scholarly journals Eruptive Styles Recognition Using High Temporal Resolution Geostationary Infrared Satellite Data

2019 ◽  
Vol 11 (6) ◽  
pp. 669 ◽  
Author(s):  
Valerio Lombardo ◽  
Stefano Corradini ◽  
Massimo Musacchio ◽  
Malvina Silvestri ◽  
Jacopo Taddeucci
Keyword(s):  
Temporal Resolution ◽  
Hot Spot ◽  
High Temporal Resolution ◽  
Spot Detection ◽  
Infrared Imager ◽  
Mt Etna ◽  
Flow Through ◽  
Radiance Distribution ◽  
Strombolian Explosions ◽  
Infrared Data

The high temporal resolution of the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument aboard Meteosat Second Generation (MSG) provides the opportunity to investigate eruptive processes and discriminate different styles of volcanic activity. To this goal, a new detection method based on the wavelet transform of SEVIRI infrared data is proposed. A statistical analysis is performed on wavelet smoothed data derived from SEVIRI Mid-Infrared( MIR) radiances collected from 2011 to 2017 on Mt Etna (Italy) volcano. Time-series analysis of the kurtosis of the radiance distribution allows for reliable hot-spot detection and precise timing of the start and end of eruptive events. Combined kurtosis and gradient trends allow for discrimination of the different activity styles of the volcano, from effusive lava flow, through Strombolian explosions, to paroxysmal fountaining. The same data also allow for the prediction, at the onset of an eruption, of what will be its dominant eruptive style at later stages. The results obtained have been validated against ground-based and literature data.

Automated volcanic hot-spot detection based on FY-4A/AGRI infrared data

2019 ◽  
Vol 41 (6) ◽  
pp. 2410-2438 ◽  
Author(s):  
S. S. Chu ◽  
L. Zhu ◽  
H. F. Sun ◽  
Q. W. Li ◽  
X. R. Zhang ◽  
...  
Keyword(s):  
Hot Spot ◽  
Spot Detection ◽  
Infrared Data

scholarly journals Hot-spot detection and characterization of strombolian activity from MODIS infrared data

2014 ◽  
Vol 35 (9) ◽  
pp. 3403-3426 ◽  
Author(s):  
D. Coppola ◽  
M. Laiolo ◽  
D. Delle Donne ◽  
M. Ripepe ◽  
C. Cigolini
Keyword(s):  
Hot Spot ◽  
Strombolian Activity ◽  
Spot Detection ◽  
Infrared Data

scholarly journals Kalman filter physical retrieval of geophysical parameters from high temporal resolution geostationary infrared radiances: the case of surface emissivity and temperature

2013 ◽  
Vol 6 (4) ◽  
pp. 6873-6933 ◽  
Author(s):  
G. Masiello ◽  
C. Serio ◽  
I. De Feis ◽  
M. Amoroso ◽  
S. Venafra ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Temporal Resolution ◽  
High Temporal Resolution ◽  
Target Area ◽  
In Situ Data ◽  
Infrared Imager ◽  
New Strategy ◽  
Filter Approach ◽  
Infrared Radiances

Abstract. The high temporal resolution of the data acquisition by geostationary satellites and their capability to resolve the diurnal cycle are a precious source of information which could be suitably used to retrieve geophysical parameters. Currently this information is for the most part considered as uncorrelated, both in space and time: each pixel is treated independently from its neighbors and the present events are not linked to past or future ones. In this paper we develop a Kalman filter approach to apply spatial and temporal constraints to estimate the geophysical parameters from radiance measurements made from geostationary platforms. We apply the new strategy to a particular case study, i.e. the retrieval of emissivity and surface temperature from SEVIRI (Spinning Enhanced Visible and InfraRed Imager) observations over a target area encompassing the Iberian Peninsula and Northwestern Africa. The retrievals are then compared with in situ data, and other similar satellite products.

High Temporal Resolution Monitoring of Fermentations Using an On-Line Amperometric Flow-Through Microdetector

2007 ◽  
Vol 19 (1) ◽  
pp. 43-48 ◽  
Author(s):  
Kinga Zór ◽  
Szilveszter Gáspár ◽  
Masatoshi Hashimoto ◽  
Hiroaki Suzuki ◽  
Elisabeth Csöregi
Keyword(s):  
Temporal Resolution ◽  
High Temporal Resolution ◽  
On Line ◽  
Flow Through

scholarly journals Applying machine learning methods to detect convection using Geostationary Operational Environmental Satellite-16 (GOES-16) advanced baseline imager (ABI) data

2021 ◽  
Vol 14 (4) ◽  
pp. 2699-2716
Author(s):  
Yoonjin Lee ◽  
Christian D. Kummerow ◽  
Imme Ebert-Uphoff
Keyword(s):  
Temporal Resolution ◽  
Radar Data ◽  
Probability Of Detection ◽  
High Temporal Resolution ◽  
Convective System ◽  
Mountainous Regions ◽  
Convective Clouds ◽  
Geostationary Operational Environmental Satellite ◽  
Resolution Data ◽  
Infrared Data

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 Satellite-17, provide high-spatial- and high-temporal-resolution data but only of cloud top properties; 1 min 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 min to an hour. This study represents a proof of concept that artificial intelligence (AI) is able, when given high-spatial- and high-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 a 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 in a 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.

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

2020 ◽  
Author(s):  
Yoonjin Lee ◽  
Christian D. Kummerow ◽  
Imme Ebert-Uphoff
Keyword(s):  
Temporal Resolution ◽  
Radar Data ◽  
Probability Of Detection ◽  
High Temporal Resolution ◽  
Convective System ◽  
Cloud Optical Depth ◽  
Mountainous Regions ◽  
Convective Clouds ◽  
Resolution Data ◽  
Infrared Data

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.

Multidetector-row Computed Tomography in the Assessment of Coronary Artery Disease – New Techniques and Insights

2010 ◽  
Vol 6 (2) ◽  
pp. 43 ◽  
Author(s):  
Andreas H Mahnken ◽  
Keyword(s):  
Computed Tomography ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Data Acquisition ◽  
Temporal Resolution ◽  
High Temporal Resolution ◽  
Multidetector Row ◽  
Ct Scanners ◽  
Artery Disease ◽  
Cardiac Mdct

Over the last decade, cardiac computed tomography (CT) technology has experienced revolutionary changes and gained broad clinical acceptance in the work-up of patients suffering from coronary artery disease (CAD). Since cardiac multidetector-row CT (MDCT) was introduced in 1998, acquisition time, number of detector rows and spatial and temporal resolution have improved tremendously. Current developments in cardiac CT are focusing on low-dose cardiac scanning at ultra-high temporal resolution. Technically, there are two major approaches to achieving these goals: rapid data acquisition using dual-source CT scanners with high temporal resolution or volumetric data acquisition with 256/320-slice CT scanners. While each approach has specific advantages and disadvantages, both technologies foster the extension of cardiac MDCT beyond morphological imaging towards the functional assessment of CAD. This article examines current trends in the development of cardiac MDCT.

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