Correcting rainfall forecasts of a numerical weather prediction model using generative adversarial networks

Jeong, Chang-Hoo; Yi, Mun Yong

doi:10.1007/s11227-022-04686-y

Cited by 11 publications

(2 citation statements)

References 39 publications

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“…The application of deep learning techniques for weather forecasting has grown dramatically in recent years; nevertheless, current machine learning algorithms based on observable data are only appropriate for very short-term forecasting. For medium-and short-term forecasting, numerical models are more stable, although the results may differ from the data that have been observed (Jeong and Yi, 2023).…”

Section: Introductionmentioning

confidence: 67%

Rainfall classification and forecasting based on a novel voting adaptive dynamic optimization algorithm

Elkenawy,

Alhussan,

Eid

et al. 2024

Front. Environ. Sci.

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Environmental issues of rainfall are basic in terms of understanding and management of ecosystems and natural resources. The rainfall patterns significantly affect soil moisture, vegetation growth and biodiversity in the ecosystems. In addition, proper classification of rainfall types helps in the evaluation of the risk of flood, drought, and other extreme weather events’ risk, which immensely affect the ecosystems and human societies. Rainfall classification can be improved by using machine learning and metaheuristic algorithms. In this work, an Adaptive Dynamic Puma Optimizer (AD-PO) algorithm combined with Guided Whale Optimization Algorithm (Guided WOA) introduces a potentially important improvement in rainfall classification approaches. These algorithms are to be combined to enable researchers to comprehend and classify rain events by their specific features, such as intensity, duration, and spatial distribution. A voting ensemble approach within the proposed (AD-PO-Guided WOA) algorithm increases its predictive performance because of the combination of predictions from several classifiers to localize the dominant rainfall class. The presented approach not only makes the classifying of rain faster and more accurate but also strengthens the robustness and trustworthiness of the classification in this regard. Comparison to other optimization algorithms validates the effectiveness of the AD-PO-Guided WOA algorithm in terms of performance metrics with an outstanding 95.99% accuracy. Furthermore, the second scenario is applied for forecasting based on the long short-term memory networks (LSTM) model optimized by the AD-PO-Guided WOA algorithm. The AD-PO-Guided WOA- LSTM algorithm produces rainfall prediction with an MSE of 0.005078. Wilcoxon rank test, descriptive statistics, and sensitivity analysis are applied to help evaluating and improving the quality and validity of the proposed algorithm. This intensive method facilitates rainfall classification and is a base for suggested measures that cut the hazards of extreme weather events on societies.

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Section: Introductionmentioning

confidence: 67%

Rainfall classification and forecasting based on a novel voting adaptive dynamic optimization algorithm

Elkenawy,

Alhussan,

Eid

et al. 2024

Front. Environ. Sci.

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“…Several deep learning methods have applied in regional precipitation forecasts, including Generative adversarial network (GAN) for hourly and daily precipitation downscaling and correction (Harris et al, 2022;Jeong & Yi, 2023;Price & Rasp, 2022), convolutional long short-term memory network for precipitation nowcasting (Shi et al, 2015(Shi et al, , 2017, and conditional GAN for probabilistic precipitation forecasting (Ravuri et al, 2021). However, these studies mainly focus on short-and medium-term forecasting (including hourly and daily), and the potential of deep learning-based models for long-term forecasting (monthly, seasonal, up to annual) is yet to be further explored.…”

Section: Introductionmentioning

confidence: 99%

Monthly precipitation prediction at regional scale using deep convolutional neural networks

Ni,

Wang,

Singh

et al. 2023

Hydrological Processes

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Variations in monthly precipitation are associated with climate extremes having significant socio‐economic and eco‐environmental impacts. Knowledge of monthly precipitation information is therefore valuable for policy making. Extensive research has been conducted on dynamic prediction using state‐of‐the‐art coupled climate models. However, the skilful prediction of monthly precipitation with dynamical models remains a challenge. With the development of machine learning tools, statistical predictions show comparable performance with dynamic models, but they are limited to at‐site monthly prediction, due to the lack of ability for processing spatially connected geophysical data. To improve monthly precipitation forecasting and provide regional forecasts, we propose a model termed UNet‐RegPre based on convolutional neural network and U‐net architecture. The model shows comparable prediction skills with recurrent state‐of‐the‐art dynamic forecasts (CFSv2), and has the capability to capture spatiotemporal patterns of precipitation and reproduce the main process of representative droughts. The key precursors for rainfall development identified by UNet‐RegPre shows that the constructed model can detect the climatic connection between rainfall in eastern China and summer monsoon, showing the potential to advance hydrometeorological understanding with a deep learning‐based model. These results suggest that the proposed model can be a potential tool for precipitation prediction.

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Machine Learning Applications for Renewable-Based Energy Systems

Graditi

Buonanno

Caliano

et al. 2023

EAI/Springer Innovations in Communication and Computing

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Correcting rainfall forecasts of a numerical weather prediction model using generative adversarial networks

Cited by 11 publications

References 39 publications

Rainfall classification and forecasting based on a novel voting adaptive dynamic optimization algorithm

Rainfall classification and forecasting based on a novel voting adaptive dynamic optimization algorithm

Monthly precipitation prediction at regional scale using deep convolutional neural networks

Machine Learning Applications for Renewable-Based Energy Systems

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