Attention-Unet-Based Near-Real-Time Precipitation Estimation from Fengyun-4A Satellite Imageries

Gao, Yuan; Guan, Jiping; Zhang, Fuhan; Wang, Xiaodong; Long, Zhilin

doi:10.3390/rs14122925

Cited by 25 publications

(24 citation statements)

References 36 publications

(37 reference statements)

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“…Attention U-Net is a transformation of U-Net which introduces an attention mechanism to process encoder and decoder feature maps. As shown in Figure 4 , the U-Net model is the main architecture, and the attention gates denoted by the red circles are integrated to automatically adjust the feature weight in different locations before skipping the connection of the encoder and decoder [ 36 ]. Compared to the U-Net networks, Attention U-Net can focus on the region of interest by putting more weight on features that are passed through the skip connections.…”

Section: Methodsmentioning

confidence: 99%

Radar Composite Reflectivity Reconstruction Based on FY-4A Using Deep Learning

Yang

Zhao

Xue

et al. 2022

Sensors

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Weather radars are commonly used to track the development of convective storms due to their high resolution and accuracy. However, the coverage of existing weather radar is very limited, especially in mountainous and ocean areas. Geostationary meteorological satellites can provide near global coverage and near real-time observations, which can compensate for the lack of radar observations. In this paper, a deep learning method was used to estimate the radar composite reflectivity from observations of China’s new-generation geostationary meteorological satellite FY-4A and topographic data. The derived radar reflectivity products from satellite observations can be used over regions without radar coverage. In general, the deep learning model can reproduce the overall position, shape, and intensity of the radar echoes. In addition, evaluation of the reconstruction radar observations indicates that a modified model based on the attention mechanism (Attention U-Net model) has better performance than the traditional U-Net model in terms of all statistics such as the probability of detection (POD), critical success index (CSI), and root-mean-square error (RMSE), and the modified model has stronger capability on reconstructing details and strong echoes.

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Section: Methodsmentioning

confidence: 99%

Radar Composite Reflectivity Reconstruction Based on FY-4A Using Deep Learning

Yang

Zhao

Xue

et al. 2022

Sensors

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“…Environmental science examples: Attention U-nets have been used to estimate radar reflectivity (Yang et al, 2023), precipitation (Trebing et al, 2021; Gao et al, 2022a), and cloud detection (Guo et al, 2020) from satellite imagery. U-Nets with bidirectional LSTM and attention mechanism (Garnot and Landrieu, 2021; Ghosh et al, 2021) leverage features from time-series satellite data to identify temporal patterns of each land cover class and automate land cover classification.…”

Section: A New Generation Of Neural Networkmentioning

confidence: 99%

Leveraging spatiotemporal information in meteorological image sequences: From feature engineering to neural networks

Bansal

Lee

Hilburn

et al. 2023

Environ. Data Science

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Atmospheric processes involve both space and time. Thus, humans looking at atmospheric imagery can often spot important signals in an animated loop of an image sequence not apparent in an individual (static) image. Utilizing such signals with automated algorithms requires the ability to identify complex spatiotemporal patterns in image sequences. That is a very challenging task due to the endless possibilities of patterns in both space and time. Here, we review different concepts and techniques that are useful to extract spatiotemporal signals from meteorological image sequences to expand the effectiveness of AI algorithms for classification and prediction tasks. We first present two applications that motivate the need for these approaches in meteorology, namely the detection of convection from satellite imagery and solar forecasting. Then we provide an overview of concepts and techniques that are helpful for the interpretation of meteorological image sequences, such as (a) feature engineering methods using (i) meteorological knowledge, (ii) classic image processing, (iii) harmonic analysis, and (iv) topological data analysis; (b) ways to use convolutional neural networks for this purpose with emphasis on discussing different convolution filters (2D/3D/LSTM-convolution); and (c) a brief survey of several other concepts, including the concept of “attention” in neural networks and its utility for the interpretation of image sequences and strategies from self-supervised and transfer learning to reduce the need for large labeled datasets. We hope that presenting an overview of these tools—many of which are not new but underutilized in this context—will accelerate progress in this area.

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“…In the study by Zhang et al (2021), geographic information (latitude and longitude) was used to merge multiple satellite-based precipitation products and measurement gauges, which improved the accuracy of satellite Earth and Space Science 10.1029/2023EA003311 precipitation estimation. Additionally, Gao et al (2022) proposed a model for estimating precipitation in nearreal-time based on Attention-Net for FY-4A/AGRI data; their model outperformed FY-4A's operational precipitation estimation products in terms of precipitation recognition and estimation accuracy. Zhao et al (2022) proposed a comprehensive utilization of satellite infrared observations and multi-source data for high-resolution typhoon precipitation estimation.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, Gao et al. (2022) proposed a model for estimating precipitation in near‐real‐time based on Attention‐Net for FY‐4A/AGRI data; their model outperformed FY‐4A's operational precipitation estimation products in terms of precipitation recognition and estimation accuracy. Zhao et al.…”

Section: Introductionmentioning

confidence: 99%

FY‐4A/AGRI Infrared Brightness Temperature Estimation of Precipitation Based on Multi‐Model Ensemble Learning

Wang,

Han,

et al. 2024

Earth and Space Science

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Satellite infrared detectors cannot penetrate clouds, especially precipitating clouds. Improving precipitation estimation accuracy based on infrared brightness temperature has always been important but challenging. In this paper, based on the infrared brightness temperature of the Advanced Geosynchronous Radiation Imager (AGRI) onboard China's Feng‐Yun 4A satellite, we develop and evaluate a new precipitation estimation method. First, using static data, physical characteristics of clouds, cloud image texture features, temporal motion features, and AGRI infrared channel brightness temperature, we construct features for a machine learning model. Then, we develop precipitation estimation methods. Precipitation is estimated in two steps: classification and regression. We employ a random forest classification model to identify whether there is precipitation in a given field of view. If there is precipitation, a multi‐model ensemble regression learning method is used to estimate the areas with this precipitation. The ensemble learning method uses convex optimization to integrate prediction results based on the optimization of hyperparameters of five basic models (i.e., those of random forest, XGBoost, LightGBM, decision tree, and extra tree models). Furthermore, two regression stacking ensemble models—the Least Absolute Shrinkage and Selection Operator (herein referred to as Stacking1‐LASSO) and K‐nearest neighbor (herein referred to as Stacking2‐KNN)—are used to predict the results of the aforementioned basic models. The results of basic models are used as inputs of these two stacking models. Finally, based on the Integrated Multi‐satellitE Retrievals for GPM (IMERG) precipitation product and rain gauge precipitation data, we conduct precipitation estimation experiments and evaluate our methods. The results show that ensemble learning models have greater accuracy in estimating precipitation than the basic models. When using IMERG precipitation as the target precipitation, ensemble learning models can estimate the central area of heavy precipitation during typhoons Ampil and Maria. The ensemble learning estimation effect is better than that of Stacking2‐KNN. Moreover, when rain gauge data is used as the target precipitation, ensemble learning can also estimate the center of heavy precipitation and with good consistency with recorded satellite brightness temperature data.

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Attention-Unet-Based Near-Real-Time Precipitation Estimation from Fengyun-4A Satellite Imageries

Cited by 25 publications

References 36 publications

Radar Composite Reflectivity Reconstruction Based on FY-4A Using Deep Learning

Radar Composite Reflectivity Reconstruction Based on FY-4A Using Deep Learning

Leveraging spatiotemporal information in meteorological image sequences: From feature engineering to neural networks

FY‐4A/AGRI Infrared Brightness Temperature Estimation of Precipitation Based on Multi‐Model Ensemble Learning

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