Propagation of electromagnetic signals in Earth's neutral atmosphere inflicts errors in space geodetic observations. To model these errors accurately, it is necessary to use a signal tracing algorithm which is informed of the state of the atmosphere. We developed such an algorithm and tested it by processing Global Navigation Satellite System network observations. Our algorithm's main advantage is lossless utilization of atmospheric information provided by numerical weather prediction models.

We model Earth's lithosphere as a thin elastic shell and present numerical methods of isogeometric finite-element analysis to simulate its deformation in isostatic equilibrium using technologies from computer-aided design. The simulations also serve as a basis for identifying parameters of the model that are most plausible to explain observed data. This research has been done to showcase the capabilities of isogeometric analysis in solving higher-order problems in geoscientific applications.

A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH₃ emissions from livestock farming. It is estimated that about 30 % of excreted N from livestock is lost due to NH₃ emissions from housing, manure management and land application of manure. High NH₃ volatilization often occurs in hot regions, while poor management practices also result in significant N losses through NH₃ emissions.

Large-scale wave modeling is essential for science and society, typically relying on resource-intensive numerical methods to simulate wave dynamics. In this study, we introduce a rolling AI-based method for modeling global significant wave height. Our model achieves accuracy comparable to traditional numerical methods while significantly improving speed, making it operable on standard laptops. This work demonstrates AI's potential to enhance the accuracy and efficiency of global wave modeling.