerning space-borne total lightning observations [11].…”

Section: Overview Of Lmi and Blnet Detection During The Main Convecti...mentioning

confidence: 99%

“…They compared the LMI lightning products with observations from ground-based lightning location systems (LLSs), such as the World Wide Lightning Location Network (WWLLN) and the Advanced Direction and Time-ofarrival Detecting (ADTD), and showed that the LMI performed reasonably well in terms of the spatial distribution and temporal evolution of cloud-to-ground (CG) lightning activities. Given the encouraging results, some attempts have been made to assimilate LMI lightning observations into numerical weather prediction (NWP) models with the aim to improve severe weather forecasting [10][11][12]. While these studies were promising, the overall performance and detection efficiency of LMI have not been thoroughly investigated in comparison with the existed lightning observations.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Fengyun-4A Lightning Mapping Imager (LMI) Performance during Multiple Convective Episodes over Beijing

et al. 2021

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This study investigates the characteristics of space-borne Lightning Mapping Imager (LMI) lightning products and their relationships with cloud properties using ground-based total lightning observations from the Beijing Broadband Lightning Network (BLNET) and cloud information from S-band Doppler radar data. LMI showed generally consistent lightning spatial distributions with those of BLNET, and yielded a considerable lightning detection capability over regions with complex terrain. The ratios between the LMI events, groups and flashes were approximately 9:3:1, and the number of LMI-detected flashes was roughly one order of magnitude smaller than the number of BLNET-detected flashes. However, in different convective episodes, the LMI detection capability was likely to be affected by cloud properties, especially in strongly electrified convective episodes associated with frequent lightning discharging and thick cloud depth. As a result, LMI tended to detect lightning flashes located in weaker and shallower cloud portions associated with fewer cloud shielding effects. With reference to the BLNET total lightning data as the ground truth of observation (both intra-cloud lightning and cloud-to-ground lightning flashes), the LMI event-based detection efficiency (DE) was estimated to reach 28% under rational spatiotemporal matching criteria (1.5 s and 65 km) over Beijing. In terms of LMI flash-based DE, it was much reduced compared with event-based DE. The LMI flash-based ranged between 1.5% and 3.5% with 1.5 s and 35–65 km matching scales. For 330 ms and 35 km, the spatiotemporal matching criteria used to evaluate Geostationary Lightning Mapper (GLM), the LMI flash-based DE was smaller (<1%).

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LD‐Net: A novel one‐stage knowledge distillation algorithm for lightning detection network

Fu,

Li,

Liu

et al. 2024

Meteorological Applications

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Lightning often causes death, injury, and damage to various facilities and equipment. Accurately detecting the spatial location of lightning occurrence by predicting thunderstorms and lightning is of great significance. Traditional lightning detection systems detect lightning by measuring the sound, light, and electromagnetic field information radiated by lightning. These methods typically have two problems. First, the detection process of lightning signals is susceptible to electromagnetic interference. Second, the equipment cost is high and is not friendly to some lightning detection tasks only targeted at specific scenarios. In order to detect lightning more conveniently, we propose a lightning detection model based on deep learning networks. With the increase in the use of cameras in modern society, designing lightning object detection networks based on deep learning is possible. However, two problems have been found in existing practice: (1) When strong lightning meteorological phenomena occur, the lightning features in the image are covered by bright electric lights, and convolutional neural networks cannot distinguish between strong lightning scenes and strong ultraviolet scenes. (2) The performance of convolutional neural networks is often related to the model's size. The larger the model, the stronger the performance of the network. However, in practical application scenarios, computing resources are insufficient to use sufficiently large networks. In this paper, we propose a simple and effective lightning object detection network (LD‐Net) and use a foreground‐background segmentation algorithm to locate frames containing lightning in the video. After using the knowledge distillation‐based model compression method, the mAP of the lightning object detection network with a backbone net of resnet with 18‐layer (LD‐Net‐18) can reach 82.4%. We hope that the proposed LD‐Net can serve as a simple and powerful alternative to traditional lightning detection methods, enhancing efficiency in lightning detection tasks.

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A Method to Update Model Kinematic States by Assimilating Satellite‐Observed Total Lightning Data to Improve Convective Analysis and Forecasting

Cited by 17 publications

References 70 publications

Assimilation of the Maximum Vertical Velocity Converted From Total Lightning Data Through the EnSRF Method

Assimilation of the Maximum Vertical Velocity Converted From Total Lightning Data Through the EnSRF Method

Evaluation of Fengyun-4A Lightning Mapping Imager (LMI) Performance during Multiple Convective Episodes over Beijing

LD‐Net: A novel one‐stage knowledge distillation algorithm for lightning detection network

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