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Can Multi-Mission Altimeter Datasets Accurately Measure Long-Term Trends in Wave Height?

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A long-duration, multi-mission altimeter dataset is analyzed to determine its accuracy in determining long-term trends in significant wave height. Two calibration methods are investigated: “altimeter–buoy” calibration and “altimeter–altimeter” calibration. The “altimeter–altimeter” approach shows larger positive trends globally, but both approaches are subject to temporal non-homogeneity between altimeter missions. This limits the accuracy of such datasets to approximately ±0.2 cm/year in determining trends in significant wave height. The sampling pattern of the altimeters is also investigated to determine if under-sampling impacts the ability of altimeters to measure trends for higher percentiles. It is concluded that, at the 99th percentile level, sampling issues result in a positive bias in values of trend. At lower percentiles (90th and mean), the sampling issues do not bias the trend estimates significantly.
Title: Can Multi-Mission Altimeter Datasets Accurately Measure Long-Term Trends in Wave Height?
Description:
A long-duration, multi-mission altimeter dataset is analyzed to determine its accuracy in determining long-term trends in significant wave height.
Two calibration methods are investigated: “altimeter–buoy” calibration and “altimeter–altimeter” calibration.
The “altimeter–altimeter” approach shows larger positive trends globally, but both approaches are subject to temporal non-homogeneity between altimeter missions.
This limits the accuracy of such datasets to approximately ±0.
2 cm/year in determining trends in significant wave height.
The sampling pattern of the altimeters is also investigated to determine if under-sampling impacts the ability of altimeters to measure trends for higher percentiles.
It is concluded that, at the 99th percentile level, sampling issues result in a positive bias in values of trend.
At lower percentiles (90th and mean), the sampling issues do not bias the trend estimates significantly.

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