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Waveform retracking is the process by which a simple mathematical model is fitted to altimeter returns. Over the ocean, the waveform location, the amplitude, and the shape can be fitted by models with 3–5 free parameters, which may, in turn, be linked to geophysical properties of the surface of interest—principally sea surface height (SSH), wave height, and normalized backscatter strength ($\sigma ^{0}$ , related to wind speed). However, random multiplicative noise, which is due to the summation of power from multiple differently orientated surfaces, produces errors in the estimation of these model parameters. Examination of the correlations among parameters estimated for each waveform leads to simple empirical corrections that reduce the waveform-to-waveform noise in geophysical estimates, resulting in smoother (and more realistic) along-track profiles of $\sigma ^{0}$ and SSH. These adjustments are fundamentally dependent upon the waveform model and retracker implemented, but when applied show improved agreement between near-simultaneous measurements from different altimeter missions. The effectiveness of these empirical adjustments is documented fully for MLE-4 retracking of the Jason-3 altimeter, with a reduction in the 1-s variance of $\sigma ^{0}$ by 97%. However, the ideas are applicable and beneficial for data from other altimeters, with small improvements in $\sigma ^{0}$ for MLE-3 and for AltiKa at Ka-band, while reductions in range variance of ~40% are noted for most retrackers evaluated [ABSTRACT FROM AUTHOR]

Analysis of the radar echoes from a spaceborne altimeter gives information on sea surface height, wave height and wind speed, as well as other parameters over land and ice. The first spaceborne radar altimeter was pioneered on Skylab in 1974. Since then, there have been about 20 further missions, with several advances in the sophistication of hardware and complexity of processing with the aim of increased accuracy and precision. Because of that, the importance of regular and precise calibration and validation ("cal/val") remains undiminished, especially with efforts to merge altimetric records from multiple missions spanning different domains and time periods. This special issue brings together 19 papers, with a focus on the recent missions (Jason-2, Jason-3, Sentinel-3A and HY-2B) as well as detailing the issues for anticipated future missions such as SWOT. This editorial provides a brief guide to the approaches and issues for cal/val of the various different derived parameters, including a synopsis of the papers in this special issue. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of how the selection of buoys and the calculation of altimeter averages affect the metrics characterising the errors of altimetric wave height estimates. The use of a 51-point median reduces the sensitivity to occasional outliers, but the quality of this measure can be improved by demanding that there is a minimum number of valid measurements. This had a marked impact in both the open ocean and the coastal zone. It also affected the relative ordering of algorithms' performances, as some fared poorly when a representative value was gleaned from a single waveform inversion, but had a much better ranking when a minimum of 20 values were used. Validation procedures could also be improved by choosing altimeter-buoy pairings that showed a good consistency. This paper demonstrated an innovative procedure using the median of the different retrackers analysed, which can be easily extended to other data validation exercises. This led to improved comparison statistics for all algorithms in the open ocean, with many showing errors less than 0.2 m, but there was only one strong change in the relative performance of the 11 Jason-3 retrackers. For Sentinel-3A, removing the inconsistent coastal buoys showed that all of the new algorithms had similar errors of just over 0.2 m. Thus, although improvements were found in the procedure used for the Sea State Round Robin exercise, the relative rankings for the buoy calibrations are mostly unaffected. [ABSTRACT FROM AUTHOR]

The echo waveforms received by conventional radar altimeters are interpreted by retracking algorithms to give estimates of range, wave height, and backscatter strength. However, in response to fading noise on the waveform leading edge, common retrackers, such as MLE-3 and MLE-4, show correlated errors in wave height and range. This correlation is used to develop a correction to the wave height data that reduces the high-frequency variability by ∼22%, without affecting the global distribution of values. This correction also results in a closer matchup of Jason-2 and Jason-3 data during their tandem phase. Although the correction is quite straightforward in practice, the appropriate conversion factor has to be determined for each combination of altimeter and retracker. There are also remaining open questions concerning the needed low-pass filtering. [ABSTRACT FROM AUTHOR]