Retrieving ocean surface wind speed from the TRMM Precipitation Radar measurements

Spaceborne scatterometery has been used for many years now to retrieve the ocean surface wind field from normalized radar cross-section measurements of the ocean surface. Though designed specifically for the measurement of precipitation profiles in the atmosphere, the Precipitation Radar (PR) of the Tropical Rainfall Measuring Mission (TRMM) also acquires surface backscattering measurements of the global oceans. As such, this instrument provides an interesting opportunity to explore the benefits and pitfalls of alternative radar configurations in the satellite remote sensing of ocean winds. In this paper, a technique was developed for retrieving ocean surface winds using surface backscattering measurements from the TRMM PR. The wind retrieval algorithm developed for TRMM PR makes use of a maximum-likelihood estimation technique to compensate for the low backscattering associated with the PR configuration. The high vertical resolution of the PR serves to filter-out rain-contaminated cells normally integrated into Ku-band scatterometer measurements. The algorithm was validated through comparisons of ocean surface wind speeds derived from PR with remotely measured winds from TMI and QuikSCAT, as well as in situ observations from oceanographic buoys, revealing good agreements in wind speed estimations.     

利用浮标和NWP风场对HY-2散射计联合定标验证

本文对海洋二号卫星微波散射计(Haiyang-2 Scatterometer,HY-2 SCAT)进行了海洋定标算法研究,并使用数值天气预报模型风场(Numerical Weather Prediction,NWP)和浮标数据对定标后反演风场进行联合验证.通过匹配2012年12月份的HY-2 SCAT反演风场、NWP风场及浮标的观测数据,共得到无降雨条件下的3112个25km分辨率的匹配数据.对匹配数据进行分析时,采用基于变量的误差分析方法能够得到比传统线性回归方法更精确的验证结果.选取在风场U、V分量进行联合验证能得到较在风速、风向上更为有利的验证结果.验证结果表明,经过海洋定标法之后的HY-2 SCAT测量后向散射系数的误差残余小于0.15dB,其反演风场与浮标及NWP数据相吻合,U、V分量相对浮标及NWP数据偏差均小于0.23m/s,验证了该定标算法的有效性及定标后反演风场的高精度.     

Evaluation of high-resolution ocean surface vector winds measured by QuikSCAT scatterometer in coastal regions

The SeaWinds scatterometer onboard QuikSCAT covers approximately 90% of the global ocean under clear and cloudy condition in 24 h, and the standard data product has 25-km spatial resolution. Such spatial resolution is not sufficient to resolve small-scale processes, especially in coastal oceans. Based on range-compressed normalized backscatter and a modified wind retrieval algorithm, a coastal wind dataset at 12.5-km resolution was produced. Even with larger error, the high-resolution winds, in medium to high strength, would still be useful over coastal ocean. Using measurements from moored buoys from the National Buoy Data Center, the high-resolution QuikSCAT wind data are found to have similar accuracy as standard data in the open ocean. The accuracy of both high- and standard-resolution winds, particularly in wind directions, is found to degrade near shore. The increase in error is likely caused by the inadequacy of the geophysical model function/ambiguity removal scheme in addressing coastal conditions and light winds situations. The modified algorithm helps to bring the directional accuracy of the high-resolution winds to the accuracy of the standard-resolution winds in near-shore regions, particularly in the nadir and far zones across the satellite track.     

Evaluation of hurricane ocean vector winds from WindSat

The ability to accurately measure ocean surface wind vectors from space in all weather conditions is important in many scientific and operational usages. One highly desirable application of satellite-based wind vector retrievals is to provide realistic estimates of tropical cyclone intensity for hurricane monitoring. Historically, the extreme environmental conditions in tropical cyclones (TCs) have been a challenge to traditional space-based wind vector sensing provided by microwave scatterometers. With the advent of passive microwave polarimetry, an alternate tool for estimating surface wind conditions in the TC has become available. This paper evaluates the WindSat polarimetric radiometer's ability to accurately sense winds within TCs. Three anecdotal cases studies are presented from the 2003 Atlantic Hurricane season. Independent surface wind estimates from aircraft flights and other platforms are used to provide surface wind fields for comparison to WindSat retrievals. Results of a subjective comparison of wind flow patterns are presented as well as quantitative statistics for point location comparisons of wind speed and direction.     

An advanced ambiguity selection algorithm for SeaWinds

SeaWinds on QuikSCAT, a spaceborne Ku-band scatterometer, estimates ocean winds via the relationship between the normalized radar backscatter and the vector wind. Scatterometer wind retrieval generates several possible wind vector solutions or ambiguities at each resolution cell, requiring a separate ambiguity selection step to give a unique solution. In processing SeaWinds on QuikSCAT data, the ambiguity selection is "nudged" or initialized using numerical weather prediction winds. We describe a sophisticated new ambiguity selection approach developed at Brigham Young University (BYU) that does not require nudging. The BYU method utilizes a low-order data-driven Karhunen-Loeve wind field model to promote self-consistency. Ambiguity selected winds from the BYU method and standard SeaWinds processing are compared over a set of 102 revs. A manual examination of the data suggests that the nonnudging BYU method selects a more self-consistent wind field in the absence of cyclonic storms. Over a set of cyclonic storm regions, BYU performs better in 9% of the cases and worse in 20% of the cases. Overall, the BYU algorithm selects 93% of the same ambiguities as the standard dataset. This comparison helps validate both nonnudging and nudging techniques and indicates that SeaWinds ambiguity selection can be generally accomplished without nudging.     

Wind direction over the ocean determined by an airborne, imaging,polarimetric radiometer system

The speed and direction of winds over the ocean can be determined by polarimetric radiometers. This has been established by theoretical work and demonstrated experimentally using airborne radiometers carrying out circle flights and thus measuring the full 360° azimuthal response from the sea surface. An airborne experiment, with the aim of measuring wind direction over the ocean using an imaging polarimetric radiometer, is described. A polarimetric radiometer system of the correlation type, measuring all four Stokes brightness parameters, is used. Imaging is achieved using a 1-m aperture conically scanning antenna. The polarimetric azimuthal signature of the ocean is known from modeling and circle flight experiments. Combining the signature with the measured brightness data from just a single flight track enables the wind direction to be determined on a pixel-by-pixel basis in the radiometer imagery     

QuikSCAT geophysical model function for tropical cyclones andapplication to Hurricane Floyd

The QuikSCAT radar measurements of several tropical cyclones in 1999 have been studied to develop the geophysical model function (GMF) of Ku-band radar σ₀ values (normalized radar cross section) for extreme high wind conditions. To account for the effects of precipitation, the authors analyze the co-located rain rates from the Special Sensor Microwave/Imager (SSM/I) and propose the rain rate as a parameter of the GMF. The analysis indicates the deficiency of the NSCAT2 GMF developed for the NASA scatterometer, which overestimates the ocean σ₀ for tropical cyclones and ignores the influence of rain. It is suggested that the QuikSCAT σ₀ is sensitive to the wind speed of up to about 40-50 m s^-1. The authors introduce modifications to the NSCAT2 GMF and apply the modified GMF to the QuikSCAT observations of Hurricane Floyd. The QuikSCAT wind estimates for Hurricane Floyd in 1999 was improved with the maximum wind speed reaching above 60 m s^-1. The authors perform an error analysis by comparing the QuikSCAT winds with the analyses fields from the National Oceanic and Atmospheric Administration (NOAA) Hurricane Research Division (HRD). The reasonable agreement between the improved QuikSCAT winds and the HRD analyses supports the applications of scatterometer wind retrievals for hurricanes     

Simultaneous wind and rain retrieval using SeaWinds data

The SeaWinds scatterometers onboard the QuikSCAT and the Advanced Earth Observing Satellite 2 measure ocean winds on a global scale via the relationship between the normalized radar backscattering cross section of the ocean and the vector wind. The current wind retrieval method ignores scattering and attenuation of ocean rain, which alter backscatter measurements and corrupt retrieved winds. Using a simple rain backscatter and attenuation model, two methods of improving wind estimation in the presence of rain are evaluated. First, if no suitable prior knowledge of the rain rate is available, a maximum-likelihood estimation technique is used to simultaneously retrieve the wind velocity and rain rate. Second, when a suitable outside estimate of the rain rate is available, wind retrieval is performed by correcting the wind geophysical model function for the known rain via the rain backscatter model. The new retrieval techniques are evaluated via simulation and validation with data from the National Centers for Environmental Prediction and the Tropical Rainfall Measuring Mission Precipitation Radar. The simultaneous wind/rain estimation method yields most accurate winds in the "sweet spot" of SeaWinds' swath. On the outer-beam edges of the swath, simultaneous wind/rain estimation is not usable. Wind speeds from simultaneous wind/rain retrieval are nearly unbiased for all rain rates and wind speeds, while conventionally retrieved wind speeds become increasingly biased with rain rate. A synoptic example demonstrates that the new method is capable of reducing the rain-induced wind vector error while producing a consistent (yet noisy) estimate of the rain rate.     

一种新的海洋风场矢量估计算法

基于合成孔径雷达(SAR)图像的海面风场估计已经得到广泛认可。多数风速反演算法是以估计的风向、校正的δvv为先验条件,应用海风模型计算而得的。在相同风向的情况下,应用不同的海风模型会得到不同的风速反演值,因此选择合适的模型是风场估计的关键。同时,风向数据的精确度也很重要,即使不大的误差也会给风速的反演结果带来明显偏差。为解决上述问题这里提出一种不需要预先已知风向数据的风场估计算法。该算法将基于海洋SAR图像中风浪的条纹信息,以及风浪条纹生成的自相关函数的周期性估计风速数据,同时由风浪条纹的最短周期方向估计风向数据,从而估计出完整的风场矢量。仿真结果显示,该算法对风速和风向数据有较高的估计精度。     

Land-Contamination Compensation for QuikSCAT Near-Coastal Wind Retrieval

The QuikSCAT scatterometer is used to accurately retrieve winds over the ocean at both high (2.5 km) and low (25 km) resolutions. In near-coastal regions, land contamination of measurements results in inaccurate wind estimates using current techniques. Here, we show that identifying land-contaminated measurements allows wind retrieval to be accurately achieved in near-coastal regions using QuikSCAT data at up to 2.5-km resolution using the AVE algorithm. To identify and remove land contamination, two metrics are compared, namely, the minimum distance to land and the land contribution ratio (LCR). The LCR is used as a metric to identify and remove land-contaminated backscatter $sigma^{o}$ measurements before wind retrieval by discarding measurements with LCR values above a threshold. LCR thresholds used to remove land-contaminated measurements are determined using Monte Carlo simulations and set during processing using a lookup table based on the local wind speed, land brightness, and the cross-track swath location. Wind retrieval from $sigma^{o}$ fields generated using the LCR is more accurate closer to the coast than previously achieved using both low- and high-resolution processing.      

Studies of the sea using HF radio scatter

Radio signals of decameter wavelength resonantly scattered from waves on the sea surface are used to measure precisely the wave. length, frequency, and direction of travel of those waves. These measurements are not only important in themselves, but are also used to deduce currents, winds, and perhaps wind stress at the sea surface. Techniques for obtaining these measurements, as well as experiments to evaluate these techniques are discussed. Finally, scatter has been used to produce the first high-resolution measurements of the directional distribution of large ocean waves, measurements of ocean surface currents at ranges of 20 km, and of surface winds at ranges of 3000 km.     

Estimation of sea-surface winds using backscatter cross-sectionmeasurements from airborne research weather radar

A technique is presented for estimation of sea-surface winds using backscatter cross-section measurements from an airborne research weather radar. The technique is based on an empirical relation developed for use with satellite-borne microwave scatterometers which derives sea-surface winds from radar backscatter cross-section measurements. Unlike a scatterometer, the airborne research weather radar is a Doppler radar designed to measure atmospheric storm structure and kinematics. Designed to scan the atmosphere, the radar also scans the ocean surface over a wide range of azimuths, with the incidence angle and polarization angle changing continuously during each scan. The new sea-surface wind estimation technique accounts for these variations in incidence angle and polarization and derives the atmospheric surface winds. The technique works well over the range of wind conditions over which the wind speed-backscatter cross-section relation holds, about 2-20 m/s. The problems likely to be encountered with this new technique are evaluated and it is concluded that most problems are those which are endemic to any microwave scatterometer wind estimation technique. The new technique will enable using the research weather radar to provide measurements which would otherwise require use of a dedicated scatterometer     

Model-based estimation of wind fields over the ocean from windscatterometer measurements. II. Model parameter estimation

For pt.I see ibid., vol.28, no.3, p.349-360 (1990). The feasibility of a model-based approach to wind field estimation is discussed. In this approach the parametric model for near-surface mesoscale wind fields developed in pt.I is used. The parameters of the model are estimated from the wind scatterometer measurements; the wind field estimate is then computed from the estimated model parameters. Unlike the traditional pointwise approach, this approach takes advantage of the inherent correlation in the winds at different sample points to estimate the wind field more accurately and resolve directional ambiguity. The accuracies of wind field estimates obtained using the traditional pointwise estimation scheme and the model-based approach using simulated scatterometer measurements are compared     

Assessing the quality of SeaWinds rain measurements

While SeaWinds was designed to measure ocean winds, it can also measure rain over the ocean. SeaWinds on QuikSCAT active measurements of integrated columnar rain rate obtained via simultaneous wind/rain retrieval are evaluated via Monte Carlo simulation and the Crame/spl acute/r-Rao lower bound on estimate accuracy. Although sufficiently accurate in many conditions, the simultaneous wind/rain retrieval method used with SeaWinds on QuikSCAT data is ill-conditioned for certain wind directions and measurement geometries, sometimes yielding spurious rain rates in zero-rain conditions. To assess the validity of SeaWinds-derived rain rates, a simple empirically based rain thresholding scheme is presented, derived from simulated data. Thresholded QuikSCAT rain rates are compared to Tropical Rainfall Measuring Mission Microwave Imager monthly-averaged data, demonstrating good correlation for monthly-averaged data.     

A Wind and Rain Backscatter Model Derived From AMSR and SeaWinds Data

The SeaWinds scatterometer was originally designed to measure wind vectors over the ocean by exploiting the relationship between wind-induced surface roughening and the normalized radar backscatter cross section. Rain can degrade scatterometer wind estimation; however, the simultaneous wind/rain (SWR) algorithm was developed to enable SeaWinds to simultaneously retrieve wind and rain rate data. This algorithm is based on colocating data from the Precipitation Radar on the Tropical Rainfall Measuring Mission and SeaWinds on QuikSCAT. This paper develops a new wind and rain radar backscatter model for SWR using colocated data from the Advanced Microwave Scanning Radiometer (AMSR) and SeaWinds aboard the Advanced Earth Observing Satellite II. This paper accounts for rain height in the model in order to calculate surface rain rate from the integrated rain rate. The performance of SWR using the new wind/rain model is measured by comparison of wind vectors and rain rates to the previous SWR algorithm, AMSR rain rates, and National Center for Environmental Prediction numerical weather prediction winds. The new SWR algorithm produces more accurate rain estimates and improved winds, and detects rain with a low false alarm rate.      

Electromagnetic induction effects at an ocean coast

This paper reviews recent progress in model calculations towards understanding electromagnetic induction effects at ocean coasts. Early models consisted of two adjacent quarter-spaces of different conductivity, whereas the newer models simulate the ocean with a very thin sheet of a perfect conductor placed on top of a uniform Earth medium. The inducing field is assumed to arise from a monochromatic plane wave incident vertically from above. With any of these models one succeeds at once in explaining the occurrence of large vertical magnetic fields when the inducing electric field is polarized parallel to the coast (E-polarization), thereby also confirming the highly directional character of the coast effects as discovered a few years before by Parkinson. Another important step was made when, first numerically, then analytically, the behavior of the horizontal component of the magnetic field at the surface was rigorously calculated. For H-polarization (inducing magnetic field parallel to shore) this horizontal surface field is uniform, but is not so for E-polarization. Indeed, it has now been shown that the surface field can vary appreciably close to the coast, particularly on the ocean side of shore. With E-polarization, very large currents flow in the ocean, parallel to shore, with the result that the effect of the coast is felt at very large distances over both land and sea. Under H-polarization induction the range of the coast effect is very much shorter, in fact almost an order of magnitude shorter over the land and even reducing to zero at the surface of the perfectly conducting model ocean. The magnetic fields at the ocean floor have also been calculated, which should be of interest in the rapidly expanding field of marine survey and prospecting.     

Measurement of oceanic wind vector using satellite microwaveradiometers

The possibility of retrieving both wind speed and direction from microwave radiometer measurements of the ocean is studied using Special Sensor Microwave/Imager (SSM/I) measurements collocated with buoy reports from the National Data Buoy Center (NDBC). A physically based algorithm is used to retrieve the wind speed. The RMS difference between the SSM/I and buoy wind speed is 1.6 m/s for 3321 comparisons. It is found that the SSM/I minus buoy wind speed difference is correlated with wind direction. When this wind direction signal is removed, the RMS difference between the SSM/I and buoy winds reduces to 1.3 m/s. The wind direction signal is used to make global, low-resolution maps of the monthly mean oceanic vector. The wind direction sensing capability of a prospective two-look satellite radiometer is also processed     

Detection and Processing of bistatically reflected GPS signals from low Earth orbit for the purpose of ocean remote sensing

We will show that ocean-reflected signals from the global positioning system (GPS) navigation satellite constellation can be detected from a low-earth orbiting satellite and that these signals show rough correlation with independent measurements of the sea winds. We will present waveforms of ocean-reflected GPS signals that have been detected using the experiment onboard the United Kingdom's Disaster Monitoring Constellation satellite and describe the processing methods used to obtain their delay and Doppler power distributions. The GPS bistatic radar experiment has made several raw data collections, and reflected GPS signals have been found on all attempts. The down linked data from an experiment has undergone extensive processing, and ocean-scattered signals have been mapped across a wide range of delay and Doppler space revealing characteristics which are known to be related to geophysical parameters such as surface roughness and wind speed. Here we will discuss the effects of integration time, reflection incidence angle and examine several delay-Doppler signal maps. The signals detected have been found to be in general agreement with an existing model (based on geometric optics) and with limited independent measurements of sea winds; a brief comparison is presented here. These results demonstrate that the concept of using bistatically reflected global navigation satellite systems signals from low earth orbit is a viable means of ocean remote sensing.     

Development and validation of altimeter wind speed algorithms usingan extended collocated Buoy/Topex dataset

The development and validation of altimeter wind speed algorithms is investigated following the collation of the largest dataset to-date of coincident altimeter/buoy open ocean measurements. Nonlinear relationships between buoy wind and Topex backscatter are fitted to the 4500 points dataset using least-squares (LSQ). The addition of altimeter significant wave height (SWH) information causes a small but significant reduction of about 10% in root-mean-square (rms) error. The new LSQ algorithms yield significant improvement of the global wind speed bias and rms error compared to earlier models, but describe the wind to backscatter relationship poorly at extreme wind speeds. Best results are obtained with the Gourrion et al. (2000) model, improving on the Witter and Chelton (WC91) (1991) model used operationally. A residual dependence on sea state persists in all wind algorithms, which underestimate winds in young sea conditions on average by 1-1.5 m/s. A case study confirms that ordinary LSQ attribute excessive weight to the peak of the wind speed histogram and yield algorithms with poor performance at extreme winds. Measurement errors are shown to greatly influence the fitted models performance, as accounting for normally distributed errors in both altimeter and buoy measurements with orthogonal distance regressions (ODRs) yields significant improvements