Wet tropospheric delay estimation of satellite altimeters based on machine learning

Abstract

Wet path delay (WPD) is an important source of satellite altimeters’ transmission error, which must be fully corrected to ensure data quality. After decades of study, WPD can now be accurately negated by measuring the brightness temperature with a microwave radiometer (MWR). However, due to hardware and financial limitations, many satellites are not equipped with MWRs, and WPD can only be corrected by mathematical models. Currently available empirical models, which are mostly established by traditional fitting methods, are unable to reach the accuracy required by altimeters, so it is necessary to further improve the correction accuracy of the models. Based on three machine learning algorithms, we established three high-precision wet tropospheric correction (WTC) models suitable for satellite altimeters. We analyzed the global navigation satellite system dataset and three MWR datasets, i.e., Jason-3, Sentinel-3A, and Sentinel-3B, for data quality and consistency and combined these datasets for modeling. In addition, several input features were extracted from existing knowledge on tropospheric delay. Through a comparative analysis of different feature combinations, temperature (T), water vapor pressure (e), and total content of water vapor were selected as the input features. Then we established three WTC models based on multilayer perceptron, random forest, and the adaptive network-based fuzzy inference system (ANFIS), respectively. With a dataset from Jason-2 that was not used during modeling as the verification set, we verified the generalizability of these WTC models from the global, latitudinal, and seasonal perspectives. Moreover, three traditional WTC models were compared with the ones developed here. The results show that the WTC models established based on machine learning algorithms outperform traditional WTC models. The WTC model based on ANFIS boasts the best calculation accuracy in all aspects, over 50% higher than that of traditional WTC models.