Abstract's details

Altimeter 1D-Var Tropospheric Correction for Sentinel-3

Ralf Bennartz (Vanderbilt University, United States)


Bruno Picard (Fluctus SAS, France); Frank Fell (Informus Gmbh, Germany); Estelle Obligis (Eumetsat, Germany)

Event: 2019 Ocean Surface Topography Science Team Meeting

Session: Instrument Processing: Propagation, Wind Speed and Sea State Bias

Presentation type: Type Oral

Contribution: PDF file


A major source of uncertainty for SSH estimates from radar altimetry is the wet tropospheric correction (WTC). The spatial and temporal variability of water vapour is such that an instantaneous estimation ofits impact is needed.

Since February 2016, these observations are being provided by the SRAL / MWR pair of instruments flown on-board the Sentinel-3A (S3-A) satellite, which in April 2018 has been complimented by the identically equipped Sentinel-3B (S3-B) satellite. S3-A, S3-B (and, in the future S3-C) thus provide critical continuing radar altimeter and MWR coverage at a 10:00 local time sun-synchronous high inclination orbit for the next ten or more years.

The ESA GMES Sentinel-3 System Requirements Document has defined the requirements for the topography ission over ocean. The overall objective is a measurement of the altimeter range with an error below 3 cm . Apart from the sea state bias which absorbs the remaining errors on the altimeter measurements, the WTC is indeed the major source of error, associated with a requirement on retrieval accuracy of 1.4 cm.

The current retrieval approach for Sentinel-3 is based on a “mixed” approach successfully applied since ERS-1, the adjective “mixed” referring to the joint use of statistical and physical methods. These operationally applied algorithms provide WTC estimates with a good accuracy over the open ocean. However, systematic errors may occur at regional scales, where atmospheric characteristics are not well represented in the learning database.

These errors are propagated into the final sea level maps, leading to local biases. Land contamination in radiometer observation near coasts is another source of degradation of WTC retrievals, caused by the sharp gradient between land and sea brightness temperatures (land surface emissivity is about 2-3 times higher than sea surface emissivity).

Previous studies have already shown the potential of variational methods such as one-dimensional variational approaches (1D-Var) to retrieve temperature, humidity and cloud vertical profiles. Over ocean, SSMIS measurements were assimilated under clear and cloudy non-precipitating conditions by Deblonde and English [2003] to retrieve temperature and humidity profiles as well as liquid water content. Hewison [2007] assimilated ground-based microwave observations as well as other IR and surface sensor measurements in a 1D-Var scheme to retrieve temperature, humidity and cloud profiles using a specific cloud classification scheme.

Finally, the study conducted by Bennartz et al. [2017] aims at retrieving TCWV and WTC over ocean using measurements from the Microwave Radiometers (MWR) onboard the ERS-1/-2 and Envisat platforms.
The mentioned studies demonstrate the potential of the 1D-Var approach as a relevant global method to retrieve WTC and its particular value for creating inter-calibrated long-term datasets from multiple satellites that are essential to climate studies. In addition to the retrieval, this method also provides a-posteriori retrieval uncertainties with all retrieved parameters, which are not available from the current empirical algorithms.

Herein, we propose to apply an existing 1D-Var solution originally developed by the authors for the MWRs onboard ERS-1, ERS-2, and Envisat [Bennartz et al., 2017] also to Sentinel-3 MWR observations to retrieve TCWV and WTC over the open ocean. The performance will be validated against GNSS observations and using the usual altimetry metrics, such as the difference of SSH variance at cross-overs.

Oral presentation show times:

Room Start Date End Date
The Forum Tue, Oct 22 2019,14:15 Tue, Oct 22 2019,14:30
Ralf Bennartz
Vanderbilt University
United States