Evaluating the utility of multispectral information in delineating the areal extent of precipitation

Data from geosynchronous Earth-orbiting (GEO) satellites equipped with visible (VIS) and infrared (IR) scanners are commonly used in rain retrieval algorithms. These algorithms benefit from the high spatial and temporal resolution of GEO observations, either in stand-alone mode or in combination with higher-quality but less frequent microwave observations from low Earth-orbiting (LEO) satellites. In this paper, a neural network-based framework is presented to evaluate the utility of multispectral information in improving rain/no-rain (R/NR) detection. The algorithm uses the powerful classification features of the self-organizing feature map (SOFM), along with probability matching techniques to map single- or multispectral input space into R/NR maps. The framework was tested and validated using the 31 possible combinations of the five Geostationary Operational Environmental Satellite 12 (GOES-12) channels. An algorithm training and validation study was conducted over the conterminous United States during June-August 2006. The results indicate that during daytime, the visible channel (0.65 μm) can yield significant improvements in R/NR detection capabilities, especially when combined with any of the other four GOES-12 channels. Similarly, for nighttime detection the combination of two IR channels - particularly channels 3 (6.5 μm) and 4 (10.7 μm)-resulted in significant performance gain over any single IR channel. In both cases, however, using more than two channels resulted only in marginal improvements over two-channel combinations. Detailed examination of event-based images indicate that the proposed algorithm is capable of extracting information useful to screen no-rain pixels associated with cold, thin clouds and identifying rain areas under warm but rainy clouds. Both cases have been problematic areas for IR-only algorithms. © 2009 American Meteorological Society

PERSIANN-MSA: A precipitation estimation method from satellite-based multispectral analysis

Visible and infrared data obtained from instruments onboard geostationary satellites have been extensively used for monitoring clouds and their evolution. The Advanced Baseline Imager (ABI) that will be launched onboard the Geostationary Operational Environmental Satellite-R (GOES-R) series in the near future will offer a larger range of spectral bands; hence, it will provide observations of cloud and rain systems at even finer spatial, temporal, and spectral resolutions than are possible with the current GOES. In this paper, a new method called Precipitation Estimation from Remotely Sensed information using Artificial Neural Networks-Multispectral Analysis (PERSIANN-MSA) is proposed to evaluate the effect of using multispectral imagery on precipitation estimation. The proposed approach uses a self-organizing feature map (SOFM) to classify multidimensional input information, extracted from each grid box and corresponding textural features of multispectral bands. In addition, principal component analysis (PCA) is used to reduce the dimensionality to a few independent input features while preserving most of the variations of all input information. The above method is applied to estimate rainfall using multiple channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite. In comparison to the use of a single thermal infrared channel, the analysis shows that using multispectral data has the potential to improve rain detection and estimation skills with an average of more than 50% gain in equitable threat score for rain/no-rain detection, and more than 20% gain in correlation coefficient associated with rain-rate estimation. © 2009 American Meteorological Society

Advancing the remote sensing of precipitation

Satellite-based global precipitation data has addressed the limitations of rain gauges and weather radar systems in forecasting applications and for weather and climate studies. Inspite of this ability, a number of issues that require the development of advanced concepts to address key challenges in satellite-based observations of precipitation were identified during the Advanced Concepts Workshop on Remote Sensing of Precipitation at Multiple Scales at the University of California. These include quantification of uncertainties of individual sensors and their propagation into multisensor products warrants a great deal of research. The development of metrics for validation and uncertainty analysis are of great importance. Bias removal, particularly probability distribution function (PDF)-based adjustment, deserves more in-depth research. Development of a near-real-time probabilistic uncertainty model for satellitebased precipitation estimates is highly desirable

WCDRR and the CEOS activities on disaters

Agencies from CEOS (Committee on Earth Observation Satellites) have traditionally focused their efforts on the response phase. Rapid urbanization and increased severity of weather events has led to growing economic and human losses from disasters, requiring international organisations to act now in all disaster risk management (DRM) phases, especially through improved disaster risk reduction policies and programmes. As part of this effort, CEOS agencies have initiated a series of actions aimed at fostering the use of Earth observation (EO) data to support disaster risk reduction and at raising the awareness of policy and decision-makers and major stakeholders of the benefits of using satellite EO in all phases of DRM. CEOS is developing a long-term vision for sustainable application of satellite EO to all phases of DRM. CEOS is collaborating with regional representatives of the DRM user community, on a multi-hazard project involving three thematic pilots (floods, seismic hazards and volcanoes) and a Recovery Observatory that supports resilient recovery from one major disaster. These pilot activities are meant as trail blazers that demonstrate the potential offered by satellite EO for comprehensive DRM. In the framework of the 2015 3rd World Conference on Disaster Risk Reduction (WCDRR), the CEOS space agencies intend to partner with major stakeholders, including UN organizations, the Group on Earth Observations (GEO), international relief agencies, leading development banks, and leading regional DRM organisations, to define and implement a 15-year plan of actions (2015- 2030) that responds to high-level Post-2015 Framework for Disaster Risk Reduction priorities. This plan of action will take into account lessons learned from the CEOS pilot activities

Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies

Background Quality assurance (QA) and quality control (QC) are two quality management processes that are integral to the success of metabolomics including their application for the acquisition of high quality data in any high-throughput analytical chemistry laboratory. QA defines all the planned and systematic activities implemented before samples are collected, to provide confidence that a subsequent analytical process will fulfil predetermined requirements for quality. QC can be defined as the operational techniques and activities used to measure and report these quality requirements after data acquisition. Aim of review This tutorial review will guide the reader through the use of system suitability and QC samples, why these samples should be applied and how the quality of data can be reported. Key scientific concepts of review System suitability samples are applied to assess the operation and lack of contamination of the analytical platform prior to sample analysis. Isotopically-labelled internal standards are applied to assess system stability for each sample analysed. Pooled QC samples are applied to condition the analytical platform, perform intra-study reproducibility measurements (QC) and to correct mathematically for systematic errors. Standard reference materials and long-term reference QC samples are applied for inter-study and inter-laboratory assessment of data

High Oxygen Does Not Increase Reperfusion Injury Assessed with Lipid Peroxidation Biomarkers after Cardiac Arrest: A Post Hoc Analysis of the COMACARE Trial

The products of polyunsaturated fatty acid peroxidation are considered reliable biomarkers of oxidative injury in vivo. We investigated ischemia-reperfusion-related oxidative injury by determining the levels of lipid peroxidation biomarkers (isoprostane, isofuran, neuroprostane, and neurofuran) after cardiac arrest and tested the associations between the biomarkers and different arterial oxygen tensions (PaO2). We utilized blood samples collected during the COMACARE trial (NCT02698917). In the trial, 123 patients resuscitated from out-of-hospital cardiac arrest were treated with a 10–15 kPa or 20–25 kPa PaO2 target during the initial 36 h in the intensive care unit. We measured the biomarker levels at admission, and 24, 48, and 72 h thereafter. We compared biomarker levels in the intervention groups and in groups that differed in oxygen exposure prior to randomization. Blood samples for biomarker determination were available for 112 patients. All four biomarker levels peaked at 24 h; the increase appeared greater in younger patients and in patients without bystander-initiated life support. No association between the lipid peroxidation biomarkers and oxygen exposure either before or after randomization was found. Increases in the biomarker levels during the first 24 h in intensive care suggest continuing oxidative stress, but the clinical relevance of this remains unresolved

High Oxygen Does Not Increase Reperfusion Injury Assessed with Lipid Peroxidation Biomarkers after Cardiac Arrest: A Post Hoc Analysis of the COMACARE Trial

The products of polyunsaturated fatty acid peroxidation are considered reliable biomarkers of oxidative injury in vivo. We investigated ischemia-reperfusion-related oxidative injury by determining the levels of lipid peroxidation biomarkers (isoprostane, isofuran, neuroprostane, and neurofuran) after cardiac arrest and tested the associations between the biomarkers and different arterial oxygen tensions (PaO2). We utilized blood samples collected during the COMACARE trial (NCT02698917). In the trial, 123 patients resuscitated from out-of-hospital cardiac arrest were treated with a 10–15 kPa or 20–25 kPa PaO2 target during the initial 36 h in the intensive care unit. We measured the biomarker levels at admission, and 24, 48, and 72 h thereafter. We compared biomarker levels in the intervention groups and in groups that differed in oxygen exposure prior to randomization. Blood samples for biomarker determination were available for 112 patients. All four biomarker levels peaked at 24 h; the increase appeared greater in younger patients and in patients without bystander-initiated life support. No association between the lipid peroxidation biomarkers and oxygen exposure either before or after randomization was found. Increases in the biomarker levels during the first 24 h in intensive care suggest continuing oxidative stress, but the clinical relevance of this remains unresolved

Metabolomics 2022 workshop report: state of QA/QC best practices in LC-MS-based untargeted metabolomics, informed through mQACC community engagement initiatives.

IntroductionThe Metabolomics Quality Assurance and Quality Control Consortium (mQACC) organized a workshop during the Metabolomics 2022 conference.ObjectivesThe goal of the workshop was to disseminate recent findings from mQACC community-engagement efforts and to solicit feedback about a living guidance document of QA/QC best practices for untargeted LC-MS metabolomics.MethodsFour QC-related topics were presented.ResultsDuring the discussion, participants expressed the need for detailed guidance on a broad range of QA/QC-related topics accompanied by use-cases.ConclusionsOngoing efforts will continue to identify, catalog, harmonize, and disseminate QA/QC best practices, including outreach activities, to establish and continually update QA/QC guidelines

Investigation of potential cognition factors correlated to fire evacuation

The design of a navigation system to support indoor fire evacuation depends not only on speed but also a relatively thorough consideration of the cognition factors. This study has investigated potential cognition factors which can affect the human behaviours and decision making during fire evacuation by taking a survey among indoor occupants in age of 20s under designed virtual scenarios. It mainly focuses on two aspects of Fire Responses Performances (FRP), i.e. indoor familiarity (spatial cognition) and psychological stress (situ-ated cognition). The collected results have shown that these cognition factors can be affected by gender and user height and they are correlated with each other in certain ways. It has also investigated users‟ attitudes to the navigation services under risky and non-risky conditions. The collected answers are also found to be correlated with the selected FRP factors. These findings may help to further design of personalized indoor navigation support for fire evacuation

Ecology of Juvenile Salmon in Shallow Tidal Freshwater Habitats of the Lower Columbia River, 2007?2010

The TFM study was designed to investigate the ecology and early life history of juvenile salmonids within shallow (<5 m) tidal freshwater habitats of the LCRE. We started collecting field data in June 2007. Since then, monthly sampling has occurred in the vicinity of the Sandy River delta (rkm 192–208) and at other sites and times in lower river reaches of tidal freshwater (rkm 110 to 141). This report provides a comprehensive synthesis of data covering the field period from June 2007 through April 2010