Multi-Classification of Rainfall Weather Based on Deep Learning-Mod

Lu, Zhengding; Ding, Xudong; Ren, Yimo; Sun, Xiaolei

doi:10.23919/ccc50068.2020.9188517

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…In the meteorological field, the percentage of doom (POD), false alarm rate (FAR), and critical success index (critical success index) (CSI) can accurately evaluate the recognition and prediction effect of short-term heavy precipitation [47], which facilitates the assessment of the identification and forecasting effectiveness of severe convective weather. The formulas are as follows ( 2)-( 5…”

Section: Model Validation Indicatorsmentioning

confidence: 99%

The Application Research of FCN Algorithm in Different Severe Convection Short-Time Nowcasting Technology in China, Gansu Province

Huang,

Fu,

Feng

et al. 2024

Atmosphere

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This study explores the application of the fully convolutional network (FCN) algorithm to the field of meteorology, specifically for the short-term nowcasting of severe convective weather events such as hail, convective wind gust (CG), thunderstorms, and short-term heavy rain (STHR) in Gansu. The training data come from the European Center for Medium-Range Weather Forecasts (ECMWF) and real-time ground observations. The performance of the proposed FCN model, based on 2017 to 2021 training datasets, demonstrated a high prediction accuracy, with an overall error rate of 16.6%. Furthermore, the model exhibited an error rate of 18.6% across both severe and non-severe weather conditions when tested against the 2022 dataset. Operational deployment in 2023 yielded an average critical success index (CSI) of 24.3%, a probability of detection (POD) of 62.6%, and a false alarm ratio (FAR) of 71.2% for these convective events. It is noteworthy that the predicting performance for STHR was particularly effective with the highest POD and CSI, as well as the lowest FAR. CG and hail predictions had comparable CSI and FAR scores, although the POD for CG surpassed that for hail. The FCN model’s optimal performances in terms of hail prediction occurred at the 4th, 8th, and 10th forecast hours, while for CG, the 6th hour was most accurate, and for STHR, the 2nd and 4th hours were most effective. These findings underscore the FCN model’s ideal suitability for short-term forecasting of severe convective weather, presenting extensive prospects for the automation of meteorological operations in the future.

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Section: Model Validation Indicatorsmentioning

confidence: 99%

The Application Research of FCN Algorithm in Different Severe Convection Short-Time Nowcasting Technology in China, Gansu Province

Huang,

Fu,

Feng

et al. 2024

Atmosphere

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“…To render their method immune to global intensity transfer, Lu et al [21] suggest a new data augmentation scheme to significantly enhance the training data, which is then utilized for training a latent SVM framework. Extensive experimental work has been done to prove the efficacy of their approach.…”

Section: Review Of Literaturementioning

confidence: 99%

Optimized Deep Learning with Learning without Forgetting (LwF) for Weather Classification for Sustainable Transportation and Traffic Safety

Dalal

Seth²,

Rădulescu

et al. 2023

Sustainability

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Unfortunately, accidents caused by bad weather have regularly made headlines throughout history. Some of the more catastrophic events to recently make news include a plane crash, ship collision, railway derailment, and several vehicle accidents. The public’s attention has been directed to the severe issue of safety and security under extreme weather conditions, and many studies have been conducted to highlight the susceptibility of transportation services to environmental factors. An automated method of determining the weather’s state has gained importance with the development of new technologies and the rise of a new industry: intelligent transportation. Humans are well-suited for determining the temperature from a single photograph. Nevertheless, this is a more challenging problem for a fully autonomous system. The objective of this research is developing a good weather classifier that uses only a single image as input. To resolve quality-of-life challenges, we propose a modified deep-learning method to classify the weather condition. The proposed model is based on the Yolov5 model, which has been hyperparameter tuned with the Learning-without-Forgetting (LwF) approach. We took 1499 images from the Roboflow data repository and divided them into training, validation, and testing sets (70%, 20%, and 10%, respectively). The proposed model has gained 99.19% accuracy. The results demonstrated that the proposed model gained a much higher accuracy level in comparison with existing approaches. In the future, this proposed model may be implemented in real-time.

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“…Hence, we aim to employ deep learning techniques for extrapolating radar echoes to enhance the accuracy of severe convection nowcasting. Lu et al [8] investigated a model for recognizing heavy precipitation weather in severe convective weather conditions, utilizing physical parameters and the deep learning model DBNs. Zhou et al [9] conducted short-term lightning forecasting by employing a deep learning fusion of a semantic segmentation model and multisource observation data.…”

Section: Introductionmentioning

confidence: 99%

Study on Nowcasting Method of Severe Convective Weather Based on SA-PredRNN++

Xue,

Fang

2024

JDSIS

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Severe convective weather, characterized by short-term intense precipitation, thunderstorms, and strong winds, poses significant threats to human life and property. Therefore, accurate and efficient prediction of severe convective weather is crucial for disaster prevention. Currently, utilizing deep learning for radar echo extrapolation stands as the primary method for forecasting severe convective weather. We propose a predictive recurrent neural network model that integrates a self-attention mechanism, specifically designed for radar echo extrapolation in severe convective weather forecasting. The self-attention mechanism offers the advantage of being lightweight, as it does not substantially increase the model parameters. Additionally, it facilitates global attention extraction, thereby enhancing the model's accuracy to some extent. By utilizing radar echo images from the previous hour as input, the model undergoes self-learning to achieve the best forecast for radar echo extrapolation in the subsequent two hours. Research findings demonstrate that our model outperforms other models in accurately predicting severe convective weather within this two-hour timeframe.

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Multi-Classification of Rainfall Weather Based on Deep Learning-Mod

Cited by 4 publications

References 11 publications

The Application Research of FCN Algorithm in Different Severe Convection Short-Time Nowcasting Technology in China, Gansu Province

The Application Research of FCN Algorithm in Different Severe Convection Short-Time Nowcasting Technology in China, Gansu Province

Optimized Deep Learning with Learning without Forgetting (LwF) for Weather Classification for Sustainable Transportation and Traffic Safety

Study on Nowcasting Method of Severe Convective Weather Based on SA-PredRNN++

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