Normalized Radar Cross Section Data Research Articles

Ocean Surface Wind Speed Dependence and Retrieval From Off‐Nadir CFOSAT SWIM Data

AbstractIn this study, we analyzed the ocean surface wind speed dependence of off‐nadir data from the Surface Waves Investigation and Monitoring (SWIM) aboard the Chinese‐French Oceanography Satellite (CFOSAT). When the data were analyzed, the SWIM normalized radar cross section (NRCS) at most incidence angles decreased along the NCEP Global Forecast System wind speed. This trend is consistent with that in a Ku‐band geophysical model function at low incidence angles (KuLMOD2S), although there was a systematic bias between the two. The consistency indicates that the SWIM data quality was normal in terms of wind speed dependence. The bias was recalibrated using a numerical weather prediction‐based ocean calibration method and the wind speeds were retrieved from the recalibrated NRCS data using the KuLMOD2S model. Finally, the retrievals at off‐nadir as well as the wind products at nadir were validated using collocated CFOSAT scatterometer (CSCAT) wind measurements. When the results were validated, the retrievals at 0.5° were consistent with the averaged wind products at nadir and both had a root mean square difference (RMSD) around 1.4 m/s compared to the CSCAT winds. Moreover, the winds retrieved from the SWIM data at most incidence angles were in good agreement with the CSCAT winds,and the RMSD values were within 2.0 m/s for incidence angles less than 7.5°, although the retrieval accuracy was poor at larger incidence angles. These results demonstrate that the SWIM data at incidence angles below 7.5° have an ability to retrieve the wind speed.

Relationship between Horizontal Wind Velocity and Normalized Surface Cross Section Using Data from the HIWRAP Dual-Frequency Airborne Radar

AbstractThe High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) dual-frequency conically scanning airborne radar provides estimates of the range-profiled mean Doppler and backscattered power from the precipitation and surface. A velocity–azimuth display analysis yields near-surface estimates of the mean horizontal wind vector υh in cases in which precipitation is present throughout the scan. From the surface return, the normalized radar cross section (NRCS) is obtained, which, by a method previously described, can be corrected for path attenuation. Comparisons between υh and the attenuation-corrected NRCS are used to derive transfer functions that provide estimates of the wind vector from the NRCS data under both rain and rain-free conditions. A reasonably robust transfer function is found by using the mean NRCS (⟨NRCS⟩) over the scan along with a filtering of the data based on a Fourier series analysis of υh and the NRCS. The approach gives good correlation coefficients between υh and ⟨NRCS⟩ at Ku band at incidence angles of 30° and 40°. The correlation degrades if the Ka-band data are used rather than the Ku band.

Evaluation of Geolocation Errors of the Chinese HY-2A Satellite Microwave Scatterometer

The Chinese HY-2A satellite microwave scatterometer (HSCAT) is designed to measure the ocean surface wind vector with a large observation swath. HSCAT is a Ku-band scatterometer that has continuously observing the earth’s surface for more than 6 years. Approximately, 30 000 orbit revolutions of scientific data are archived in its ground application system. To further understand the characteristics of the instrument, deeper analyses of these scientific data must be carried out. Currently, the geolocation accuracy of the normalized radar cross section (NRCS) has yet to be documented. However, geolocation errors can cause significant measurement bias, and have large impacts on the calibration and validation results. In this paper, the coastline crossing detection method is used to evaluate the geolocation errors of the NRCS data. Some coastal areas and islands where the NRCS data have large gradients are selected for the geolocation error analysis. The coastline inflection points can be identified on the basis that the radar backscatter of land and water exhibits a significant difference when the ocean surface wind speed is relatively low. The longitudes and latitudes of these points from different time periods are then fit to the global self-consistent hierarchical high-resolution shorelines map. The comparison results show that the overall distance error of HSCAT is approximately 2.59 km. This geolocation accuracy meets the instrument design requirements. The sources of geolocation errors are also analyzed in this paper in order to improve the geolocation accuracy.

An Electromagnetic Detection Case Study on Crude Oil Injection in a Young Sea Ice Environment

This paper presents a multidisciplinary case study on a crude oil injection experiment in an artificially grown young sea ice environment under controlled conditions. In particular, the changes in the geophysical and electromagnetic responses of the sea ice to oil introduction are investigated for this experiment. Furthermore, we perform a preliminary study on the detection of oil spills utilizing the normalized radar cross section (NRCS) data collected by a C-band scatterometer is presented. To this end, an inversion scheme is introduced that retrieves the effective complex permittivity of the domain prior and after oil injection by comparing the simulated and calibrated measured NRCS data, while roughness parameters calculated using lidar are utilized as prior information. Once the complex permittivity values are obtained, the volume fraction of oil within the sea ice is found using a mixture formula. Based on this volume fraction, a binary detection of oil presence seems to be possible for this test case. Finally, the possible sources of error in the retrieved effective volume fraction, which is an overestimate of the actual value, are identified and discussed by macrolevel and microlevel analyses through bulk salinity measurements and X-ray imagery of the samples, as well as a brief chemical analysis.

Landfast First-Year Snow-Covered Sea Ice Reconstruction via Electromagnetic Inversion

The inversion of the monostatic normalized radar cross section (NRCS) data collected by an on-site C-band scatterometer and also RADARSAT-2 satellite are investigated to reconstruct some parameters of interest associated with landfast snow-covered sea ice in Cambridge Bay, Nunavut, Canada. The parameters of interest are temperature, density, salinity, and snow grain size. To this end, this remote sensing problem is cast as an inverse scattering problem in which a data misfit cost functional is to be minimized using a differential evolution algorithm. This minimization requires repetitive calls to an appropriate electromagnetic forward solver. The utilized electromagnetic forward solver attempts to model both surface and volume scattering components associated with the irradiated rough multilayered medium under investigation. The reconstruction results demonstrate the ability of this inversion algorithm to retrieve the parameters of interest with reasonable accuracy. In particular, the best performance of the inversion algorithm occurs when both the scatterometer and satellite NRCS data are simultaneously used in the inversion process.

Impact of rain cell on scatterometer data: 2. Correction of Seawinds measured backscatter and wind and rain flagging

Rain can strongly modify the normalized radar cross section (NRCS) measured by Ku‐band scatterometers and alter the wind vector retrieval. Part 1 of this paper (Tournadre and Quilfen, 2003) presented a theoretical model of interaction between rain and scatterometer signal and used it to quantify the effect of rain on the backscatter and on wind vectors. Their results showed that the scatterometer data are strongly affected by rain, that they are extremely sensitive to the rain distribution within scatterometer resolution cells, and that the normalized radar cross section (NRCS) variability induced by rain could be a good indicator for rain flagging. The model is further tested and validated on a tropical cyclone case using colocated high resolution rain and Seawinds NRCS data. The model is used to compute attenuation and volume scattering from Tropical Rainfall Mapping Mission Precipitation Radar (TRMM PR) rain data. The comparison of the high‐resolution (4 km) NRCS to synthetic NRCS computed from National Hurricane Center (NHC) winds and modeled rain terms shows a good qualitative agreement. The rain terms are used to correct the measured NRCS, to infer corrected winds which are significantly improved compared to NHC winds, especially for high winds. The wind correction using low‐resolution rain data (such as Special Sensor Microwave Imager (SSM/I) ones) is also investigated using rain data averaged over wind scatterometer cells. This can also significantly improve the rain retrieval. A new rain flag based on the NRCS variability within wind vector cells is presented and shown to perform better than the operational one.

A two-scale model to predict C-band VV and HH normalized radar cross section values over the ocean

This paper presents a two-scale model that can predict C-band, VV polarized (C-VV), normalized radar cross section values generated from the CMOD4 model and calibrated normalized radar cross section values derived from C-band, HH polarized (C-HH), RADARSAT-1 synthetic aperture radar imagery with coincident buoy observations of the local wind. The model is based on standard composite models that incorporate tilt modulations, with a new approach for incorporating hydrodynamic modulations. It is shown that inclusion of the hydrodynamic term does not significantly impact the fit to normalized radar cross section (NRCS) values, but does allow the model to accurately predict the upwind to downwind C-VV ratios and improves the model fit to simultaneous C-VV and C-HH NRCS observations. The model uses a new wave height spectrum that is a linear combination of the Apel and Romeiser spectra, weighted heavily toward the Apel form, and has modified values within the C-band Bragg wavenumber regime which are close to midway between the two spectra. The final model can represent the CMOD4 C-VV NRCS results with a root-mean-square error (RMSE) of 0.47 dB and can represent the RADARSAT-1 C-HH NRCS observations, without any change to the model parameters, with an RMSE of 1.9 dB. The model can accurately reproduce the upwind to downwind C-VV NRCS ratios from CMOD4 (RMSE = 0.15 dB) and fits simultaneous C-VV to C-HH ratios derived from aircraft observations to within 1 dB (RMSE = 0.65 dB). If the C-HH hydrodynamic term is allowed to be scaled differently than the C-VV hydrodynamic term, it lowers the RMSE for the simultaneous C-VV to C-HH aircraft observations to 0.49 dB while not affecting the fit to C-VV and C-HH NRCS values. Compared to other C-HH models published in the literature, this model provides a better fit to the RADARSAT-1 C-HH NRCS data across a larger range of conditions than any other single model, provides a better fit to simultaneous C-VV and C-HH NRCS data at an incidence angle of 20°, and reproduces the decreasing trend in the C-VV to C-HH ratio with increasing wind speed observed in data.

Satellite scatterometer studies of ocean surface stress and drag coefficients using a direct model

This study evaluates the accuracy of estimates of sea surface friction velocity derived using a new model function that operates directly on the NASA scatterometer normalized radar cross‐section (NRCS) measurements. These NRCS data are first collocated with numerous National Data Busy Center buoys (within ±0.5° latitude and longitude) and are then processed using the new Weissman et al. [1994] Frontal Air‐Sea Interaction Experiment model to produce 25‐km by 25‐km spatial averages of the friction velocity for several contiguous areas within this region. One method of validation of these estimates involves comparisons with friction velocity estimates derived from data collected by the buoys using bulk methods. This was conducted at numerous locations; the Atlantic and Pacific coastlines and within the Gulf of Mexico. It is found that there is excellent agreement between these two different measurements. In addition, this approach enabled the derivation of estimates of the surface drag coefficient, with the use of the buoy winds, neutral stability winds at 10‐m elevation, U10N, and the scatterometer surface friction velocity u*. This permitted the study of the dependence of this drag coefficient CD on wind speed in these different regions on spatial scales, which had not previously been possible. Comparisons are made to previously published models of CD that only depend on wind speed, with generally good agreement at winds over 6 m−1 s, but with significant regional differences at winds less than 6 m−1 s.