scholarly journals Use of Geostationary Super Rapid Scan Satellite Imagery by the Storm Prediction Center*

2016 ◽  
Vol 31 (2) ◽  
pp. 483-494 ◽  
Author(s):  
William E. Line ◽  
Timothy J. Schmit ◽  
Daniel T. Lindsey ◽  
Steven J. Goodman
Keyword(s):  
Temporal Resolution ◽  
Satellite Imagery ◽  
Rapid Change ◽  
Active Regions ◽  
Severe Weather ◽  
High Temporal Resolution ◽  
Convective Initiation ◽  
Rapid Scan ◽  
Storm Prediction ◽  
Insight Into

Abstract The Geostationary Operational Environmental Satellite-14 (GOES-14) Imager was operated by the National Oceanic and Atmospheric Administration (NOAA) in an experimental rapid scan 1-min mode during parts of 2012, 2013, 2014, and 2015. This scan mode, known as the Super Rapid Scan Operations for GOES-R (SRSOR), emulates the high-temporal-resolution sampling that will be provided by the Advanced Baseline Imager on the next-generation GOES-R series. NOAA/National Weather Service/Storm Prediction Center (SPC) forecasters utilized the 1-min imagery extensively in operations when available over convectively active regions. They found it provided them with unique insight into relevant features and processes before, during, and after convective initiation. This paper introduces how the SRSOR datasets from GOES-14 were used by SPC forecasters and how these data are likely to be applied when available operationally from GOES-R. Several animations, included as supplemental material, showcase the rapid change of severe weather–related phenomena observed during the 2014 and 2015 SRSOR campaigns from the GOES-14 Imager.

scholarly journals Examining Deep Convective Cloud Evolution Using Total Lightning, WSR-88D, and GOES-14 Super Rapid Scan Datasets*

2015 ◽  
Vol 30 (3) ◽  
pp. 571-590 ◽  
Author(s):  
Kristopher M. Bedka ◽  
Cecilia Wang ◽  
Ryan Rogers ◽  
Lawrence D. Carey ◽  
Wayne Feltz ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Convective Cloud ◽  
Severe Weather ◽  
High Temporal Resolution ◽  
Lightning Flash ◽  
Rapid Scan ◽  
Wind Events ◽  
Total Lightning ◽  
Cloud Tops ◽  
Anvil Cirrus

Abstract The Geostationary Operational Environmental Satellite-14 (GOES-14) Imager operated in 1-min Super Rapid Scan Operations for GOES-R (SRSOR) mode during summer and fall of 2012 to emulate the high temporal resolution sampling of the GOES-R Advanced Baseline Imager (ABI). The current GOES operational scan interval is 15–30 min, which is too coarse to capture details important for severe convective storm forecasting including 1) when indicators of a severe storm such as rapid cloud-top cooling, overshooting tops, and above-anvil cirrus plumes first appear; 2) how satellite-observed cloud tops truly evolve over time; and 3) how satellite cloud-top observations compare with radar and lightning observations at high temporal resolution. In this paper, SRSOR data, radar, and lightning observations are used to analyze five convective storms, four of which were severe, to address these uncertainties. GOES cloud-top cooling, increased lightning flash rates, and peak precipitation echo tops often preceded severe weather, signaling rapid intensification of the storm updraft. Near the time of several severe hail or damaging wind events, GOES cloud-top temperatures and radar echo tops were warming rapidly, which indicated variability in the storm updraft that could have allowed the hail and wind gusts to reach the surface. Above-anvil cirrus plumes were another prominent indicator of impending severe weather. Detailed analysis of storms throughout the 2012 SRSOR period indicates that 57% of the plume-producing storms were severe and 85% of plumes from severe storms appeared before a severe weather report with an average lead time of 18 min, 9 min earlier than what would be observed by GOES operational scanning.

scholarly journals The Above-Anvil Cirrus Plume: An Important Severe Weather Indicator in Visible and Infrared Satellite Imagery

2018 ◽  
Vol 33 (5) ◽  
pp. 1159-1181 ◽  
Author(s):  
Kristopher Bedka ◽  
Elisa M. Murillo ◽  
Cameron R. Homeyer ◽  
Benjamin Scarino ◽  
Haiden Mersiovsky
Keyword(s):  
Satellite Imagery ◽  
Wave Breaking ◽  
Lead Time ◽  
Doppler Radar ◽  
Severe Weather ◽  
Rapid Scan ◽  
The World ◽  
Total Lightning ◽  
Anvil Cirrus ◽  
Rapid Scanning

Abstract Intense tropopause-penetrating updrafts and gravity wave breaking generate cirrus plumes that reside above the primary anvil. These “above anvil cirrus plumes” (AACPs) exhibit unique temperature and reflectance patterns in satellite imagery, best recognized within 1-min “super rapid scan” observations. AACPs are often evident during severe weather outbreaks and, due to their importance, have been studied for 35+ years. Despite this research, there is uncertainty regarding why some storms produce AACPs but other nearby storms do not, exactly how severe are storms with AACPs, and how AACP identification can assist with severe weather warning. These uncertainties are addressed through analysis of severe weather reports, NOAA/National Weather Service (NWS) severe weather warnings, metrics of updraft cloud height, intensity, and rotation derived from Doppler radars, as well as ground-based total lightning observations for 4583 storms observed by GOES super rapid scanning, 405 of which produced an AACP. Datasets are accumulated throughout storm lifetimes through radar object tracking. It is found that 1) AACP storms generated 14 times the number of reports per storm compared to non-AACP storms; 2) AACPs appeared, on average, 31 min in advance of severe weather; 3) 73% of significant severe weather reports were produced by AACP storms; 4) AACP recognition can provide comparable warning lead time to that provided by a forecaster; and 5) the presence of an AACP can increase forecaster confidence that large hail will occur. Given that AACPs occur throughout the world, and most of the world is not observed by Doppler radar, AACP-based severe storm identification and warning would be extremely helpful for protecting lives and property.

Rapid Refresh Information of Significant Events: Preparing Users for the Next Generation of Geostationary Operational Satellites

2015 ◽  
Vol 96 (4) ◽  
pp. 561-576 ◽  
Author(s):  
Timothy J. Schmit ◽  
Steven J. Goodman ◽  
Mathew M. Gunshor ◽  
Justin Sieglaff ◽  
Andrew K. Heidinger ◽  
...  
Keyword(s):  
Rapid Change ◽  
Hurricane Sandy ◽  
High Temporal Resolution ◽  
Next Generation ◽  
Rapid Scan ◽  
Rim Fire ◽  
Ocean Prediction ◽  
The Many ◽  
Nearly Continuous ◽  
National Hurricane Center

Abstract The Geostationary Operational Environmental Satellite-14 (GOES-14) imager was operated by the National Oceanic and Atmospheric Administration (NOAA) in an experimental rapid scan 1-min mode during parts of the summers of 2012 and 2013. This scan mode, known as the super rapid scan operations for GOES-R (SRSOR), emulates the high-temporal-resolution sampling of the mesoscale region scanning of the Advanced Baseline Imager (ABI) on the next-generation GOES-R series. This paper both introduces these unique datasets and highlights future satellite imager capabilities. Many phenomena were observed from GOES-14, including fog, clouds, severe storms, fires and smoke (including the California Rim Fire), and several tropical cyclones. In 2012 over 6 days of SRSOR data of Hurricane Sandy were acquired. In 2013, the first two days of SRSOR in June observed the propagation and evolution of a mid-Atlantic derecho. The data from August 2013 were unique in that the GOES imager operated in nearly continuous 1-min mode; prior to this time, the 1-min data were interrupted every 3 h for full disk scans. Used in a number of NOAA test beds and operational centers, including NOAA’s Storm Prediction Center (SPC), the Aviation Weather Center (AWC), the Ocean Prediction Center (OPC), and the National Hurricane Center (NHC), these experimental data prepare users for the next-generation imager, which will be able to routinely acquire mesoscale (1,000 km × 1,000 km) images every 30 s (or two separate locations every minute). Several animations are included, showcasing the rapid change of the many phenomena observed during SRSOR from the GOES-14 imager.

scholarly journals Evolution of Severe and Nonsevere Convection Inferred from GOES-Derived Cloud Properties

2013 ◽  
Vol 52 (9) ◽  
pp. 2009-2023 ◽  
Author(s):  
John L. Cintineo ◽  
Michael J. Pavolonis ◽  
Justin M. Sieglaff ◽  
Andrew K. Heidinger
Keyword(s):  
Temporal Trends ◽  
Weather Radar ◽  
Severe Weather ◽  
False Alarms ◽  
High Temporal Resolution ◽  
Convective Initiation ◽  
Temporal Sampling ◽  
Cloud Properties ◽  
Storm Evolution ◽  
Echo Threshold

AbstractGeostationary satellites [e.g., the Geostationary Operational Environmental Satellite (GOES)] provide high temporal resolution of cloud development and motion, which is essential to the study of many mesoscale phenomena, including thunderstorms. Initial research on thunderstorm growth with geostationary imagery focused on the mature stages of storm evolution, whereas more recent research on satellite-observed storm growth has concentrated on convective initiation, often defined arbitrarily as the presence of a given radar echo threshold. This paper seeks to link the temporal trends in robust GOES-derived cloud properties with the future occurrence of severe-weather radar signatures during the development phase of thunderstorm evolution, which includes convective initiation. Two classes of storms (severe and nonsevere) are identified and tracked over time in satellite imagery, providing distributions of satellite growth rates for each class. The relationship between the temporal trends in satellite-derived cloud properties and Next Generation Weather Radar (NEXRAD)-derived storm attributes is used to show that this satellite-based approach can potentially be used to extend severe-weather-warning lead times (with respect to radar-derived signatures), without a substantial increase in false alarms. In addition, the effect of varying temporal sampling is investigated on several storms during a period of GOES super-rapid-scan operations (SRSOR). It is found that, from a satellite perspective, storms evolve significantly on time scales shorter than the current GOES operational scan strategies.

scholarly journals An automatic contrail tracking algorithm

2010 ◽  
Vol 3 (4) ◽  
pp. 1089-1101 ◽  
Author(s):  
M. Vazquez-Navarro ◽  
H. Mannstein ◽  
B. Mayer
Keyword(s):  
Spatial Resolution ◽  
Temporal Resolution ◽  
High Spatial Resolution ◽  
High Temporal Resolution ◽  
Cirrus Cloud ◽  
Linear Stage ◽  
Geostationary Satellites ◽  
Rapid Scan ◽  
Temporal And Spatial ◽  
Resolution Data

Abstract. A method designed to track the life cycle of contrail-cirrus using satellite data with high temporal and spatial resolution, from its formation to the final dissolution of the aviation-induced cirrus cloud is presented. The method follows the evolution of contrails from their linear stage until they are undistinguishable from natural cirrus clouds. Therefore, the study of the effect of aircraft-induced clouds in the atmosphere is no longer restricted to linear contrails and can include contrail-cirrus. The method takes advantage of the high spatial resolution of polar orbiting satellites and the high temporal resolution of geostationary satellites to identify the pixels that belong to an aviation induced cloud. The high spatial resolution data of the MODIS sensor is used for contrail detection, and the high temporal resolution of the SEVIRI sensor in the Rapid Scan mode is used for contrail tracking. An example is included in which the method is applied to the study of a long lived contrail over the bay of Biscay.

scholarly journals An automatic contrail tracking algorithm

2010 ◽  
Vol 3 (2) ◽  
pp. 1439-1494
Author(s):  
M. Vazquez-Navarro ◽  
H. Mannstein ◽  
B. Mayer
Keyword(s):  
Spatial Resolution ◽  
Temporal Resolution ◽  
High Spatial Resolution ◽  
High Temporal Resolution ◽  
Cirrus Cloud ◽  
Linear Stage ◽  
Geostationary Satellites ◽  
Rapid Scan ◽  
Temporal And Spatial ◽  
Resolution Data

Abstract. A method designed to track the life cycle of contrail-cirrus using satellite data with high temporal and spatial resolution, from its formation to the final dissolution of the aviation-induced cirrus cloud is presented. The method follows the evolution of contrails from their linear stage until they are undistinguishable from natural cirrus clouds. Therefore, the study of the effect of aircraft-induced clouds in the atmosphere is no longer restricted to linear contrails and can include contrail-cirrus. The method takes advantage of the high spatial resolution of polar orbiting satellites and the high temporal resolution of geostationary satellites to identify the pixels that belong to an aviation induced cloud. The high spatial resolution data of the MODIS sensor is used for contrail detection, and the high temporal resolution of the SEVIRI sensor in the Rapid Scan mode is used for contrail tracking. An example is included in which the method is applied to the study of a long lived contrail over the bay of Biscay.

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.

Sign in / Sign up

Export Citation Format

Share Document