Cross-over analysis of altimetry over ocean and investigating the orbital error’s effect on inter-mission/track bias in inland altimetry

Abstract

The largest part of Earth’s surface (approximately 71%) is covered with water. Given the constantly changing environment, particularly amidst accelerated climate change, it is crucial to continuously measure the water levels of oceans and lakes. Therefore, satellite altimetry becomes essential. The orbits of the altimetry satellites are selected in a way that allows satellites to pass over the same locations after a specific interval. These orbits are termed as repeat orbits, facilitating the creation of time series measurements. Over the past 40 years, numerous altimetry satellite missions have been launched. When multi-mission monitoring of water bodies is targeted, each satellite altimeter possesses its own biases, which should be removed for comparability among different missions. This ensures the creation of long-term data records by combining data from various missions. Over open oceans, this is typically achieved through a cross-calibration method. However, these methods prove effective for ocean data but not for inland altimetry. In this thesis, I investigated the reasons for the bias among water values measured by different satellites. Additionally, I explored potential solutions to merge the data. The main focus lays on the tandem phases of Jason 1 and 2, as well as Jason 2 and 3. The study area focused on Lake Erie, situated in the Great Lakes region in the northwest of the US. To reduce the bias, I employed a cross-calibration method to estimate and reduce radial error components. As this approach does not resolve the entire bias problem, I investigated the retracking algorithms by comparing their results. Differences between height measurements of Jason 2 and Jason 3, both using MLE4, were identified. It could be determined that MLE4 in Jason 3 finds systematically lower values compared to Jason 2. Over the whole tandem phase, Jason 3 finds the retracking point approximately 20%, in respect to the leading edge, lower than Jason 2. The influence of this systematic difference on the SSH/LLH remains unclear, as no further investigations are done. To get a better understanding if the bias can be reduced when the mid-height point is used, two simple threshold retracking algorithms are employed. The outcome is, that the difference between Jason 2 and Jason 3 increased to 18.3 cm on average. Lastly, I examined the corrections that need to be added to the range measurement of the satellite. This includes the geoid undulation, tidal height variations, the ocean surface response caused by atmospheric pressure and propagation delay due to the atmosphere. I found differences of 5 to 8 cm over Lake Erie in the atmosphere corrections. Employing the same corrections for two satellites yielded the most effective bias reduction. However, employing this technique, necessitates satellites passing the same location within a few minutes of each other. Consequently, the Jason satellites were selected during their tandem phases. On average the bias could be reduced from 7 cm to 2.4 cm. The study delved into understanding and reducing biases in satellite altimetry measurements, particularly focusing on the tandem phases of Jason satellites, revealing challenges and promising methods to significantly reduce biases.