Convective Storm VIL and Lightning Nowcasting Using Satellite and Weather Radar Measurements Based on Multi-Task Learning Models

Li, Yang; Liu, Yubao; Sun, Rongguo; Guo, Fengjing; Xu, Xiaofeng; Xu, Haixiang

doi:10.1007/s00376-022-2082-6

Cited by 10 publications

(4 citation statements)

References 44 publications

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“…We adopt a U-Net for the representation learning of the radar echo data (Figure 1). The U-Net deep network is a convolutional neural network (CNN) variant originating from biomedical image segmentation [49] and is here repurposed for a regression task as in many previous studies [31,33,50,51]. It preserves the hierarchical convolutional structure of a CNN in its left contracting path, and uses upsampling operations in successive layers to form a right expansive path.…”

Section: Deep Network Modelmentioning

confidence: 99%

Multiscale Representation of Radar Echo Data Retrieved through Deep Learning from Numerical Model Simulations and Satellite Images

et al. 2023

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Radar reflectivity data snapshot fine-grained atmospheric variations that cannot be represented well by numerical weather prediction models or satellites, which poses a limit for nowcasts based on model–data fusion techniques. Here, we reveal a multiscale representation (MSR) of the atmosphere by reconstructing the radar echoes from the Weather Research and Forecasting (WRF) model simulations and the Himawari-8 satellite products using U-Net deep networks. Our reconstructions generated the echoes well in terms of patterns, locations, and intensities with a root mean square error (RMSE) of 5.38 dBZ. We find stratified features in this MSR, with small-scale patterns such as echo intensities sensitive to the WRF-simulated dynamic and thermodynamic variables and with larger-scale information about shapes and locations mainly captured from satellite images. Such MSRs with physical interpretations may inspire innovative model–data fusion methods that could overcome the conventional limits of nowcasting.

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Section: Deep Network Modelmentioning

confidence: 99%

Multiscale Representation of Radar Echo Data Retrieved through Deep Learning from Numerical Model Simulations and Satellite Images

et al. 2023

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“…Despite the greatest efforts in recent years to improve weather forecasts, the uncertainties that still exist must be considered [2,3]. In addition, new challenges arise from climatic changes, extreme weather conditions, strong wind shear at low altitudes [4,5], lateral boundary perturbations [6], or general hazardous meteorological conditions [7] that require variation or extension of previous models [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…Advances in machine learning and deep learning have led to many methods being developed in recent years to improve weather forecasts and support air traffic management (ATM) [13]. These methods are correspondingly diverse, such as an encoder-decoder U-net neural network to forecast convective storms and lightning [10], offline model-free reinforcement learning, or eXtreme Gradient Boosting (XGBoost), to support runway configuration management (RCM) [14,15], detection of adverse weather with EEG-enabled Bayesian neural networks [7], or anomaly detection and hierarchical clustering to spot anomalous Eng. Proc.…”

Section: Introductionmentioning

confidence: 99%

Intrinsic Explainable Self-Enforcing Networks Using the ICON-D2-Ensemble Prediction System for Runway Configurations

Zinkhan,

Greisbach,

Zurmaar

et al. 2023

Itise 2023

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“…Through statistical analysis of 67,384 convective cells, Mosier et al pointed out that 30-dBZ radar echoes can be detected by at least two continuous body scans at temperature levels of −15 °C and −20 °C. It can be used as a lightning warning indicator, and the critical success index of the indicator is 68% (Li et al, 2023). The lightning warning system of the Chinese Academy of Meteorological Sciences uses the double-echo intensity threshold and the echo intensity threshold at a certain temperature level, combined with the lightning location data, to predict the probability of a future lightning occurrence (Dokic et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Optimization of the lightning warning model for distribution network lines based on multiple meteorological factor thresholds

Wan,

Fu,

et al. 2023

Front. Energy Res.

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Lightning is one of the frequent natural disasters, which seriously affects the secure and stable operation of the power system, especially the distribution network lines with weak reliability. In order to improve the power supply reliability of the distribution network, higher requirements are put forward for the accuracy of lightning warning. Therefore, this paper establishes a lightning warning model based on comprehensive multi-meteorological factor thresholds and analyzes the meteorological factor data such as atmospheric field strength, echo intensity, echo-top height, and vertical cumulative liquid water content under thunderstorm weather. The threshold value of each factor warning is obtained, and the corresponding threshold weight is calculated by the entropy weight method. According to the weight of each threshold, the comprehensive threshold index of lightning warning is obtained, and the lightning warning is based on this index. A total of 105 lightning data from May to June 2022 in Nanchang city were analyzed as samples. The thresholds of atmospheric field strength, echo intensity, echo-top height, and vertical cumulative liquid water content were 1.2 kV/m, 40 dBZ, 8 km, and 5.2 kg·m−2, respectively. The corresponding weights of each factor were 0.4188, 0.2056, 0.2105, and 0.165, respectively. This model was used to warn a thunderstorm event in July 2022 in Nanchang area. The success rate of the model warning was 0.91, the false alarm rate (FAR) was 0.11, and the critical success index (CSI) was 0.80. Compared with the single-factor threshold lightning warning model, the warning FAR is decreased by 6%, and CSI is increased by 14% while ensuring the high warning success rate.

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Convective Storm VIL and Lightning Nowcasting Using Satellite and Weather Radar Measurements Based on Multi-Task Learning Models

Cited by 10 publications

References 44 publications

Multiscale Representation of Radar Echo Data Retrieved through Deep Learning from Numerical Model Simulations and Satellite Images

Multiscale Representation of Radar Echo Data Retrieved through Deep Learning from Numerical Model Simulations and Satellite Images

Intrinsic Explainable Self-Enforcing Networks Using the ICON-D2-Ensemble Prediction System for Runway Configurations

Optimization of the lightning warning model for distribution network lines based on multiple meteorological factor thresholds

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