Conditional Generative Adversarial Networks (cGANs) for Near Real-Time Precipitation Estimation from Multispectral GOES-16 Satellite Imageries—PERSIANN-cGAN

Hayatbini, Negin; Kong, Bailey; Hsu, Kuolin; Nguyen, Phu; Sorooshian, Soroosh; Stephens, Graeme L.; Fowlkes, Charless C.; Nemani, Ramakrishna R.

doi:10.3390/rs11192193

Cited by 51 publications

(33 citation statements)

References 35 publications

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“…proposed an approach to obtain waterlogging depth from video images using CNN. Hong et al, 2004;Hayatbini et al, 2019;Pan et al, 2019;Potnis et al, 2019;Jain et al, 2020;Jiang et al, 2020 Knowledge-based approaches Kurte et al (2017) used a semantics-driven framework to enable spatial relationships based semantic queries to detect flooded regions from satellite imagery and further extended the framework (Kurte et al, 2019) to accommodate temporal dimension that enabled spatio-temporal queries over flooded regions. In a similar approach, Potnis et al (2018) developed a flood scene ontology (FSO) which formally defines complex classes such as Accessible Residential Buildings, to classify flooded regions in urban area from satellite imagery.…”

Section: Xu Et Al 2019amentioning

confidence: 99%

“…The authors stated that the method outperformed reanalysis precipitation products as well as statistical downscaling (SD) products using linear regression, nearest neighbors, random forests, or fully connected deep neural networks. In an another recent work, Hayatbini et al (2019) proposed a precipitation estimation framework using a fully convolutional neural network and the advanced baseline imager data from GOES-16, a multispectral geostationary satellite. Specifically, they proposed that the U-net CNN architecture could perform rain/no-rain classification using satellite imagery.…”

Section: Flood Managementmentioning

confidence: 99%

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Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards

Allen‐Dumas

Kurte

et al. 2021

Front. Water

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Due to the complex interactions of human activity and the hydrological cycle, achieving urban water security requires comprehensive planning processes that address urban water hazards using a holistic approach. However, the effective implementation of such an approach requires the collection and curation of large amounts of disparate data, and reliable methods for modeling processes that may be co-evolutionary yet traditionally represented in non-integrable ways. In recent decades, many hydrological studies have utilized advanced machine learning and information technologies to approximate and predict physical processes, yet none have synthesized these methods into a comprehensive urban water security plan. In this paper, we review ways in which advanced machine learning techniques have been applied to specific aspects of the hydrological cycle and discuss their potential applications for addressing challenges in mitigating multiple water hazards over urban areas. We also describe a vision that integrates these machine learning applications into a comprehensive watershed-to-community planning workflow for smart-cities management of urban water resources.

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Section: Xu Et Al 2019amentioning

confidence: 99%

Section: Flood Managementmentioning

confidence: 99%

Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards

Allen‐Dumas

Kurte

et al. 2021

Front. Water

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“…We used the proposed DeepCTC model to evaluate the performance of the operational PERSIANN-CCS product based on different cloud types with respect to MRMS rain rate data as a reference, at a half-hourly scale over the CONUS. Both the MRMS and DeepCTC datasets are resampled to 0.04 • for each 30 min to be comparable with the PERSIANN-CCS product [62]. Table 5 provides the verification statistics including Pearson correlation coefficient (Corr), bias ratio (bias) and root mean square error (RMSE) of the satellite-based precipitation estimates compared to MRMS for individual cloud types for July 2018.…”

Section: Evaluation Of Half-hourly Persiann-ccs According To Cloud-tymentioning

confidence: 99%

Deep Neural Network Cloud-Type Classification (DeepCTC) Model and Its Application in Evaluating PERSIANN-CCS

et al. 2020

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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 tested using coincident data from both CloudSat and ABI over the CONUS region. Evaluations of DeepCTC indicate that the model performs well for a variety of cloud types including Altostratus, Altocumulus, Cumulus, Nimbostratus, Deep Convective and High clouds. However, capturing low-level clouds remains a challenge for the model. Results from simulated GOES-16 ABI imageries of the Hurricane Harvey event show a large-scale perspective of the rapid and consistent cloud-type monitoring is possible using the DeepCTC model. Additionally, assessments using half-hourly Multi-Radar/Multi-Sensor (MRMS) precipitation rate data (for Hurricane Harvey as a case study) show the ability of DeepCTC in identifying rainy clouds, including Deep Convective and Nimbostratus and their precipitation potential. We also use DeepCTC to evaluate the performance of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) product over different cloud types with respect to MRMS referenced at a half-hourly time scale for July 2018. Our analysis suggests that DeepCTC provides supplementary insights into the variability of cloud types to diagnose the weakness and strength of near real-time GEO-based precipitation retrievals. With additional training and testing, we believe DeepCTC has the potential to augment the widely used PERSIANN-CCS algorithm for estimating precipitation.

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“…Machine learning, and in particular neural networks, are emerging in many remote sensing applications for clouds (Mahajan and Fataniya, 2020). Application of neural networks has led to more use of geostationary satellite data in cloudrelated products such as cloud type classification or rainfall rate estimation which has been challenging in the past (Bankert et al, 2009;Gorooh et al, 2020;Hayatbini et al, 2019;Hirose et al, 2019). Especially using GOES-16, raining cloud is detected by Liu et al 2019 with a deep neural network model, and radar reflectivity is estimated by Hilburn et al 2020 using a model with convolutional layers.…”

Section: Minutes Over Contiguous Unitedmentioning

confidence: 99%

Applying machine learning methods to detect convection using GOES-16 ABI data

Lee

Kummerow

Ebert‐Uphoff

2020

Preprint

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Abstract. An ability to accurately detect convective regions is essential for initializing models for short term precipitation forecasts. Radar data are commonly used to detect convection, but radars that provide high temporal resolution data are mostly available over land and the quality of the data tends to degrade over mountainous regions. On the other hand, geostationary satellite data are available nearly anywhere and in near-real time. Current operational geostationary satellites, the Geostationary Operational Environmental Satellite-16 (GOES-16) and -17 provide high spatial and temporal resolution data, but only of cloud top properties. One-minute data, however, allow us to observe convection from visible and infrared data even without vertical information of the convective system. Existing detection algorithms using visible and infrared data look for static features of convective clouds such as overshooting top or lumpy cloud top surface, or cloud growth that occurs over periods of 30 minutes to an hour. This study represents a proof-of-concept that Artificial Intelligence (AI) is able, when given high spatial and temporal resolution data from GOES-16, to learn physical properties of convective clouds and automate the detection process. A neural network model with convolutional layers is proposed to identify convection from the high-temporal resolution GOES-16 data. The model takes five temporal images from channel 2 (0.65 μm) and 14 (11.2 μm) as inputs and produces a map of convective regions. In order to provide products comparable to the radar products, it is trained against Multi-Radar Multi-Sensor (MRMS), which is a radar-based product that uses rather sophisticated method to classify precipitation types. Two channels from GOES-16, each related to cloud optical depth (channel 2) and cloud top height (channel 14), are expected to best represent features of convective clouds: high reflectance, lumpy cloud top surface, and low cloud top temperature. The model has correctly learned those features of convective clouds, and resulted reasonably low false alarm ratio (FAR) and high probability of detection (POD). However, FAR and POD can vary depending on the threshold, and a proper threshold needs to be chosen based on the purpose.

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Conditional Generative Adversarial Networks (cGANs) for Near Real-Time Precipitation Estimation from Multispectral GOES-16 Satellite Imageries—PERSIANN-cGAN

Cited by 51 publications

References 35 publications

Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards

Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards

Deep Neural Network Cloud-Type Classification (DeepCTC) Model and Its Application in Evaluating PERSIANN-CCS

Applying machine learning methods to detect convection using GOES-16 ABI data

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