Understanding atmospheric conditions is one of the fundamental steps in creating accurate weather forecasts. However, the relatively limited number of ground-based measuring instruments significantly hampers this task, leading to a reliance on weather models that are not always precise. GNSS satellite techniques are increasingly being used as an alternative to classical meteorological instruments, due to the vulnerability of electromagnetic waves to varying atmospheric parameters, including water vapor. One of the most widely used to ols for monitoring the state of the atmosphere with GNSS observations is GNSS tomography.
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From 16th to 18th September 2024 was held the first edition of the International Summer School, organized jointly by the Polish Society for Photogrammetry and Remote Sensing (PTFiT) and German Society for Photogrammetry, Remote Sensing, and Geoinformation (DGPF). The event took place at the Department of Geodesy and Offshore Survey at the Maritime University of Szczecin. Dr hab. inż. Grzegorz Jóźków oraz dr inż. Małgorzata Jarząbek-Rychard from the Institute of Geodesy and Geoinformatics were involved in preparing lectures and tutorials.
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IGiG is pleased to invite you to a scientific seminar, which will take place in a hybrid format, on Thursday (September 19th, 2024) at 9:00 AM. Prof. Kefei Zhang (School of Environment and Spatial Informatics, China University of Mining and Technology) will present the topic "Space Resources Exploitation and Utilization - A Geodetic Perspective".
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Weather phenomena forecasting is undergoing a significant transformation with the latest research, introducing a AI-based to atmospheric prediction. The new study, published in IEEE Transactions on Geoscience and Remote Sensing, marks the pioneering use of GNSS tomography for ensemble forecasting, setting a new pathway in the field. Traditional troposphere models often struggle with low spatial resolution and irregular data distribution. By leveraging GNSS troposphere tomography, the study generates high-resolution, three-dimensional wet refractivity fields, effectively addressing these critical challenges. Unlike previous deterministic models, the research employs ensemble forecasting using Generative Adversarial Networks (GAN), enhancing predictive accuracy by generating realistic time series data and providing probability-based forecasts. At the core of the model are Long Short Time Memory (LSTM) networks, optimized with Genetic Algorithms (GA), ensuring robust predictive performance
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INSTITUTE OF GEODESY AND GEOINFORMATICS
Wroclaw University of Environmental and Life Sciences Grunwaldzka 53 50-357 Wroclaw NIP: 896-000-53-54, REGON: 00000 18 67 Phone +48 71 3205617 Fax +48 71 3205617 e-mail: igig@upwr.edu.pl |