A Convection Nowcasting Method Based on Machine Learning

Aifang, Su; Li, Han; Cui, Liman; Chen, Yungang

doi:10.1155/2020/5124274

Cited by 20 publications

(14 citation statements)

References 21 publications

(27 reference statements)

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“…Now we compare the performance of the proposed chart with the chart proposed by Aslam and Khan 39 and X-bar control chart under classical statistics using the simulated data. Let n N ǫ [6,8] , w N ǫ [3,5] and r 0N ǫ[370, 370] The 20 observations are generated from the neutrosophic normal distribution with µ N ǫ[0, 0] and variance σ 2 N ǫ [1,1] . Later 20 observations are generated from the process with a shift of 0.30.…”

Section: Resultsmentioning

confidence: 99%

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RETRACTED ARTICLE: Monitoring the temperature through moving average control under uncertainty environment

Aslam

Shareef

Khan

2020

Sci Rep

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the existing moving average control charts can be only applied when all observations in the data are determined, precise, and certain. But, in practice, the data from the weather monitoring is not exact and express in the interval. in this situation, the available monitoring plans cannot be applied for the monitoring of weather data. A new moving average control chart for the normal distribution is offered under the neutrosophic statistics. The parameters of the offered chart are determined through simulation under neutrosophic statistics. the comparison study shows the superiority of the proposed chart over the moving average control chart under classical statistics. A real example from the weather is chosen to present the implementation of the chart. from the simulation study and real data, the proposed chart is found to be effective to be applied for temperature monitoring than the existing control chart.

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Section: Resultsmentioning

confidence: 99%

“…Ye et al 7 applied quality control methods for air data. Su et al 8 used the machine learning technique for metrology data.…”

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confidence: 99%

RETRACTED ARTICLE: Monitoring the temperature through moving average control under uncertainty environment

Aslam

Shareef

Khan

2020

Sci Rep

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“…Ayzel et al (2020), Agrawal et al (2019), andSamsi et al (2019) trained CNNs with a similar U-Net architecture as in this paper for the problem of nowcasting using radar data. Su et al (2020) approached the nowcasting problem using a recurrent architecture, which should better capture temporally evolving features than a standard feedforward architecture. There are a number of commercial entities seeking to provide proxy global radar datasets.…”

Section: Introductionmentioning

confidence: 99%

Development and Interpretation of a Neural-Network-Based Synthetic Radar Reflectivity Estimator Using GOES-R Satellite Observations

Hilburn

Ebert‐Uphoff

Miller

2021

Journal of Applied Meteorology and Climatology

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The objective of this research is to develop techniques for assimilating GOES-R Series observations in precipitating scenes for the purpose of improving short-term convective-scale forecasts of high impact weather hazards. Whereas one approach is radiance assimilation, the information content of GOES-R radiances from its Advanced Baseline Imager (ABI) saturates in precipitating scenes, and radiance assimilation does not make use of lightning observations from the GOES Lightning Mapper (GLM). Here, a convolutional neural network (CNN) is developed to transform GOES-R radiances and lightning into synthetic radar reflectivity fields to make use of existing radar assimilation techniques. We find that the ability of CNNs to utilize spatial context is essential for this application and offers breakthrough improvement in skill compared to traditional pixel-by-pixel based approaches. To understand the improved performance, we use a novel analysis methodology that combines several techniques, each providing different insights into the network’s reasoning. Channel withholding experiments and spatial information withholding experiments are used to show that the CNN achieves skill at high reflectivity values from the information content in radiance gradients and the presence of lightning. The attribution method, layer-wise relevance propagation, demonstrates that the CNN uses radiance and lightning information synergistically, where lightning helps the CNN focus on which neighboring locations are most important. Synthetic inputs are used to quantify the sensitivity to radiance gradients, showing that sharper gradients produce a stronger response in predicted reflectivity. Finally, lightning observations are found to be uniquely valuable for their ability to pinpoint locations of strong radar echoes.

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“…The results of the TSWP model outperform the comparative methods, such as MLP, DNN, and Logistic Regression.In addition to the aforementioned studies, various works have been published for different meteorological tasks Su et al (2020). use the pyramid delaminating technique to generate the global optical flow field.…”

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confidence: 99%

“…Su et al (2020) use the pyramid delaminating technique to generate the global optical flow field. A ConvLSTM model takes the RGB image and generated flow field in order to improve the forecast accuracy of echo position and intensity.…”

mentioning

confidence: 99%

A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resources

Sit¹,

Demiray²,

Xiang³

et al. 2020

Preprint

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The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume, variety, and velocity of water-related data are increasing due to large-scale sensor networks and increased attention to topics such as disaster response, water resources management, and climate change. Combined with the growing availability of computational resources and popularity of deep learning, these data are transformed into actionable and practical knowledge, revolutionizing the water industry. In this article, a systematic review of literature is conducted to identify existing research which incorporates deep learning methods in the water sector, with regard to monitoring, management, governance and communication of water resources. The study provides a comprehensive review of state-of-the-art deep learning approaches used in the water industry for generation, prediction, enhancement, and classification tasks, and serves as a guide for how to utilize available deep learning methods for future water resources challenges. Key issues and challenges in the application of these techniques in the water domain are discussed, including the ethics of these technologies for decision-making in water resources management and governance. Finally, we provide recommendations and future directions for the application of deep learning models in hydrology and water resources.

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A Convection Nowcasting Method Based on Machine Learning

Cited by 20 publications

References 21 publications

RETRACTED ARTICLE: Monitoring the temperature through moving average control under uncertainty environment

RETRACTED ARTICLE: Monitoring the temperature through moving average control under uncertainty environment

Development and Interpretation of a Neural-Network-Based Synthetic Radar Reflectivity Estimator Using GOES-R Satellite Observations

A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resources

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