Very Short-term Prediction of Weather Radar-Based Rainfall Distribution and Intensity Over the Korean Peninsula Using Convolutional Long Short-Term Memory Network

Kim, Yeonjun; Hong, Sungwook

doi:10.1007/s13143-022-00269-2

Cited by 8 publications

(6 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…southern cases, which encompass relatively broader regions (Kim and Hong, 2022a;Oh et al, 2023). To further improve the performance of nowcasting models, enhancing the prediction capabilities for isolated precipitation patterns is crucial.…”

Section: Modelmentioning

confidence: 99%

“…Recently, deep learning-based models have been studied to predict the non-linear evolution of precipitation patterns (e.g., Shi et al, 2015;Shi et al, 2017;Agrawal et al, 2019;Lebedev et al, 2019;Ayzel et al, 2020;Sønderby et al, 2020;Ravuri et al, 2021;Kim and Hong, 2022a;Kim and Hong, 2022b;Choi and Kim, 2022;Ko et al, 2022;Oh et al, 2023). These models are data-driven and physics-free, as they can be trained using high-resolution radar data that contain spatiotemporal information on instantaneous rainfall.…”

mentioning

confidence: 99%

“…S-band radar has a spatiotemporal resolution of approximately 10-min intervals and 1-km scale (Thorndabl et al, 2017), enabling sufficiently high-resolution predictions within 0-6h lead times. In particular, models based on convolutional long short-term memory (Conv-LSTM) and U-Net have previously been proposed, wherein multiple past radar images are used as input data to train the model and predict future timestep images (Shi et al, 2015;Agrawal et al, 2019;Lebedev et al, 2019;Ayzel et al, 2020;Sønderby et al, 2020;Kim and Hong, 2022a;Ko et al, 2022;Oh et al, 2023). These models learn the complex evolution of rainfall patterns over time, resulting in better predictive performance than baseline models, such as NWP, radar echo tracking, and Eulerian persistence.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Advancing very short-term rainfall prediction with blended U-Net and partial differential approaches

Ha,

Park

2024

Front. Earth Sci.

View full text Add to dashboard Cite

Accurate and timely prediction of short-term rainfall is crucial for reducing the damages caused by heavy rainfall events. Therefore, various precipitation nowcasting models have been proposed. However, the performance of these models still remains limited. In particular, the current operational precipitation nowcasting method, which is based on radar echo tracking, such as the McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE), has a critical drawback when predicting newly developed or decayed precipitation fields. Recently proposed deep learning models, such as the U-Net convolutional neural network outperform the models based on radar echo tracking. However, these models are unsuitable for operational precipitation nowcasting due to their blurry predictions over longer lead times. To address these blurry effects and enhance the performance of U-Net-based rainfall prediction, we propose a blended model that combines a partial differential equation (PDE) model based on fluid dynamics with the U-Net model. The evaluation of the forecast skill, based on both qualitative and quantitative methods for 0–3-h lead times, demonstrates that the blended model provides less blurry and more accurate rainfall predictions compared with the U-Net and partial differential equation models. This indicates the potential to enhance the field of very short-term rainfall prediction. Additionally, we also evaluated the monthly-averaged forecast skills for different seasons, and confirmed the operational feasibility of the blended model, which contributes to the performance enhancement of operational nowcasting.

show abstract

Section: Modelmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Advancing very short-term rainfall prediction with blended U-Net and partial differential approaches

Ha,

Park

2024

Front. Earth Sci.

View full text Add to dashboard Cite

show abstract

“…Their method improved accuracy by 50% for real weather datasets. Kim Y et al [20] adopted a ConvLSTM network to predict the presence of rainfall and classify rainfall intensity. The experimental results showed that for longer-time predictions, lower rainfall intensities were predicted even if the rainfall was heavy, and for lighter rainfall intensities, the prediction time increased.…”

Section: Related Workmentioning

confidence: 99%

GraphAT Net: A Deep Learning Approach Combining TrajGRU and Graph Attention for Accurate Cumulonimbus Distribution Prediction

Zhang,

Liew,

et al. 2023

Atmosphere

View full text Add to dashboard Cite

In subtropical regions, heavy rains from cumulonimbus clouds can cause disasters such as flash floods and mudslides. The accurate prediction of cumulonimbus cloud distribution is crucial for mitigating such losses. Traditional machine learning approaches have been used on radar echo data generated by constant altitude plan position indicator (CAPPI) radar systems for predicting cumulonimbus cloud distribution. However, the results are often too foggy and fuzzy. This paper proposes a novel approach that integrates graph convolutional networks (GCN) and trajectory gated recurrent units (TrajGRU) with an attention mechanism to predict cumulonimbus cloud distribution from radar echo data. Experiments were conducted using the moving modified National Institute of Standards and Technology (moving MNIST) dataset and real-world radar echo data, and the proposed model showed a 59.12% improvement in mean square error (MSE) and a 16.26% improvement in structure similarity index measure (SSIM) on average in the moving MNIST dataset, a 65.40% improvement in MSE, and an 10.29% improvement in SSIM on average in the radar echo dataset. These results demonstrate the effectiveness of the proposed approach for improving the prediction accuracy of cumulonimbus cloud distribution.

show abstract

“…Most precipitation products offer data at 5-or 10-minute intervals [11,12] to address the need for real-time monitoring of rapid precipitation changes. Moreover, numerous studies leverage high-temporal-resolution data to enhance spatial resolution, accuracy, and predictive capabilities in precipitation products [13,14].…”

Section: Introductionmentioning

confidence: 99%

Temporal Resolution Enhancement of COMS Satellite Using Geo-Kompsat-2A Satellite Through Data-to-Data Translation

Kim,

Ryu,

Han

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

This study introduces a data-to-data (D2D) translation approach that utilizes a conditional adversarial learning framework to generate hypothetical data at the Meteorological Imager (MI) sensor on the Communication, Ocean, and Meteorological Satellite (COMS). The proposed D2D model produces virtual 10-minute data from actual 30-minute data by exploiting the 10-minute temporal resolution (TR) of the advanced MI (AMI) on GEO-KOMPSAT-2A (GK2A) during the overlapping observation period of the two satellites. Specifically, the D2D model uses one visible (VIS) at 0.64 µm channel and four infrared (IR) channels (3.8, 6.9, 10.8, and 12.3 µm) from AMI of the actual 30minute data from April 2020 to April 2022 to train and test the model. Subsequently, the D2D model is applied to simulate hypothetical 10 min-TR COMS data using the 30-minute COMS observation data from September 2019 to March 2020 during the coexistence period of the two satellites. Regression-calibrated COMS data alleviated the spectral response function differences between MI and AMI sensors. The proposed D2D method exhibits excellent statistical performance, with an average root mean square error of 0.056 for the VIS channel and 3.237 K,

show abstract

Very Short-term Prediction of Weather Radar-Based Rainfall Distribution and Intensity Over the Korean Peninsula Using Convolutional Long Short-Term Memory Network

Cited by 8 publications

References 42 publications

Advancing very short-term rainfall prediction with blended U-Net and partial differential approaches

Advancing very short-term rainfall prediction with blended U-Net and partial differential approaches

GraphAT Net: A Deep Learning Approach Combining TrajGRU and Graph Attention for Accurate Cumulonimbus Distribution Prediction

Temporal Resolution Enhancement of COMS Satellite Using Geo-Kompsat-2A Satellite Through Data-to-Data Translation

Contact Info

Product

Resources

About