A continuous vertically resolved ozone dataset from the fusion of chemistry climate models with observations using a Bayesian neural network

Continuous historic datasets of vertically resolved stratospheric ozone, support the case for ozone recovery, are necessary for the running of offline models and increase understanding of the impacts of ozone on the wider atmospheric system. Vertically resolved ozone datasets are typically constructed from multiple satellite, sonde and ground-based measurements that do not provide continuous coverage. As a result, several methods have been used to infill these gaps, most commonly relying on regression against observed time series. However, these existing methods either provide low accuracy infilling especially over polar regions, unphysical extrapolation, or an incomplete estimation of uncertainty. To address these methodological shortcomings we used and further developed an infilling framework that fuses observations with output from an ensemble of chemistry-climate models within a Bayesian neural network. We used this deep learning framework to produce a continuous record of vertically resolved ozone with uncertainty estimates. Under rigorous testing the infilling framework extrapolated and interpolated skillfully and maintained realistic interannual variability due to the inclusion of physically and chemically realistic models. This framework and the ozone dataset it produced, enables a more thorough investigation of vertically resolved trends throughout the atmosphere.

An efficient algorithm for solving the constellation-to-ground region coverage problem based on longitude strip division

Aiming at the constellation-to-ground region coverage problem, an efficient solution method called the longitude strip method is proposed. By dividing the ground region into several longitude strips, the latitude range of each strip is computed according to the region. Similarly, concerning the coverage area of the satellite, the latitude range of each strip is also calculated. On this basis, the coverage rate can be obtained by comprehensive statistics. Additionally, the upper and lower bounds for problems of continuous coverage and accumulative coverage are solved. Numerical simulation experiments show the proposed algorithm has higher accuracy and it is also efficient in resolving the ground area with arbitrary shapes.

USING MODIS AEROSOL OPTICAL DEPTH TO PREDICT PM10 OVER AL AIN REGION, UAE

PM10 concentrations are essential for assessing air quality in arid areas. They are usually measured at air quality monitoring stations. The limited number of monitoring stations can make difficult to study significantly the spatial variability of PM10 over relatively large areas. This study aimed at evaluating the use of Aerosol Optical Depth derived from satellite data to estimate PM10 concentrations. The continuous coverage offered by remote sensing data helps to address the limitation encountered with the spatial distribution of relevant monitoring stations. In the current study we compared MODIS AOD at 550 nm included in MCD19A2 and we established a regression equation between AOD and PM10. The use of daily AOD at 1 km resolution helped establish regression with acceptable correlation coefficient. The regression equation is then used to create daily maps of estimated PM10 concentrations over the study area and helped assessing their variability.

CDIP observations of recent extreme wave conditions on U.S. coasts

The Coastal Data Information Program (CDIP) maintains wave gauge stations for continuous coverage, with precision instruments and dedicated telemetry and dissemination infrastructure. Decades of this persistent, quality-controlled wave monitoring effort has provided the data required to generate metrics for wave climate at coastal locations across the United States and identify and characterize extreme wave events. During the extremely active 2020 North Atlantic hurricane season, the CDIP East Coast array recorded significantly elevated wave conditions generated by no fewer than 15 named storms. In California, meanwhile, long-term monitoring stations have measured new all-time maximum wave heights during recent storm events. Complete quality-controlled directional spectra and displacement data sets, as well as sea surface temperature and surface current data from the wave buoys, are publicly available at http://cdip.ucsd.edu.

Impacts of a large and decentralized telepathology network in Canada

Background: Telepathology is one of the fast growing segment of the telemedicine field and Canada is recognized as a world leader in this particular domain. . Introduction: We report a benefits evaluation study of a decentralized telepathology network deployed in Eastern Quebec. The project involves 18 hospitals, making it one of the largest telepathology networks in the world. Materials and Methods: We first conducted 43 semi-structured interviews with telepathology users and managers. Hard data on the impacts of the telepathology network (e.g. the number of service disruptions, the average time between initial diagnosis and surgery) was also extracted and analyzed, where available. Results: Users found the system to be easy to use and the quality of the virtual slides and images was also considered satisfactory by pathologists. A key objective was to provide continuous coverage of intraoperative consultations in hospitals with no pathologist. Our findings show that no service disruptions were recorded in the se sites. Surgeons agreed that the use of telepathology helped avoid second surgeries and improved accessibility to care services. Telepathology was also perceived by respondents as having positive impacts on remote hospitals’ ability to retain and recruit specialists. Discussion: The observed benefits should not leave the impression that implementing telepathology is a trivial matter. Indeed, many technical, human and organizational challenges may be encountered. Conclusions: Telepathology can be highly useful in regional hospitals that do not have a pathologist on site. More research is needed to investigate the challenges and benefits associated with this growing form of telemedicine.

Impacts of a large and decentralized telepathology network in Canada

Background: Telepathology is one of the fast growing segment of the telemedicine field and Canada is recognized as a world leader in this particular domain. . Introduction: We report a benefits evaluation study of a decentralized telepathology network deployed in Eastern Quebec. The project involves 18 hospitals, making it one of the largest telepathology networks in the world. Materials and Methods: We first conducted 43 semi-structured interviews with telepathology users and managers. Hard data on the impacts of the telepathology network (e.g. the number of service disruptions, the average time between initial diagnosis and surgery) was also extracted and analyzed, where available. Results: Users found the system to be easy to use and the quality of the virtual slides and images was also considered satisfactory by pathologists. A key objective was to provide continuous coverage of intraoperative consultations in hospitals with no pathologist. Our findings show that no service disruptions were recorded in the se sites. Surgeons agreed that the use of telepathology helped avoid second surgeries and improved accessibility to care services. Telepathology was also perceived by respondents as having positive impacts on remote hospitals’ ability to retain and recruit specialists. Discussion: The observed benefits should not leave the impression that implementing telepathology is a trivial matter. Indeed, many technical, human and organizational challenges may be encountered. Conclusions: Telepathology can be highly useful in regional hospitals that do not have a pathologist on site. More research is needed to investigate the challenges and benefits associated with this growing form of telemedicine.