Precipitation nowcasting using ground radar data and simpler yet better video prediction deep learning

Han, Daehyeon; Choo, Minki; Im, Jungho; Shin, Yeji; Lee, Juhyun; Jung, Sihun

doi:10.1080/15481603.2023.2203363

Cited by 11 publications

(2 citation statements)

References 53 publications

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“…The reflectivity maps (also known as radar echo maps) play an important role in in identifying different weather conditions. Furthermore, the radar reflectivity factor has also been applied to data assimilation, shortterm and impending weather forecasts, and other fields [7,8]. However, deploying and operating weather radars in complex terrains, such as mountainous areas, is extremely challenging [9].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Reconstruction of Radar Composite Reflectivity Based on Satellite Observations and Deep Learning

Zhao,

Tan,

Chen

et al. 2024

Remote Sensing

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Weather radar is a useful tool for monitoring and forecasting severe weather but has limited coverage due to beam blockage from mountainous terrain or other factors. To overcome this issue, an intelligent technology called “Echo Reconstruction UNet (ER-UNet)” is proposed in this study. It reconstructs radar composite reflectivity (CREF) using observations from Fengyun-4A geostationary satellites with broad coverage. In general, ER-UNet outperforms UNet in terms of root mean square error (RMSE), mean absolute error (MAE), structural similarity index (SSIM), probability of detection (POD), false alarm rate (FAR), critical success index (CSI), and Heidke skill score (HSS). Additionally, ER-UNet provides the better reconstruction of CREF compared to the UNet model in terms of the intensity, location, and details of radar echoes (particularly, strong echoes). ER-UNet can effectively reconstruct strong echoes and provide crucial decision-making information for early warning of severe weather.

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Section: Introductionmentioning

confidence: 99%

Intelligent Reconstruction of Radar Composite Reflectivity Based on Satellite Observations and Deep Learning

Zhao,

Tan,

Chen

et al. 2024

Remote Sensing

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“…Thus, using solar position information in advanced empirical models may improve the spatiotemporal continuity of ocean-fog. In this study, machine learning approaches (random forest, RF; extreme gradient boost, XGB; and logistic regression, LR), which have been demonstrated to be effective for detecting meteorological features under complex conditions [21]- [26] were utilized to detect ocean-fog with various characteristics. However, non-fog phenomena (e.g., clear skies and clouds) were trained to mitigate the confusion between ocean-fog and other phenomena.…”

Section: Introductionmentioning

confidence: 99%

Improved Ocean–Fog Monitoring Using Himawari-8 Geostationary Satellite Data Based on Machine Learning With SHAP-Based Model Interpretation

Sim,

2023

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

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Ocean-fog is a type of fog that forms over the ocean and has a visibility of less than 1 km. Ocean-fog frequently causes incidents over oceanic and coastal regions; ocean-fog detection is required regardless of the time of day. Ocean-fog has distinct thermo-optical properties, and spatially and temporally extensive ocean-fog detection methods based on geostationary satellites are typically employed. Infrared channels of Himawari-8 were used to construct three machine learning models for the continuous detection of ocean-fog. In contrast, visible channels are valid only during the daytime. As control models, we used fog products from the National Meteorological Satellite Center (NMSC) and machine learning models trained by adding a visible channel. The extreme gradient boosting model utilizing infrared channels corrected ocean-fog perfectly day and night, with the highest F1 score of 97.93% and a proportion correct of 98.59% throughout the day. In contrast, the NMSC product had a probability of detection of 87.14%, an F1 score of 93.13%, and a proportion correct of 71.9%. As demonstrated by the qualitative evaluation, the NMSC product overestimates clouds over small and coarsely textured ocean-fog regions. In contrast, the proposed model distinguishes between ocean-fog, clear skies, and clouds at the pixel scale. The Shapley additive explanation analysis demonstrated that the difference between channels 14 and 7 was very useful for ocean-fog detection at night, and its extremely low values contributed significantly to distinguishing non-fog during the daytime. Channel 15, affected by water vapor absorption, contributed most to ocean-fog detection among atmospheric window channels. The research findings can be used to improve operational ocean-fog detection and forecasting.

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A new statistical downscaling approach for short‐term forecasting of summer air temperatures through a fusion of deep learning and spatial interpolation

Cho,

Im,

Jung

2024

Quart J Royal Meteoro Soc

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Reliable early forecasting of extreme summer air temperatures is essential for effectively managing and mitigating the socioeconomic damage caused by thermal disasters. Numerical weather prediction models have become valuable tools for forecasting air temperature; however, they incur high computational costs, resulting in coarse spatial resolution and systematic bias owing to imperfect parameterization. To address these problems, we developed a novel statistical downscaling and bias correction method (named DeU‐Net) for the maximum and minimum air temperature (Tmax and Tmin, respectively) forecasts obtained from the Global Data Assimilation and Prediction System with a spatial resolution of 10 km to 1.5 km over South Korea through the fusion of deep learning (i.e., U‐Net) and spatial interpolation. In this study, we used a methodology to decompose statistically downscaled Tmax and Tmin forecasts into temporal dynamics over South Korea and spatial fluctuations by pixels. When comparing the proposed DeU‐Net with the dynamical downscaling model (i.e., Local Data Assimilation and Prediction System) and support vector regression (SVR)‐based statistical downscaling model at the seen and unseen stations for forecasting the next‐day Tmax and Tmin, respectively, DeU‐Net showed the highest spatial correlation and the lowest RMSE in all cases. In a qualitative evaluation, DeU‐Net successfully produced a detailed spatial distribution most similar to the observations. A further comparison extending the forecast lead time to seven days indicated that the proposed DeU‐Net is a better downscaling approach than SVR, regardless of the forecast lead time. These results demonstrate that bias‐corrected high spatial resolution air temperature forecasts with relatively long forecast lead times in summer can be effectively produced using the proposed model for operational forecasting.This article is protected by copyright. All rights reserved.

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Precipitation nowcasting using ground radar data and simpler yet better video prediction deep learning

Cited by 11 publications

References 53 publications

Intelligent Reconstruction of Radar Composite Reflectivity Based on Satellite Observations and Deep Learning

Intelligent Reconstruction of Radar Composite Reflectivity Based on Satellite Observations and Deep Learning

Improved Ocean–Fog Monitoring Using Himawari-8 Geostationary Satellite Data Based on Machine Learning With SHAP-Based Model Interpretation

A new statistical downscaling approach for short‐term forecasting of summer air temperatures through a fusion of deep learning and spatial interpolation

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