Wind Vector Cell Research Articles

Validation and intercomparison of HY-2A/MetOp-A/Oceansat-2 scatterometer wind products

Sea surface winds are of great significance in scientific research. In the last few years, three series of scatterometers were launched to measure these winds, including the Advanced Scatterometer (ASCAT) aboard Meteorological Operational Satellite A (MetOp-A) and MetOp-B, Oceansat-2 Scatterometer (OSCAT), and HY-2A Scatterometer (HY-2A SCAT). Based on buoy wind data, validation and intercomparison of these scatterometers were performed. Scatterometer-derived wind and buoy wind data were collected only if the spatial difference was less than 0.1 degree and temporal difference less than 5 min. After discarding wind direction data outside five times the standard deviation, ASCAT wind products showed high accuracy in both wind speed and direction, with root-mean-square error (RMSE) 0.86 m/s and 17.97 degrees, respectively. HY-2A SCAT nearly meets the mission requirement, with RMSE for wind speed 1.23 m/s and 22.85 degrees for wind direction. OSCAT had poor performance when compared with the others. RMSE for wind speed was 1.54 m/s and 39.86 degrees for wind direction, which greatly exceeds the mission requirement of 20 degrees. In addition, the RMSE for wind direction shows a high-low pattern on buoy wind speed. However, a wind speed range from 14 to 15 m/s was found to be abnormal, and the reason remains unclear. There was no systematic dependency of both wind speed and direction residuals on buoy wind speed and cross-track location of the wind vector cells across the entire range. No seasonal variation was found for any scatterometer.

In-orbit calibration of Haiyang-2 scatterometer using natural land-extended targets

海洋二号卫星微波散射计(HY-2 Scatterometer, HY-2 SCAT)是一种旋转扫描笔形波束散射计, 能够对同一观测面元提供4次方位角和入射角的观测组合, 并通过地球物理模型反演海面风场. 为达到设计的风场反演精度, 要求其系统定标精度达到0.5dB. 利用不同区域自然扩展目标对HY-2 SCAT进行在轨外定标, 并与OSCAT散射计在同时期内的测量结果进行了对比. 定标结果显示可以消除因散射计天线指向偏差带来的方位向测量误差. 针对HY-2 SCAT方位向测量偏差进行了误差分析, 利用仿真方法以及海洋二号卫星雷达高度计同期测量数据的反演结果进行比对, 验证了误差来源.

Calibration and Estimation of Attitude Errors for a Rotating Fan-Beam Scatterometer Using Calibration Ground Stations

The rotating fan-beam scatterometer (RFSCAT) onboard Chinese-French Oceanic SATellite (CFOSAT) due to launch in 2018 is a new type of radar scatterometer system for ocean surface wind vector measurement. It can give observations with more azimuth and incidence angles for a single wind vector cell (WVC) than other available scatterometers. This has been proved effective in bettering the retrieved wind quality by the simulation approach. However, its innovative observing geometry is challenging for the coming in-orbit external calibration. In this paper, CFOSAT attitude errors are estimated, and its antenna gain pattern is monitored and verified based on the external calibration strategy of a Ku-band scatterometer employing calibration ground stations (CGSs). The effects of satellite attitude errors on the measurements are also analyzed, together with simulation results for the external calibration. It is shown that a gain pattern with accuracy of 0.08 dB and attitude errors within 0.025° are achieved.

Optimized Tropical Cyclone Winds From QuikSCAT: A Neural Network Approach

We have developed a neural network technique for retrieving accurate 12.5-km resolution wind speeds from Ku-band scatterometer measurements in tropical cyclone conditions including typical rain events in such storms. The method was shown to retrieve accurate wind speeds up to 40 m/s when compared with aircraft reconnaissance data, including GPS dropwindsondes and Stepped-Frequency Microwave Radiometer surface wind speed measurements, and when compared to global best track maximum wind speeds. Wind directions were unchanged from the current (version 3) Jet Propulsion Laboratory (JPL) global wind vector product. The technique removes positive biases with respect to best track winds in the developing phase of tropical cyclones that occurred in the nominal (version 2) JPL QuikSCAT product. The new technique also reduces negative biases with respect to best track wind speeds that occurred in the nominal product (both versions 2 and 3) during the most extreme period of the lifetime of intense storms. The wind regime with the most notable improvement is 20–40 m/s (40–80 kn), with more modest improvement for higher winds and the improvement at lower winds comparable to that achieved previously by the version 3 JPL global rain-corrected product. The net effect of all the wind speed improvements is a much better measurement of storm intensity over time in the new product than what has been previously available. When compared with speed data from aircraft flights in Atlantic hurricanes, the new product exhibited a 1–2-m/s positive overall bias and a 3-m/s mean absolute error. The random error and systematic positive bias in the new scatterometer wind product is similar to that of the Hurricane Research Division H*WIND analyses when aircraft data are available for assimilation. This similarity may be explained by the fact that H*WIND data are used as ground truth to fit the coefficients used by the new technique to map radar measurements to wind speed. The fact that H*WIND was designed to match maximum winds while preserving radial symmetry may explain the overall positive biases that we observe in both H*WIND and the new scatterometer wind product which compared to aircraft reconnaissance data. The new scatterometer product could also be inheriting systematic biases in the presence of rain from H*WIND. Under the most extreme rain conditions, the radar signal from the surface can be lost. In such cases, the technique makes use of measurements in the 87.5-km region comprising the 7 $\times$ 7 neighboring cells around the target 12.5-km wind vector cell. In so doing, we sacrifice resolution in cases where the highest resolution region has no useful measurements. Even so, the most extreme rain conditions can result in reduced accuracy. The new technique has been used to retrieve wind fields for every tropical cyclone of tropical storm force or above that has been observed by QuikSCAT during the period of time from October 1999 to November 2009. The resulting data set has been made available online for use by the tropical cyclone research community.

Rain Identification in ASCAT Winds Using Singularity Analysis

The Advanced Scatterometer (ASCAT) onboard the Metop satellite series is designed to measure the global ocean surface wind vector. Generally, ASCAT provides wind products at excellent quality. Occasionally, though, ASCAT-derived winds are degraded by rain. Therefore, identification of rain can help to better understand the rain impact on scatterometer wind quality and to develop a proper quality control (QC) approach for scatterometer data processing. In this letter, an image processing method, known as singularity analysis (SA), is used to detect the presence of rain such that rain-contaminated wind vector cells are flagged. The performance of SA for rain detection is validated using ASCAT Level-2 data collocated with satellite radiometer rain data. The rain probability as a function of SA singularity exponent is calculated and compared with other rain sensitive parameters, such as the wind inversion residual or maximum-likelihood estimator (MLE). The results indicate that the SA is effective in detecting ASCAT rain-contaminated data. Moreover, SA is a complementary rain indicator to the MLE parameter, thus showing great potential for an improved scatterometer QC.

Insights on the OAFlux ocean surface vector wind analysis merged from scatterometers and passive microwave radiometers (1987 onward)

AbstractA high‐resolution global daily analysis of ocean surface vector winds (1987 onward) was developed by the Objectively Analyzed air‐sea Fluxes (OAFlux) project. This study addressed the issues related to the development of the time series through objective synthesis of 12 satellite sensors (two scatterometers and 10 passive microwave radiometers) using a least‐variance linear statistical estimation. The issues include the rationale that supports the multisensor synthesis, the methodology and strategy that were developed, the challenges that were encountered, and the comparison of the synthesized daily mean fields with reference to scatterometers and atmospheric reanalyses. The synthesis was established on the bases that the low and moderate winds (<15 m s−1) constitute 98% of global daily wind fields, and they are the range of winds that are retrieved with best quality and consistency by both scatterometers and radiometers. Yet, challenges are presented in situations of synoptic weather systems due mainly to three factors: (i) the lack of radiometer retrievals in rain conditions, (ii) the inability to fill in the data voids caused by eliminating rain‐flagged QuikSCAT wind vector cells, and (iii) the persistent differences between QuikSCAT and ASCAT high winds. The study showed that the daily mean surface winds can be confidently constructed from merging scatterometers with radiometers over the global oceans, except for the regions influenced by synoptic weather storms. The uncertainties in present scatterometer and radiometer observations under high winds and rain conditions lead to uncertainties in the synthesized synoptic structures.

Diagnostics of T1279 ECMWF analysis winds in the Mediterranean basin by comparison with ASCAT 12.5 km winds

This article aims to understand to what extent the winds fields from an advanced numerical weather prediction system and from a satellite scatterometer describe the same spatial and temporal features of the wind in the Mediterranean Sea. We investigated wind fields for the period February 2010 to February 2012 using the ASCAT scatterometer data with 12.5 km wind vector cells and the analysis wind fields from the ECMWF T1279 global model. The ASCAT–ECMWF mean relative bias and centred root mean square deviation of wind speed, normalized by scatterometer wind speed wsc, Δws/wsc and , have been found to be 7 and 23%. An interesting result is the identification of dependence of both Δws/wsc and on the distance from the coast, indicating the coastal areas as the main source of discrepancy between the two datasets. From 50 to 200 km away from coast, decreases from 40 to 25% and Δws/wsc from 8 to 4%. These results gain more importance considering that the Mediterranean Sea is essentially a coastal sea (50% of its surface lies within 50 km from the coast). Both Δws and have been found to depend nonlinearly on the wind speed. The seasonal variation of Δws/wsc and shows that they are in phase opposition, with higher values of during the warm season (April to October). It is hypothesized that local coastal circulations like land/sea breezes could explain the observed mismatch between model and observations. The reported findings emphasize a common feature of the present atmospheric models forecasting winds over regional basins like the Mediterranean. Copyright © 2010 Royal Meteorological Society

Prior Selection for QuikSCAT Ultra-High Resolution Wind and Rain Retrieval

QuikSCAT was designed for ocean wind retrieval. However, its wind estimation performance is limited in rainy conditions. Several estimation techniques have been proposed: wind-only (WO), simultaneous wind and rain (SWR), and rain-only, which are appropriate for different levels of rain contamination. To exploit the strengths of each estimation method at mitigating rain contamination, a Bayes estimator selection (BES) technique has been developed for 25-km wind products to select from among the several estimation techniques for each wind vector cell. This paper adapts the BES concept [1] to QuikSCAT ultra-high resolution (UHR) 2.5-km, products and extends BES to include prior selection and noise reduction. Prior selection and noise reduction exploit general spatial characteristics of wind and rain fields to improve the accuracy of estimator selections. Together these techniques enable improved estimator selection performance so that the probability of selecting the estimate with minimum squared error approaches optimal levels. Optimal estimator selection reduces variability of wind estimates during rainy conditions and provides rain estimates when possible without using additional sources of information. Overall, UHR wind estimation performance with the new technique has improved bias and root mean-squared error, -0.16 m/s and 2.15 m/s, respectively, which are lower than either of the UHR WO and UHR SWR estimates.

Self-Consistency of Marine Surface Wind Vectors Observed by ASCAT

Marine surface wind vectors observed by the Advanced Scatterometer on the MetOp-A satellite are evaluated by assessing their self-consistency. Global statistics on wind speeds and directions were calculated from the data for a period of one year. The wind speed histograms exhibited a clear dependence on the cross-track wind vector cell (WVC) location, which corresponds to the incidence angle. This trend was reflected in the higher order statistics (standard deviation, skewness, and kurtosis) of the wind speed distribution. The histograms of the wind directions relative to the satellite flight direction clearly showed systematic errors relative to the antenna beam directions. The number density of the wind directions exhibited a systematic pattern relative to the antenna beam directions, and the pattern varied with wind speed and cross-track WVC location. These systematic errors in the wind speed and direction may affect the divergence/convergence and rotation of the wind field. The results of this study suggest the need for further refinements of the wind retrieval algorithms and the C-band geophysical model function. It was also confirmed that the evaluation technique based on the statistical distributions of scatterometer-derived vector winds is effective for identifying systematic errors in the wind speeds and directions.

High-Resolution ASCAT Scatterometer Winds Near the Coast

The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility delivers operational wind products from the Advanced Scatterometer (ASCAT) at 25 km and 12.5 km Wind Vector Cell (WVC) spacing. In these products, based on the backscatter processing performed at EUMETSAT, data closer than ~ 70 km (25 km products) or ~ 35 km (12.5 km products) to the coast are flagged because of land contamination. An alternative wind product is presented here which uses a different way of averaging the full resolution (FR) backscatter measurements from ASCAT. The FR backscatter measurements are screened for land contamination in the coastal zone, thus allowing the construction of WVCs that follow the coast line. The implied alternative spatial averaging allows good quality winds over sea as close as 15-20 km to the shore. The alternative (coastal) and nominal products are compared, and the resulting winds are validated with buoy winds, both in coastal and open sea regions. In regions far away from the coast, the ASCAT coastal and nominal products appear to be of identical quality, but fewer WVCs pass the quality control steps for the nominal product, indicating that the coastal product better resolves sub-WVC wind variability. In the coastal region, we anticipate enhanced wind variability due to katabatic and sea breeze effects, among others. However, the quality of the coastal winds in terms of buoy wind component difference standard deviation is almost as good as for the open sea winds.

Improved ASCAT Wind Retrieval Using NWP Ocean Calibration

The Advanced Scatterometer (ASCAT) wind data processor (AWDP) currently uses the so called CMOD5n geophysical model function (GMF), which was originally derived for the European Remote Sensing (ERS) scatterometers. In order to deliver a high-quality ASCAT wind product, the operational AWDP uses backscatter measurement corrections that are estimated visually (VOC) for each wind vector cell. We propose an alternative and previously established method for estimating correction tables based on numerical weather prediction ocean calibration residuals (NOC). It embodies a smooth incidence-angle dependent part that could serve as an appropriate ASCAT GMF correction, and a radar-beam-dependent residual. The incidence-angle-dependent part of these correction tables is due to differences in calibration procedure of the ERS and ASCAT scatterometers. For the high ASCAT incidence angles for which the GMF has not been assessed by ERS data, the modification is quite large, almost 1 dB. The incidence angle-dependent part is derived by fitting the OC residuals of all beams obtained over one year of data. It is subsequently used to adapt the GMF (yielding CMOD5na). The remaining radar-beam-dependent residual (NOCa) shows a wiggle pattern as function of incidence angle that is very persistent over time, apart from a seasonally varying offset. Both the effects of the GMF modification and the beam-dependent residual on the wind retrieval quality are investigated in this paper. Overall, the performance of NOC is better than that obtained with the previously used VOC calibration method, and the wind statistics show a much better symmetry of the left and right swath for NOC. The beam-dependent corrections improve the quality of the retrieved winds. NOC may thus be used for the intercalibration of the ERS and ASCAT scatterometers.

A Neural Network Technique for Improving the Accuracy of Scatterometer Winds in Rainy Conditions

We exhibit a technique for improving wind accuracy in Ku-band ocean wind scatterometers in the presence of rain. The technique is autonomous in that it only makes use of measurements made by the scatterometer itself, so that no colocation of an external data set (e.g., rain radiometers) is required to perform the correction. The only inputs to the technique are the normalized radar cross-section measurements for each wind vector cell, the cross-track distance of the cell as a proxy for measurement geometry, and the nominal retrieved wind vector for the cell without rain correction. This last input is used to avoid modifying winds not contaminated by rain. The technique was applied to QuikSCAT data for the month of January 2008, resulting in a marked improvement to rainy data. For data that were determined to be rain contaminated by the Jet Propulsion Laboratory rain flag, the rms speed error with respect to National Data Buoy Center buoy winds improved from 8.9 to 3.5 m/s for colocations within 25 km. The rms speed error in rain also improved when compared with the European Centre Medium-Range Weather Forecast winds from 7 to 3 m/s. Data that were not flagged as rain contaminated were not significantly changed, despite the fact that the technique does not make use of the rain flag. The technique was able to distinguish between rain-contaminated wind cells and rain-free wind cells and to substantially improve the wind speed accuracy of the former using QuikSCAT data alone without recourse to any external information about the extent of the rain.

Scatterometer Backscatter Uncertainty Due to Wind Variability

Wind retrieval from scatterometer backscatter measurements is not trivial. A good assessment of the different measurement uncertainties inherent in scatterometer systems is very important for successful wind retrieval and quality control. One source of these uncertainties, i.e., geophysical noise, is dominated by the subcell wind variability. Although the latter is known to dominate the total measurement noise at low winds, no attempt to fully model such effect has yet been performed. In this paper, a simple method to derive a model of geophysical noise for the European Remote Sensing Satellite (ERS) scatterometer is proposed. It is assumed that this noise is mainly due to the spatial distribution of the backscatter footprints and the wind variability within the wind vector cell. In a simulation experiment these parameters were varied, and the values for which the simulation compares best to real data in the three-dimensional measurement space were selected. The resulting geophysical noise model is dependent on wind speed and across subsatellite track location. The empirical method presented here is straightforward and could be applied to other scatterometer systems

Evaluation of marine surface winds observed by SeaWinds and AMSR on ADEOS-II

Marine surface winds observed by two microwave sensors, SeaWinds and Advanced Microwave Scanning Radiometer (AMSR), on the Advanced Earth Observing Satellite-II (ADEOS-II) are evaluated by comparison with off-shore moored buoy observations. The wind speed and direction observed by SeaWinds are in good agreement with buoy data with root-mean-squared (rms) differences of approximately 1 m s−1 and 20°, respectively. No systematic biases depending on wind speed or cross-track wind vector cell location are discernible. The effects of oceanographic and atmospheric environments on the scatterometry are negligible. Though the wind speed observed by AMSR also showed agreement with buoy observations with rms difference of 1.27 m s−1, the AMSR wind speed is systematically lower than the buoy data for wind speeds lower than 5 m s−1. The AMSR wind seems to have a discontinuous trend relative to the buoy data at wind speeds of 5–6 m s−1. Similar results have been obtained in an intercomparison of wind speeds globally observed by SeaWinds and AMSR on the same orbits. A global wind speed histogram of the AMSR wind shows skewed features in comparison with those of SeaWinds and European Centre for Medium-range Weather Forecasts (ECMWF) analyses.

Impact of rain cell on scatterometer data: 2. Correction of Seawinds measured backscatter and wind and rain flagging

Rain can strongly modify the normalized radar cross section (NRCS) measured by Ku‐band scatterometers and alter the wind vector retrieval. Part 1 of this paper (Tournadre and Quilfen, 2003) presented a theoretical model of interaction between rain and scatterometer signal and used it to quantify the effect of rain on the backscatter and on wind vectors. Their results showed that the scatterometer data are strongly affected by rain, that they are extremely sensitive to the rain distribution within scatterometer resolution cells, and that the normalized radar cross section (NRCS) variability induced by rain could be a good indicator for rain flagging. The model is further tested and validated on a tropical cyclone case using colocated high resolution rain and Seawinds NRCS data. The model is used to compute attenuation and volume scattering from Tropical Rainfall Mapping Mission Precipitation Radar (TRMM PR) rain data. The comparison of the high‐resolution (4 km) NRCS to synthetic NRCS computed from National Hurricane Center (NHC) winds and modeled rain terms shows a good qualitative agreement. The rain terms are used to correct the measured NRCS, to infer corrected winds which are significantly improved compared to NHC winds, especially for high winds. The wind correction using low‐resolution rain data (such as Special Sensor Microwave Imager (SSM/I) ones) is also investigated using rain data averaged over wind scatterometer cells. This can also significantly improve the rain retrieval. A new rain flag based on the NRCS variability within wind vector cells is presented and shown to perform better than the operational one.

Wind Stress Curl and Wind Stress Divergence Biases from Rain Effects on QSCAT Surface Wind Retrievals

Abstract Surface vector wind datasets from scatterometers provide essential high-resolution surface forcing information for analyses and models of global atmosphere–ocean processes affecting weather and climate. The importance of realistic amplitude, high-wavenumber, surface wind forcing from scatterometer data has been demonstrated in a variety of ocean modeling applications. However, the radar backscatter signal from which surface vector wind estimates are retrieved is attenuated and/or contaminated in heavy rain. The QuikSCAT (QSCAT) dataset flags rain-contaminated wind vector cells where retrievals are either highly uncertain or not available. Zonal and annual averages of wind stress curl and divergence for 2000, 2001, and 2002 are derived and compared across three surface wind datasets: QSCAT only, reanalysis winds from the National Centers for Environmental Prediction (NCEP reanalysis), and blended QSCAT+NCEP. Missing QSCAT surface wind retrievals due to rain contamination lead to statistically sign...

An experimental approach to correct the microwave radiometer wind speed affected by the change of the brightness temperature due to the relative wind direction

[1] We try to correct the change of the brightness temperature of the spaceborne microwave radiometer caused by the angle between the wind and the sensor azimuth (relative wind direction) and to reduce the effect on the wind speed by an experimental way. We make the preliminary approach by comparing the SSM/I brightness temperature collocated with the QuikSCAT wind vector cell. The change of brightness temperature in 37/19 GHz in the horizontal polarization caused by wind is defined as the shift from the relationship between the horizontal and the vertical polarizations without surface wind (PS37/PS19). We propose two hopeful approaches to correct the brightness temperatures. By comparing the observed PS37/PS19 with the value in the PS37/PS19 tables, the correction is done without knowing the information of the in situ vector wind. The relative wind direction effect on the retrieved wind speed is almost halved by this approach.

A comparison of a two‐dimensional variational analysis method and a median filter for NSCAT ambiguity removal

The ocean surface vector wind can be measured from space by scatterometers. For a set of measurements observed from several viewing directions and collocated in space and time, there will usually exist two, three, or four consistent wind vectors. These multiple wind solutions are known as ambiguities. Ambiguity removal procedures select one ambiguity at each location. We compare results of two different ambiguity removal algorithms, the operational median filter (MF) used by the Jet Propulsion Laboratory (JPL) and a two‐dimensional variational analysis method (2d‐VAR). We applied 2d‐VAR to the entire NASA Scatterometer (NSCAT) mission, orbit by orbit, using European Centre for Medium‐Range Weather Forecasts (ECMWF) 10‐m wind analyses as background fields. We also applied 2d‐VAR to a 51‐day subset of the NSCAT mission using National Centers for Environmental Prediction (NCEP) 1000‐hPa wind analyses as background fields. This second data set uses the same background fields as the MF data set. When both methods use the same NCEP background fields as a starting point for ambiguity removal, agreement is very good: Approximately only 3% of the wind vector cells (WVCs) have different ambiguity selections; however, most of the WVCs with changes occur in coherent patches. Since at least one of the selections is in error, this implies that errors due to ambiguity selection are not isolated, but are horizontally correlated. When we examine ambiguity selection differences at synoptic scales, we often find that the 2d‐VAR selections are more meteorologically reasonable and more consistent with cloud imagery.

Physically based modeling of QuikSCAT SeaWinds passive microwave measurements for rain detection

We present a method for detecting rain‐contaminated wind vector cells in QuikSCAT SeaWinds scatterometer observations. This rain detection method uses passive measurements of microwave brightness temperature obtained as a signal processing by‐product from the standard SeaWinds active scatterometer measurements. The rain flag is developed theoretically first by calibrating the SeaWinds brightness temperatures using Special Sensor Microwave Imager (SSM/I) observations and then by using physically based simulations including the effects of both rain and ice precipitation. Rain retrievals are validated by comparison to SSM/I‐observed rain rates and to other independently produced SeaWinds rain flags and produce rain maps that agree well with the SSM/I estimates. The rain detection method may be used to complement existing rain flags in the current operational QuikSCAT data product. In addition, an atmospheric correction algorithm was developed to dynamically adjust the backscatter coefficient measurements for variations in water vapor and cloud liquid water; results are not significantly different from the climatological correction currently implemented.

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.