Satellite altimetry plays an important role in monitoring global sea level and its changes over time. The sea level is one of the most important indicators of global climate change and has been defined as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). To accurately quantify the long-term changes in global mean sea level (GMSL), observations need to be as accurate as possible and also extremely stable over long periods of time. Most past and present satellite altimetry missions use radar signals to measure the distance between the satellite and the sea surface based on the travel time of short microwave pulses. To achieve the required range accuracy of few centimeters, the measurements must be corrected for delays due to atmospheric refraction effects, among others, in the Earth's ionosphere.

The aim of this project is to investigate, evaluate, and combine different ionospheric corrections for satellite altimetry measurements, with the overall objective of developing an innovative improved ionospheric delay correction that is not only of the best possible quality but also consistent over all surface types (open ocean, coastal and polar areas, inland) for the entire altimetry era (1992 to present) and applicable to all satellite altimetry missions.

Ionospheric corrections for along-track satellite altimetry are derived from DORIS observations. In addition, two different combination approaches are developed to couple on-board altimetry and DORIS observations with GNSS-based GIM information: (1) a machine learning-based method and (2) an innovative analytical solution based on localized B-spline functions. Both methods are compared with respect to accuracy, long-term stability, and efficiency.

Based on the developed innovative methods, the project answers the following research questions:

• How do the existing ionospheric corrections differ in terms of accuracy and applicability for specific studies (e.g., sea level trend studies, ocean tide estimation)?
• What is the benefit of using DORIS observations to correct satellite altimetry measurements for ionospheric effects?
• How can machine learning help to improve ionospheric corrections for satellite altimetry?
• How can an optimal ionospheric correction for satellite altimetry be derived, and what is its impact on scientific sea level studies, e.g., on GMSL and ocean tides?