Artificial intelligence systems for rainy areas detection and convective cells' delineation for the south shore of Mediterranean Sea during day and nighttime using MSG satellite images

Tebbi, Mohsene Abdelfettah; Haddad, Boualem

doi:10.1016/j.atmosres.2016.04.013

Cited by 26 publications

(10 citation statements)

References 44 publications

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“…For instance, Hameg et al [19] used a naive Bayes classifier and northern Algeria weather radar observations to find the relationship between predefined spectral parameters from MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager) for convective clouds differentiation. Tebbi and Haddad [20] focused on the combination of two machine learning models and Lazri and Ameur [21] utilized an ensemble of learning algorithms to identify convective or stratiform precipitation over a small Mediterranean region. It should be pointed out that the computational expenses and complexity of the implementation of these models are challenging.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…of the huge application fields of artificial intelligence [2]- [6]. Because of its intuitive, accurate, timely and rich content, it is widely used in many occasions, but the huge video data brings the difficulty of video analysis which is hard to overcome by manpower.…”

Section: Introductionmentioning

confidence: 99%

Face Detection in Security Monitoring Based on Artificial Intelligence Video Retrieval Technology

2020

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With the rapid development of video monitoring, the massive information of the monitoring image has far exceeded the effective processing range of human resources. Intelligent video retrieval technology has become an increasingly indispensable part of video monitoring system. Intelligent video retrieval technology integrates video processing, computer vision and artificial intelligence, which greatly improves the efficiency of monitoring and the accuracy and linkage of monitoring system. Face recognition and other emerging technologies continue to rise and apply to the security monitoring system. Based on deep learning theory and face detection neural network, this paper proposes a video oriented cascaded intelligent face detection algorithm, which builds deep learning network by cascading multiple features, from edge features, contour features, local features to semantic features, and advances layer by layer. According to the last semantic features, the information of the input data is obtained to accurately realize the face detection under the non ideal condition. Simulation results show that the algorithm has good detection performance for single face and multi face images, and has strong robustness for rotating face. At the same time, the algorithm is fast and can basically meet the requirements of real-time face detection. INDEX TERMS Artificial intelligence, video retrieval, security monitoring, face detection.

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“…Rainfall estimates using other machine-learning methods have been attempted (Capacci and Conway 2005); however, the RF method can process a large number of inputs with substantially lower calculation costs compared to conventional methods. Tebbi and Haddad (2016) investigated the cloud classification potential using high-resolution, MSG SEVIRI images from northern Algeria. They estimated rainfall using neural networks with MSG and rain gauge observations as training data.…”

Section: Introductionmentioning

confidence: 99%

High Temporal Rainfall Estimations from Himawari-8 Multiband Observations Using the Random-Forest Machine-Learning Method

Hirose

Shige

Yamamoto

et al. 2019

Journal of the Meteorological Society of Japan

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We introduce a novel rainfall-estimating algorithm with a random-forest machine-learning method only from Infrared (IR) observations. As training data, we use nine-band brightness temperature (BT) observations, obtained from IR radiometers, on the third-generation geostationary meteorological satellite (GEO) Himawari-8 and precipitation radar observations from the Global Precipitation Measurement core observatory. The Himawari-8 Rainfall-estimating Algorithm (HRA) enables us to estimate the rain rate with high spatial and temporal resolution (i.e., 0.04° every 10 min), covering the entire Himawari-8 observation area (i.e., 85°E -155°W, 60°S -60°N) based solely on satellite observations. We conducted a case analysis of the Kanto-Tohoku heavy rainfall event to compare HRA rainfall estimates with the near-real-time version of the Global Satellite Mapping of Precipitation (GSMaP_NRT), which combines global rainfall estimation products with microwave and IR BT observations obtained from satellites. In this case, HRA could estimate heavy rainfall from warm-type precipitating clouds. The GSMaP_NRT could not estimate heavy rainfall when microwave satellites were unavailable. Further, a statistical analysis showed that the warm-type heavy rain seen in the Asian monsoon region occurred frequently when there were small BT differences between the 6.9-μm and 7.3-μm of water vapor (WV) bands (ΔT6.9 -7.3). Himawari-8 is the first GEO to include the 6.9-μm band, which is sensitive to middle-to-upper tropospheric WV. An analysis of the WV multibands' weighting functions revealed that ΔT6.9 -7.3 became small when the WV amount in the middle-to-upper troposphere was small and there were optically thick clouds with the cloud top near the middle troposphere. Statistical analyses during boreal summer (indicate that HRA has higher estimation accuracy for heavy rain from warm-type precipitating clouds than a conventional rain estimation method based on only one IR band.

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Artificial intelligence systems for rainy areas detection and convective cells' delineation for the south shore of Mediterranean Sea during day and nighttime using MSG satellite images

Cited by 26 publications

References 44 publications

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

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

Face Detection in Security Monitoring Based on Artificial Intelligence Video Retrieval Technology

High Temporal Rainfall Estimations from Himawari-8 Multiband Observations Using the Random-Forest Machine-Learning Method

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