Twelve years (1991-2003) of ERS-1 and ERS-2 altimetry data have been reprocessed within the European Space Agency (ESA) reprocessing altimeter products for ERS (REAPER) project using an updated, modern set of algorithms and auxiliary models. The reprocessed data set (identified as RP01) has been cross-calibrated against the reprocessed ENVISAT V2.1 data. The format of this reprocessed data set is network common data form (version 3). The new data set shows a clear improvement in data quality beyond that of previous releases. The product validation shows reduction of the mean standard deviation of the sea-surface height differences from 8.1 (previously available product) to 6.7 cm (RP01). This paper presents the details of how the reprocessing was conducted and shows selected results from the validation and quality-assurance processes. The major improvements of the REAPER RP01 data set with respect to the previous ESA ERS radar altimetry (RA) products are due to the use of four ENVISAT RA-2 retrackers, RA calibration improvements, new reprocessed precise orbit solutions, ECMWF ERA-interim model for meteorological corrections, new ionospheric corrections, and new sea state. The intent of this paper is to aid the reader in understanding the benefits of the new data set for their particular use-case.

During its commissioning phase, the Copernicus Sentinel-3B satellite has been placed in a tandem formation with Sentinel-3A for a period of 6 months. This configuration allowed a direct comparison of measurements obtained by the two satellites. The purpose of this paper was to present the range of analyses that can be performed from this dataset, highlighting methodology aspects and the main outcomes for each instrument. We examined, in turn, the benefit of the tandem in understanding instrument operational modes differences, in assessing inter-satellite differences, and in validating measurement uncertainties. The results highlighted the very good consistency of the Sentinel-3A and B instruments, ensuring the complete inter-operability of the constellation. Tandem comparisons also pave the way for further improvements through harmonization of the sensors (OLCI), correction of internal stray-light sources (SLSTR), or high-frequency processing of SRAL SARM data. This paper provided a comprehensive overview of the main results obtained, as well as insights into some of the results. Finally, we drew the main lessons learned from the Sentinel-3 tandem phase and provided recommendations for future missions.

Following the successful launch of the SARAL space mission in February 2013, the reliability of the innovative AltiKa altimeter has been demonstrated for deep ocean applications, where Ka-band performances are excellent. With the objective to ensure the complementarity but also the continuity with the altimeter Level-2 products provided in the open ocean, the Prototype for Expertise on AltiKa for Coastal, Hydrology and Ice (PEACHI) project has been set up as an initiative of the French space agency, CNES, to provide a data set of research-grade Level-2 parameters that might be interesting for SARAL secondary objectives on the study of coastal dynamics, inland waters, polar oceans, or continental and sea ices. Thus, the PEACHI prototype has been developed to process and accurately tune dedicated algorithms for the assessment of Ka-band parameters, from the instrument processing to geophysical corrections. As a result, the PEACHI prototype routinely provides end users with new or improved altimeter corrections for scientific applications dedicated to mesoscale monitoring but also synergistic science.

The wet tropospheric path delay is presently the main source of error in the estimation of the mean sea level by satellite altimetry. This correction on altimetric measurements, provided by a dedicated radiometer aboard the satellite, directly depends on the atmospheric water vapor content. Nowadays, water vapor products from microwave radiometers are rather consistent but important discrepancies remain. Understanding these differences can help improve the retrieval of water vapor and reduce at the same time the error on the mean sea level. Three radiometers are compared: the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), Jason-1 microwave radiometer (JMR), and Envisat microwave radiometer (MWR). Water vapor products are analyzed both in terms of spatial and temporal distribution over the period 2004–10, using AMSR-E as a reference. The Interim ECMWF Re-Analysis (ERA-Interim) data are also included in the study as an additional point of comparison. Overall, the study confirms the general good agreement between the radiometers: similar patterns are observed for the spatial distribution of water vapor and the correlation of the times series is better than 0.90. However, regional discrepancies are observed and a quantitative agreement on the trend is not obtained. Regional discrepancies are driven by the annual cycle. The JMR product shows discrepancies are highly dependent on water vapor, which might be related to calibration issues. Furthermore, triple collocation analysis suggests a possible drift of JMR. MWR discrepancies are located in coastal regions and follow a seasonal dynamic with stronger differences in summer. It may result from processing of the brightness temperatures.

This paper addresses the subject of the regression models used for the wet troposphere path delay correction for range measurements by satellite radar altimeters. The objective of this study is twofold: 1) to find which regression method is better suited for the retrieval between a neural network algorithm and a log-linear regression and 2) to determine whether the use of the altimeter backscattering coefficient at Ka- or Ku-band can substitute for the use of the radiometer brightness temperature at 18 GHz as an input for the retrieval. Several configurations of algorithms, including those used in the operational processing of altimetry missions such as Jason-1 or Envisat, are built and compared on the same learning and test database to determine which retrieval strategy is more appropriate. The importance of each input is analyzed and the performances of the different algorithms are assessed in terms of bias and standard of the errors and also in terms of their geographical distribution and correlations with other environmental variables. Their performances are then assessed on Jason-2 radiometer measurements using the criterion of variance in sea-surface height differences at crossovers. The study shows that the neural network formalism is better suited for the retrieval of the wet tropospheric path delay than the log-linear regression. In terms of variable selection, better results were obtained when the brightness temperature at 18 GHz was used instead of the backscattering coefficient. Overall, the best results are obtained with the combination of a three-channel radiometer and a neural network algorithm.