An analysis of SeaWinds-based rain retrieval in severe weather events

The Ku-band SeaWinds scatterometer estimates near-surface ocean wind vectors by relating measured backscatter to a geophysical model function for the near-surface vector wind. The conventional wind retrieval algorithm does not explicitly account for SeaWinds' sensitivity to rain, resulting in rain-caused wind retrieval error. A new retrieval method, termed "simultaneous wind/rain retrieval," that estimates both wind and rain from rain-contaminated measurements has been previously proposed and validated with Tropical Rain Measuring Mission data. Here, the accuracy of rains retrieved by the new method is validated through comparison with the Next Generation Weather Radar (NEXRAD) in coastal storm events. The rains detected by both sensors are comparable, though SeaWinds-estimated rains exhibit greater variability. The performance of simultaneous wind/rain retrieval in flagging excessively rain-contaminated winds is discussed and compared to existing methods. A new rain-only retrieval algorithm for use in rain-backscatter-dominated areas is proposed and tested. A simple noise model for SeaWinds rain estimates is developed, and Monte Carlo simulation is employed to verify the model. The model shows that SeaWinds rain estimates have a standard deviation of 2.5 mm/h, which is higher than the NEXRAD measurements. Thresholding SeaWinds rain estimates at 2 mm/h yields a better rain flag than current rain flag algorithms.     

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.     

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.     

An Ocean Surface Wind Vector Model Function for a Spaceborne Microwave Radiometer

Surface wind vector measurements over the oceans are vital for scientists and forecasters to understand the Earth's global weather and climate. In the last two decades, operational measurements of global ocean wind speeds were obtained from passive microwave radiometers (Special Sensor Microwave/ Imagers); and over this period, full ocean surface wind vector data were obtained from several National Aeronautics and Space Administration and European Space Agency scatterometry missions. However, since SeaSat-A in 1978, there have not been other combined active and passive wind measurements on the same satellite until the launch of Japan Aerospace Exploration Agency's Advanced Earth Observing Satellite-II in 2002. This mission provided a unique data set of coincident measurements between the SeaWinds scatterometer and the Advanced Microwave Scanning Radiometer (AMSR). The AMSR instrument measured linearly polarized brightness temperatures (TB) over the ocean. Although these measurements contained wind direction information, the overlying atmospheric influence obscured this signal and made wind direction retrievals not feasible. However, for radiometer channels between 10 and 37 GHz, a certain linear combination of vertical and horizontal brightness temperatures causes the atmospheric dependence to cancel and surface parameters such as wind speed and direction and sea surface temperature to dominate the resulting signal. In this paper, an empirical relationship between AMSR T_B's (specifically A ^. T_BV - T_BH) and surface wind vectors (inferred from SeaWinds' retrievals) is established for three microwave frequencies: 10, 18, and 37 GHz. This newly developed wind vector model function for microwave radiometers can serve as a basis for wind vector retrievals either separately or in combination with active scatterometer measurements.     

Polarimetric radar remote sensing of ocean surface wind

Experimental data are presented to support the development of a new concept for ocean wind velocity measurement (speed and direction) with the polarimetric microwave radar technology. This new concept has strong potential for improving the wind direction accuracy and extending the useful swath width by up to 30% for follow-on NASA spaceborne scatterometer mission to SeaWinds series. The key issue is whether there is a relationship between the polarization state of ocean backscatter and surface wind velocity at NASA scatterometer frequencies (13 GHz). An airborne Ku-band polarimetric scatterometer (POLSCAT) was developed for proof-of-concept measurements. A set of aircraft flights indicated repeatable wind direction signals in the POLSCAT observations of sea surfaces at 9-11 m/s wind speed. The correlation coefficients between co- and cross-polarized radar response of ocean surfaces have a peak-to-peak amplitude of about 0.4 and are shown to have an odd-symmetry with respect to the wind direction, unlike the normalized radar cross sections     

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     

Impact of rain on spaceborne Ku-band wind scatterometer data

The accuracy of Ku-band ocean wind scatterometers (i.e., NSCAT and SeaWinds) is impacted to varying degrees by rain. In order to determine how to best flag rain-contaminated wind vector cells and ultimately to calibrate out the effects of rain as much as possible, we must understand the impact of rain on the backscatter measurements that are used to retrieve wind vectors. This study uses collocated SSM/I rain rate measurements, NCEP wind fields, and SeaWinds on QuikSCAT backscatter measurements to empirically fit a simple theoretical model of the effect of rain on /spl sigma//sub 0/, and to check the validity of that model. The chief findings of the study are (1) horizontal polarization measurements are more sensitive to rain than vertical polarization, (2) sensitivity to rain varies dramatically with wind speed, and (3) the additional backscatter due to rain overshadows the rain-related attenuation.     

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.     

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.     

Improved resolution backscatter measurements with the SeaWindspencil-beam scatterometer

The SeaWinds scatterometer was launched on the NASA QuikSCAT spacecraft in June 1999 and is planned for the Japanese ADEOS-II mission in 2000. In addition to generating a global Ku-band backscatter data set useful for a variety of climate studies, these flights will provide ocean-surface wind estimates for use in operational weather forecasting. SeaWinds employs a compact “pencil-beam” design rather than the “fan-beam” approach previously used with SASS on Seasat, NSCAT on ADEOS-I, and the AMI scatterometer on ERS-1, 2. As originally envisioned and reported, the resolution of the SeaWinds backscatter measurements were to be antenna-beamwidth limited. In order to satisfy an emerging demand for higher resolution backscatter data, however, the SeaWinds signal-processing design has been significantly modified. Here, the various options considered for improving the resolution of the SeaWinds measurements are discussed, and the selected hardware modification (the addition of deramp processing for range discrimination) is described. The radar equation specific to a rotating pencil-beam scatterometer with digital range filtering is developed, and the new challenges associated with calibrating the resulting improved resolution measurements are discussed. A formulation for assessing the variance of the measurements due to fading and thermal noise is presented. Finally, the utility of improved resolution SeaWinds measurements for land and ice studies is demonstrated by simulated enhanced-resolution imaging of a synthetic Earth backscatter scene     

Radar backscatter measurement accuracy for a spaceborne pencil-beamwind scatterometer with transmit modulation

Scatterometers are remote sensing radars designed to measure near-surface winds over the ocean. The difficulties of accommodating traditional fan-beam scatterometers on spacecraft has lead to the development of a scanning pencil-beam instrument known as SeaWinds. SeaWinds will be part of the Japanese Advanced Earth Observing Satellite II (ADEOS-II) to be launched in 1999. To analyze the performance of the SeaWinds design, a new expression for the measurement accuracy of a pencil-beam system is required. In this paper the authors derive a general expression for the backscatter measurement accuracy for a pencil-beam scatterometer which includes the effects of transmit signal modulation with simple power detection. Both separate and simultaneous signal+noise and noise-only measurements are considered. The utility of the new expression for scatterometer design tradeoffs is demonstrated using a simplified geometry. A separate paper, ibid., 1997, describes detailed tradeoffs made to develop the SeaWinds design     

A C-Band Wind/Rain Backscatter Model

With the confirmed evidence of rain surface perturbation in recent studies, the rain effects on C-band scatterometer measurements are reevaluated. By using colocated Tropical Rainfall Measuring Mission Precipitation Radar, ESCAT on European Remote Sensing Satellites, and European Centre for Medium-Range Weather Forecasts data, we evaluate the sensitivity of C-band sigmadeg to rain. We develop a low-order wind/rain backscatter model with inputs of surface rain rate, incidence angle, wind speed, wind direction, and azimuth angle. We demonstrate that the wind/rain backscatter model is accurate enough for describing the total backscatter in raining areas with relatively low variance. We also show that the rain surface perturbation is a dominating factor of the rain-induced backscatter. Using three distinct regimes, we show under what conditions the wind, rain, and both wind and rain can be retrieved from the measurements. We find that the effect of rain has a more significant impact on the measurements at high incidence angles than at low incidence angles     

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     

Tradeoffs in the design of a spaceborne scanning pencil beamscatterometer: application to SeaWinds

SeaWinds is a spaceborne wind scatterometer to be flown on the second Japanese Advanced Earth Observing Satellite (ADEOS-II) in 1999. An important international element of NASA's Earth Observing System (EOS), SeaWinds is an advanced follow-on to the NASA scatterometer (NSCAT) on the first ADEOS platform. Unlike previous operational spaceborne scatterometer systems. SeaWinds employs a scanning “pencil-beam” antenna rather than a “fan-beam” antenna, making the instrument more compact and yielding greater ocean coverage. The goals of this paper are twofold. First, the overall SeaWinds functional design and backscatter measurement approach are described, and the relative advantages of the pencil-beam technique are outlined. Second, the unique aspects of measurement accuracy optimization and signal processing for the SeaWinds instrument are discussed. Applying the results of a separate companion paper, ibid., 1997, a technique to significantly improve measurement accuracy by modulating the transmit pulse is described. Trade-offs to optimize the transmit modulation bandwidth are presented     

Comparisons of scatterometer models for the AMI on ERS-1: thepossibility of systematic azimuth angle biases of wind speed anddirection

There have been many different attempts to develop a model to relate the normalized radar backscatter values for the C-band radars of the active microwave instrument (AMI) on ERS-1 as a function of 10-m wind speed, azimuth angle, which is wind direction relative to radar-beam direction and incidence angle. The first two models, namely CMOD-1, Long (1985) and CMOD-2, are analyzed, and modifications of them are used to show by means of Monte Carlo methods that it is important to be able to define the backscatter model for all azimuth angles in addition to obtaining good agreement at upwind, downwind, and crosswind relative to the radar-beam direction. Methods are described to compare one model to another and to show how to systematic discrepancies, which are the result of model differences, can be found. These discrepancies are also expected when various models are employed to recover winds from real backscatter data. Discrepancies between a model and an unknown “true” model can introduce systematic biases in the recovered wind vectors as opposed to random errors, which result from sampling variability. The validation of the vector winds from scatterometer data requires a comparison of these winds with accurate conventionally measured winds. The data buoys deployed by various nations can serve as the appropriate data because ship reports are not accurate enough     

Microwave backscatter modeling of erg surfaces in the Sahara desert

The Sahara desert includes large expanses of sand dunes called ergs. These dunes are formed and constantly reshaped by prevailing winds. Previous study shows that Saharan ergs exhibit significant radar backscatter (/spl sigma//spl deg/) modulation with azimuth angle (f). We use /spl sigma//spl deg/ measurements observed at various incidence angles and f from the NASA Scatterometer (NSCAT), the SeaWinds scatterometer, the ERS scatterometer (ESCAT), and the Tropical Rainfall Measuring Mission's Precipitation Radar to model the /spl sigma//spl deg/ response from sand dunes. Observations reveal a characteristic relationship between the backscatter modulation and the dune type, i.e., the number and orientation of the dune slopes. Sand dunes are modeled as a composite of tilted rough facets, which are characterized by a probability distribution of tilt with a mean value, and small ripples on the facet surface. The small ripples are modeled as cosinusoidal surface waves that contribute to the return signal at Bragg angles only. Longitudinal and transverse dunes are modeled with rough facets having Gaussian tilt distributions. The model results in a /spl sigma//spl deg/ response similar to NSCAT and ESCAT observations over areas of known dune types in the Sahara. The response is high at look angles equal to the mean tilts of the rough facets and is lower elsewhere. This analysis provides a unique insight into scattering by large-scale sand bedforms.     

Calibration of spaceborne scatterometers using tropical rainforests

Wind scatterometers are radar systems designed specifically to measure the normalized radar backscatter coefficient (σ°) of the ocean's surface in order to determine the near-surface wind vector. Postlaunch calibration of a wind scatterometer can be performed with an extended-area natural target such as the Amazon tropical rain forest. Rain forests exhibit a remarkably high degree of homogeneity in their radar response over a very large area though some spatial and temporal variability exist. The authors present a simple technique for calibrating scatterometer data using tropical rain forests, Using a polynomial model for the rolloff of σ° with incidence angle, the technique determines gain corrections to ensure consistency between different antennas and processing channels. Corrections for the time varying instrument gain are made consistent with a seasonally fixed rain forest response; however, without ground stations or aircraft flights, it is difficult to uniquely distinguish between seasonal variations in the rain forest and slow variations of the system gain. Applying the corrections, the intrinsic variability of the σ° of the rain forest is estimated to be ±0.15 dB, which is the limit of the accuracy of calibration using the rain forest. The technique is illustrated with Seasat scatterometer (SASS) data and applied to ERS-1 Active Microwave Instrument scatterometer (Escat) data. Gain corrections of up to several tenths of a decibel are estimated for SASS. Corrections for Escat data are found to be very small, suggesting that Escat data is well calibrated     

A new wind vector algorithm for C-band SAR

Ocean wind speed and wind direction are estimated simultaneously using the normalized radar cross sections /spl sigma//sup 0/ corresponding to two neighboring (25-km) blocks, within a given synthetic aperture radar (SAR) image, having slightly different incidence angles. This method is motivated by the methodology used for scatterometer data. The wind direction ambiguity is removed by using the direction closest to that given by a buoy or some other source of information. We demonstrate this method with 11 ENVISAT Advanced SAR sensor images of the Gulf of Mexico and coastal waters of the North Atlantic. Estimated wind vectors are compared with wind measurements from buoys and scatterometer data. We show that this method can surpass other methods in some cases, even those with insufficient visible wind-induced streaks in the SAR images, to extract wind vectors.     

Vegetation studies of the Amazon basin using enhanced resolutionSeasat scatterometer data

The Seasat-A scatterometer (SASS) was designed to measure the near-surface wind field over the ocean by inferring the wind from measurements of the surface radar backscatter. While backscatter measurements were also made over land, they have been primarily used for the calibration of the instrument. This has been due in part to the low resolution of the scatterometer measurements (nominally 50 km). In a separate paper the present authors introduced a new method for generating enhanced resolution radar measurements of the Earth's surface using spaceborne scatterometry. In the present paper, the method is used with SASS data to study vegetation classification over the extended Amazon basin using the resulting medium-scale radar images. The remarkable correlation between the Ku-band radar images and vegetation formations is explored, and the results of several successful experiments to classify the general vegetation classes using the image data are presented. The results demonstrate the utility of medium-scale radar imagery in the study of tropical vegetation and permit utilization of both historic and contemporary scatterometer data for studies of global change. Because the scatterometer provides frequent, wide-area coverage at a variety of incidence angles, it can supplement higher resolution instruments which often have narrow swaths with limited coverage and incidence angle diversity     

Observation and characterization of radar backscatter over Greenland

Characterization of the microwave signature of the Greenland snow surface enables delineation of the different snow facies and is a tool for tracking the effects of climate change. A new empirical observation model is introduced that uses a limited number of parameters to characterize the snow surface based on the dependence of radar backscatter on incidence angle, azimuth angle, spatial gradient, and temporal rate of change. The individual model parameters are discussed in depth with examples using data from the NASA Scatterometer (NSCAT) and from the C-band European Remote Sensing (ERS) satellite Advanced Microwave Instrument in scatterometer mode. The contribution of each model term to the overall accuracy of the model is evaluated. The relative contributions of the modeled dependencies vary by region. Two studies illustrating applications of the model are included. First, interannual changes over the Greenland ice sheet are investigated using nine years of ERS data. Surface changes are observed as anomalies in the /spl sigma//spl deg/ model parameters. Second, intraannual variations of the surface are investigated. These changes are observed in the average backscatter normalized to a given observation geometry. The results indicate that the model can be used to obtain a more complete understanding of multiyear change and to obtain low-variance high temporal resolution observations of intraannual changes. The model may be applied for increased accuracy in scatterometer, synthetic aperture radar (SAR), and wide-angle SAR studies.