Improving Altimeter Wind Speed Retrievals Using Ocean Wave Parameters

Abstract

Spaceborne altimeters are an important data source for obtaining global sea surface wind speeds (U10). Although many altimeter U10 algorithms have been proposed and they perform well, there is still room for improvement. In this study, the data from ten altimeters were collocated with buoys to investigate the error of the altimeter U10 retrievals. The U10 residuals were found to be significantly dependent on many oceanic and atmospheric parameters. Because these oceanic and atmospheric parameters are intercorrelated, an asymptotic strategy was used to isolate the impact of different parameters and establish a neural-networkbased correction model of altimeter U10. The results indicated that significant wave heights and mean wave periods are effective in correcting U10 retrievals, probably due to the tilting modulation of long-waves on the sea surface. After the wave correction, the rootmean-square error between the U10 from altimeters and buoys was reduced from 1.45 to 1.24 m/s and the impacts of thermodynamic parameters, such as sea surface (air) temperate, became negligible. The U10 residuals after correction showed that the atmospheric instability can lead to errors on extrapolated buoy U10. The buoy measurements with large air-sea temperature differences need to be excluded in the Cal/Val of remotely sensed U10.

Wind speed and direction estimation from wave spectra using deep learning

“…These are probably because the NDBC buoys do not respond well to the small waves generated by very low wind while the geophysical noises such as ocean currents have a large impact on the wind estimation during low wind speed. Meanwhile, it is noted that other indirect methods for wind speed estimation, such as remote sensing, also always overestimate low wind speed (e.g., Stopa et al, 2017;Jiang et al, 2020). Both the bias and SD increase with the U 10 when U 10 > 10 m s −1 .…”

“…The five Fourier coefficients of different frequencies are the minimum requirement to reconstruct the directional wave spectrum. These NDBC data, especially the offshore data, are widely used in the validation of wind and wave remote sensing and numerical weather and wave models (e.g., Jiang et al, 2016;Jiang, 2020;Wang et al, 2021). The wave data and the wind data were collocated if their ends of sampling time were within 10 min (the sampling duration is ∼ 20 min for wave measurements and ∼ 10 min for wind measurement).…”

“…As a nonparametric model, a DNN can theoretically be used to fit any form of function with any number of input parameters provided the network is wide and deep enough. The DNN has been proved to be effective for regression problems with more than two input parameters and is widely used in the training of retrieval models and correction models in studies of ocean remote sensing (e.g., Wang et al, 2020;Jiang et al, 2020). A DNN is a useful tool for the problem that there are causal relationships between inputs and outputs (in this study, wave spectra and winds, respectively), but the explicit form of the relationship is not known.…”

“…Although this model has good theoretical support, its accuracy is lower than typical remote sensing retrievals. For example, altimeter-retrieved wind speed has a typical overall RMSE of 1.2-1.5 m s −1 (e.g., Jiang et al, 2020) and scatterometer-retrieved wind speed and wind directions have a typical overall RMSE of ∼ 1 m s −1 and 15 • (e.g., Wang et al, 2021) when using buoys' anemometer data as the reference.…”

Wind Speed and Direction Estimation from Wave Spectra using Deep Learning

“…Although this model has good theoretical support, the accuracy of this model is lower than typical remote sensing retrievals. For example, altimeter-retrieved wind speed has a typical overall RMSE of 1.2-1.5 m/s (e.g., Jiang et al 2020) and scatterometer-retrieved wind speed and wind directions has a typical overall RMSE of ~1 m/s and 16° (e.g., Wang et al 2021) when using buoys' anemometer data as the reference.…”