On the role of the troposphere in satellite altimetry

Satellite radar altimetry is nowadays a powerful and attractive Earth Observation technique of major importance in several applications such as the study of mean sea level change (a crucial climate change indicator), the observation of continental ice sheets' melting or the management of highly sensitive coastal zones and continental water resources. This paper aims at presenting an overview of the effects of the troposphere in satellite radar altimetry range observations, i.e. the tropospheric path delays (PD), and the corresponding corrections required to account for these effects, including those due to neutral gases, the DTC (dry tropospheric correction) and those related with water vapour and cloud liquid water content, the WTC (wet tropospheric correction). Starting with a summary of satellite altimeter missions since 1991 and corresponding relevant characteristics for DTC and WTC estimation, the tropospheric PD are discussed in the more general context of the atmospheric corrections. Then, for each of the tropospheric PD, the following topics are inspected and discussed: relevant background theory required to estimate each of the PD; methods used in the PD retrievals either from observations or from atmospheric models; specific problems encountered in the retrieval of each PD in coastal and inland water regions; errors still present in some altimeter products and proposed approaches to mitigate them. Most relevant conclusions are highlighted, including the future challenges and trends in DTC and WTC estimation.

Automated summary

Satellite radar altimetry is a powerful technique for Earth Observation, with applications in studying sea level change, ice sheets melting, and managing coastal zones and water resources. This paper provides an overview of the effects of the troposphere in altimetry range observations, discussing relevant theory, retrieval methods, problems in coastal and inland water regions, errors in altimeter products, and future challenges in estimation.