Sensitivity of Altimeter Wave Height Assessment to Data Selection

Abstract

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.

The Sea State CCI dataset v1: towards a sea state climate data record based on satellite observations

“…Not surprisingly, the list of altimeter data sources is very similar to that used by the Sea Level CCI ( Fig. 1a in Quartly et al, 2017), except that project could not utilise the instruments in very long repeat cycles.…”

Evaluation of the Worldwide Wave Energy Distribution Based on ERA5 Data and Altimeter Measurements

“…The statistical parameters show a good agreement between altimeter and in situ measurements, with low values for bias (±0.1 m) and root mean square errors (around 0.22 m), and high correlation coefficients (around 0.97). An analysis of these data was recently made [52,53].…”

Reliability of Extreme Significant Wave Height Estimation from Satellite Altimetry and In Situ Measurements in the Coastal Zone

“…It provides calibrated and quality controlled 1 Hz along-track observations from 10 satellite missions (provided separately) including: ERS-1, TOPEX/Poseidon, ERS-2, GFO, Envisat, Jason-1, Jason-2, Cryosat-2, Jason-3 and SARAL/Altika (note SENTINEL-3 missions A and B are not included). Throughout this paper the calibrated 1 Hz along-track measurements are compared with in situ observations at fixed locations by aggregation and averaging over a 50 km radius, chosen in accordance with common convention [22]. We acknowledge that evaluation over smaller scales might well be desirable for some applications, for example leading to closer correlation to point observations (from buoys), but this typically leads to impractical levels of sampling and is particularly problematic for the analysis of extremes.…”

“…In (rare) cases, where more than one track segment lies within a 1-h bin, in order to avoid statistical biases associated with weighting heavily in favour of track segments that happen to have a much larger number of points, we take the mean of the median values. Note also that conventionally where specific buoy and satellite measurement "match ups" are identified for precise calibration (e.g., [22]), a time window of 30 min is adopted. In this case however we are working with long time series that makes this impractical.…”

“…Within the ESA Sea State CCI project, competing coastal re-tracking algorithms were evaluated against a range of criteria for implementation in future versions of the Sea State CCI product. Schlembach et al [21] describe this "round-robin" process in detail, noting that while improvements over operational re-tracking algorithms were widely obtained, the exact performance improvements between algorithms was strongly sensitive to proximity to the coast (see also [22]). While such efforts focus on the detailed performance of altimetry measurements they do not necessarily provide insight into their overall limitations when employing a larger combined dataset, such as the Sea State CCI product, across a range of coastal regions and conditions.…”