scholarly journals Wildfire detection notifications for impact-based decision support services in Oklahoma using geostationary super rapid scan satellite imagery

2016 ◽  
Vol 04 (14) ◽  
pp. 182-191 ◽  
Author(s):  
Todd Lindley ◽  
Aaron Anderson ◽  
Vivek Mahale ◽  
Thomas Curl ◽  
William Line ◽  
...  
Keyword(s):  
Decision Support ◽  
Satellite Imagery ◽  
Support Services ◽  
Rapid Scan

Mapping invasive Phragmites australis in the coastal Great Lakes with ALOS PALSAR satellite imagery for decision support

2013 ◽  
Vol 39 ◽  
pp. 65-77 ◽  
Author(s):  
Laura L. Bourgeau-Chavez ◽  
Kurt P. Kowalski ◽  
Martha L. Carlson Mazur ◽  
Kirk A. Scarbrough ◽  
Richard B. Powell ◽  
...  
Keyword(s):  
Decision Support ◽  
Great Lakes ◽  
Phragmites Australis ◽  
Satellite Imagery ◽  
Alos Palsar

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.

A Case for Enterprise Interoperability in Healthcare IT

2014 ◽  
pp. 192-215 ◽  
Author(s):  
Mustafa Yuksel ◽  
Asuman Dogac ◽  
Cebrail Taskin ◽  
Anil Yalcinkaya
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Medical Devices ◽  
Support Services ◽  
Clinical Decision ◽  
Medical Data ◽  
Physiological Status ◽  
Healthcare Applications ◽  
It Systems ◽  
Healthcare It

The PHR systems need to be integrated with a wide variety of healthcare IT systems including EHRs, electronic medical devices, and clinical decision support services to get their full benefit. It is not possible to sustain the integration of PHRs with other healthcare IT systems in a proprietary way; this integration has to be achieved by exploiting the promising interoperability standards and profiles. This chapter provides a survey and analysis of the interoperability standards and profiles that can be used to integrate PHRs with a variety of healthcare applications and medical data resources, including EHR systems to enable access of a patient to his own medical data generated by healthcare professionals; personal medical devices to obtain the patient’s instant physiological status; and the clinical decision support services for patient-physician shared decision making.

Benchmarking Optical/Thermal Satellite Imagery for Estimating Evapotranspiration and Soil Moisture in Decision Support Tools

2015 ◽  
Vol 52 (1) ◽  
pp. 89-119 ◽  
Author(s):  
Jan M.H. Hendrickx ◽  
Richard G. Allen ◽  
Al Brower ◽  
Aaron R. Byrd ◽  
Sung-ho Hong ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Decision Support ◽  
Satellite Imagery ◽  
Decision Support Tools ◽  
Support Tools

From Adverse Drug Event Detection to Prevention

2014 ◽  
Vol 53 (06) ◽  
pp. 482-492 ◽  
Author(s):  
P. McNair ◽  
V. Kilintzis ◽  
K. Skovhus Andersen ◽  
J. Niès ◽  
J.-C. Sarfati ◽  
...  
Keyword(s):  
Patient Safety ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Adverse Drug Event ◽  
Support Services ◽  
Medication Safety ◽  
Point Of Care ◽  
Clinical Decision ◽  
Substantial Contribution ◽  
Drug Event

Summary Background: Errors related to medication seriously affect patient safety and the quality of healthcare. It has been widely argued that various types of such errors may be prevented by introducing Clinical Decision Support Systems (CDSSs) at the point of care. Objectives: Although significant research has been conducted in the field, still medication safety is a crucial issue, while few research outcomes are mature enough to be considered for use in actual clinical settings. In this paper, we present a clinical decision support framework targeting medication safety with major focus on adverse drug event (ADE) prevention. Methods: The novelty of the framework lies in its design that approaches the problem holistically, i.e., starting from knowledge discovery to provide reliable numbers about ADEs per hospital or medical unit to describe their consequences and probable causes, and next employing the acquired knowledge for decision support services development and deployment. Major design features of the frame-work’s services are: a) their adaptation to the context of care (i.e. patient characteristics, place of care, and significance of ADEs), and b) their straightforward integration in the healthcare information technologies (IT) infrastructure thanks to the adoption of a service-oriented architecture (SOA) and relevant standards. Results: Our results illustrate the successful interoperability of the framework with two commercially available IT products, i.e., a Computerized Physician Order Entry (CPOE) and an Electronic Health Record (EHR) system, respectively, along with a Web prototype that is independent of existing health-care IT products. The conducted clinical validation with domain experts and test cases illustrates that the impact of the framework is expected to be major, with respect to patient safety, and towards introducing the CDSS functionality in practical use. Conclusions: This study illustrates an important potential for the applicability of the presented framework in delivering contextualized decision support services at the point of care and for making a substantial contribution towards ADE prevention. None-theless, further research is required in order to quantitatively and thoroughly assess its impact in medication safety.

scholarly journals Forecaster Evaluations of High Temporal Satellite Imagery for the GOES-R Era at the NWS Operations Proving Ground

2016 ◽  
Vol 31 (4) ◽  
pp. 1157-1177 ◽  
Author(s):  
Chad M. Gravelle ◽  
Kim J. Runk ◽  
Katie L. Crandall ◽  
Derrick W. Snyder
Keyword(s):  
Decision Making ◽  
Satellite Imagery ◽  
Support Services ◽  
Radar Data ◽  
Decision Making Process ◽  
Refresh Rate ◽  
Convective Initiation ◽  
Adverse Impacts ◽  
Operational Practices ◽  
Process Feedback

Abstract Between February and April of 2015, the National Weather Service (NWS) Operations Proving Ground (OPG) facilitated an evaluation of the usefulness of 1-min satellite imagery for NWS operations in the Geostationary Operational Environmental Satellite-R (GOES-R) series era. The overarching goal of the evaluation was to provide quantitative and qualitative guidance to NWS management, including the regional NWS Scientific Services division chiefs, on how satellite imagery with a refresh rate of 1 min impacts NWS forecaster decision-making. During the simulations, forecasters evaluated 1- and 5-min satellite imagery while completing tasks ranging from aviation forecasting and wildfire decision support services to monitoring where convective initiation would occur and integrating the imagery into the convective warning decision-making process. Feedback was gathered to assess if the satellite imagery had influence on forecaster decision-making, if the satellite imagery provided them with more confidence in making those decisions, if forecasters could assimilate the data into operational practices, and if there were adverse impacts on forecaster workload. Forecasters overwhelmingly were of the opinion that 1-min satellite imagery improved their ability and increased their confidence to make effective forecast and warning decisions. The majority of participants expressed that they were able to internally assimilate the imagery with ease. However, feedback gathered when forecasters were asked how useful and easy the imagery was to use in convective warning operations was mixed. Some forecasters expressed difficulty integrating both satellite imagery and radar data while issuing convective warnings. Others felt that with ample training and experience the imagery would be invaluable in warning operations.

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