A Heterogeneous Spatiotemporal Network for Lightning Prediction

Geng, Yangli-ao; Li, Qingyong; Lin, Tianyang; Zhang, Jing; Xu, Liangtao; Yao, Wen; Zheng, Dong; Lyu, Weitao; Huang, Heng

doi:10.1109/icdm50108.2020.00121

Cited by 10 publications

(11 citation statements)

References 9 publications

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“…Several investigations (Geng et al, 2019;Wang et al, 2019;Zhou et al, 2020) have verified that integrating different data sources contributes to lightning forecasting. For example, (Wang et al, 2019) designed a DNN model combining past observations and NWP to predict temperature, relative humidity and wind at an AWS.…”

Section: Discussionmentioning

confidence: 98%

“…Although some researchers managed to apply DNNs to several weather forecasting tasks Shi et al, 2015;2017;Geng et al, 2019;Wang et al, 2019;Zhou et al, 2020, there lacks a general framework for handling various ST meteorological data from multiple sources. To fill this gap, we propose a DNN forecasting framework (referred to as LightNet+) using a lightning scenario.…”

Section: Introductionmentioning

confidence: 99%

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A deep learning framework for lightning forecasting with multi‐source spatiotemporal data

Geng

Lin

et al. 2021

Quart J Royal Meteoro Soc

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Weather forecasting requires comprehensive analysis of a variety of meteorological data. Recent decades have witnessed the advance of weather observation and simulation technologies, triggering an explosion of meteorological data which are collected from multiple sources (e.g., radar, automatic stations and numerical weather prediction) and usually characterized by a spatiotemporal (ST) structure. As a result, the adequate exploition of these multi-source ST data emerges as a promising but challenging topic for weather forecasting. To address this issue, we propose a data-driven forecasting framework (referred to as LightNet+) based on deep neural networks using a lightning scenario.Our framework design enables LightNet+ to make forecasts by mining complementary information distributed across multiple data sources, which may be heterogeneous in spatial (continuous versus discrete) and temporal (observations from the past versus simulation of the future) domains. We evaluate LightNet+ using a real-world weather dataset in North China. The experimental results demonstrate: (a) LightNet+ produces significantly better forecasts than three established lightning schemes, and (b) the more data sources are fed into LightNet+, the higher forecasting quality it achieves.

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Section: Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

A deep learning framework for lightning forecasting with multi‐source spatiotemporal data

Geng

Lin

et al. 2021

Quart J Royal Meteoro Soc

Self Cite

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“…With the renewal and iteration of the neural networks, many spatiotemporal prediction models are constantly proposed and applied to the prediction of various systems in the atmosphere [76][77][78][79][80][81][82][83]. The ensemble DL model is one of the most typical cases.…”

Section: Related Work and Research Gapmentioning

confidence: 99%

“…Lguensat et al [76] introduced Eddynet, which can automatically detect and classify eddy currents from sea surface height (SSH) maps, providing a simple and powerful tool for the marine remote sensing community. In the research of weather phenomenon prediction, a data-driven model based on a neural network, called Lightnet, for lightning prediction was proposed by [77]. The experimental results illustrated that Lightnet can achieve a threefold improvement in equitable thread score for six hours prediction compared with the other three models.…”

Section: Related Work and Research Gapmentioning

confidence: 99%

Polar Vortex Multi-Day Intensity Prediction Relying on New Deep Learning Model: A Combined Convolution Neural Network with Long Short-Term Memory Based on Gaussian Smoothing Method

Peng

Cao

Liu

et al. 2021

Entropy

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The variation of polar vortex intensity is a significant factor affecting the atmospheric conditions and weather in the Northern Hemisphere (NH) and even the world. However, previous studies on the prediction of polar vortex intensity are insufficient. This paper establishes a deep learning (DL) model for multi-day and long-time intensity prediction of the polar vortex. Focusing on the winter period with the strongest polar vortex intensity, geopotential height (GPH) data of NCEP from 1948 to 2020 at 50 hPa are used to construct the dataset of polar vortex anomaly distribution images and polar vortex intensity time series. Then, we propose a new convolution neural network with long short-term memory based on Gaussian smoothing (GSCNN-LSTM) model which can not only accurately predict the variation characteristics of polar vortex intensity from day to day, but also can produce a skillful forecast for lead times of up to 20 days. Moreover, the innovative GSCNN-LSTM model has better stability and skillful correlation prediction than the traditional and some advanced spatiotemporal sequence prediction models. The accuracy of the model suggests important implications that DL methods have good applicability in forecasting the nonlinear system and vortex spatial–temporal characteristics variation in the atmosphere.

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“…A Unet-based model on the fusion of rainfall radar images and wind velocity produced by a weather forecast model is proposed in [7] and improves the prediction for high precipitation rainfalls. Moreover, the dual-input dual-encoder network structures are also proposed to extract simulation-based and observation-based features for prediction [38,39]. The limitation of the existing deep learning models lies in the defect of the extracting ability of spatiotemporal characteristics.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Spatiotemporal Context Refinement Network for Precipitation Nowcasting

et al. 2021

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Precipitation nowcasting by radar echo extrapolation using machine learning algorithms is a field worthy of further study, since rainfall prediction is essential in work and life. Current methods of predicting the radar echo images need further improvement in prediction accuracy as well as in presenting the predicted details of the radar echo images. In this paper, we propose a two-stage spatiotemporal context refinement network (2S-STRef) to predict future pixel-level radar echo maps (deterministic output) more accurately and with more distinct details. The first stage is an efficient and concise spatiotemporal prediction network, which uses the spatiotemporal RNN module embedded in an encoder and decoder structure to give a first-stage prediction. The second stage is a proposed detail refinement net, which can preserve the high-frequency detailed feature of the radar echo images by using the multi-scale feature extraction and fusion residual block. We used a real-world radar echo map dataset of South China to evaluate the proposed 2S-STRef model. The experiments showed that compared with the PredRNN++ and ConvLSTM method, our 2S-STRef model performs better on the precipitation nowcasting, as well as at the image quality evaluating index and the forecasting indices. At a given 45dBZ echo threshold (heavy precipitation) and with a 2 h lead time, the widely used CSI, HSS, and SSIM indices of the proposed 2S-STRef model are found equal to 0.195, 0.312, and 0.665, respectively. In this case, the proposed model outperforms the OpticalFlow method and PredRNN++ model.

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A Heterogeneous Spatiotemporal Network for Lightning Prediction

Cited by 10 publications

References 9 publications

A deep learning framework for lightning forecasting with multi‐source spatiotemporal data

A deep learning framework for lightning forecasting with multi‐source spatiotemporal data

Polar Vortex Multi-Day Intensity Prediction Relying on New Deep Learning Model: A Combined Convolution Neural Network with Long Short-Term Memory Based on Gaussian Smoothing Method

Two-Stage Spatiotemporal Context Refinement Network for Precipitation Nowcasting

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