Convolutional Neural Networks Applied on Weather Radar Echo Extrapolation

Shi, En; Qian, Li; Gu, Daquan; Zhao, Zhangming

doi:10.12783/dtcse/csae2017/17544

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…However these models require very large datasets with tens of thousands of images, due the data-intensive training process. For this reason, CNNs and ConvLSTMs are mainly applied to data sets with short time intervals of no more than a few minutes between data points, which are typically much larger than data sets with longer time intervals [29][30][31][32][33][34][35][36][37][38][39][40]. For single-output prediction, a wider range of ML tools and time frames have been used, from linear methods in [17,21,41,42], to ensemble methods in [43][44][45], to hybrid methods in [28,[46][47][48], to deep models in [49][50][51][52][53][54][55][56] covering time scales from minutes to years.…”

Section: Literature Review and Scope Of The Researchmentioning

confidence: 99%

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Basic Statistical Estimation Outperforms Machine Learning in Monthly Prediction of Seasonal Climatic Parameters

et al. 2021

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Machine learning (ML) has been utilized to predict climatic parameters, and many successes have been reported in the literature. In this paper, we scrutinize the effectiveness of five widely used ML algorithms in the monthly prediction of seasonal climatic parameters using monthly image data. Specifically, we quantify the predictive performance of these algorithms applied to five climatic parameters using various combinations of features. We compare the predictive accuracy of the resulting trained ML models to that of basic statistical estimators that are computed directly from the training data. Our results show that ML never significantly outperforms the statistical baseline, and underperforms for most feature sets. Unlike previous similar studies, we provide error bars for the relative performance of different predictors based on jackknife estimates applied to differences in predictive error magnitudes. We also show that the practice of shuffling data sequences which was employed in some previous references leads to data leakage, resulting in over-estimated performance. Ultimately, the paper demonstrates the importance of using well-grounded statistical techniques when producing and analyzing the results of ML predictive models.

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Section: Literature Review and Scope Of The Researchmentioning

confidence: 99%

“…According to [66], the simplest baseline for predicting time series is to use the previous lag. For short-term image data, the previous image is used as a naive predictor for the next image [36,37,40,70]. As for monthly data, using previous lags as a baseline is not a common practice.…”

Section: Literature Review and Scope Of The Researchmentioning

confidence: 99%

Basic Statistical Estimation Outperforms Machine Learning in Monthly Prediction of Seasonal Climatic Parameters

et al. 2021

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“…For example, the authors in [10] used up to 60 images to predict rainfall on an hourly base. the authors in [3], [7], [8], [18]- [20], used 4, 4, 5, 10, 10, 20, .20 images respectively. As to image size, the authors in [10] used several image sizes between 101 × 101 and 10 × 10, and compared the performance.…”

Section: A Role Of Rainfall Maps In Water Resource Managementmentioning

confidence: 99%

Regional Rainfall Prediction Using Support Vector Machine Classification of Large-Scale Precipitation Maps

Hussein¹,

Ghaziasgar²,

Thron³

2020

Preprint

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Rainfall prediction helps planners anticipate potential social and economic impacts produced by too much or too little rain. This research investigates a class-based approach to rainfall prediction from 1-30 days in advance. The study made regional predictions based on sequences of daily rainfall maps of the continental US, with rainfall quantized at 3 levels: light or no rain; moderate; and heavy rain. Three regions were selected, corresponding to three squares from a 5 × 5 grid covering the map area. Rainfall predictions up to 30 days ahead for these three regions were based on a support vector machine (SVM) applied to consecutive sequences of prior daily rainfall map images. The results show that predictions for corner squares in the grid were less accurate than predictions obtained by a simple untrained classifier. However, SVM predictions for a central region outperformed the other two regions, as well as the untrained classifier. We conclude that there is some evidence that SVMs applied to large-scale precipitation maps can under some conditions give useful information for predicting regional rainfall, but care must be taken to avoid pitfalls.

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“…For challenging degraded echo images, the textural detail in the reconstructed echoes of traditional approaches is typically absent, resulting in unsatisfying super-resolution (SR) solutions. Furthermore, some current traditional [18][19][20] or deep learning-based [21,22] weather radar echo extrapolation methods and forecasting models were primarily based on weather radar echo maps, which are often constant altitude plan position indicator (CAPPI) images. As the prediction lead time increases, the radar echo extrapolated from these models becomes increasingly blurred and deformed.…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Networks Capabilities for Super-Resolution Reconstruction of Weather Radar Echo Images

et al. 2019

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Improving the resolution of degraded radar echo images of weather radar systems can aid severe weather forecasting and disaster prevention. Previous approaches to this problem include classical super-resolution (SR) algorithms such as iterative back-projection (IBP) and a recent nonlocal self-similarity sparse representation (NSSR) that exploits the data redundancy of radar echo data, etc. However, since radar echoes tend to have rich edge information and contour textures, the textural detail in the reconstructed echoes of traditional approaches is typically absent. Inspired by the recent advances of faster and deeper neural networks, especially the generative adversarial networks (GAN), which are capable of pushing SR solutions to the natural image manifold, we propose using GAN to tackle the problem of weather radar echo super-resolution to achieve better reconstruction performance (measured in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM)). Using authentic weather radar echo data, we present the experimental results and compare its reconstruction performance with the above-mentioned methods. The experimental results showed that the GAN-based method is capable of generating perceptually superior solutions while achieving higher PSNR/SSIM results.

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Convolutional Neural Networks Applied on Weather Radar Echo Extrapolation

Cited by 4 publications

References 15 publications

Basic Statistical Estimation Outperforms Machine Learning in Monthly Prediction of Seasonal Climatic Parameters

Basic Statistical Estimation Outperforms Machine Learning in Monthly Prediction of Seasonal Climatic Parameters

Regional Rainfall Prediction Using Support Vector Machine Classification of Large-Scale Precipitation Maps

Generative Adversarial Networks Capabilities for Super-Resolution Reconstruction of Weather Radar Echo Images

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