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

Sadeghi, Mojtaba; Asanjan, Ata Akbari; Faridzad, Mohammad; Nguyen, Phu; Hsu, Kuolin; Sorooshian, Soroosh; Braithwaite, Dan

doi:10.1175/JHM-D-19-0110.1

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

Full Record
Other Related Research

Abstract

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,more »« less

Authors:

Sadeghi, Mojtaba ^[1]; Asanjan, Ata Akbari ^[2]; Faridzad, Mohammad ^[1]; Nguyen, Phu ^[1]; Hsu, Kuolin ^[1]; Sorooshian, Soroosh ^[1]; Braithwaite, Dan ^[1]

Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California
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:: 2019-11-27

Research Org.:: Univ. of California, Oakland, CA (United States)

Sponsoring Org.:: USDOE Office of International Affairs (IA)

OSTI Identifier:: 1575959

Alternate Identifier(s):: OSTI ID: 1801038

Grant/Contract Number:: # DE-IA0000018; 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

Subject:: 54 ENVIRONMENTAL SCIENCES; Meteorology & Atmospheric Sciences; Precipitation; Hydrometeorology; Neural networks

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.

Copy to clipboard


                    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.  https://doi.org/10.1175/JHM-D-19-0110.1

Copy to clipboard


                    Sadeghi, Mojtaba, Asanjan, Ata Akbari, Faridzad, Mohammad, Nguyen, Phu, Hsu, Kuolin, Sorooshian, Soroosh, and Braithwaite, Dan. Wed .  
"PERSIANN-CNN: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Convolutional Neural Networks".  United States.  https://doi.org/10.1175/JHM-D-19-0110.1.

Copy to clipboard


                    
@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 = {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, 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        = {11}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Publisher's Version of Record
https://doi.org/10.1175/JHM-D-19-0110.1

Other availability

Search WorldCat to find libraries that may hold this journal

Citation Metrics:

Cited by: 8 works

Citation information provided by
Web of Science

Save / Share:

Export Metadata

Save to My Library

Similar Records in DOE PAGES and OSTI.GOV collections:

A Two-Stage Deep Neural Network Framework for Precipitation Estimation from Bispectral Satellite Information

Journal Article Tao, Yumeng ; Hsu, Kuolin ; Ihler, Alexander ; ... - Journal of Hydrometeorology

Compared to ground precipitation measurements, satellite-based precipitation estimation products have the advantage of global coverage and high spatiotemporal resolutions. However, the accuracy of satellite-based precipitation products is still insufficient to serve many weather, climate, and hydrologic applications at high resolutions. In this paper, the authors develop a state-of-the-art deep learning framework for precipitation estimation using bispectral satellite information, infrared (IR), and water vapor (WV) channels. Specifically, a two-stage framework for precipitation estimation from bispectral information is designed, consisting of an initial rain/no-rain (R/NR) binary classification, followed by a second stage estimating the nonzero precipitation amount. In the first stage, themore »« less
Cited by 2
https://doi.org/10.1175/JHM-D-17-0077.1
Conditional Generative Adversarial Networks (cGANs) for Near Real-Time Precipitation Estimation from Multispectral GOES-16 Satellite Imageries—PERSIANN-cGAN

Journal Article Hayatbini, Negin ; Kong, Bailey ; Hsu, Kuo-lin ; ... - Remote Sensing

In this paper, we present a state-of-the-art precipitation estimation framework which leverages advances in satellite remote sensing as well as Deep Learning (DL). The framework takes advantage of the improvements in spatial, spectral and temporal resolutions of the Advanced Baseline Imager (ABI) onboard the GOES-16 platform along with elevation information to improve the precipitation estimates. The procedure begins by first deriving a Rain/No Rain (R/NR) binary mask through classification of the pixels and then applying regression to estimate the amount of rainfall for rainy pixels. A Fully Convolutional Network is used as a regressor to predict precipitation estimates. The networkmore »« less
https://doi.org/10.3390/rs11192193

Full Text Available
Effective Cloud Detection and Segmentation Using a Gradient-Based Algorithm for Satellite Imagery: Application to Improve PERSIANN-CCS

Journal Article Hayatbini, Negin ; Hsu, Kuo-lin ; Sorooshian, Soroosh ; ... - Journal of Hydrometeorology

The effective identification of clouds and monitoring of their evolution are important toward more accurate quantitative precipitation estimation and forecast. In this study, a new gradient-based cloud-image segmentation algorithm is developed using image processing techniques. This method integrates morphological image gradient magnitudes to separate cloud systems and patches boundaries. A varying scale kernel is implemented to reduce the sensitivity of image segmentation to noise and to capture objects with various finenesses of the edges in remote sensing images. The proposed method is flexible and extendable from single to multispectral imagery. Case studies were carried out to validate the algorithm bymore »« less
Cited by 3
https://doi.org/10.1175/JHM-D-18-0197.1
Deep Neural Network Cloud-Type Classification (DeepCTC) Model and Its Application in Evaluating PERSIANN-CCS

Journal Article Afzali Gorooh, Vesta ; Kalia, Subodh ; Nguyen, Phu ; ... - Remote Sensing

Satellite remote sensing plays a pivotal role in characterizing hydrometeorological components including cloud types and their associated precipitation. The Cloud Profiling Radar (CPR) on the Polar Orbiting CloudSat satellite has provided a unique dataset to characterize cloud types. However, data from this nadir-looking radar offers limited capability for estimating precipitation because of the narrow satellite swath coverage and low temporal frequency. We use these high-quality observations to build a Deep Neural Network Cloud-Type Classification (DeepCTC) model to estimate cloud types from multispectral data from the Advanced Baseline Imager (ABI) onboard the GOES-16 platform. The DeepCTC model is trained and testedmore »« less
https://doi.org/10.3390/rs12020316

Full Text Available
Short-Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks

Journal Article Akbari Asanjan, Ata ; Yang, Tiantian ; Hsu, Kuolin ; ... - Journal of Geophysical Research: Atmospheres

Short-term Quantitative Precipitation Forecasting is important for flood forecasting, early flood warning, and natural hazard management. This study proposes a precipitation forecast model by extrapolating Cloud-Top Brightness Temperature (CTBT) using advanced Deep Neural Networks, and applying the forecasted CTBT into an effective rainfall retrieval algorithm to obtain the Short-term Quantitative Precipitation Forecasting (0–6 hr). To achieve such tasks, we propose a Long Short-Term Memory (LSTM) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), respectively. The precipitation forecasts obtained from our proposed framework, (i.e., LSTM combined with PERSIANN) are compared with a Recurrent Neural Network (RNN),more »« less
Cited by 6
https://doi.org/10.1029/2018jd028375

Full Text Available

Similar Records