Infrared precipitation estimation using convolutional neural network for FengYun satellites

Wang, Cunguang; Tang, Guoqiang; Xiong, Wentao; Ma, Zhanshan; Zhu, Siyu

doi:10.1016/j.jhydrol.2021.127113

Cited by 24 publications

(12 citation statements)

References 47 publications

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“…CNN have been successfully used in various remote sensing fields such as land cover classification (Maggiori et al., 2017), semantic segmentation (Schroff et al., 2015), object detection (Audebert et al., 2017), reconstruction of missing data (Q. Zhang et al., 2018), and image pansharpening (Masi et al., 2016). In recent years, CNNs have also played an important role in satellite precipitation estimation, outperforming other neural network architectures in terms of performance (Sadeghi et al., 2019; Wang et al., 2020).…”

Section: Methodsmentioning

confidence: 99%

“…Zhang et al, 2018), and image pansharpening (Masi et al, 2016). In recent years, CNNs have also played an important role in satellite precipitation estimation, outperforming other neural network architectures in terms of performance (Sadeghi et al, 2019;Wang et al, 2020).…”

Section: Cnnmentioning

confidence: 99%

“…Subsequently, Wang et al. (2020) proposed IR precipitation estimation using a CNN, where the CNN technique was combined with a two‐stage framework and returned a significant result in multichannel satellite imagery.…”

Section: Introductionmentioning

confidence: 99%

“…The primary concept of this framework is that a satellite precipitation algorithm is required to simultaneously achieve reliable results in rain/no-rain detection and rainfall amount estimation under the skewness of the probability density function in precipitation data (Tao et al, 2018). Subsequently, Wang et al (2020) proposed IR precipitation estimation using a CNN, where the CNN technique was combined with a two-stage framework and returned a significant result in multichannel satellite imagery.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Multi‐Task Learning for Simultaneous Retrievals of Passive Microwave Precipitation Estimates and Rain/No‐Rain Classification

Bannai

Utsumi

et al. 2023

Geophysical Research Letters

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Satellite‐based precipitation estimations provide frequent, large‐scale measurements. Deep learning has recently shown significant potential for improving estimation accuracy. Most studies have employed a two‐stage framework, which is a sequential architecture of a rain/no‐rain binary classification task followed by a rain rate regression task. This study proposes a novel precipitation retrieval framework in which these two tasks are simultaneously trained using multi‐task learning approach (MTL). Furthermore, a novel network architecture and loss function were designed to reap the benefits of MTL. The proposed two‐task model successfully achieved a better performance than the conventional single‐task model possibly due to efficient knowledge transfer between tasks. Furthermore, the product intercomparison showed that our product outperformed existing products in rain rate retrieval and also yielded better skills in the rain/no‐rain retrieval task.

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Section: Methodsmentioning

confidence: 99%

Section: Cnnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Multi‐Task Learning for Simultaneous Retrievals of Passive Microwave Precipitation Estimates and Rain/No‐Rain Classification

Bannai

Utsumi

et al. 2023

Geophysical Research Letters

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“…Chen et al (2019) proposed a two-stage hybrid neural network to estimate precipitation using ground-based radar and satellite observations. Wang et al (2021) proposed a transfer learning based method, which uses data-riched Continental US (CONUS) IR data set from the Geostationary Operational Environment Satellite for pre-training of the model, and then transferred to China through re-training with multi-band IR signals from Chinese Fangyuan (FY) satellite. Gao et al (2022) used a U-Net model combined with the attention mechanism to directly retrieve precipitation maps using multi-band FY satellite images at a near real-time scale.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Quantitative Precipitation Estimation of NWP Model With Fundamental Meteorological Variables and Transformer Based Deep Learning Model

Liu,

Fung,

Lau

et al. 2024

Earth and Space Science

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Quantitative precipitation forecasting in numerical weather prediction (NWP) models is contingent upon physicals parameterization schemes. However, uncertainties abound due to limited knowledge of the precipitating processes, leading to degraded forecasting skills. In light of this, our study explores the application of a Swin‐Transformer based deep learning (DL) model as a supplementary tool for enhancing the mapping trajectory between the NWP fundamental variables and the most downstream variable precipitation. Constrained by the observational satellite precipitation product from NOAA CPC Morphing Technique (CMORPH), the DL model serves as the post‐processing tool that can better resolve the precipitation patterns compared to solely based on NWP estimation. Compared to the baseline Weather Research and Forecasting simulation, the DL post‐processing effectively extracts features over meteorological variables, leading to improved precipitation skill scores of 21.7%, 60.5%, and 45.5% for light rain, moderate rain, and heavy rain, respectively, on an hourly basis. We also evaluate two case studies under different driven synoptic conditions and show promising results in estimating heavy precipitation during strong convective precipitation events. Overall, the proposed DL model can provide a vital reference for capturing precipitation‐triggering mechanisms and enhancing precipitation forecasting skills. Additionally, we discuss the sensitivities of the fundamental meteorological variables used in this study, training strategies, and performance limitations.

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Modeling with Artificial Neural Networks to estimate daily precipitation in the Brazilian Legal Amazon

Pinheiro Gomes,

Progênio,

da Silva Holanda

2024

Clim Dyn

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Hydrological analyzes carried out from precipitation in the Legal Amazon (AMZ-L) are essential due to their importance in climate regulation, regional and global atmospheric circulation. However, in this region, there are limitations related to data series with short periods and many gaps, especially in the daily scale. Thus, to improve precipitation analyses, a non-parametric stochastic model based on Artificial Neural Networks (ANNs) was used to estimate daily precipitation in AMZ-L. For this, 22 rainfall stations were adopted over a period of 18 years (1998-2016) and with <1% missing data, which were organized considering the complete series and the seasonal periods (rainy and dry). The results obtained demonstrate the good capacity of the model to preserve the precipitation characteristics of the evaluated rainfall stations, mainly those with a more humid climate and with more frequent precipitations during the year, as is the case of those located in the Amazon Biome. However, in regions that suffer prolonged periods of drought, such as the Amazon-Cerrado Ecotone areas, the results were less satisfactory due to the greater recurrence of zeros in the historical series. The seasonal division into dry and rainy periods did not provide better estimates to the model, except for some rainfall gauge stations located at latitudes close to the equator.However, this study may support future research on the estimation of daily precipitation in the region.

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Infrared precipitation estimation using convolutional neural network for FengYun satellites

Cited by 24 publications

References 47 publications

Multi‐Task Learning for Simultaneous Retrievals of Passive Microwave Precipitation Estimates and Rain/No‐Rain Classification

Multi‐Task Learning for Simultaneous Retrievals of Passive Microwave Precipitation Estimates and Rain/No‐Rain Classification

Enhancing Quantitative Precipitation Estimation of NWP Model With Fundamental Meteorological Variables and Transformer Based Deep Learning Model

Modeling with Artificial Neural Networks to estimate daily precipitation in the Brazilian Legal Amazon

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