Homogenization and cross-calibration are done at different steps of the processing.

The first homogenization step consists of acquiring altimeter and ancillary data from the different altimeters that are a priori as homogeneous as possible. The DUACS processing is based on the altimeter standards given by L2p products. They include the most recent standards recommended for altimeter global products by the different agencies and expert groups such as OSTST, ESA Quality Working groups. Each mission is processed separately as its requirements vary depending on the input data. When available, a specific standard recommended for regional processing can be applied by DUACS. The list of corrections applied is given in the Table below. Details of the standards used (i.e. the precise algorithm references) for the different corrections for DT and NRT production are given in the FAQ section.

 

Table : Recapitulatif of applied corrections

	Corrections	Applied or not in the DUACS processing
Environnemental corrections	Ionosphere	yes
	Dry troposphere	yes
	Wet troposphere	yes
	Sea state bias	yes
Geophysical corrections	Dynamic Atmospherical Correction	yes
	Ocean tide	yes
	Pole tide	yes
	Solid earth tide	yes
	Laoding tide	yes
	Glacial Isostatic Adjustment	No

Table: List of the different corrections that can be applied on altimeter measurements. The standards actually used for the different corrections are described in the FAQ section.

Input L2p products include a first cross-calibration processing that consists of ensuring mean sea level continuity between the altimeter reference missions. This step, crucial for climate signals, is done as accurately as possible in REP/DT conditions, taking into account both the global and regional biases, as presented in Pujol et al (2016). In NRT conditions, the accuracy of this cross-calibration step is reduced due to the temporal variability of the orbit solutions. Only the global bias between the reference mission is usually corrected.

Nevertheless, they are not always coherent at large regional scales due to various sources of geographically correlated errors (instrumental, processing, orbit residuals errors). Consequently, the DUACS multi-mission cross-calibration algorithm aims to reduce these errors in order to generate a global, consistent and accurate dataset for all altimeter constellations. This processing step consists of applying the Orbit Error Reduction (OER) algorithm. This process consists of reducing orbit errors through a global minimization of the crossover differences observed for the reference mission, and between the reference and other missions also identified as complementary and opportunity missions Traon and Ogor (1998). Multi-satellite crossover determination is performed on a daily basis. All altimeter fields (measurement, corrections and other fields such as bathymetry, MSS, etc.) are interpolated at crossover locations and dates. Crossovers are then appended to the existing crossover database as more altimeter data become available. This crossover dataset is the input of the OER method. Using the precision of the reference mission orbit (Topex/Jason series), an accurate orbit error can be estimated. This processing step is applied on GDR/NTC as well as on IGDR/STC measurements, except for some missions that present a high error level at long wavelengths (e.g., H2C). It does not concern either OGDR/NRT measurements. Specifically, to the OGDR measurements and [I]GDR measurements processing for some specific missions, the DUACS system includes SLA filtering. The reduced quality of the orbit solution indeed limits the use of the long-wavelength signal with these products. The DUACS processing extracts from these data sets the short scales (< ~900 km) which are useful to better describe the ocean variability in real time, and merges this information with a fair description of large-scale signals provided by the multi-satellite observation (i.e., L4 product) in near real time. Finally, a “hybrid” SLA is computed. This specific processing is summarized in figure.

The last step consists of applying the long wavelength error (LWE) reduction algorithm. It is based on multiscale mapping method Ubelmann et al., (2021a). This process reduces geographically correlated errors between neighbouring tracks from different sensors. This optimal interpolation based empirical correction also contributes to reduction of the residual high frequency signal that is not fully corrected by the different corrections that are applied (mainly the Dynamic Atmospheric Correction and Ocean tides).