Altimeter Wind Speed Research Articles

Assessing the performance of SWAN model for wave simulations in the Bay of Bengal

This study aims to comparatively evaluate two reanalysis wind fields, simulate waves driven by CFSv2 and ERA5, and test the simulation performance of the ST6 source term package of the SWAN model in the Bay of Bengal (BOB). A nested model using the third-generation numerical wave model SWAN is established from the Indian Ocean to the BOB, and the model calibration is adjusted based on various factors, including the driving wind field, input source function, physical parameters, and more. The importance of selecting appropriate input-dissipation parameterizations is stressed, as the ST6 source term package is better suited for BOB wave simulations, improving the modeling of wind input and swell dissipation compared to the KOMEN source function. There is a strong correlation between the wind field data from ERA5 and CFSv2 and the satellite altimeter wind speed data, with ERA5 showing superior performance. When the calibrated ST6 parameter values are combined with the ERA5 wind field, the SWAN model achieves a significant level of simulation accuracy, comparable to, or possibly slightly better than, that of the ECMWF WAM model for the specific application considered. The results offer valuable insights for improving the accuracy of wave forecasting models in the BOB region, with important implications for oceanography, fisheries, and disaster management.

Quantifying Multifrequency Ocean Altimeter Wind Speed Error Due to Sea Surface Temperature and Resulting Impacts on Satellite Sea Level Measurements

Surface wind speed measurements from a satellite radar altimeter are used to adjust altimeter sea level measurements via sea state bias range correction. We focus here on previously neglected ocean radar backscatter and subsequent wind speed variations due to sea surface temperature (SST) change that may impact these sea level estimates. The expected error depends on the radar operating frequency and may be significant at the Ka band (36 GHz) frequency chosen for the new Surface Water and Ocean Topography (SWOT) satellite launched in December 2022. SWOT is expected to revolutionize oceanography by providing wide-swath Ka band observations and enhanced spatial resolution compared to conventional Ku band (14 GHz) altimetry. The change to the Ka band suggests a reconsideration of SST impact on wind and sea level estimates, and we investigate this in advance of SWOT using existing long-term Ku and Ka band satellite altimeter datasets. This study finds errors up to 1.5 m/s in wind speed estimation and 1.0 cm in sea level for AltiKa altimeter data. Future SWOT data analyses may require consideration of this dependence prior to using its radar backscatter data in its sea level estimation.

High-resolution calibrated and validated Synthetic Aperture Radar Ocean surface wind data around Australia

The dataset consists of ocean surface wind speed and direction at 10 m height and 1 km spatial resolution around the wider Australian coastal areas, spanning 4 years (2017 to 2021) of measurements from Sentinel-1 A and B imaging Synthetic Aperture Radar (SAR) platforms. The winds have been derived using a consistent SAR wind retrieval algorithm, processing the full Sentinel-1 archive in this region. The data are appropriately quality controlled, flagged, and archived as NetCDF files representing SAR wind field maps aligned with satellite along-track direction. The data have been calibrated against Metop-A/B Scatterometer buoy-calibrated, wind measurements and examined for potential changes in calibration over the duration of the data. The calibrated data are further validated by comparisons against independent Altimeter (Cryosat-2, Jason-2, Jason-3, and SARAL) wind speeds. Several methods for data access are also listed. The database is potentially useful for offshore industries (oil and gas, fisheries, shipping, offshore wind energy), public recreational activities (fishing, sailing, surfing), and protection and management of coasts and natural habitats.

Multi-Parameter Neural Network for Altimeter Tropical Cyclone Wind Speed Estimation

The ability of satellite altimeter to estimate wind speed in tropical cyclone condition has been investigated. In the extreme condition with higher spatio-temporal variation, the ocean-atmosphere interaction is very complex and makes the existing algorithm become an ill-posed solution. In such condition, the developed algorithm from single frequency backscatter and significant waves height were insufficient. Besides, wind speed estimates become saturated at high regimes and the reflected backscatter was contaminated by rain. Therefore, other simultaneously observed parameters are needed to comprehensively account for this condition and is expected to improve the accuracy of wind speed retrieval. Aside from altimeter instrument, the microwave radiometer onboard Jason-2 concurrently records the brightness temperature and the rain information. To accommodate related multiple parameters for wind speed derivation, the neural network approach is proposed. Its unique advantage is relationship among multi-parameters can be easily established without prior knowledge on their physical attributes. Therefore, this study intended to determine the multi-parameter neural network (MPNN) model in estimating altimeter wind speed during the tropical cyclone condition. The results proved that the MPNN technique has potential in reducing the root mean square error by 30% in comparison between tropical cyclone wind speed estimate by the existing algorithm.

Validation of Sentinel-3A/3B and Jason-3 Altimeter Wind Speeds and Significant Wave Heights Using Buoy and ASCAT Data

This study validated wind speed (WS) and significant wave height (SWH) retrievals from the Sentinel-3A/3B and Jason-3 altimeters for the period of data beginning 31 October 2019 (to 18 September 2019 for Jason-3) using moored buoy data and satellite Meteorological Operational Satellite Program (MetOp-A/B) Advanced Scatterometer (ASCAT) data. The spatial and temporal scales of the collocated data were 25 km and 30 min, respectively. The statistical metrics of root mean square error (RMSE), bias, correlation coefficient (R), and scatter index (SI) were used to validate the WS and SWH accuracy. Validation of WS against moored buoy data indicated errors of 1.19 m/s, 1.13 m/s and 1.29 m/s for Sentinel-3A, Sentinel-3B and Jason-3, respectively. The accuracy of Sentinel-3A/3B WS is better than that of Jason-3. All three altimeters underestimated WS slightly in comparison with the buoy data. Errors in WS at different speeds or SWHs increased slightly as WS or SWH increased. Over time, the accuracy of the Jason-3 altimeter-derived WS improved, whereas that of Sentinel-3A showed no temporal dependence. The WSs of the three altimeters were compared with ASCAT wind data for validation purposes over the global ocean without in situ measurements. On average, the WSs of the three altimeters were lower in comparison with the ASCAT data. The accuracy of the three altimeters was found to be consistent and stable at low/medium speeds but it decreased when the WS exceeded 15 m/s. Validations of SWH against buoy wave data indicated that the accuracy of Jason-3 SWH was better than that of Sentinel-3A/3B. However, the accuracy of all three altimeters decreased when the SWH exceeded 4 m. The accuracy of Sentinel-3A and Jason-3 SWH was temporally stable, whereas that of Sentinel-3B SWH improved over time. Analyses of SWH accuracy as a function of wave period showed that the Jason-3 altimeter was better than the Sentinel-3A/3B altimeters for long-period ocean waves. Generally, the accuracy of WS and SWH data derived by the Sentinel-3A/3B and Jason-3 altimeters satisfies their mission requirements. Overall, the accuracy of WS (SWH) derived by Sentinel-3A/3B (Jason-3) is better than that retrieved by Jason-3 (Sentinel-3A/3B).

Validation of Wind Speeds From Brown-Peaky Retracker in the Gulf of Mexico and East Coast of North America

The performance of Brown-Peaky (BP) retracker on retrieving the backscatter coefficients, and then wind speeds, in coastal zones has been examined in this article. Eight years of Jason-2 waveforms are reprocessed by the BP retracker. An empirical wind speed model is used to obtain the altimeter wind speeds based on the BP-derived backscatter coefficients. The validation of BP-derived wind speeds is conducted through comparisons with wind speeds from the in situ anemometer and Jason-2 official altimeter product. The along-track root mean square errors (RMSEs) and biases between altimeter and anemometer wind speed time series are calculated pointwise to evaluate the data quality. The orthogonal regression analysis is computed to evaluate the overall data quality. The validation results show that the BP-derived backscatter coefficients are highly correlated with the square of the off-nadir angles. By removing this correlation-induced error, the quality of BP-derived backscatter coefficients is comparable to that obtained from the three-parameter maximum likelihood estimator (MLE3). The improved backscatter coefficients are beneficial for retrieving reliable along-track wind speed observations, which allows for lower and smoother RMSEs and biases. The 1- and 2-Hz BP-derived wind speeds achieve similar performance, implying that the application of high rate altimeter wind speeds in coastal zones is possible. We also find that the altimeter wind speeds are significantly dependent on the sea state in the last 20-km distance to the coast, where the bias between altimeter and anemometer wind speeds increases remarkably and varies inversely with the offshore distance.

Extreme value statistics of wind speed and wave height of the Marmara Sea based on combined radar altimeter data

Both reliable and long-term wind and wave data are necessary for the design of coastal and offshore structures. Due to lack of sufficient in-situ measurement data, modeling data have been used in the limited number of wind and wave climate studies of the Marmara Sea. Satellites equipped with instruments capable of observing marine surface wind and ocean waves like Radar Altimeter can be another source for the long term wind and wave climate of the Marmara Sea. In this study, for the first time, the altimeter wind speed and the significant wave height data from different satellite missions are attempted to use in the climate and extreme value analysis of the Marmara Sea. Altimeter wind speeds and significant wave heights are compared with the in-situ measurements and it is found that while the altimeter wind speed agrees with the measurement data, the significant wave height data should be calibrated. After the calibration of the altimeter data and the inter-calibrations of earlier satellite missions, 27 years of altimeter wind speed and wave height data are obtained to use in extreme value analysis. The wind speed and the significant wave height values corresponding to various return periods are determined as a result of extreme value statistics and those values are compared with the results of the measurements and previous studies. Consistent extreme values computed in the current study indicate that the combined radar altimeter data can be used in the wind and the wave climate calculations and the extreme value analysis of the Marmara Sea.

A physics-based dual-frequency approach for altimeter wind speed retrieval

The altimeter normalized radar cross section (NRCS) has been used to retrieve the sea surface wind speed for decades, and more than a dozen of wind speed retrieval algorithms have been proposed. Despite the continuing efforts to improve the wind speed measurements, a bias dependence on wave state persists in all wind algorithms. On the basis of recent evidence that short waves are essentially modulated by local winds and much less affected by wave state, we proposed a physics-based approach to retrieve the wind speed from the dual-frequency difference in terms of the mean square slope of short waves. A collocated dataset of coincident altimeter/buoy measurements were used to develop and validate the approach. Validation against buoy measurements indicates that the approach is almost unbiased and has an overall root mean square error of 1.24 m s−1, which is 5.3% lower than the single-parameter algorithm in operational use (Witter and Chelton, 1991) and 2.4% lower than another dual-frequency approach (Chen et al., 2002). Furthermore, the results indicate that the new approach significantly improves the wave-dependent bias compared to the single-parameter algorithm. The capacity of altimeter to retrieve sea surface wind speed appears to be limited for the case of winds below 3 m s−1. The validity of the approach at high winds needs to be further examined in the future study.

One- and Two-Dimensional Wind Speed Models for Ka-Band Altimetry

abstract SARAL—the Satellite with ARgos and ALtiKa—is the first satellite radar altimetry mission to fly a Ka-band instrument (AltiKa). Ocean backscatter measurements in the Ka band suffer larger signal attenuation due to water vapor and atmospheric liquid water than those from Ku-band altimeters. An attenuation algorithm is provided, based on radar propagation theory, which is a function of atmospheric pressure, temperature, water vapor, and liquid water content. Because of the nature of the air–sea interactions between wind and surface gravity waves, the shorter wavelength Ka-band backscatter exhibits a different relationship with wind speed than at Ku band, particularly at moderate to high wind speeds. This paper presents a new one-dimensional wind speed model, as a function of backscatter only, and a two-dimensional model, as a function of backscatter and significant wave height, tuned to AltiKa’s backscatter measurements. The performance of these new Ka-band altimeter wind speed models is assessed through validation with independent ocean buoy wind speeds. The results indicate wind measurement accuracy comparable to that observed at Ku band with only slightly elevated noise in the wind estimates.

Research on the method of variational fusion of altimeter wind speed and radiometer wind speed

In order to realize the fusion of altimeter wind speed and radiometer wind speed, the Kriging method is used to interpolate radiometer wind speed to the altimeter path to obtain the altimeter wind speed observation operator, a cost function is established, and then the variational method is adopted to construct and analyze wind speed and derive fusion results. Simulation tests are carried out when only altimeter wind speed contains error, only radiometer wind speed contains error, both altimeter and radiometer wind speed contain error. The results show that through the variational fusion, the accuracy is improved, especially in the altimeter path. The Jason-1 altimeter cycle 241 wind speed data and spatial temporally matched Special Sensor Microwave Imager Sounder wind speed data (carried by Defense Meteorological Satellite Program F17) are selected to carry out the real test, and the results show that the fused wind speed is more close to the buoy observation, so it is confirmed that the variational fusion method is effective, statics shows that 60% altimeter and radiometer data matched, which means that fusion is an important theory and has application value. The variational fusion method can be applied to the fusion of HY-2 satellite altimeter and radiometer wind inversion results.

Detection of Long-Term Instabilities on Altimeter Backscatter Coefficient Thanks to Wind Speed Data Comparisons from Altimeters and Models

As long-term stability of altimeter backscatter coefficient (σ 0 ) can impact the accuracy of the Global Mean Sea Level (GMSL) trend through the Sea State Bias (SSB) correction, it is important to accurately monitor its evolution in order to detect potential instabilities. This study aims to characterize long-term uncertainties from several altimeter missions. The originality of our approach is to perform comparisons between wind speed estimations deduced from σ 0 measurements and from ERA-interim reanalysis. Thanks to the good temporal correlation between the Global Wind Speed (GWS) evolution issued from altimeters and the atmospheric reanalysis, the detection of small drifts is possible in the altimeter time series. In particular, drifts have been detected on Jason-1 GWS between mid-2004 and 2005 and on Envisat prior to 2004. They are associated to σ 0 jumps of approximately 0.03 dB. Larger anomalies have been detected on TOPEX σ 0 data with variations reaching 0.2 dB. These low altimeter wind speed variations originate from small σ 0 changes are within the instrumental stability requirements. However, via the SSB calculation, their impact on the GMSL evolution is estimated close to 0.1 mm/yr over the altimetry 20-year period. In addition, they strongly affect inter-annual GMSL variations with associated errors reaching 2 mm.

Dependence of mean square slope on wave state and its application in altimeter wind speed retrieval

Mean square slope (MSS) of sea surface is a parameter describing the sea surface roughness and plays a key role in understanding the sea surface dynamics. Although MSS is influenced by many factors such as wind speed and sea surface waves, it has been traditionally parameterized by wind speed only. In this study, the surface wave impact on MSS is investigated using a collocated data set of altimeter and buoy measurements. It is found that the MSS detected by Ku-band altimeter is closely related to the wind wave components and increases with the degree of wind wave development; however, it is almost independent of the swell. The wave effect on MSS is mainly ascribed to the contribution of longer waves rather than shorter waves. An analytical spectral MSS model is proposed, and it is shown that the MSS calculated from the model agrees well with the altimeter-measured MSS when the pseudo-wave age is adjusted to the wave age corresponding to wind waves. This model is applied to derive wind speeds from altimeter data including the normalized radar cross section and the significant wave height of sea surface waves. With the collocated data set, it is shown that this new algorithm performs better than previous empirical algorithms.

Response to Comment on “Global Trends in Wind Speed and Wave Height”

We acknowledge that the special sensor microwave/imager (SSM/I) studies identified by Wentz and Ricciardulli were overlooked. These studies report wind speed trends 1.4 to 2.4 times smaller than our altimeter data. However, the reported altimeter wind speed trends are consistent with limited buoy data and exhibit scatter consistent with the calculated error statistics.

Estimating Gale to Hurricane Force Winds Using the Satellite Altimeter

Abstract A new model is provided for estimating maritime near-surface wind speeds (U10) from satellite altimeter backscatter data during high wind conditions. The model is built using coincident satellite scatterometer and altimeter observations obtained from QuikSCAT and Jason satellite orbit crossovers in 2008 and 2009. The new wind measurements are linear with inverse radar backscatter levels, a result close to the earlier altimeter high wind speed model of Young (1993). By design, the model only applies for wind speeds above 18 m s−1. Above this level, standard altimeter wind speed algorithms are not reliable and typically underestimate the true value. Simple rules for applying the new model to the present-day suite of satellite altimeters (Jason-1, Jason-2, and Envisat RA-2) are provided, with a key objective being provision of enhanced data for near-real-time forecast and warning applications surrounding gale to hurricane force wind events. Model limitations and strengths are discussed and highlight the valuable 5-km spatial resolution sea state and wind speed altimeter information that can complement other data sources included in forecast guidance and air–sea interaction studies.

A new wind retrieval algorithm for Jason-1 at high wind speeds

The altimeter wind speed algorithm at high wind speeds remains unsolved because of lack of observed data. In this study data at high wind speeds were generated using Yin's typhoon model, which consists of the Rankine vortex model and the angular momentum model with typhoon parameters, provided by the Joint Typhoon Warning Centre (JTWC). The accuracy of Yin's typhoon model can be validated by comparing it with recorded data from a weather station. By comparing the normalized radar backscatter cross‐section (NRCS) detected by the Jason‐1 altimeter with wind speed data inferred by Yin's typhoon model, an empirical algorithm valid for a range of wind speeds between 10 and 40 m s–1 is developed and proposed. The proposed algorithm is compared with the Jason‐1 operational algorithm and Young's altimeter wind retrieval algorithm. The study shows that, for the proposed algorithm and the operational algorithm for Jason‐1, the root mean square (RMS) errors are 3.38 and 3.60 m s–1, respectively, and the average relative errors are 18% and 19%, respectively, for wind speeds less than 27 m s–1. Hence, the proposed algorithm is in agreement with the operational algorithm for the Jason‐1 altimeter for wind speeds in the range 10–27 m s–1. However, the Jason‐1 operational algorithm is inaccurate for wind speeds above 27 m s–1 because the wind speeds used in the algorithm training process came from scatterometer wind products, and are significantly lower than those in strong wind and heavy rain conditions. Comparison of the proposed algorithm and Young's algorithm shows that the RMS errors are 6.27 and 15.18 m s–1, respectively, and the average relative errors are 16% and 59%, respectively, for wind speeds greater than 20 m s–1. The Holland typhoon model cannot accurately determine the outer wind field of typhoons since it extends cyclonic wind speeds to infinity. Young's altimeter wind retrieval algorithm depends on the Holland typhoon model, and the latter results in some errors. Compared with Young's altimeter wind retrieval algorithm, the proposed algorithm retrieves wind speeds with better accuracy. Therefore, the proposed algorithm, suitable for retrieving sea surface wind speeds in typhoons and other strong wind conditions, can be considered as supplementary to the Jason‐1 operational algorithm.

A new approach to adjusting sea surface wind using altimeter wind data by variational regularization method

To adjust non-divergent and divergent background wind fields by altimeter wind data, a novel approach, the variational regularization method, is presented. Numerical simulation results show that it is positive to use altimeter wind speed to adjust the background wind field, particularly effective in the altimeter track region. At the same time, the radar backscattering cross-section sensitivity is evaluated, when the backscattering cross-section contains noise, and the result proves that the above method can be used to suppress the noise. Finally, a real case shows that the above method is feasible and effective. This approach is an effective and reliable method of using the altimeter wind data to adjust the sea surface wind.

Observing and Studying Extreme Low Pressure Events with Altimetry

The ability of altimetry to detect extreme low pressure events and the relationship between sea level pressure and sea level anomalies during extra-tropical depressions have been investigated. Specific altimeter treatments have been developed for tropical cyclones and applied to obtain a relevant along-track sea surface height (SSH) signal: the case of tropical cyclone Isabel is presented here. The S- and C-band measurements are used because they are less impacted by rain than the Ku-band, and new sea state bias (SSB) and wet troposphere corrections are proposed. More accurate strong altimeter wind speeds are computed thanks to the Young algorithm. Ocean signals not related to atmospheric pressure can be removed with accuracy, even within a Near Real Time context, by removing the maps of sea level anomaly (SLA) provided by SSALTO/Duacs. In the case of Extra-Tropical Depressions, the classical altimeter processing can be used. Ocean signal not related to atmospheric pressure is along-track filtered. The sea level pressure (SLP)-SLA relationship is investigated for the North Atlantic, North Pacific and Indian oceans; three regression models are proposed allowing restoring an altimeter SLP with a mean error of 5 hPa if compared to ECMWF or buoys SLP. The analysis of barotropic simulation outputs points out the regional variability of the SLP/Model Sea Level relationship and the wind effects.

An analytical algorithm with a wave age factor for altimeter wind speed retrieval

Based on the specular reflection theory of electromagnetic waves at rough sea surface and the wind wave spectrum model with a wave age factor, the sea surface wind speeds are retrieved from the normalized radar backscatter cross‐section (NRCS) measured by TOPEX/Poseidon (T/P) Ku‐band altimeter using the mean square slope (MSS) calculated from the spectrum models of the wind waves and the gravity‐capillary waves. A relationship between wave age and non‐dimensional wave height is applied to compute the wave age factor using the significant wave height (SWH) and wind speeds obtained from buoy or altimeter simultaneously. The study indicates that the wave age factor has a significant impact on the retrieval of altimeter wind speed. Compared with the operational algorithm for retrieving altimeter wind speed, the wind speed retrieved from the new analytical algorithm based on the wind wave spectrum model with the wave age factor, proposed in this study, can match the buoy measurements better. The effects of the wave age factor on altimeter wind speed retrieval are also shown quantitatively through a series of experiments and measurements. The comparison with the operational algorithm indicates that both the bias and root mean square error (RMSE) between wind speeds retrieved by the proposed analytical algorithm and those observed by the buoy decrease significantly. In the Gulf of Mexico, with the new analytical algorithm, more accurate altimeter wind speeds are retrieved.

A Two-Parameter Wind Speed Algorithm for Ku-Band Altimeters

Globally distributed crossovers of altimeter and scatterometer observations clearly demonstrate that ocean altimeter backscatter correlates with both the near-surface wind speed and the sea state. Satellite data from TOPEX/Poseidon and NSCAT are used to develop an empirical altimeter wind speed model that attenuates the sea-state signature and improves upon the present operational altimeter wind model. The inversion is defined using a multilayer perceptron neural network with altimeter-derived backscatter and significant wave height as inputs. Comparisons between this new model and past single input routines indicates that the rms wind error is reduced by 10%–15% in tandem with the lowering of wind error residuals dependent on the sea state. Both model intercomparison and validation of the new routine are detailed, including the use of large independent data compilations that include the SeaWinds and ERS scatterometers, ECMWF wind fields, and buoy measurements. The model provides consistent improvement against these varied sources with a wind-independent bias below 0.3 m s−1. The continuous form of the defined function, along with the global data used in its derivation, suggest an algorithm suitable for operational application to Ku-band altimeters. Further model improvement through wave height inclusion is limited due to an inherent multivaluedness between any single realization of the altimeter measurement pair [σo, HS] and observed near-surface winds. This ambiguity indicates that HS is a limited proxy for variable gravity wave properties that impact upon altimeter backscatter.

A dual‐frequency approach for retrieving sea surface wind speed from TOPEX altimetry

More than a dozen of wind speed (U) algorithms have been proposed during the past 2 decades, as a result of a continuing effort to improve altimeter wind measurement. The progress in terms of accuracy, however, is seen to be rather slow. The reported root mean square (RMS) error of prevailing algorithms varies mostly between 1.6 and 2.0 m/s for the dominant wind regime. As far as the TOPEX altimeter is concerned, three measured quantities, namely, the radar cross sections from Ku and C band (σKu and σC), as well as the significant wave height (Hs), have been used in previous algorithm developments, resulting in a variety of single‐, dual‐, and three‐parameter model functions. On the basis of the finding of a banded dependency of the U–σKu relationship on σC a new approach for retrieving altimeter wind speed, termed linear composite method (LCM), is proposed in this study. The LCM model function appears as a set of σC‐dependent linear relations between U and σKu. A unique advantage of this approach is that it allows the algorithm to be tuned or expanded for a given range of wind speed without affecting the rest. Over 1.7 million coincident TOPEX/NASA scatterometer (NSCAT) and TOPEX/QuikSCAT data covering a period of 2.5 years are used to adjust the model. Validation against extensive buoy measurements indicates that the LCM algorithm is almost unbiased and has an overall RMS error of 1.56 m/s, which is 12% lower compared to the algorithm in operational use [Witter and Chelton, 1991]. In addition, a small (2.5–6%, depending on the reference data set) but significant improvement is found for the LCM when compared to the most recent dual‐parameter algorithm [Gourrion et al., 2002].