Lightning is an instantaneous, intense, and convective weather phenomenon that can produce great destructive power and easily cause serious economic losses and casualties. It always occurs in convective storms with small spatial scales and short life cycles. Weather radar is one of the best operational instruments that can monitor the detailed 3D structures of convective storms at high spatial and temporal resolutions. Thus, extracting the features related to lightning automatically from 3D weather radar data to identify lightning strike locations would significantly benefit future lightning predictions. This article makes a bold attempt to apply three-dimensional radar data to identify lightning strike locations, thereby laying the foundation for the subsequent accurate and real-time prediction of lightning locations. First, that issue is transformed into a binary classification problem. Then, a suitable dataset for the recognition of lightning strike locations based on 3D radar data is constructed for system training and evaluation purposes. Furthermore, the machine learning methods of a convolutional neural network, logistic regression, a random forest, and k-nearest neighbors are employed to carry out experiments. The results show that the convolutional neural network has the best performance in identifying lightning strike locations. This technique is followed by the random forest and k-nearest neighbors, and the logistic regression produces the worst manifestation.

/pdf/lightning-strike-location-identification-based-on-3d-weather-438yfnm5fs.pdf

Lightning Strike Location Identification Based on 3D Weather Radar Data

A vision transformer for lightning intensity estimation using 3D weather radar

Human life and property are put at risk by lightning, heavy hail, and strong winds caused by thunderstorms. Accurate prediction of thunderstorms has long been a difficult problem. In difference from conventional methods, the modern methods for predicting thunderstorms utilize a combination of computer models, observation (via the use of radars and satellite photos), and modern knowledge and patterns employed by thunderstorms stations. Observations: Using these methods, it is possible to make reasonably accurate predictions. Prediction models based on complex formulas run the majority of forecasting computer models. Artificial neural networks, machine learning, deep learning, and statistics are all examples of adaptive systems that alter their structure in response to information flowing through the network. In this reviewed paper, different Artificial intelligence techniques are used such as machine learning, deep learning, and ANN (Artificial neural network). Different statistical models were presented to predict thunderstorm systems. Various researchers achieved technical milestones by presenting statistical methods on thunderstorm systems using satellite data.

/pdf/thunderstorms-prediction-using-satellite-images-2hgdn9so.pdf

Thunderstorms Prediction Using Satellite Images

Thunderstorm Recognition Based on Neural Network PRDsNET Models

Sample imbalance prevents the Bayesian model from making effective potential forecasts of gale. An approach is thus proposed to modify the Bayesian model to deal with sample imbalance and verified using years (2015–2019) of reanalysis and radar data. The approach reduces sample imbalance by the resampling based on the value ranges of environmental parameters and the application of multi-layer conditional probabilities. The samples that are insensitive to gale forecasts are excluded according to the value ranges of environmental parameters for gale occurrence. This resampling greatly improves gale occurrence hits of and suppresses its false alarms, leading to significant improvement in gale forecasting skill. On the basis of the resampling, the application of the multi-layer conditional probabilities that forecast gale occurrence balances the samples to an equivalent magnitude. Consequently, the false alarms are further suppressed although some hits are reduced, resulting in a higher forecasting skill of gale. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2022GL100019

A Bayes‐Based Approach Against Sample Imbalance to Improving the Potential Forecasts of Gale

The local thunderstorms and gale weather occurring frequently has brought huge losses to the agriculture and transportation industries. This paper presents a method of forecasting the local thunderstorms and gale weather, in which a multisource convolution neural network is constructed to extract the features of weather-related data with multiple types from Doppler radar. To improve the discriminative power of features, Center-Loss and Softmax were jointly used as loss function in the training, and then the features obtained are combined with SVM for classification. Furthermore, a comparative experiment of multisource convolution neural network based on CNN-4, ResNet30, ResNet50, and VGG16 is designed, in which the ResNet30 achieves the highest accuracy. The experimental results show that the multisource convolution neural network avoids the limitation of using one type of data and improves the accuracy of forecasting local thunderstorms and gale.

/pdf/a-method-of-forecasting-thunderstorms-and-gale-weather-based-55uwhh1m5c.pdf

A Method of Forecasting Thunderstorms and Gale Weather Based on Multisource Convolution Neural Network

The invention discloses a thunderstorm strong wind grade prediction classification method based on a multi-source convolutional neural network. According to the method, a multi-source convolutional neural network model is adopted to carry out feature extraction on various data images obtained by the Doppler meteorological mine, more meteorological data information can be fused, and the extractionof difference features is improved. Meanwhile, the method is combined with a classification method in a support vector machine, and a model obtained on a meteorological data training set of small andmedium samples has a very good thunderstorm strong wind level prediction classification effect.

Thunderstorm strong wind grade prediction classification method based on multi-source convolutional neural network

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Jiang Yanping

Author Tools

Chat about Author

Papers

A Method of Forecasting Thunderstorms and Gale Weather Based on Multisource Convolution Neural Network

Thunderstorm strong wind grade prediction classification method based on multi-source convolutional neural network