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Title: PERSIANN-CNN: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Convolutional Neural Networks

Abstract

Accurate and timely precipitation estimates are critical for monitoring and forecasting natural disasters such as floods. Despite having high-resolution satellite information, precipitation estimation from remotely sensed data still suffers from methodological limitations. State-of-the-art deep learning algorithms, renowned for their skill in learning accurate patterns within large and complex datasets, appear well suited to the task of precipitation estimation, given the ample amount of high-resolution satellite data. In this study, the effectiveness of applying convolutional neural networks (CNNs) together with the infrared (IR) and water vapor (WV) channels from geostationary satellites for estimating precipitation rate is explored. The proposed model performances are evaluated during summer 2012 and 2013 over central CONUS at the spatial resolution of 0.08° and at an hourly time scale. Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)–Cloud Classification System (CCS), which is an operational satellite-based product, and PERSIANN–Stacked Denoising Autoencoder (PERSIANN-SDAE) are employed as baseline models. Results demonstrate that the proposed model (PERSIANN-CNN) provides more accurate rainfall estimates compared to the baseline models at various temporal and spatial scales. Specifically, PERSIANN-CNN outperforms PERSIANN-CCS (and PERSIANN-SDAE) by 54% (and 23%) in the critical success index (CSI), demonstrating the detection skills of the model. Furthermore, themore » root-mean-square error (RMSE) of the rainfall estimates with respect to the National Centers for Environmental Prediction (NCEP) Stage IV gauge–radar data, for PERSIANN-CNN was lower than that of PERSIANN-CCS (PERSIANN-SDAE) by 37% (14%), showing the estimation accuracy of the proposed model.« less

Authors:
 [1];  [2];  [1];  [1];  [1];  [1];  [1]
  1. Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California
  2. Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, and Universities Space Research Association, Mountain View, California
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1575959
Grant/Contract Number:  
# DE-IA0000018
Resource Type:
Published Article
Journal Name:
Journal of Hydrometeorology
Additional Journal Information:
Journal Name: Journal of Hydrometeorology Journal Volume: 20 Journal Issue: 12; Journal ID: ISSN 1525-755X
Publisher:
American Meteorological Society
Country of Publication:
United States
Language:
English

Citation Formats

Sadeghi, Mojtaba, Asanjan, Ata Akbari, Faridzad, Mohammad, Nguyen, Phu, Hsu, Kuolin, Sorooshian, Soroosh, and Braithwaite, Dan. PERSIANN-CNN: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Convolutional Neural Networks. United States: N. p., 2019. Web. doi:10.1175/JHM-D-19-0110.1.
Sadeghi, Mojtaba, Asanjan, Ata Akbari, Faridzad, Mohammad, Nguyen, Phu, Hsu, Kuolin, Sorooshian, Soroosh, & Braithwaite, Dan. PERSIANN-CNN: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Convolutional Neural Networks. United States. doi:10.1175/JHM-D-19-0110.1.
Sadeghi, Mojtaba, Asanjan, Ata Akbari, Faridzad, Mohammad, Nguyen, Phu, Hsu, Kuolin, Sorooshian, Soroosh, and Braithwaite, Dan. Sun . "PERSIANN-CNN: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Convolutional Neural Networks". United States. doi:10.1175/JHM-D-19-0110.1.
@article{osti_1575959,
title = {PERSIANN-CNN: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Convolutional Neural Networks},
author = {Sadeghi, Mojtaba and Asanjan, Ata Akbari and Faridzad, Mohammad and Nguyen, Phu and Hsu, Kuolin and Sorooshian, Soroosh and Braithwaite, Dan},
abstractNote = {Accurate and timely precipitation estimates are critical for monitoring and forecasting natural disasters such as floods. Despite having high-resolution satellite information, precipitation estimation from remotely sensed data still suffers from methodological limitations. State-of-the-art deep learning algorithms, renowned for their skill in learning accurate patterns within large and complex datasets, appear well suited to the task of precipitation estimation, given the ample amount of high-resolution satellite data. In this study, the effectiveness of applying convolutional neural networks (CNNs) together with the infrared (IR) and water vapor (WV) channels from geostationary satellites for estimating precipitation rate is explored. The proposed model performances are evaluated during summer 2012 and 2013 over central CONUS at the spatial resolution of 0.08° and at an hourly time scale. Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)–Cloud Classification System (CCS), which is an operational satellite-based product, and PERSIANN–Stacked Denoising Autoencoder (PERSIANN-SDAE) are employed as baseline models. Results demonstrate that the proposed model (PERSIANN-CNN) provides more accurate rainfall estimates compared to the baseline models at various temporal and spatial scales. Specifically, PERSIANN-CNN outperforms PERSIANN-CCS (and PERSIANN-SDAE) by 54% (and 23%) in the critical success index (CSI), demonstrating the detection skills of the model. Furthermore, the root-mean-square error (RMSE) of the rainfall estimates with respect to the National Centers for Environmental Prediction (NCEP) Stage IV gauge–radar data, for PERSIANN-CNN was lower than that of PERSIANN-CCS (PERSIANN-SDAE) by 37% (14%), showing the estimation accuracy of the proposed model.},
doi = {10.1175/JHM-D-19-0110.1},
journal = {Journal of Hydrometeorology},
number = 12,
volume = 20,
place = {United States},
year = {2019},
month = {12}
}

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This content will become publicly available on November 27, 2020
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