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.      

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     

Accuracy of scatterometer-derived winds using the Cramer-Rao bound

A wind scatterometer makes measurements of the normalized radar-backscatter coefficient σ° of the ocean surface. To retrieve the wind, a geophysical model function (GMF), which relates σ° to the near-surface wind, is used. The wind vector can be estimated using maximum-likelihood techniques from several σ° measurements made at different azimuth angles. The probability density of the measured σ° is assumed to be Gaussian with a variance that depends on the true σ° and therefore, depends on the wind through the GMF. With this model for wind estimation, the Cramer-Rao (C-R) bound is derived for wind estimation, and its implications for wind retrieval are discussed. As part of this discussion, the role of geophysical modeling error is considered and shown to play a significant role in the performance of near-surface wind estimates. The C-R bound is illustrated using parameters from the ERS AMI, NSCAT, and Sea Winds scatterometers     

Polarimetric microwave wind radiometer model function and retrieval testing for WindSat

A geophysical model function (GMF), relating the directional response of polarimetric brightness temperatures to ocean surface winds, is developed for the WindSat multifrequency polarimetric microwave radiometer. This GMF is derived from the WindSat data and tuned with the aircraft radiometer measurements for very high winds from the Hurricane Ocean Wind Experiment in 1997. The directional signals in the aircraft polarimetric radiometer data are corroborated by coincident Ku-band scatterometer measurements for wind speeds in the range of 20-35 m/s. We applied an iterative retrieval algorithm using the polarimetric brightness temperatures from 18-, 23-, and 37-GHz channels. We find that the root-mean-square direction difference between the Global Data Assimilation System winds and the closest WindSat wind ambiguity is less than 20/spl deg/ for above 7-m/s wind speed. The retrieval analysis supports the consistency of the Windrad05 GMF with the WindSat data.     

Emission cross sections and spectroscopy of Ho3+ laserchannels in KGd(WO4)2 single crystal

The spectroscopic properties of Ho³⁺ laser channels in KGd(WO₄)₂ crystals have been investigated using optical absorption, photoluminescence, and lifetime measurements. The radiative lifetimes of Ho³⁺ have been calculated through a Judd-Ofelt (JO) formalism using 300-K optical absorption results. The JO parameters obtained were Ω₂=15.35×10^-20 cm², Ω ₄=3.79×10^-20 cm², Ω₆ =1.69×10^-20 cm². The 7-300-K lifetimes obtained in diluted (8·10¹⁸ cm^-3) KGW:0.1% Ho samples are: τ(⁵F₃)≈0.9 μs, τ( ⁵S₂)=19-3.6 μs, and τ(⁵F₅ )≈1.1 μs. For Ho concentrations below 1.5×10²⁰ cm^-3, multiphonon emission is the main source of non radiative losses, and the temperature independent multiphonon probability in KGW is found to follow the energy gap law τ_ph ^-1(0)=βexp(-αΔE), where β=1.4×10^-7 s^-1, and α=1.4×10³ cm. Above this holmium concentration, energy transfer between Ho impurities also contributes to the losses. The spectral distributions of the Ho³⁺ emission cross section σ_EM for several laser channels are calculated in σ- and π-polarized configurations. The peak a σ_EM values achieved for transitions to the ⁵I₈ level are ≈2×10^-20 cm² in the σ-polarized configuration, and three main lasing peaks at 2.02, 2.05, and 2.07 μm are envisaged inside the ⁵I₇→⁵I₈ channel     

An empirical model for interpreting the relationship betweenbackscattering and arid land surface roughness as seen with the SAR

The nature of the surface in a flat arid zone located in the western Sahara desert was studied and its roughness parameters defined. The main geological units were compared with backscattering data from ERS-1 images. Results showed a close correlation between the backscattering coefficient, σ₀, and the maximum height, h_max, of the rocks making up the arid landscape. The simplified relationship σ₀=4.63.1og h_max-15.29 (with h_max in cm and σ₀ in dB) was established from field measurements, giving an r² of about 88%. This empirical relationship is confirmed by another close relation between σ₀ and the statistical parameter s, the height standard deviation. The advantage of h_max is that it can be extracted for any site, whatever its roughness may be. The spatial distribution of the rock facets and the geometric characteristics of the incidence angle of the radar signal seem to explain the results     

Remote sensing of water salinity at decameter wavelengths

The possibility of remote sensing of surface water salinity in the decametric wave range is studied. The theoretical consideration of sensitivities to salinity variations proves the decametric range to be best for measuring small water salinities [0.3-5 parts per thousand ( ⁰/₀₀) at 30 MHz]. The conclusion is confirmed by 30 MHz radar experiments, which show a good agreement with the in situ data in the range 0.3-30 ⁰/₀₀. Radar contrasts of about 0.35 db and 1.6 db are observed when salinity changes from 0.3 ⁰/₀₀ to 1.2 ⁰/₀₀ and to 30 ⁰/₀₀, correspondingly     

Wind vector retrieval using ERS-1 synthetic aperture radar imagery

An automated algorithm intended for operational use is developed and tested for estimating wind speed and direction using ERS-1 SAR imagery. The wind direction comes from the orientation of low frequency, linear signatures in the SAR imagery that the authors believe are manifestations of roll vortices within the planetary boundary layer. The wind direction thus has inherently a 180° ambiguity since only a single SAR image is used. Wind speed is estimated by using a new algorithm that utilizes both the estimated wind direction and σ ₀ values to invert radar cross section models. The authors show that: 1) on average the direction of the roll vortices signatures is approximately 11° to the right of the surface wind direction and can be used to estimate the surface wind direction to within ±19° and 2) utilizing these estimated wind directions from the SAR imagery subsequently improves wind speed estimation, generating errors of approximately ±1.2 m/s, for ERS-1 SAR data collected during the Norwegian Continental Shelf Experiment in 1991     

Calibrated imaging radar polarimetry: technique, examples, andapplications

The authors developed a calibration procedure for imaging radar polarimeters and applied it to a set of images acquired by the NASA DC-8 multifrequency radar system. The technique requires the use of ground reflectors of known cross-section for absolute calibration, that is, solution for σ⁰; however, the image data themselves can usually provide all information necessary for phase calibration and for antenna crosstalk correction. The accuracy of the approach, as measured by calculating the cross-section residuals of known targets in each calibrated scene, is on the order of ±1-2 dB at the P- and C-band, but improves to ±0.5 dB at the L-band. The authors present the results of applying this technique to radar scenes of lava flows of varying roughness, temperate and tropical rain forests, and ocean water surfaces. They also present several example applications which are feasible with calibrated data but which would be difficult to implement with uncalibrated data     

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.     

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.     

Error-erasure decoding of binary cyclic codes, up to a particularinstance of the Hartmann-Tzeng bound

It is shown that error-erasure decoding for a cyclic code allows the correction of a combination of t errors and r erasures when 2t+r<σ₀; the parameter σ₀ denotes a particular instance of the Hartmann-Tzeng bound. This procedure is an improvement on the error-erasure decoding algorithm developed by G.D. Forney (1965), which works when 2t+r<σ, where σ denotes the BCH-bound of the code     

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.     

A Comparison of the Impact of QuikScat and WindSat Wind Vector Products on Met Office Analyses and Forecasts

Several studies have demonstrated that retrievals of wind vectors from the WindSat polarimetric radiometer are of sufficient quality to be considered for assimilation in operational numerical weather prediction models. In this paper, WindSat data are used in a state-of-the-art global meteorological analysis and forecasting system. Each wind vector contains a directional ambiguity and so is assimilated in a similar way to that of scatterometer data. The forecast impact of using analyses containing information from WindSat data was investigated for a period during August and September of 2005, when a large number of tropical cyclones were present. Forecast errors were reduced in the surface pressure fields, and the average improvement across the forecast range was found to be 1.0%. This is comparable to the improvement of 1.1% found in the same fields when winds were assimilated from the QuikScat scatterometer. The impact on tropical cyclone tracks in the forecasts was also studied. The scatterometer improved (reduced) the track errors markedly by 25% in the analyses. When impacts across the forecast range out to five days were also included, the improvement was found to be 8%. In contrast, the assimilation of WindSat data improved the analysis track errors by 7%, although this figure was found to be 10% across the complete forecast range.      

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.     

Measuring soil moisture with imaging radars

An empirical algorithm for the retrieval of soil moisture content and surface root mean square (RMS) height from remotely sensed radar data was developed using scatterometer data. The algorithm is optimized for bare surfaces and requires two copolarized channels at a frequency between 1.5 and 11 GHz. It gives best results for kh⩽2.5, μ_υ⩽35%, and &thetas;⩾30°. Omitting the usually weaker hv-polarized returns makes the algorithm less sensitive to system cross-talk and system noise, simplifies the calibration process and adds robustness to the algorithm in the presence of vegetation. However, inversion results indicate that significant amounts of vegetation (NDVI>0.4) cause the algorithm to underestimate soil moisture and overestimate RMS height. A simple criteria based on the σ_hv⁰/σ_vv⁰ ratio is developed to select the areas where the inversion is not impaired by the vegetation. The inversion accuracy is assessed on the original scatterometer data sets but also on several SAR data sets by comparing the derived soil moisture values with in-situ measurements collected over a variety of scenes between 1991 and 1994. Both spaceborne (SIR-C) and airborne (AIRSAR) data are used in the test. Over this large sample of conditions, the RMS error in the soil moisture estimate is found to be less than 4.2% soil moisture     

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     

An ERS-1 synthetic aperture radar image of a tropical squall linecompared with weather radar data

A radar image acquired by the C-band synthetic aperture radar (SAR) aboard the European Remote Sensing satellite ERS-2 over the coastal waters south of Singapore showing radar signatures of a strong tropical squall line (“Sumatra Squall”) is compared with coincident and collocated weather radar data. Squall line features such as the gust front, areas of updraft convergence, and rain areas are identified. Possible attenuation effects from the rain drops in the atmosphere under very heavy rain (rain rate >100 mm/h) is suggested. In addition, the possibility of extracting the associated geophysical parameters, i.e., rain rate and wind speed from SAR imagery is investigated. The rain rate is estimated from the attenuation signature in the SAR image. Comparison between the estimated rain rate and weather radar rain rate shows consistency. Wind speed associated with the squall line is estimated based on the CMOD4 wind scatterometer model. The estimated wind speed pattern appears to be in agreement with the observed squall line structure. Possible errors in the wind estimation due to effects of rain are suggested     

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.