Geodesy contributes to atmospheric science by providing some of the essential climate variables of the Global Climate Observing System. Water vapor is currently under-sampled in meteorological and climate observing systems. Thus, obtaining more high-quality humidity observations is essential for weather forecasting and climate monitoring. The production, exploitation, and evaluation of operational GNSS Meteorology for weather forecasting is well established in Europe thanks to a long-lasting cooperation between the geodetic community and the meteorological services. Improving the skill of numerical weather prediction (NWP) models, e.g., to forecast extreme precipitation, requires GNSS products with higher spatio-temporal resolution and shorter turnaround. Homogeneously reprocessed GNSS data (e.g., IGS repro3) have high potential for monitoring water vapor climatic trends and variability. Advances in SLR atmospheric delay modelling are using NWP data and 3D ray-tracing to improve tropospheric corrections. With shorter orbit repeat periods, SAR measurements are a source of information to improve NWP models. Additionally, emerging LEO-PNT missions offer capabilities for atmospheric and environmental monitoring due to their dense geometry, rapid revisit times and new signals that will be defined. Their integration with GNSS and other geodetic techniques could open new possibilities for real-time correction models. Reflected signals of GNSS and future LEO-PNT provide additional opportunities for remote sensing of the Earth system. GNSS-R contributes to environmental monitoring with estimates of soil moisture, snow depth, ocean wind speed, sea ice concentration and can be used to retrieve near-surface water vapor. We welcome, but do not limit, contributions on:
-Estimates of the neutral atmosphere using ground- and space-based geodetic data
-Retrieval and comparison of tropospheric parameters from multi-GNSS, VLBI, DORIS and multi-sensor observations
-Nowcasting, forecasting, and climate research using RT and repro tropospheric products, employing NWP and machine learning
-Assimilation of GNSS tropospheric products in NWP and in climate reanalysis
-Production of SAR tropospheric parameters and assimilation in NWP
-Homogenization of long-term GNSS, VLBI tropospheric products
-Detection and characterization of sea level, snow depth, and sea ice changes, using GNSS-R
-Monitoring of soil moisture and ground-atmosphere boundary interactions using GNSS data