The effect of a non-Gaussian point target response function on radar altimeter returns from the sea surface

Challenor, Peter; Greco, B.; Srokosz, Meric

doi:10.1080/01431168708948643

Cited by 6 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Typically this model is fitted (in the least-square sense) to the amplitude waveforms to derive the elapsed travel time (related to the range), the maximum amplitude of the signal (related to wind speed at the sea surface) and the variability in surface height denoted by the significant wave height (SWH). [6] and [7] developed the theory for determining another parameter, SSH skewness, which was successfully implemented for Jason-1 [8] and Envisat RA-2 [9] However pulse echoes are more complex and variable when there is significant spatial variation of properties within the full altimetric footprint i.e. that portion of the surface area contributing to any of the altimeter's waveform sampling gates (a disc ~14 km across for Envisat RA-2).…”

Section: Introductionmentioning

confidence: 99%

Modeling Envisat RA-2 Waveforms in the Coastal Zone: Case Study of Calm Water Contamination

Gómez-Enri

Vignudelli

Quartly

et al. 2010

IEEE Geosci. Remote Sensing Lett.

View full text Add to dashboard Cite

Abstract-Radar altimeters have so far had limited use in the coastal zone, the area with most societal impact. This is due to both lack of, or insufficient accuracy in the necessary corrections, and more complicated altimeter signals. This letter examines waveform data from the Envisat RA-2 as it passes regularly over Pianosa (a 10-km 2 island in the northwestern Mediterranean). Forty-six repeat passes were analyzed, with most showing a reduction in signal upon passing over the island, with weak early returns corresponding to the reflections from land. Intriguingly, one third of cases showed an anomalously bright hyperbolic feature. This feature may be due to extremely calm waters in the Golfo della Botte (northern side of the island), but the cause of its intermittency is not clear. The modeling of waveforms in such a complex land/sea environment demonstrates the potential for sea surface height retrievals much closer to the coast than is achieved by routine processing. The long-term development of altimetric records in the coastal zone will not only improve the calibration of altimetric data with coastal tide gauges but also greatly enhance the study of storm surges and other coastal phenomena.Index Terms-Coastal altimetry, digital elevation model (DEM), Envisat RA-2, northwestern Mediterranean, waveform analysis.

show abstract

Section: Introductionmentioning

confidence: 99%

Modeling Envisat RA-2 Waveforms in the Coastal Zone: Case Study of Calm Water Contamination

Gómez-Enri

Vignudelli

Quartly

et al. 2010

IEEE Geosci. Remote Sensing Lett.

View full text Add to dashboard Cite

show abstract

“…We may also use a maximum likelihood (ML) fit, first introduced by Rodriguez (1988) and particularly developed for the ERS-1 altimeter by Challenor and Srokosz (1989). ML is the optimal method for a uniform sea state with fluctuations in the waveforms dominated by speckle noise.…”

Section: Definitions Of Retracking Cost Functionsmentioning

confidence: 99%

Along-track resolution and uncertainty of altimeter-derived wave height and sea level: re-defining the significant wave height in extreme storms

Ardhuin,

Carlo

2024

Preprint

View full text Add to dashboard Cite

Retracking of altimeter waveforms yields fluctuations in wave height and sea level, correlated at the scale of the effective footprint• A good approximation for the effective footprint diameter is the square root of the product of wave height and satellite altitude• An estimation of phenomenal wave heights precise to better than 2% requires filtering over wave groups, typically over a distance of 20 to 50 km

show abstract

“…We may also use a maximum likelihood (ML) fit, first introduced by Rodriguez (1988) and particularly developed for the ERS-1 altimeter by Challenor and Srokosz (1989), and later used by Gómez-Enri et al (2007). ML is the optimal method for a uniform sea state with fluctuations in the waveforms dominated by speckle noise.…”

Section: Definitions Of Retracking Cost Functionsmentioning

confidence: 99%

“…We may also use a maximum likelihood (ML) fit, first introduced by Rodriguez (1988) and particularly developed for the ERS‐1 altimeter by Challenor and Srokosz (1989), and later used by Gómez‐Enri et al. (2007).…”

Section: Waveforms and Their Retracking Over Wave Height Gradientsmentioning

confidence: 99%

Along‐Track Resolution and Uncertainty of Altimeter‐Derived Wave Height and Sea Level: Re‐Defining the Significant Wave Height in Extreme Storms

De Carlo,

Ardhuin

2024

JGR Oceans

View full text Add to dashboard Cite

Satellite altimeters are the most common source of wave measurement in phenomenal sea states, with significant wave heights exceeding 14 m. Unfortunately their data is still considered with skepticism, because there is usually no other data to verify the accuracy of the largest values. Here we investigate the self‐consistency of the measurement, and their small scale variability, in order to define an estimate of satellite altimeter precision. Using numerical simulations of ocean surfaces and the processing involved in satellite retracking, we find that wave groups are responsible for a variance in estimated altimeter wave heights that is proportional to the square of the spectral peakedness parameter and the significant wave height. Additional variance induced by speckle noise is proportional to the wave height. The effect of wave groups generally dominates in the most severe storms. This variability requires a relatively large scale smoothing or filtering to yield accurate wave height estimates. For example, the largest ever reported 1 s average significant wave height from altimeters sampled by Jason‐2 in the North Atlantic in 2011, at m, is now interpreted to correspond to a true wave height Hs = 18.5 ± 0.3 m. The difference between 20.1 and 18.5 m is mostly due to wave group contributions to the raw measurement. We argue that wave group effects should not be included in the definition of the significant wave height, just like the maximum wave height differs from the significant wave height.

show abstract

The effect of a non-Gaussian point target response function on radar altimeter returns from the sea surface

Cited by 6 publications

References 8 publications

Modeling Envisat RA-2 Waveforms in the Coastal Zone: Case Study of Calm Water Contamination

Modeling Envisat RA-2 Waveforms in the Coastal Zone: Case Study of Calm Water Contamination

Along-track resolution and uncertainty of altimeter-derived wave height and sea level: re-defining the significant wave height in extreme storms

Along‐Track Resolution and Uncertainty of Altimeter‐Derived Wave Height and Sea Level: Re‐Defining the Significant Wave Height in Extreme Storms

Contact Info

Product

Resources

About