The Meteosat Third Generation (MTG) Program is a EUMETSAT geostationary satellite mission. The satellites are developed and procured by the European Space Agency (ESA). It will ensure the future continuity with, and enhancement of, operational meteorological (and climate) data from Geostationary Orbit as currently provided by the Meteosat Second Generation (MSG) system. The MTG satellites series is composed of 4 MTG-I (Horizontal 2D Imagery) and 2 MTG-S (Horizontal and Vertical Sounding 3D) to bring to the meteorological community a continuous Imagery and Sounding capabilities with high spatial, spectral and temporal resolution observations and geophysical parameters of the Earth based on state-of-the-art sensors.This invited paper will address the impact of the geostationary sounding and what a game changer it will be for weather forecasting.

Meteosat Third Generation (MTG) is a joint programme of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and the European Space Agency (ESA). Its purpose is to ensure the continuity and enhance the quality of geostationary meteorological observations currently provided by its predecessor, Meteosat Second Generation (MSG). The MTG space segment, developed under ESA leadership, consists of two kinds of geostationary satellites: an imager (MTG-I) and sounder (MTG-S) variant. The first MTG-I satellite was launched on December 13, 2022, with start of operational service expected in Q2 2024. The first MTG-S satellite is in the final stages of testing, and currently planned to launch by Q2 2025. Differently from MSG and MTG-I, the principal payload of MTG-S (Infrared Sounder; IRS) is an imaging Fourier transform spectrometer. This paper presents MTG-S and the IRS instrument, with a particular focus on the current state of development and testing.

OBJECTIVE: This study aimed to develop a novel, regulatory-compliant approach for openly exposing integrated clinical and environmental exposures data: the Integrated Clinical and Environmental Exposures Service (ICEES). MATERIALS AND METHODS: The driving clinical use case for research and development of ICEES was asthma, which is a common disease influenced by hundreds of genes and a plethora of environmental exposures, including exposures to airborne pollutants. We developed a pipeline for integrating clinical data on patients with asthma-like conditions with data on environmental exposures derived from multiple public data sources. The data were integrated at the patient and visit level and used to create de-identified, binned, "integrated feature tables," which were then placed behind an OpenAPI. RESULTS: Our preliminary evaluation results demonstrate a relationship between exposure to high levels of particulate matter ≤2.5 µm in diameter (PM2.5) and the frequency of emergency department or inpatient visits for respiratory issues. For example, 16.73% of patients with average daily exposure to PM2.5 >9.62 µg/m3 experienced 2 or more emergency department or inpatient visits for respiratory issues in year 2010 compared with 7.93% of patients with lower exposures (n = 23 093). DISCUSSION: The results validated our overall approach for openly exposing and sharing integrated clinical and environmental exposures data. We plan to iteratively refine and expand ICEES by including additional years of data, feature variables, and disease cohorts. CONCLUSIONS: We believe that ICEES will serve as a regulatory-compliant model and approach for promoting open access to and sharing of integrated clinical and environmental exposures data.

This special communication describes activities, products, and lessons learned from a recent hackathon that was funded by the National Center for Advancing Translational Sciences via the Biomedical Data Translator program ('Translator'). Specifically, Translator team members self-organized and worked together to conceptualize and execute, over a five-day period, a multi-institutional clinical research study that aimed to examine, using open clinical data sources, relationships between sex, obesity, diabetes, and exposure to airborne fine particulate matter among patients with severe asthma. The goal was to develop a proof of concept that this new model of collaboration and data sharing could effectively produce meaningful scientific results and generate new scientific hypotheses. Three Translator Clinical Knowledge Sources, each of which provides open access (via Application Programming Interfaces) to data derived from the electronic health record systems of major academic institutions, served as the source of study data. Jupyter Python notebooks, shared in GitHub repositories, were used to call the knowledge sources and analyze and integrate the results. The results replicated established or suspected relationships between sex, obesity, diabetes, exposure to airborne fine particulate matter, and severe asthma. In addition, the results demonstrated specific differences across the three Translator Clinical Knowledge Sources, suggesting cohort- and/or environment-specific factors related to the services themselves or the catchment area from which each service derives patient data. Collectively, this special communication demonstrates the power and utility of intense, team-oriented hackathons and offers general technical, organizational, and scientific lessons learned.