Ocean Surface Wind Direction Research Articles

Unambiguous Wind Direction Estimation Method for Shipborne HFSWR Based on Wind Direction Interval Limitation

Due to its maneuverability and agility, the shipborne high-frequency surface wave radar (HFSWR) provides a new way of monitoring large-area marine dynamics and environment information. However, wind direction ambiguity is problematic when using monostatic shipborne HFSWR for wind direction inversion. In this article, an unambiguous wind direction measurement method based on wind direction interval limitation is proposed. The two first-order spectral wind direction estimation methods are first presented using the relationship between the normalized amplitude differences or ratios of the broadened Doppler spectrum and the wind direction. Moreover, based on the characteristic of a small wind direction estimation error in a large included angle between the spectral wind direction and the radar beam, the wind direction interval is obtained by counting the distribution of radar-measured wind direction within this included angle. Furthermore, the eliminated ambiguity of wind direction is transformed to judge the relationship between the wind direction interval and the two curves, which represent the relationship between the spreading parameter and the wind direction. Therefore, the remote sensing monitoring of ocean surface wind direction fields can be realized by shipborne HFSWR. The simulation results are used to evaluate the performance of the proposed method and the multi-beam sampling method for wind direction inversion. The experimental results show that the errors of wind direction estimated by the multi-beam sampling method and the equivalent dual-station model are large, and the proposed method can improve the accuracy of wind direction measurement. Three widely used wave directional spreading models have been applied for performance comparison. The wind direction field measured by the proposed method under a modified cosine model agrees well with that observed by the China-France Oceanography Satellite (CFOSAT).

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.

Comparison of Multiple Surface Ocean Wind Products with Buoy Data over Blue Amazon (Brazilian Continental Margin)

Remote sensing data for space-time characterization of wind fields in extensive oceanic areas have been shown to be increasingly useful. Orbital sensors, such as radar scatterometers, provide data on ocean surface wind speed and direction with spatial and temporal resolutions suitable for multiple applications and air-sea studies. Even considering the relevant role of orbital scatterometers to estimate ocean surface wind vectors on a regional and global scale, the products must be validated regionally. Six different ocean surface wind datasets, including advanced scatterometer (ASCAT-A and ASCAT-B products) estimates, numerical modelling simulations (BRAMS), reanalysis (ERA5), and a blended product (CCMP), were compared statistically with in situ measurements obtained by anemometers installed in fifteen moored buoys in the Brazilian margin (8 buoys in oceanic and 7 in shelf waters) to analyze which dataset best represents the wind field in this region. The operational ASCAT wind products presented the lowest differences in wind speed and direction from the in situ data (0.77 ms−1 < RMSEspd < 1.59 ms−1, 0.75 < Rspd < 0.96, −0.68 ms−1 < biasspd < 0.38 ms−1, and 12.7° < RMSEdir < 46.8°). CCMP and ERA5 products also performed well in the statistical comparison with the in situ data (0.81 ms−1 < RMSEspd < 1.87 ms−1, 0.76 < Rspd < 0.91, −1.21 ms−1 < biasspd < 0.19 ms−1, and 13.7° < RMSEdir < 46.3°). The BRAMS model was the one with the worst performance (RMSEspd > 1.04 m·s−1, Rspd < 0.87). For regions with a higher wind variability, as in the southern Brazilian continental margin, wind direction estimation by the wind products is more susceptible to errors (RMSEdir > 42.4°). The results here presented can be used for climatological studies and for the estimation of the potential wind power generation in the Brazilian margin, especially considering the lack of availability or representativeness of regional data for this type of application.

Orientation of marine atmospheric rolls in a SAR imagery using wavelet transform: A case study over Bay of Bengal

An objective wavelet based algorithm has been developed to find the orientation of rolls in Synthetic Aperture Radar (SAR) imagery using Radar Imaging Satellite (RISAT-1) data. The orientation of rolls plays a very crucial role for the estimation of ocean surface wind direction. It also affects the shape and size of a tropical cyclone. In this manuscript, SAR image was decomposed using Haar Wavelet Transform (HWT) and optimal thresholding was done to delineate the direction of propagating rolls. The roll features were found oriented in the range 26°–31° from the along track direction. These features are very useful to retrieve wind direction and hence the vector wind observation over the ocean surface.

First‐order ocean surface cross‐section for shipborne HFSWR incorporating a horizontal oscillation motion model

To investigate the characteristic of ocean surface backscatter echo, the first-order ocean surface cross-section for shipborne high-frequency surface wave radar (HFSWR) incorporating a horizontal oscillation motion model is mathematically derived. The horizontal oscillation motion includes yaw, sway and surge. Simulation results show that the horizontal oscillation motion can induce more additional peaks in Doppler spectrum due to combined oscillation motions, and the amplitudes as well as frequency locations of these motion-induced peaks are determined by the amplitude and frequency of the oscillation motion. Furthermore, the Bragg and motion-induced peaks are spread due to the forward movement of the ship. These spreading peaks overlap each other as the ship speed increases, which may severely influence moving target detection and ocean remote sensing. However, different characteristics of the first-order cross-sections for the true and ambiguous wind directions provide a new idea to measure ocean surface wind direction by shipborne HFSWR with a single receiving antenna.

Ocean Surface Wind Direction Inversion Using Shipborne High-Frequency Surface Wave Radar

Shipborne high-frequency surface wave radar (SHFSWR) has exhibited great advantages over onshore HFSWR (OHFSWR) in ocean remote sensing. Unlike OHFSWR, SHFSWR suffers the problem of Doppler spectrum spread owing to platform movement, which is a great challenge preventing the extraction of ocean surface parameters for SHFSWR. To address this challenge, in this letter, the mathematical model of ocean surface wind direction is first investigated based on the first-order SHFSWR cross section. Furthermore, a method for the wind direction inversion without ambiguity from the spread Doppler spectrum is proposed using a single receiving antenna. Meanwhile, the wind directions of the sea area covered by radar can be obtained by sequentially utilizing the proposed method, which is more appropriate for the application of SHFSWR with limited deck space and less cost. Experimental results of the real data collected in Taiwan Strait preliminarily verify the detection accuracy and the distance limit of the wind direction inversion, as the root-mean-square error and the detection range are 9.85° and 120 km, respectively

Second-Order Ocean Surface Cross Section for Shipborne HFSWR

To investigate the characteristics of sea clutter in monostatic shipborne high-frequency surface-wave radar (HFSWR) with uniform linear motion, the second-order patch scatter cross section is derived. With the previously proposed first-order ocean surface cross section, both cross sections could be reduced to the existing results in land-based HFSWR. Illustrations and discussions are provided on the basis of the simulation results for different situations. Although the spread Bragg lines vary with the ocean surface wind direction, the second-order contributions contaminate the energies of the Bragg lines in the high sea state, which may affect the application of shipborne HFSWR to wind direction extraction from the ratio of the positive and negative Bragg line energies. The derivations and analyses would be of importance to future investigations and applications, not only for target detection but also for ocean remote sensing.

Use of the ocean surface wind direction signal in microwave radiance assimilation

We developed an empirical relative wind direction (RWD) model function to represent azimuthal variations of oceanic microwave radiances of vertical and horizontal polarizations. The RWD model function was based on radiance measurements from the Advanced Microwave Scanning Radiometer and Special Sensor Microwave Imager/Sounder (SSMIS). Ocean surface wind vector data from SeaWinds on board the Advanced Earth Observing Satellite‐II and European Centre for Medium‐range Weather Forecasts (ECMWF) Integrated Forecasting System were utilized for the RWD model function development. The RWD model function was introduced to a microwave ocean emissivity model: a FAST microwave Emissivity Model (FASTEM) in a radiative transfer model for satellite radiance assimilation. Performances of the RWD model function were much more realistic than present azimuthal model functions in FASTEM for low wind speed and high‐frequency channels. Assimilation experiments using the RWD model function were performed in the ECMWF system. The experiment demonstrated reductions of first‐guess departure biases arising from modelling of the azimuthal variations in areas of high wind‐speed and low variability of wind direction. For example, bias reductions in ascending and descending SSMIS 19 GHz vertically polarized radiance in the Somali jet over the Arabian Sea were approximately 0.6 and 0.7 K. The bias reductions were found for all assimilated microwave imager channels in a wide wind‐speed range. Moreover, analysis increments of specific humidity in the lower troposphere were reduced (e.g. 0.2 g kg−1 reduction at 1000 hPa in the Somali jet). We found improvements of relative humidity and temperature in short‐range forecasts in the lower troposphere. The experiment results clearly showed the importance of modelling the azimuthal variation of emissivity for assimilation of microwave imager observations. The new RWD model function, combined with the other components of FASTEM, will be available as FASTEM‐6.

The Airborne Multiangle SpectroPolarimetric Imager (AirMSPI): a new tool for aerosol and cloud remote sensing

Abstract. The Airborne Multiangle SpectroPolarimetric Imager (AirMSPI) is an eight-band (355, 380, 445, 470, 555, 660, 865, 935 nm) pushbroom camera, measuring polarization in the 470, 660, and 865 nm bands, mounted on a gimbal to acquire multiangular observations over a ±67° along-track range. The instrument has been flying aboard the NASA ER-2 high altitude aircraft since October 2010. AirMSPI employs a photoelastic modulator-based polarimetric imaging technique to enable accurate measurements of the degree and angle of linear polarization in addition to spectral intensity. A description of the AirMSPI instrument and ground data processing approach is presented. Example images of clear, hazy, and cloudy scenes over the Pacific Ocean and California land targets obtained during flights between 2010 and 2012 are shown, and quantitative interpretations of the data using vector radiative transfer theory and scene models are provided to highlight the instrument's capabilities for determining aerosol and cloud microphysical properties and cloud 3-D spatial distributions. Sensitivity to parameters such as aerosol particle size distribution, ocean surface wind speed and direction, cloud-top and cloud-base height, and cloud droplet size is discussed. AirMSPI represents a major step toward realization of the type of imaging polarimeter envisioned to fly on NASA's Aerosol-Cloud-Ecosystem (ACE) mission in the next decade.

CMOD5.H—A High Wind Geophysical Model Function for C-Band Vertically Polarized Satellite Scatterometer Measurements

The Advanced Scatterometer (ASCAT) on the MetOp-A satellite is a radar instrument designed specifically to retrieve the ocean surface wind speed and direction. The ASCAT wind vector products are produced and utilized operationally in support of the National Oceanic and Atmospheric Administration (NOAA)'s weather forecasting and warning mission. The standard ASCAT winds at NOAA are produced using the ASCAT wind data processor developed at the Royal Netherlands Meteorological Institute (KNMI) utilizing the CMOD5.n geophysical model function (GMF). Recent validation of the ASCAT wind retrievals revealed a low bias at high wind speeds when compared to both the QuikSCAT winds and the National Centers for Environmental Prediction numerical weather prediction (NWP) model winds. The goal of this paper is to investigate the ASCAT high-wind-speed performance and to modify, as appropriate, the high-wind-speed portion of CMOD5.n GMF. This effort would potentially improve the utility of ASCAT wind retrievals in supporting wind warning and analysis and thus better mitigate the loss of QuikSCAT data products. Traditionally, the GMF is developed empirically by collocating scatterometer measurements and other truth data such as buoy and NWP model winds. However, NWP models are known to underestimate the intensity of higher wind speeds, and data sources such as ship-based or buoy-based observations provide an inadequate quantity of measurements for empirical GMF development. In this paper, a method utilizing aircraft-based scatterometer measurements in the high-wind-speed regimes is used in conjunction with satellite scatterometer measurements to refine the satellite GMF. As a result of this paper, a high wind C-band satellite GMF, CMOD5.h, was developed and implemented in NOAA's ASCAT processor. The validation comparison of the high wind and standard ASCAT wind products revealed 0.6-m/s reduction in the wind speed bias for winds greater than 15 m/s with respect to QuikSCAT, WindSat, and Step Frequency Microwave Radiometer high wind measurements.

Assimilating QuikSCAT Ocean Surface Winds with the Weather Research and Forecasting Model for Surface Wind-Field Simulation over the Chukchi/Beaufort Seas

To achieve a high-quality simulation of the surface wind field in the Chukchi/Beaufort Sea region, quick scatterometer (QuikSCAT) ocean surface winds were assimilated into the mesoscale Weather Research and Forecasting model by using its three-dimensional variational data assimilation system. The SeaWinds instrument on board the polar-orbiting QuikSCAT satellite is a specialized radar that measures ice-free ocean surface wind speed and direction at a horizontal resolution of 12.5 km. A total of eight assimilation case studies over two five-day periods, 1–5 October 2002 and 20–24 September 2004, were performed. The simulation results with and without the assimilation of QuikSCAT winds were then compared with QuikSCAT data available during the subsequent free-forecast period, coastal station observations, and North American Regional Reanalysis data. It was found that QuikSCAT winds are a potentially valuable resource for improving the simulation of ocean near-surface winds in the Chukchi/Beaufort Seas region. Specifically, the assimilation of QuikSCAT winds improved, (1) offshore surface winds as compared to unassimilated QuikSCAT winds, (2) sea-level pressure, planetary boundary-layer height, as well as surface heat fluxes, and (3) low-level wind fields and geopotential height. Verification against QuikSCAT data also demonstrated the temporal consistency and good quality of QuikSCAT observations.

Ocean surface wind direction retrieval from multi-polarization airborne SAR based on wavelet

An ocean surface wind direction retrieval method from multi-polarization airborne synthetic aperture radar images is presented in order to retrieve high precision wind direction SAR image and improve the accuracy of ocean surface wind. The method relies on the ability of two-dimensional (2D) continuous wavelet technique with better time-frequency than Fourier transform and local gradient, which combines 2D continuous wavelet and FFT to exact the ocean surface wind direction from airborne SAR images. The proposed method is executed by using several kinds of mother wavelet functions for the C-band co-polarization and cross-polarization airborne sounding images, and the retrieved ocean surface wind direction is compared with NCEP data and buoy data. The verification results show that the wavelet based ocean surface wind direction retrieval algorithm is suited to retrieve wind direction from airborne SAR sounding data. The choice of mother wavelet function has a certain influence on the results, and 2D Mexican-Hat is the best mother wavelet for wind retrieval. Both co-polarization and cross-polarization airborne sounding images are suited to retrieve ocean wind direction.

Sensing the Earth using Global Navigation Satellite System signals

International Workshop on GNSS Remote Sensing for Future Missions and Sciences; Shanghai, China, 7–9 August 2011 The Global Navigation Satellite System (GNSS) has been widely used in navigation, positioning, and geoscience applications. Recently, the versatility of GNSS as a new remote sensing tool has been demonstrated with the use of refracted, reflected, and scattered GNSS signals to sound the atmosphere and ionosphere, ocean, land surfaces (including soil moisture), and cryosphere. Existing GPS radio occultation (RO) missions—e.g., the U.S.‐Argentina SAC‐C, German Challenging Minisatellite Payload (CHAMP), U.S.‐Germany Gravity Recovery and Climate Experiment (GRACE), Taiwan‐U.S. Formosa Satellite Mission‐3/Constellation Observing System for Meteorology, Ionosphere, and Climate (FORMOSAT‐3/COSMIC) satellites, German TerraSAR‐X satellite, and European MetOp—together with groundbased GNSS observations, have provided precise and high‐resolution information on tropospheric water vapor, pressure, temperature, tropopause parameters, ionospheric total electron content, and electron density profiles. GNSS signals reflected from the ocean and land surface can determine the ocean height, ocean surface wind speed and wind direction, soil moisture, and ice and snow thickness. With improvement expected due to the next generation of multifrequency GNSS systems and receivers, and new space‐based instruments tracking GNSS reflected and refracted signals, new scientific applications of GNSS are expected in the near future across a number of environmental remote sensing fields.

Use of scatterometer based surface vorticity fields in forecasting genesis of tropical cyclones over the north Indian Ocean

Utilizing surface vorticity fields computed with the ocean surface wind speed and direction data obtained from QuikSCAT, a study has been undertaken to investigate the increase in surface vorticity during the genesis phase of tropical cyclones over the north Indian Ocean. Six named tropical cyclones; Agni, Hibaru, Mala, Akash, Nargis and Phyan which formed over the region during 2004-2009 have been selected for this purpose. It has been found that there was a steep rise in scatterometer based surface vorticity before the formation of a cyclone in the cyclogenesis region. The peak surface vorticity in the genesis region was observed on the day of intensification of the vortex to the depression stage or a day earlier. However, the rising trend in the genesis region begins a few days before the formation of the system. Thus, the surface vorticity fields derived on the basis of scatterometer data can provide predictive indication of the genesis of tropical cyclones over the Bay of Bengal and Arabian Sea with a lead time of 2-3 days. Using this technique it is possible to increase the lead time of pre-cyclone watch period over the north Indian Ocean.No relationship was found between the peak surface vorticity anomaly during the genesis phase and the surface vorticity anomaly at the time of peak intensity of the system during its life cycle. In other words, the peak surface vorticity anomaly during genesis phase does not provide any indication of future maximum intensity of the cyclone.

Use of scatterometer based surface vorticity fields in forecasting genesis of tropical cyclones over the north Indian Ocean

. Utilizing surface vorticity fields computed with the ocean surface wind speed and direction dataobtained from QuikSCAT, a study has been undertaken to investigate the increase in surface vorticity during the genesisphase of tropical cyclones over the north Indian Ocean. Six named tropical cyclones; Agni, Hibaru, Mala, Akash, Nargisand Phyan which formed over the region during 2004-2009 have been selected for this purpose. It has been found thatthere was a steep rise in scatterometer based surface vorticity before the formation of a cyclone in the cyclogenesisregion. The peak surface vorticity in the genesis region was observed on the day of intensification of the vortex to thedepression stage or a day earlier. However, the rising trend in the genesis region begins a few days before the formationof the system. Thus, the surface vorticity fields derived on the basis of scatterometer data can provide predictiveindication of the genesis of tropical cyclones over the Bay of Bengal and Arabian Sea with a lead time of 2-3 days. Usingthis technique it is possible to increase the lead time of pre-cyclone watch period over the north Indian Ocean.
 No relationship was found between the peak surface vorticity anomaly during the genesis phase and the surfacevorticity anomaly at the time of peak intensity of the system during its life cycle. In other words, the peak surfacevorticity anomaly during genesis phase does not provide any indication of future maximum intensity of the cyclone.

Passive and Active L-Band Microwave Observations and Modeling of Ocean Surface Winds

L-band microwave backscatter and brightness temperature of sea surfaces acquired using the Passive/Active L-band Sensor during the High Ocean Wind campaign are reported in terms of their dependence on ocean surface wind speed and direction. We find that the L-band VV, HH, and HV radar backscatter data increase by 6-7 dB from 5 to 25 m/s wind speed at a 45° incidence angle. The data suggest the validity of Phased Array type L-band Synthetic Aperture Radar (PALSAR) HH model function between 5 and 15 m/s wind speeds, but show that the extrapolation of PALSAR model at above 20 m/s wind speeds overpredicts <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> coefficients. There is wind direction dependence in the radar backscatter with about 4 dB differences between upwind and crosswind observations at 24 m/s wind speed for VV and HH. The passive brightness temperatures show about a 5-K change for <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</sub> and a 7-K change for <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</sub> for a wind speed increasing from 5 to 25 m/s. Circle flight data suggest a wind direction response of about 1-2 K in <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</sub> and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</sub> at 14 and 24 m/s wind speeds. The L-band microwave data show excellent linear correlation with the surface wind speed with a correlation better than 0.95. The results support the use of L-band radar data for estimating the wind-driven excess brightness temperature of sea surfaces. The data also support the applications of L-band microwave signals for high-resolution (kilometer scale) observation of ocean surface winds under high wind conditions (10-28 m/s).

Wind-Vector Retrievals Under Rain With Passive Satellite Microwave Radiometers

We have developed algorithms that retrieve ocean-surface wind speed and direction under rain using brightness-temperature (TB) measurements from passive satellite microwave radiometers. For accurate radiometer retrievals of wind speeds in the rain, it is essential to use TB signals at different frequencies, whose spectral signature makes it possible to find channel combinations that are sufficiently sensitive to wind speed but little or not sensitive to rain. The wind-speed retrieval accuracy of an algorithm that utilizes C-band frequencies and is trained for tropical cyclones ranges from 2.0 m/s in light rain to 4.0 m/s in heavy rain. We have also trained and tested global algorithms that are less accurate in tropical storms but can be applied under all conditions. The wind-direction retrieval accuracy degrades from about 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deg</sup> in light rain to 30 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deg</sup> at the onset of heavy rain. We compare the performance of wind-vector retrievals under rain from microwave radiometers with those from scatterometers and discuss advantages and shortcomings of both instruments. We have also analyzed the wind-induced sea-surface emissivity, including its wind-direction dependence for wind speeds up to 45 m/s.

WindSat Passive Microwave Polarimetric Signatures of the Greenland Ice Sheet

WindSat has systematically collected the first global fully polarimetric passive microwave data over both land and ocean. As the first spaceborne polarimetric microwave radiometer, it was designed to measure ocean surface wind speed and direction by including the third and fourth Stokes parameters, which are mostly related to the asymmetric structures of the ocean surface roughness. Although designed for wind vector retrieval, WindSat data are also collected over land and ice, and this new data has revealed, for the first time, significant land signals in the third and fourth Stokes parameter channels, particularly over Greenland and the Antarctic ice sheets. The third and fourth Stokes parameters show well-defined large azimuth modulations that appear to be correlated with geophysical variations, particularly snow structure, melting, and metamorphism, and have distinct seasonal variation. The polarimetric signatures are relatively weak in the summer and are strongest around spring. This corresponds well with the formation and erosion of the sastrugi in the dry snow zone and snowmelt in the soaked zone. In this paper, we present the full polarimetric signatures obtained from WindSat over Greenland, and use a simple empirical observation model to quantify the azimuthal variations of the signatures in space and time.

Development of a neural network algorithm for the retrieval of TPW from NOAA16 AMSU measurements

A neural‐network‐based algorithm for the retrieval of Total Precipitable Water (TPW) using Advanced Microwave Sounding Unit (AMSU) data available in real time from NOAA16 satellite has been developed. The retrieval method benefits from reliable surface observations, which includes the skin temperature and ocean surface wind speed and direction. The algorithm uses the simulated brightness temperatures at four frequencies, 23.4 Ghz, 31.4 Ghz, 50.3 Ghz, and 89.0 Ghz, of AMSU‐A as input and TPW derived from Radiosonde Observations (RAOB) profiles as output. The pairs of input and output are restricted to the homogeneous emitting areas, e.g. over Bay of Bengal and Arabian Sea. The performance of the algorithm is assessed using independent RAOB measurements. The bias and rms differences are found to be 0.21 mm and 2.03 mm respectively over the range of 15 and 75 mm. Further, extensive comparisons are made between the TPW obtained from the neural network algorithm and those obtained using other satellite instruments like the Tropical Rainfall Measuring Mission Microwave Imager, Advanced Infrared Sounder, and Moderate Resolution Imaging Spectroradiometer, in which the results are found to be in close agreement.

Retrieval of Ocean Surface Wind Speed and Wind Direction Using Reflected GPS Signals

Global positioning system (GPS) signals reflected from the ocean surface can be used for various remote sensing purposes. Some possibilities include measurements of surface roughness characteristics from which the rms of wave slopes, wind speed, and direction could be determined. In this paper, reflected GPS measurements that were collected using aircraft with a delay mapping GPS receiver are used to explore the possibility of determining ocean surface wind speed and direction during flights to Hurricanes Michael and Keith in October 2000. To interpret the GPS data, a theoretical model is used to describe the correlation power of the reflected GPS signals for different time delays as a function of geometrical and sea-roughness parameters. The model employs a simple relationship between surface-slope statistics and both a wind vector and wave age or fetch. Therefore, for situations when this relationship holds there is a possibility of indirectly measuring the wind speed and the wind direction. Wind direction estimates are based on a multiple-satellite nonlinear least squares solution. The estimated wind speed using surface-reflected GPS data collected at wind speeds between 5 and 10 m s−1 shows an overall agreement of better than 2 m s−1 with data obtained from nearby buoy data and independent wind speed measurements derived from TOPEX/Poseidon, European Remote Sensing (ERS), and QuikSCAT observations. GPS wind retrievals for strong winds in the close vicinity to and inside the hurricane are significantly less accurate. Wind direction agreement with QuikSCAT measurements appears to be at the 30° level when the airplane has both a stable flight level and a stable flight direction. Discrepancies between GPS retrieved wind speeds/directions and those obtained by other means are discussed and possible explanations are proposed.