scholarly journals HF RADAR MAPPING OF EXTENSIVE OCEAN WINDFIELDS

1980 ◽  
Vol 1 (17) ◽  
pp. 20 ◽  
Author(s):  
P.E. Dexter ◽  
R.C. Casey
Keyword(s):  
Meteorological Data ◽  
Wave Spectrum ◽  
Surface Wind ◽  
Doppler Frequency ◽  
Hf Radar ◽  
Sea Surface ◽  
Propagation Mode ◽  
Wave Spectra ◽  
Radio Waves ◽  
Sea Wave

The possibility of deriving parameters of sea wave spectra remotely from characteristics of radio waves at high frequency (HF) scattered from the sea surface was first raised when Crombie (1955) correctly deduced that Doppler frequency shifts in the signal returned from short range in his HF radar resulted uniquely from components of the sea wave spectrum having wavelengths exactly one-half the radio wavelength, and travelling radially with respect to the radar. Since then the technique has been expanded in two directions: (a) The use of ionospherically propagated-JiF radio waves ('Skywave' HF radar) to^ examine extensive ocean areas out to some 4000 km from the observing site, to obtain oceanographic and meteorological data suitable for input to synoptic observation systems. This approach has been developed through the experimental work of Tveten (1967) and Ward (1969), and the empirical technique proposed by Long and Trizna (1973) to allow the simple extraction of sea surface wind vectors from Doppler spectra of the backscattered radio signals. (b) The determination of directional sea wave spectra and sea surface currents at short ranges with HF radars operating in the groundwave propagation mode, based on theoretical analyses of the scattering process such as those of Barrick (1972). The HF Skywave radar constructed and operated at Townsville by the Physics Department of James Cook University has been employed for some years now on research into the possibilities for mapping sea states and sea surface winds over ocean areas surrounding Australia (Ward, 1969; Ward and Dexter, 1976; Dexter and Casey, 1978).

Assessment of the sea surface wind and current retrieval from ALOS-2 and Sentinel-1 SAR data over coastal areas

2021 ◽  
Author(s):  
Anis Elyouncha ◽  
Leif E. B. Eriksson
Keyword(s):  
Wind Speed ◽  
Frequency Shift ◽  
Doppler Shift ◽  
Surface Wind ◽  
Surface Current ◽  
Doppler Frequency ◽  
Doppler Frequency Shift ◽  
Sea Surface ◽  
Surface Velocity ◽  
L Band

<p><span>Synthetic aperture radar (SAR) has become an essential component in ocean remote sensing due it’s </span><span>high</span> <span>sensitivity</span><span> to sea surface dynamics and its high spatial resolution. </span><span>The ALOS-</span><span>2 SAR</span><span> data are </span> underutilized <span>for</span><span> ocean surface wind and current retrieval. Althou</span><span>g</span><span>h the primary goals of the </span><span>ALOS-2</span><span> mission are focused on land applications, the extension of the satellite scenes over the coast</span><span>al areas</span><span> offers an opportunity for ocean applications. Th</span><span>e</span><span> underutilization </span><span>of ALOS-2 data </span><span>is mainly due to the fact that at low radar frequencies, e.g. L-band, the sensitivity of the radar scattering coefficient to wind speed and the sensitivity of the Doppler frequency shift to sea surface velocity is lower than at higher frequencies, e.g. C- </span><span>and</span><span> X-</span><span>band</span><span>. </span><span>This is also due to the fact that most of ALOS-2 images are acquired in HH or HV polarization while the VV polarization is often preferred in ocean applications due the higher signal to noise ratio. </span></p><p>The wind speed is retrieved from Sentinel-1 and ALOS-2 using the existing empirical C- and L-band geophysical model functions. For Sentinel-1, the Doppler frequency shift provided in the OCN product is used. For ALOS-2, the Doppler frequency shift is estimated from the single look complex data using the pulse-pair processing method. The estimated Doppler shift converted to the surface radial velocity and the velocity is calibrated using land as a reference. The estimated L-band Doppler shift and surface velocity is compared to the C-band Doppler shift provided in the Sentinel-1 OCN product. Due the difference in the local time of ascending node (about 6 hours at the equator) of the two satellites, a direct pixel-by-pixel comparison is not possible, i.e. the wind and surface current can not be assumed to be constant during such a large time difference. Thus, the retrieved wind from each sensor is compared separately to model data and in-situ observations.</p><p>In this paper, the quality of the wind speed retrieved from the L-band SAR (ALOS-2) in coastal areas is assessed and compared to the C-band SAR (Sentinel-1). In addition, the feasibility of the surface current retrieval from the L-band Doppler frequency shift is investigated and also compared to Sentinel-1. Examples will be shown and discussed. This opens an opportunity for synergy between L-band and C-band SAR missions to increase the spatial and temporal coverage, which is one of the main limitations of SAR application in ocean remote sensing.</p>

scholarly journals Characteristics and Applications of the Ground-Based X Band Low Elevation Angle Brightness Temperatures under Low Sea State Based on Measured Data

2020 ◽  
Vol 12 (11) ◽  
pp. 1736
Author(s):  
Zhongqing Cao ◽  
Lixin Guo ◽  
Shifeng Kang ◽  
Xianhai Cheng ◽  
Qingliang Li ◽  
...  
Keyword(s):  
Wind Speed ◽  
Meteorological Data ◽  
Surface Wind ◽  
Elevation Angle ◽  
Surface Wind Speed ◽  
Sea Surface ◽  
Sea State ◽  
Sea Surface Wind Speed ◽  
Sea Surface Wind ◽  
X Band

In ground-based microwave radiometer remote sensing, low-elevation-angle (−3°~3°) radiation data are often discarded because they are considered to be of little value and are often difficult to model due to the complicated mechanism. Based on the observed X-band horizontal polarization low elevation angle microwave radiation data and the meteorological data at the same time, this study investigated the generation mechanism of low elevation angle brightness temperature (LEATB) and its relationship with meteorological data, i.e., temperature, humidity, and wind speed, under low sea state. As a result, one could find that the LEATB was sensitive to the atmosphere at the elevation angle between 1° to 3°, and a diurnal variation of the LEATB reached up to 10 K. This study also found a linear relationship between the LEATB and sea surface wind speed under low sea state at an elevation range from −3° to 0°, i.e., the brightness temperature decreased as the wind speed increased, which was inconsistent with the observations at the elevation angle from −10° to −5°. The variation of the LEATB difference according to the change in the over-the-horizon detection capability (OTHDC) of the shipborne microwave radar was examined to identify the reason for this phenomenon theoretically. The results showed that the LEATB difference was significantly influenced by a change in the OTHDC. Further, this study examined a remote sensing method to extract the sea surface wind speed data from experimental LEATB data under low sea state. The results demonstrated that the X-band horizontal polarization LEATBs were useful to retrieve the sea surface wind speed data at a reasonable accuracy—the root mean square error of 0.02408 m/s. Overall, this study proved the promising potential of the LEATB data for retrieving temperature profiles, humidity profiles, sea surface winds, and the OTHDC.

scholarly journals Sea Surface Wind Correction Using HF Ocean Radar and Its Impact on Coastal Wave Prediction

2017 ◽  
Vol 34 (9) ◽  
pp. 2001-2020 ◽  
Author(s):  
Yukiharu Hisaki
Keyword(s):  
Wind Direction ◽  
Surface Wind ◽  
Hf Radar ◽  
Sea Surface ◽  
Wave Parameter ◽  
Simple Method ◽  
Wind Speeds ◽  
Wave Prediction ◽  
Sea Surface Wind ◽  
The Impact

AbstractBoth wind speeds and wind directions are important for predicting wave heights near complex coastal areas, such as small islands, because the fetch is sensitive to the wind direction. High-frequency (HF) radar can be used to estimate sea surface wind directions from first-order scattering. A simple method is proposed to correct sea surface wind vectors from reanalysis data using the wind directions estimated from HF radar. The constraints for wind speed corrections are that the corrections are small and that the corrections of horizontal divergences are small. A simple algorithm for solving the solution that minimizes the weighted sum of the constraints is developed. Another simple method is proposed to correct sea surface wind vectors. The constraints of the method are that corrections of wind vectors and horizontal divergences from the reanalysis wind vectors are small and that the projection of the corrected wind vectors to the direction orthogonal to the HF radar–estimated wind direction is small. The impact of wind correction on wave parameter prediction is large in the area in which the fetch is sensitive to wind direction. The accuracy of the wave prediction is improved by correcting the wind in that area, where correction of wind direction is more important than correction of wind speeds for the improvement. This method could be used for near-real-time wave monitoring by correcting forecast winds using HF radar data.

Simulation Research about HJ-1C SAR Wind Mapping Capability

2012 ◽  
Vol 500 ◽  
pp. 550-555
Author(s):  
Feng Feng Chen ◽  
Wei Gen Huang ◽  
Jing Song Yang
Keyword(s):  
Wave Spectrum ◽  
Surface Wind ◽  
Coastal Region ◽  
Sea Surface ◽  
Surface Model ◽  
Composite Surface ◽  
Envisat Asar ◽  
Wind Speeds ◽  
Sea Surface Wind ◽  
Wind Mapping

Synthetic aperture radar (SAR) on aboard Chinese Huan Jing (HJ)-1C satellite has been planed to be launched in 2010. The satellite will fly in a sun-synchronous polar orbit of about 500-km altitude. SAR will operate in S band with HH polarization. Its image mode has the incidence angles 25°and 47°at the near and far sides of the swath respectively. SAR image has a spatial resolution of 20 m with a swath of 100 km. Here, the sea surface wind mapping capability of the SAR in the Chinese Coastal Region has been examined using M4S radar imaging model developed by Romeiser et al. The model is based on Bragg scattering theory in a composite surface model expansion. It accounts for contributions of the full ocean wave spectrum to the radar backscatter from ocean surface. The model reproduces absolute normalized radar cross section (NRCS) values for wide ranges of wind speeds. The model results of HJ-1C SAR have been compared with the model results of Envisat and Radarsat SAR signals. It shows that HJ-1C SAR is as good as both Envisat ASAR and Radarsat SAR at sea surface wind mapping Capability.

scholarly journals Apply GNSS-Reflectometry Technique for wind retrieval in typhoon condition

2019 ◽  
Vol 94 ◽  
pp. 05003
Author(s):  
Hwa Chien ◽  
Quang-Huy Lu ◽  
Wen-Hao Yeh
Keyword(s):  
Wind Speed ◽  
Gravity Wave ◽  
Wave Spectrum ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Data Uncertainty ◽  
Wave Spectra ◽  
Mean Square ◽  
Time Data ◽  
Wave Directionality

Global Navigation Satellite System-Reflectometry (GNSS-R) is an innovative Earth observation technique that exploits signal from satellite constellations after reflection on the Eart h surface. The GNSS-R techniques is also known to have the potential of mapping the surface wind speed up to 70 m/s, and thus provide a promising solution. Abundant real-time data of the typhoon surface wind speed will play the crucial role on the improvement of typhoon intensification forecasting. The aim of present study is to investigate the influences of high wind speed and the corresponding giant waves during typhoon to the GNSS-R wind speed retrieval algorithms. The Mean Square Slope that altered by the complex wave directionality is discussed. Finally, the uncertainties of wind speed retrieval with respect to the influences of wave directionality is assessed. The level 1 product from the GNSS-R receiver is a map of GPS signal power scattered from the sea surface, as a 2D function of delay and Doppler frequency, which is known as a Delay-Doppler Map, or DDM. Based on Zavorotny-Voronovich model, the DDMs are simulated from the Directional Mean Square Slope (DMSS) that obtained from the combination of capillary wave spectra and gravity wave spectra. The gravity wave spectra were calculated using a 3rd generation numerical wave model that driven by the Dujuan typhoon (2015) wind fields with super-fine resolution. The complexity of directional wave spectrum, such as extreme spatial heterogeneity, bimodal spectra and varying directional spreading alter the DMSS and DDM. Various observables, e.g. DDM Average (DDMA), and Leading Edge Slope (LES) are then applied to the simulated DDM. Regression-based wind retrievals are developed for each individual observable using empirical geophysical model functions. The wind speed retrieval in case of Dujuan typhoon are compared with the target data uncertainty assessment.

scholarly journals VERIFICATION OF A MODIFIED BAYESIAN METHOD FOR ESTIMATING DIRECTIONAL WAVE SPECTRA FROM HF RADAR

2011 ◽  
Vol 1 (32) ◽  
pp. 65 ◽  
Author(s):  
Lukijanto Lukijanto ◽  
Noriaki Hashimoto ◽  
Masaru Yamashiro
Keyword(s):  
Field Data ◽  
Bayesian Method ◽  
Wave Spectrum ◽  
Coastal Engineering ◽  
Hf Radar ◽  
Ocean Wave ◽  
Wave Spectra ◽  
Directional Spectrum ◽  
Radio Science ◽  
Directional Wave

A Modified Bayesian Method (MBM) for estimating directional wave spectra from Doppler spectra obtained by HF radar is examined using field data which were employed in the verification of Bayesian Method (BM). Applicability, validity and accuracy of the MBM are demonstrated compared with the directional wave spectra estimated by BM and observed by buoy acquired from the reliable field data obtained from Surface Current and Wave Variability Experiments (SCAWVEX) project. The necessary conditions of the Doppler spectral components to be used to estimate a reliable directional spectrum are correspondingly estimated by BM. The results clearly demonstrate that directional wave spectra can be estimated by MBM on the basis of Doppler spectra. In addition, though BM shows very time consuming in computations, BM is more robust against the presence of noise than MBM. References Akaike, H. (1980). Likelihood and Bayesian procedure, Bayesian statistics. In J.M. Bernardo, M.H. De Groot, D.U. Lindley, and A.F.M. Smith (Eds.), 143-166. Valencia: University Press. PMid:6252024 Barrick, D. E. (1972a). First order theory and analysis of MF/HF/VHF scatter from sea. IEEE Trans., Antennas Propagation, 20, 2-10. http://dx.doi.org/10.1109/TAP.1972.1140123 Barrick, D. E. (1977). Extraction of wave parameters from measured HF radar sea-echo Doppler spectra. Radio Science, 12(3), 415–424. http://dx.doi.org/10.1029/RS012i003p00415 Crombie, D. (1955). Doppler spectrum of sea echo at 13.56Mc/s. Nature, 175, 681-682. http://dx.doi.org/10.1038/175681a0 Hashimoto, N. and Kobune, K. (1986). Estimation of directional spectra from the maximum entropy principle. Proceedings of 5th International Offshore Mechanics and Arctic Engineering Symposium, 1, 80-85. Hashimoto, N., Kobune, K., and Kameyama, Y. (1987). Estimation of directional spectrum using the Bayesian approach, and its application to field data analysis. Report of P.H.R.I., 26(5), 57-100. Hashimoto N., and Tokuda M., (1999): A Bayesian Method Approach for Estimation of Directional Wave Spectra with HF radar, Coastal Engineering Journal, vol. 41, 137-147. http://dx.doi.org/10.1142/S0578563499000097 Hashimoto, N., Wyatt, L and Kojima, S. (2003): Verification of Bayesian Method for Estimating Directional Spectra from HF Radar Surface. Coastal Engineering Journal, 45(2), 255-274. http://dx.doi.org/10.1142/S0578563403000725 Hashimoto, N., Lukijanto, and Yamashiro, M. (2008). Development of a practical method for estimating directional spectrum from HF radar backscatter. Annual Journal of Coastal Engineering (in Japanese), 55(1), 1451-1455. http://dx.doi.org/10.2208/proce1989.55.1451 Hisaki, Y. (1996). Nonlinear inversion of the integral equation to estimate ocean wave spectra from HF radar. Radio science, 31(1), 25-39. http://dx.doi.org/10.1029/95RS02439 Howell, R., and Walsh, J. (1993). Measurement of ocean wave spectra using a ship mounted HF radar. IEEE Journal of Oceanic Engineering, 18(3), 306-310. http://dx.doi.org/10.1109/JOE.1993.236369 Lipa, B. J. and Barrick, D.E. (1982) : Analysis Methods for Narrow-Beam High-Frequency Radar Sea Echo, NOAA Technical Report ERL 420-WPL 56, 1-55. Lukijanto, Hashimoto, N., and Yamashiro, M. (2009a). Further modification practical method for estimating directional wave spectrum by HF radar. Proc. of 19 th ISOPE, 898-905. Lukijanto, Hashimoto, N., and Yamashiro, M. (2009b). An improvement of Modified Bayesian Method for estimating directional wave spectra from HF radar backscatter. Proceedings of 5 th APAC (Asian and Pacific Coasts), 105-111. Lukijanto, Hashimoto, N., and Yamashiro, M. (2009c). A comparison of analysis methods for estimating directional wave spectrum from HF ocean radar. Journal of Memoirs of the Faculty of Engineering, 69(4). Kyushu University, 163-185. Wyatt, L.R. (1990). A relaxation method for integral inversion applied to HF radar measurement of the ocean wave directional spectrum. International Journal Remote Sensing, 11(8), 1481-1494. http://dx.doi.org/10.1080/01431169008955106 Wyatt, L. R. Gurgel, K.W., Peters, H.C., Prandle, D., Krogstad, H.E., Haug, O., Gerritsen, H., Wensink, G.J. (1997b). The SCAWVEX Project. Proceedings of WAVES97, ASCE.

scholarly journals CONDITIONS OF MARITIME METEOROLOGY AND OCEANOGRAPHY IN THE VICINITY OF KOTOK ISLAND, SERIBU ISLANDS: APRIL 2011

2012 ◽  
Vol 4 (1) ◽  
Author(s):  
Atika Lubis ◽  
Mira Yosi
Keyword(s):  
Coral Reef ◽  
Air Temperature ◽  
Meteorological Data ◽  
Meteorological Parameter ◽  
Surface Wind ◽  
Water Area ◽  
Air Pressure ◽  
Sea Surface ◽  
Monsoon Period ◽  
Oceanographic Parameter

Meteorological data measurement by an Automatic Weather Station (AWS) were carried out at the pier of Kotok Island for East Monsoon period on April 2011 to obtain the changes phenomenon of maritime meteorological parameter and their correlation with the changes of oceanographic condition in shallow waters area and its surrounding. The measurement consists of surface wind parameter, air temperature, humidity, air pressure, and oceanographic data observations such as batimetry, tide, sea current, sea surface temperature, and salinity distributions. Result of maritime meteorological assessment obtained from previous reports (1980, 1998, and 2001) showed that the changes of the maximum air temperature were relatively significant. Furthermore, the air pressure data at the sea surface increased approximately 1.3 mBar. Nevertheless, other meteorological data seemed to be less significant in changes, but indicated a strong correlation with the pattern of tidal current and propagated waves to the shoreline. The changes of oceanographic parameter in the shallow water area were triggered the sedimentation processes, so that the coral reef growth might be decreased.Keywords: Maritime meteorology, oceanographic parameter, flat coral reef

scholarly journals Impacts of Extreme Weather on Sea Surface Temperature in the Western Waters of Sumatera and the Southern Waters of Java in June 2016

2017 ◽  
Vol 31 (1) ◽  
pp. 108-117
Author(s):  
Martono Martono
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Wave Height ◽  
Surface Wind ◽  
Extreme Weather ◽  
Ocean Dynamics ◽  
Sea Surface ◽  
Atmospheric Conditions ◽  
The Impact ◽  
Sea Wave

Ocean dynamics is affected by atmospheric conditions. Surface wind is one of atmospheric variables that has an important role in the ocean dynamics. This study was conducted to determine impacts of extreme weather on sea surface temperature in the western waters of Sumatera and the southern waters of Java in June 2016. Daily surface wind (2007-2016), sea surface temperature (1987-2016), sea wave height (1-10 June 2016) and surface current (1994-2016) were analyzed using anomaly analysis to assess the impact of surface winds on surface ocean. The result showed that in June 2016 extreme weather occurred in these waters that was characterized by sea wave height reached 2.6 m. Impacts of extreme weather in these waters cause upwelling intensity weakened that was marked by increase of sea surface temperature. The increases of sea surface temperature in the middle to north of western waters of Sumatera reached 0.9 OC, in the middle to south of western waters of Sumatera reached 1.8 OC and in the southern waters of Java reached 1.6 OC.

Analysis of microwave backscattering from nonlinear sea surface with currents: doppler spectrum and SAR images

2020 ◽  
Vol 12 (7) ◽  
pp. 598-608
Author(s):  
Xiang Su ◽  
Xiaoxiao Zhang ◽  
Hongxing Dang ◽  
Xiaomin Tan
Keyword(s):  
Temporal Variation ◽  
Wave Spectrum ◽  
Action Spectrum ◽  
Surface Wind ◽  
Measurement Data ◽  
Sea Surface ◽  
Doppler Spectrum ◽  
Baseline Length ◽  
Sar Images ◽  
Spatio Temporal

AbstractElectromagnetic scattering from the sea surface is of great significance in radar detection, target recognition, ocean remote sensing, etc. By introducing the action spectrum, the modified spatio-temporal variation wave spectrum is used to establish a nonlinear sea surface with currents in this paper. Traditional capillary wave modification facet scattering model (CWMFSM) can only calculate the backscattering from the wind-driven sea surface. By using the new spatio-temporal variation wave spectrum to modify the scattering amplitude of every facet, the new CWMFSM can be used to calculate the nonlinear sea surface scattering with surface currents. Therefore, the model simultaneously considers the modulation of sea surface wind and currents to the radar back echo. The dependence of backscattering coefficient from nonlinear sea surface on the incident angle and the polarization are discussed. The results verify that the nonlinear model is more consistent with the measurement data. This paper also investigates the Doppler spectrum characteristics of the sea with currents. It is found that the effect of wave–current interaction on Doppler spectra is weaker than that of wave–wave interaction. The SAR images of nonlinear sea surfaces are also simulated and different bands, polarizations, and baseline length effects on sea current detection performance of along-track interference SAR are analyzed.

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