Preliminary validation of ocean surface vector winds estimated from China’s HY-2A scatterometer

The microwave scatterometer on the Haiyang-2A (HY-2A) satellite is designed to provide global sea surface wind field data. The accuracy of HY-2A scatterometer wind retrievals is determined through various comparisons with moored buoys and the European Centre for Medium Range Weather Forecasting (ECMWF) reanalysis data. These comparisons were made in wide regions, including open sea and coastal areas, over a four-month period from January to March 2012 and August 2012. The retrieved wind speed results agree well with in situ observations and model data with respective biases ?0.19 m s^?1 and 0.01 m s^?1 and root mean square error 2.02 m s^?1 and 1.81 m s^?1. However, the wind direction errors are a little higher. The overall bias and root mean square deviation of wind direction are ?2.24°, 1.74°, and 40.28°, 38.56°, respectively. The wind speed and direction residuals are higher in low- and high-wind speed ranges. In addition, the wind speed and direction are relatively more accurate for open sea than those in coastal regions.     

Use of an optimum interpolation method to construct a high-resolution global ocean surface vector wind dataset from active scatterometers and passive radiometers

This study presents a new 0.25° gridded 6-hourly global ocean surface wind vector dataset from 2000 to 2015 produced by blending satellite wind retrievals from five active scatterometers (QuikSCAT, ASCAT-A, ASCAT-B, OSCAT, and HY-2A), nine passive radiometers (four SSM/I sensors, two SSMIS sensors, TMI, AMSR-E, and AMSR2) and one polarimetric radiometer (WindSat) with reanalysis from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) employing an optimum interpolation method (OIM). The accuracy of this wind product is determined through various comparisons with buoy measurements, NCEP/NCAR reanalysis and the cross-calibrated multi-platform (CCMP) winds. The comparisons indicate that OIM winds agree well with buoys, showing a root-mean-squared difference of 1.32 m s^?1 for wind speed and 24.73° for wind direction over 0–30 m s^?1 wind speed range. And the quality of OIM winds is improved significantly relative to NCEP/NCAR reanalysis and can be comparable with CCMP winds. Furthermore, OIM winds can reveal abundant small-scale features that are not visible in reanalysis data. In addition, the wind speed and direction retrievals of most satellites are proved to play an important role in generating the high-quality product, but the procedure for including HY-2A winds and WindSat wind directions should be further explored.     

Comparison of wind data from QuikSCAT and buoys in the Indian Ocean

The performance of QuikSCAT‐derived wind vectors is evaluated using in‐situ data from moored buoys over the Indian Ocean. The results show that the mean differences for wind speed and wind direction are 0.37 ms^?1 and 5.8°, root mean square deviations are 1.57 ms^?1 and 44.1° and corresponding coefficients of correlation are 0.87 and 0.75, respectively. The matching between in‐situ and satellite estimates seems to be better in the North Indian Ocean than in the Equatorial Indian Ocean. The effects of sea surface temperature and air–sea temperature difference on wind residuals were also investigated. In general, QuikSCAT is found to overestimate the winds. It is speculated that low wind speed during rain‐free conditions and high wind speed, normally associated with rain, may be the reason for the less accurate estimation of the wind vector from QuikSCAT over the Indian Ocean.     

Sensitivity analysis of high-resolution Oceansat-2 scatterometer winds on Thane cyclone simulation

Ocean surface wind vectors retrieved from the Oceansat-2 scatterometer (OSCAT) are used in this study to evaluate their impact on Thane cyclone simulation. The Weather Research and Forecasting (WRF) model and its three-dimensional variational (3D-Var) data assimilation system are adapted to evaluate the sensitivity of OSCAT observations. Simulated track error and landfall forecast are considered as standard measurements to assess the impact of 50 km and ~15 km spacing grid OSCAT winds along and across the swath. Significant improvement is obtained in track forecasting, when high-resolution vector winds (HVW; composite slice-level winds, ~15 km) are used for assimilation rather than coarser-resolution (50 km) operational OSCAT winds. Forecasting sensitivity to observations (OSCAT winds) using WRF tangent linear and adjoint modelling is used to quantify the impact of two different resolutions of OSCAT winds. WRF adjoint modelling is used here as a diagnostic tool, which indicates that high-resolution OSCAT winds have a more positive impact on the track prediction of Thane tropical cyclone.     

Assessment of the offshore wind resource in Japan with the ASCAT microwave scatterometer

We analysed wind speed and direction off the coast of Japan using data from the satellite-borne Advanced Scatterometer (ASCAT) and the Weather Research and Forecasting model (WRF), validated these data using in situ wind measurements from 20 buoys, and evaluated the effect of the long time intervals from ASCAT observations on wind resource assessment. More than 25 km from the coast, and at heights of 10 m, the ASCAT wind speed has negative biases of up to 3.4% and root mean square errors of up to 18.5%; its wind direction has 11° to 27° of mean absolute error compared to buoy measurements at a height of 10 m. These accuracies are better than either the expected accuracies reported in the technical manual or those simulated with WRF with its spatial resolution of 10 km. We also evaluated long-term average ASCAT wind speeds in comparison to 4- and 5-year averages of in situ buoy wind speeds measured at three buoys, with resulting differences of –0.3%, –6.3%, and – 1.6%. Furthermore, wind roses show that appearance frequencies of the ASCAT wind direction for the long term are in a good agreement with those of the measurements at the three buoys. Our results show that the ASCAT-derived wind speed and direction are appropriate more than 25 km from the coast, and that the long time interval between ASCAT observations has an insignificant effect on wind resource assessment, if at least 4 or 5 years of averaged ASCAT data are used.     

Fusion of sea surface wind vector data acquired by multi-source active and passive sensors in China sea

This work is the first to analyse the sea surface wind vector (SSWV) data acquisition capabilities of eight satellites carrying microwave scatterometer (scanning scatterometer carried by Haiyang satellite 2A, advanced scatterometer carried by Metop satellite A, advanced scatterometer carried by Metop satellite B and scanning scatterometer carried by Oceansat satellite 2) or radiometers (Special Sensor Microwave Imager carried by Meteorological Satellite Program satellites F15 and F17, advanced microwave scanning radiometer 2 carried by GCOM-W1 satellite, and windsat polarimetric radiometer carried by Coriolis satellite) and investigate a SSWV fusion algorithm for active and passive remote-sensing data. We found that combining observations of the eight satellites can provide an SSWV data product with spatial resolution of 25 km × 25 km and temporal resolution of 3 h. Sea surface wind speed and direction data were obtained from multi-source active and passive sensors using a spatiotemporally weighted fusion algorithm. An adaptive sliding window was introduced for calculating effective observation data within spatial/temporal radii, which can effectively improve calculation efficiency for wind field fusion. Comparing the fused and buoy observation results, the root-mean-square errors of the wind direction and speed were 20.6° and 1.2 m s^–1, respectively, indicating that the fusion results can meet most application requirements for wind vector. Meanwhile, the space coverage, accuracy of merged wind speed and wind direction can be improved comparing to a single sensor.     

Using sentinel-1A SAR wind retrievals for enhancing scatterometer and radiometer regional wind analyses

Scatterometer surface wind speed and direction observations in combination with radiometer wind speeds allow to generate surface wind analyses with high space and time resolutions over global as well as at regional scales. Regarding scatterometer sampling schemes and physics, the resulting surface wind analyses suffer from lack of accuracy in areas near coasts. The use of the synthetic aperture radar (SAR) onboard the Sentinel-1A satellite attempts to address the enhancement of surface wind analyses issues. In this study, SAR wind speeds and directions retrieved from backscatter coefficients acquired in interferometric wide (IW) swath mode are used. Their accuracy is determined through comprehensive comparisons with moored buoy wind measurements. SAR and buoy winds agree well at offshore and nearshore locations. The statistics characterizing the comparison of SAR and buoy wind speeds and directions are of the same order as those obtained from scatterometer (Advanced SCATterometer (ASCAT) and RapidScat) and buoy wind comparisons. The main discrepancy between SAR and buoy data are found for high wind speeds. SAR wind speeds exceeding 10 m s^–1 tend to be underestimated. A similar conclusion is drawn from SAR and scatterometer wind speed comparisons. It is based on the underestimation of SAR backscatter coefficient (σ°) with respect to σ° estimated from scatterometer winds and the geophysical model function (GMF) named CMOD-IFR2 (Ifremer C band MODel). New SAR wind speeds are retrieved using CMOD-IFR2. The corrected SAR retrievals allow better determination of the spatial characteristics of surface wind speeds and of the related wind components in near-coast areas. They are used for enhancing the determination of the spatial structure function required for the estimation of wind fields gridded in space and time at the regional scale. The resulting wind fields are only determined from scatterometer wind observations in combination with radiometer retrievals. Their qualities are determined through comparisons with SAR wind speeds and directions, and through their application for determination of wind power off Brittany coasts.     

A geometrical optics model based on the non-Gaussian probability density distribution of sea surface slopes for wind speed retrieval at low incidence angles

In this study, a large amount of data from precipitation radar (PR) and National Data Buoy Center (NDBC) buoys are collocated for the development and validation of a Geometrical Optics Model, in order to retrieve wind speed at small incidence angles. The omni-directional model is developed based on the combination of the quasi-specular scattering theory and non-Gaussian probability density distribution of ocean surface slope, and can be applied at incidence angles as high as 15°. There are four parameters included in the proposed model: the effective Fresnel reflection coefficient, the mean square slope, and the two coefficients associated with the kurtosis of the sea surface slope distribution. Using one half of the collocated data, the dependence of the four parameters on the in situ wind speed is acquired. The results show that the effective Fresnel reflection coefficient has a decrease relative to that obtained in previous studies. We combine the proposed model with the maximum-likelihood estimation (MLE) technique to retrieve the ocean surface wind speed at the 10 m height. The retrieved wind speeds are then validated against those measured by the NDBC buoys. The comparison shows that the root mean square error (RMSE) and bias between the model retrievals and buoy observations are 1.54 m s^–1 and 0.1 m s^–1, respectively, revealing high agreements in the wind speed estimations. The results of this study indicate that the proposed model and the PR measurements at low incidence angles can provide reasonably accurate estimates of the surface wind speeds within the range of 0–20 m s^–1.     

Response of OceanSat II scatterometer winds in the Bay of Bengal circulation

Surface wind is one of the major forcing factors in any ocean circulation model. The response of satellite-derived OceanSat II scatterometer (OSCAT) winds during spring (February and March) 2010 in the Bay of Bengal (BOB) surface circulation is described in this study. Wind stress is calculated from wind speed derived from OSCAT by the bulk-aerodynamic formula. The Regional Ocean Modeling System is used in this study because it is a free-surface, terrain-following, primitive-equations ocean model widely used by the scientific community for a diverse range of applications. The model is used after a climatological simulation with Comprehensive Ocean–Atmosphere Data Set (COADS) forcing when the model simulation reached the annual cycle. The paper also carried out a comparative study of National Centers for Environmental Prediction (NCEP) forcing over the same time period. The comparison of model-simulated surface temperature to National Oceanic and Atmospheric Administration (NOAA) sea surface temperature (SST) indicates that meso-scale features in the BOB are resolved due to the finer resolution of this model. Comparisons of water mass characteristics to the available ARGO floats show good agreement in different locations within the BOB. This study confirms the usefulness of OSCAT winds in simulating the meso-scale feature in the BOB.     

Global structure of extreme wind and wave climate derived from TOPEX altimeter data

Sea surface wind speed and significant wave height (SWH) are two basic parameters, in addition to sea surface height, which can be inferred from satellite altimeter measurements. Traditionally, altimeter-derived wind and wave data are less extensively used compared to sea surface height, as they are sometimes considered as by-products of satellite altimetry (in contrast to, for example, the dedicated scatterometer missions for marine wind observations). However, it is clear that altimeter-based wind and wave data have the unique advantage of being concurrent and collocated with each other. Using eight years (1993–2000) of TOPEX altimeter data with unprecedented accuracy and continuity, the 10-, 50- and 100-year return values of global wind speed and SWH are derived, their characteristics are discussed in relation to wind climatology and wind variability. Validations against in situ observations indicate that the uncertainties of altimeter-derived extreme winds and waves are at the levels of 10% and 5%, respectively. These results suggest that satellite altimeter data, with present quality and duration, can be very useful in many aspects of coastal engineering and marine technology such as design of offshore facilities, ship routing, and preparation of other sea-going activities.     

Observations of wind and ocean wave fields using ERS Synthetic Aperture Radar imagery

The aim of the research reported here is to evaluate Synthetic Aperture Radar (SAR) capability to estimate the wind vector and associated directional wave spectrum. Two ERS–2 SAR images of the Mediterranean Sea, one over the Sicily Channel and one over the Ligurian Sea, were selected as case studies. Wind speed was estimated using SAR calibrated backscatter response, in conjunction with empirically derived ERS scatterometer models such as CMOD4 and CMOD–IFREMER. The predictions of these models were then compared with the actual sea surface wave spectra either provided by in situ measurements or resulting from the inversion of the SAR image spectrum. SAR-detected effects of both wind and wave features, induced either by atmospheric boundary layer instability or by land shadowing, were also used as reliable indicators of wind direction.     

Bimodal variation of SST and related physical processes over the North Indian Ocean: special emphasis on satellite observations

Using sea surface temperature (SST) and wind speed retrieved by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), for the period of 1998–2003, we have studied the annual cycle of SST and confirmed the bimodal distribution of SST over the north Indian Ocean. Detailed analysis of SST revealed that the summer monsoon cooling (winter cooling) over the eastern Arabian Sea (Bay of Bengal) is more prominent than winter cooling (summer monsoon cooling). A sudden drop in surface short wave radiation by 57 W m^?2 (74 W m^?2) and rise in kinetic energy per unit mass by 24 J kg^?1 (26 J kg^?1) over the eastern Arabian Sea (Bay of Bengal) is observed in summer monsoon cooling period. The subsurface profiles of temperature and density for the spring warming and summer monsoon cooling phases are studied using the Arabian Sea Monsoon Experiment (ARMEX) data. These data indicate a shallow mixed layer during the spring warming and a deeper mixed layer during the summer monsoon cooling. Deepening of the mixed layer by 30 to 40 m with corresponding cooling of 2°C is found from warming to summer monsoon cooling in the eastern Arabian Sea. The depth of the 28°C isotherm in the eastern Arabian Sea during the spring warming is 80 m and during summer monsoon cooling it is about 60 m, while over the Bay of Bengal the 28°C isotherm is very shallow (35 m), even during the summer monsoon cooling. The time series of the isothermal layer depth and mixed layer depth during the warming phase revealed that the formation of the barrier layer in the spring warming phase and the absence of such layers during the summer cooling over the Arabian Sea. However, the barrier layer does exist over the Bay of Bengal with significant magnitude (20–25 m). The drop in the heat content with in first 50 m of the ocean from warming to the cooling phase is about 2.15 × 10⁸ J m^?2 over the Arabian Sea.     

Sea state in the Baltic Sea from space-borne high-resolution synthetic aperture radar imagery

In this work, remote sensing synthetic aperture radar (SAR) data from X-band TerraSAR-X and TanDEM-X (TS-X and TD-X) satellites have been used to adopt the algorithms for estimating sea state parameters in the specific condition of the Baltic Sea with archipelago islands and where short steep sea state dominates. Since the moving targets can be defocused and shifted in SAR images, sea state consisting of short windsea waves with strong local orbital velocities and wave breaking needs additional effort for accurate estimation of the total significant wave height that consists of swell and windsea parts. The XWAVE_C algorithm, developed for the North Sea, where the long swell waves coming from the Atlantic Ocean are present during storms, was further enhanced for the short steep windsea which dominates under ordinary storm conditions in the Baltics. For the empirical XWAVE_C model function, based on the spectral analysis of subscenes as well as on local wind information, an additional term was incorporated for assessment the minimal windsea significant wave height by applying JONSWAP wave spectra. A term to compensate spectral distortions triggered by windsea waves moving in SAR flight direction has also been introduced. In total, 95 TS-X/TD-X StripMap scenes between 2012 and 2017 were acquired in Eastern Baltic Sea, processed and analysed. The wave height results from SAR images were compared with collocated in situ data from 11 available buoys. The analysed data include both high and low windsea conditions. The comparison of SAR-derived wave heights with measured wave heights shows high agreement with a correlation coefficient r of 0.88. The wind speed, estimated from SAR images, was compared to measurements from 14 collocated in situ stations, yielding a high agreement with an r value of 0.90. This article is focused on the algorithm developments; however, it is also the first study of sea state retrieval in the Baltic Sea using high-resolution satellite-based techniques. The results show the local variability in the wave fields connected to atmospheric features. The observed local wave height can increase by 1–2 m in kilometre-size cells that are accompanied by wind gusts. The developed algorithms are installed in the German Aerospace Center’s (DLR) ground station Neustrelitz and can also be used in near-real-time.     

Russian scatterometer: discussion of the concept and the numerical simulation of wind field retrieval

Orbital scatterometry is briefly overviewed and its trends are indicated. Two scatterometer concepts are currently considered for trade-offs: with fixed and rotating antenna systems. The concept with a rotating antenna system was selected and SeaWinds was chosen as the prototype for the first Russian scatterometer. The scatterometer concept was then further developed and instead of two pencil beams, a fan-beam antenna was proposed (about 1° × 6°). The fan-beam antenna allows successive measurements for horizontal and vertical polarization in each wind vector cell (WVC). This increases the number of observations of the WVC at different incidence and azimuth angles during flight. The scatterometer parameters required to implement the proposed measurement geometry for an orbit altitude of 650 km and a swath width of 1525 km are discussed. A numerical scatterometer model that accounts for both the specifications and the observation geometry is developed. The scatterometer performance, with subsequent formation of a swath and splitting into WVCs, is simulated. The procedure of wind vector retrieval includes two stages: 1) determining wind speed and wind direction in a single WVC; and 2) using the information from adjacent WVCs to correct wind direction. It is shown that the accuracy of wind direction retrieval by a WVC can be increased by simultaneous radar cross-section (RCS) measurements at vertical and horizontal polarization. The basic error in determining wind direction is due to a 180° wind direction ambiguity caused by the form of RCS azimuth dependence. Two-dimensional median filtering is commonly employed in scatterometry to increase the accuracy of wind direction retrieval. In this study, two-dimensional angular median filtering was employed and it is shown that the error in wind direction retrieval significantly decreased. The results of the research indicate that wind field can be retrieved by the new scatterometer with the level of precision required.     

Impact of sub-daily wind forcing on mixed-layer depth and sea surface temperature: study using observations and a one-dimensional upper ocean model

The impact of sub-daily wind sampling on the diurnal cycle of oceanic mixed-layer depth (MLD) and sea surface temperature (SST) is investigated using a one-dimensional upper ocean model and observations at two locations: the Central Arabian Sea (CAS) and Eastern Equatorial Indian Ocean (EEIO). Motivation to carry out this study is twofold: first, it will help in understanding the possible error in model-simulated MLD and SST due to the non-inclusion of high-temporal wind sampling; and second, it will also emphasize the requirements of temporal sampling from space-based measurements of surface winds. Temporal decorrelation analysis suggests that over a 24-hour period, auto-correlation falls rapidly in the EEIO region, whereas the fall is less even at a lag of 24 hours in CAS. Time series analysis with different sub-daily sampling rates suggests that the optimum sampling rate is three hours for MLD and SST. A suite of one-dimensional model simulations performed at the CAS and EEIO locations with sub-daily wind suggests that once-daily synoptic measurements of wind, which is the most likely scenario with one scatterometer, results in small biases but large standard deviations in MLD. In the case of SST, there is a small positive bias in the order of 0.1°C at the CAS buoy location while at the EEIO location, no such bias is observed. With two scatterometers in a constellation resulting in two observations per day, one can obtain a small standard deviation in MLD, but the bias is greater in this case. For SST, except for a small bias (about 0.1°C) at the CAS location, the distribution is mostly well-behaved Gaussian in all cases. The present study suggests the advisability of acquiring more frequent wind measurements from space-borne scatterometers. A well-coordinated satellite scatterometer constellation will help in resolving the diurnal variability and associated feedback mechanism of air–sea exchange processes, enhancing the understanding of large-scale phenomena such as the Indian summer monsoon, El Niño-southern oscillations, and the Madden–Julian oscillation.     

Improvement in air–sea flux estimates derived from satellite observations

A new method is developed to estimate daily turbulent air–sea fluxes over the global ocean on a 0.25° grid. The required surface wind speed (w ₁₀) and specific air humidity (q ₁₀) at 10 m height are both estimated from remotely sensed measurements. w ₁₀ is obtained from the SeaWinds scatterometer on board the QuikSCAT satellite. A new empirical model relating brightness temperatures (T _b) from the Special Sensor Microwave Imager (SSM/I) and q ₁₀ is developed. It is an extension of the author's previous q ₁₀ model. In addition to T _b, the empirical model includes sea surface temperature (SST) and air–sea temperature difference data. The calibration of the new empirical q ₁₀ model utilizes q ₁₀ from the latest version of the National Oceanography Centre air–sea interaction gridded data set (NOCS2.0). Compared with mooring data, the new satellite q ₁₀ exhibits better statistical results than previous estimates. For instance, the bias, the root mean square (RMS), and the correlation coefficient values estimated from comparisons between satellite and moorings in the northeast Atlantic and the Mediterranean Sea are –0.04 g kg^?1, 0.87 g kg^?1, and 0.95, respectively. The new satellite q ₁₀ is used in combination with the newly reprocessed QuikSCAT V3, the latest version of SST analyses provided by the National Climatic Data Center (NCDC), and 10 m air temperature estimated from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses (ERA-Interim), to determine three daily gridded turbulent quantities at 0.25° spatial resolution: surface wind stress, latent heat flux (LHF), and sensible heat flux (SHF). Validation of the resulting fields is performed through a comprehensive comparison with daily, in situ values of LHF and SHF from buoys. In the northeast Atlantic basin, the satellite-derived daily LHF has bias, RMS, and correlation of 5 W m^?2, 27 W m^?2, and 0.89, respectively. For SHF, the statistical parameters are –2 W m^?2, 10 W m^?2, and 0.94, respectively. At global scale, the new satellite LHF and SHF are compared to NOCS2.0 daily estimates. Both daily fluxes exhibit similar spatial and seasonal variability. The main departures are found at latitudes south of 40° S, where satellite latent and sensible heat fluxes are generally larger.     

Variations of surface boundary layer parameters associated with Cyclone Gonu over the Arabian Sea using QuikSCAT data

This study attempted to quantify the variations of the surface marine atmospheric boundary layer (MABL) parameters associated with the tropical Cyclone Gonu formed over the Arabian Sea during 30 May–7 June 2007 (just after the monsoon onset). These characteristics were evaluated in terms of surface wind, drag coefficient, wind stress, horizontal divergence, and frictional velocity using 0.5° × 0.5° resolution Quick Scatterometer (QuikSCAT) wind products. The variation of these different surface boundary layer parameters was studied for three defined cyclone life stages: prior to the formation, during, and after the cyclone passage. Drastic variations of the MABL parameters during the passage of the cyclone were observed. The wind strength increased from 12 to 22 m s^?1 in association with different stages of Gonu. Frictional velocity increased from a value of 0.1–0.6 m s^?1 during the formative stage of the system to a high value of 0.3–1.4 m s^?1 during the mature stage. Drag coefficient varied from 1.5 × 10^?3 to 2.5 × 10^?3 during the occurrence of Gonu. Wind stress values varied from 0.4 to 1.1 N m^?2. Wind stress curl values varied from 10 × 10^?7 to 45 × 10^?7 N m^?3. Generally, convergent winds prevailed with the numerical value of divergence varying from 0 to –4 × 10^?5 s^?1. Maximum variations of the wind parameters were found in the wall cloud region of the cyclone. The parameters returned to normally observed values in 1–3 days after the cyclone passage.     

Inter-comparison of wind profiles in the tropical boundary layer remotely sensed from GPS radiosonde and Doppler wind lidar

Winds play a very important role in the dynamics of the lower atmosphere, and there is a need to obtain vertical distribution of winds at high spatio-temporal resolution for various observational and modelling applications. Profiles of wind speed and direction obtained at two tropical Indian stations using a Doppler wind lidar during the Indian southwest monsoon season were inter-compared with those obtained simultaneously from GPS upper-air sounding (radiosonde). Mean wind speeds at Mahbubnagar (16.73° N, 77.98° E, 445 m above mean sea level) compare well in magnitude for the entire height range from 100 m to 2000 m. The mean difference in wind speed between the two techniques ranged from ?0.81 m s^?1 to +0.41 m s^?1, and the standard deviation of wind speed differences ranged between 1.03 m s^?1 and 1.95 m s^?1. Wind direction by both techniques compared well up to about 1200 m height and then deviated slightly from each other at heights above, with a standard deviation in difference of 19°–48°. At Pune (18^○32′ N, 73^○51′ E, 559 m above mean sea level), wind speed by both techniques matched well throughout the altitude range, but with a constant difference of about 1 m s^?1. The root mean square deviation in wind speed ranged from 1.0 to 1.6 m s^?1 and that in wind direction from 20° to 45°. The bias and spread in both wind speed and direction for the two stations were computed and are discussed. The study shows that the inter-comparison of wind profiles obtained by the two independent techniques is very good under conditions of low wind speeds, and they show larger deviation when wind speeds are large, probably due the drift of the radiosonde balloon away from the location.     

A new relationship between radar cross-section and ocean surface wind speed using ERS-t scatterometer and buoy measurements

The ERS–I spacecraft scatterometer, C-band VV polarization, acquired radar cross-section measurements over the global oceans during 1992 and 1993. We investigate the cross-section dependence on mean wind speed U using collocated buoys within ±25km of the scatterometer cells. These collocated measurements result in over 75000 matches in two diITerent oceanic regions. The buoys measure hourly mean wind speeds from 0·2–10 mS ¹ and 0·2–18ms ^-1 in the equatorial Pacific Ocean and at mid^-latitudes off the North American coasts, respectively. We present experimental evidence for a new and compact exponential model dependence on wind speed. The previously used power–law form inadequately characterizes the cross-section measurements based on a single index over a large wind speed range. The cross-sectional slope varies from about zero dB/ms^-1 at high wind speeds U=18ms ^-1 and small incidence angles 0=20° to about 1·3dB/ms ^-1 at low wind speeds U=3ms ^-1 and large incidence angles, 0=55°. The CMOD4 model significantly underestimates the radar cross section measurements for U≤3ms ^-1 whereas the exponential model exhibits less bias.     

Validation of in situ wave spectrum with JONSWAP in the Indian Ocean

The moored data buoys deployed by the National Institute of Ocean Technology (NIOT) are floating platforms designed to carry a specific suit of sensors to measure wave parameters. Waves are measured by the Motion Reference Unit (MRU), which outputs roll, pitch, compass, and heave. These data are recorded at a rate of 1 Hz for 17 minutes every three hours. For this study, wave measurements were carried out at two locations (deep and shallow water) over the same period. The wave spectra were generated from the hard disk data (roll, pitch, heave, and compass) of the moored buoys. A Matrix Laboratory (MATLAB) program was developed for generating the wave spectrum using the hard disk data. The spectrum exhibits significant features for deep- and shallow-water buoys. In a single peak spectrum the dominant peaks are observed at 0.08 and 0.1 Hz and the multi-peak spectrum energy is distributed over a wide range, from 0.05 to 0.25 Hz. The buoy spectra were compared to the Joint North Sea Wave Project (JONSWAP) spectrum in deep- and shallow-water locations in the Bay of Bengal, and also the deep-water buoy spectrum was compared to the wave model (WAM) output spectrum in the Arabian Sea. The JONSWAP spectrum mostly conforms to the buoy spectrum in regard to marine wind conditions.