HY-2散射计风速降雨影响的神经网络校正

海洋二号搭载的笔形圆锥扫描微波散射计(HY2-scat)是国内第一个业务化运行的,可提供大量实时海面风场数据的微波传感器。由于Ku波段散射计测风原理和微波传输特性,受到降雨影响的散射计反演风场数据准确度降低。降雨导致的微波传播路径衰减,雨滴对微波直接后向散射导致的回波能量增加和雨滴对海表面毛细波的干扰等综合效应,使得降雨条件下散射计测风风速计算值偏高,风向计算值偏差较大。针对散射计反演风速受降雨影响的特点引入神经网络模型,使用准确度较高的NWP数值预报模式风场数据作为参考,对受降雨影响的HY-2散射计反演L2B级标准风场数据产品进行校正,改进HY-2散射计反演风矢量在降雨条件下的准确度。与受降雨影响的散射计反演风场风速偏差相比较,经过神经网络校正后的风速偏差减小,说明该方法适用于改善受降雨影响的HY-2散射计测风风速精度。     

Ocean-atmosphere momentum coupling in the Kuroshio Extension observed from space

Using a combination of Quick Scatterometer (QuikSCAT), Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), and Lagrangian drifter measurements, we demonstrate that wind data alone are not sufficient to derive ocean surface stress (momentum flux) over mid-latitude ocean fronts, specifically the Kuroshio Extension. There was no continuous and large-scale stress measurement over ocean until the launch of the scatterometers. Stress had been derived from winds through a drag coefficient, and our concept of stress distribution may be largely influenced by our knowledge of wind distribution. QuikSCAT reveals that the variability of stress could be very different from wind. The spatial coherence between the magnitude of stress and sea surface temperature (SST), between the divergence of surface stress and the downwind SST gradient, and between the vorticity of stress and crosswind SST gradient, are the inherent characteristics of stress (turbulence production by buoyancy) that would exist even under a uniform wind field. The coherence between stress vorticity and SST gradient is masked by the rotation of ocean currents over the Kuroshio meanders. Surface stress rotates in the opposite direction to surface currents because stress is the vector difference between wind and current. The results are in agreement with a previous study of the Agulhas Extension and confirm the unique stress measuring capability of the scatterometer.     

QuikSCAT散射计矢量风统计特征及南海大风遥感分析

主要分析了QuikSCAT散射计矢量风资料的统计特征,并利用该资料分析了南海大风频数的月变化、空间分布特征以及南海各月风场的空间分布特征.结果显示QuikSCAT矢量风在南海具有可信性;利用散射计风场资料分析发现在南海主要盛行两种风,即冬季东北风和夏季西南风,东北风最大中心在巴士海峡、台湾海峡;南海中南部存在东北季风的次大中心和西南季风的极大中心.     

Identification and removal of block ambiguities in sea surface wind field with scatterometers

The solution of the current wind retrieval algorithm for scatterometers has several wind vector ambiguities, due to the bi-harmonic relationship between normalized backscattering cross section and the relative wind direction and the existence of the measurement error. In order to remove the ambiguities for a unique wind field, a circular median filter approach (CMF) is usually adopted. But under the condition for clustering distribution of the false ambiguities in some local areas, the CMF fails and thus engenders block ambiguities, which degrade the precision of the retrieved wind field. For such a situation, a technique of identification and removal of the block ambiguities is presented to further optimize the retrieved wind field after CMF. It is demonstrated in experiment that this technique can identify and remove most of the block ambiguities.     

Probability Distribution of Surface Wave Slope Derived Using Sun Glitter Images from Geostationary Meteorological Satellite and Surface Vector Winds from Scatterometers

The probability distribution of the sea surface slope has been estimated using sun glitter images derived from the visible wavelength radiometer on the Geostationary Meteorological Satellite (GMS) and surface vector winds observed by spaceborne scatterometers. The brightness of the visible images is converted to the probability of wave surfaces which reflect the sunlight toward GMS in grids of 0.25° × 0.25° (latitude × longitude). The slope and azimuth angle required for the reflection of the sun's rays toward GMS are calculated for each grid from the geometry of GMS observation and location of the sun. The GMS images are then collocated with surface wind data observed by three scatterometers. Using the collocated data set of about 30 million points obtained in a period of 4 years from 1995 to 1999, the probability distribution function of the surface slope is estimated as a function of wind speed and azimuth angle relative to the wind direction. The results are compared with those of Cox and Munk (1954a). The surface slope estimated by the present method shows a narrower distribution and much less directivity relative to the wind direction than that reported by Cox and Munk. It is expected that their data were obtained under conditions of growing wind waves. In general, wind waves are not always developing, and the slope distribution might differ from the results of Cox and Munk. Most of our data are obtained in the subtropical seas under clear-sky conditions. This difference in the conditions may be the reason for the difference of slope distribution. This revised version was published online in July 2006 with corrections to the Cover Date.     

Concurrent use of active and passive microwave remote sensing data for monitoring of rice crop

Estimation of crop area, growth and phenological information is very important for monitoring of agricultural crops. However, judicious combination of spatial and temporal data from different spectral regions is necessary to meet the requirement. This study highlights the use of active microwave QuikSCAT Ku-band scatterometer and Special Sensor Microwave/Imager (SSM/I) passive microwave radiometer data to derive information on important phenological phases of rice crop. The wetness index, a weekly composite product derived using brightness temperatures from 19, 37 and 85 GHz channels of SSM/I, was used to identify the puddling period. Ku-band scatterometer data provided the signal of transplanted rice seedlings since they acts as scatterers and increases the backscattering. Dual peak nature of temporal backscatter curve around the heading stage of rice crop was observed in Ku-band. The decrease of backscatter after first peak was associated with the threshold value of 60% crop canopy cover. The symmetric (Gaussian) and asymmetric (lognormal) curve fits were attempted to derive the date of initiation of the heading phase. The temporal signature from each of these sensors was found to complement each other in crop growth monitoring. Image showing pixel-wise timings of heading stage revealed the differences exists in various parts of the study area.     

降雨对Ku波段旋转扇形波束散射计影响

星载散射计测量的归一化后向散射截面是有关海面毛细重力波的大小和方向的函数:散射计的回波强度与海面毛细重力波的振幅成正比;风向对后向散射系数具有调制作用。因此,可以利用散射计的数据,根据地球物理模型反演出具有高精确度、无雨和中低风速条件下的海面风场矢量。然而高达10%的散射计测量数据会受到降雨影响(Nie,etal.,2008),尤其工作在Ku波段的散射计。降雨对海面散射幅度的影响主要包括:(1)降雨对雷达波的衰减和散射;(2)降雨改变海洋水面形态和海面粗糙度。     

基于HY-2A散射计数据的极区海冰范围反演方法及其评估

A sea ice extent retrieval algorithm over the polar area based on scatterometer data of HY-2A satellite has been established.Four parameters are used for distinguishing between sea ice and ocean with Fisher's linear discriminant analysis method.The method is used to generate polar sea ice extent maps of the Arctic and Antarctic regions of the full 2013–2014 from the scatterometer aboard HY-2A(HY-2A-SCAT) backscatter data.The time series of the ice mapped imagery shows ice edge evolution and indicates a similar seasonal change trend with total ice area from DMSP-F17 Special Sensor Microwave Imager/Sounder(SSMIS) sea ice concentration data.For both hemispheres,the HY-2A-SCAT extent correlates very well with SSMIS 15% extent for the whole year period.Compared with Synthetic Aperture Radar(SAR) imagery,the HY-2A-SCAT ice extent shows good correlation with the Sentinel-1 SAR ice edge.Over some ice edge area,the difference is very evident because sea ice edges can be very dynamic and move several kilometers in a single day.     

海浪对ASCAT散射计反演风场的影响研究

To improve retrieval accuracy, this paper studies wave effects on retrieved wind field from a scatterometer. First, the advanced scatterometer(ASCAT) data and buoy data of the National Data Buoy Center(NDBC) are collocated. Buoy wind speed is converted into neutral wind at 10 m height. Then, ASCAT data are compared with the buoy data for the wind speed and direction. Subsequently, the errors between the ASCAT and the buoy wind as a function of each wave parameter are used to analyze the wave effects. Wave parameters include dominant wave period(dpd), significant wave height(swh), average wave period(apd) and the angle between the dominant wave direction(dwd) and the wind direction. Collocated data are divided into sub-datasets according to the different intervals of each wave parameter. A root mean square error(RMSE) for the wind speed and a mean absolute error(MAE) for the wind direction are calculated from the sub-datasets, which are considered as the function of wave parameters. Finally, optimal wave conditions on wind retrieved from the ASCAT are determined based on the error analyses. The results show the ocean wave parameters have correlative relationships with the RMSE of the retrieved wind speed and the MAE of the retrieved wind direction. The optimal wave conditions are presented in terms of dpd, swh, apd and angle.     

Construction of surface wind stress fields with high temporal resolution using ERS-1 scatterometer data

Wind stress fields with high temporal resolution over the North Pacific have been constructured by using ERS-1 scatterometer data. A simple objective analysis, a successive correction method, was used to construct the fields. Several necessary parameters used in the method are examined by a simulation based on the climatological data. The meridional decorrelation scale of the wind stress depends strongly on the season, while the zonal decorrelation scale is highly stable. We determined the decorrelation scale depending on the location and the time and applied to the successive correction method. The monthly mean field constructed by averaging the daily mean data is free from an aliasing error, which is a serious problem if a simple monthly averaging is applied. The daily wind stress data obtained in the present study represent small time- and spatial-scale variation and large amplitudes compared with data interpolated from simple monthly mean data. The satellite-derived data are also compared with in situ data obtained by meteorological buoys. The satellite wind speeds are lower than in situ wind speeds for every buoy. This underestimation is not due to the present objective analysis, but due to the original data, the ERS-1 Scatterometer Value-Added Product.