Using sentinel-1A SAR wind retrievals for enhancing scatterometer and radiometer regional wind analyses

Scatterometer surface wind speed and direction observations in combination with radiometer wind speeds allow to generate surface wind analyses with high space and time resolutions over global as well as at regional scales. Regarding scatterometer sampling schemes and physics, the resulting surface wind analyses suffer from lack of accuracy in areas near coasts. The use of the synthetic aperture radar (SAR) onboard the Sentinel-1A satellite attempts to address the enhancement of surface wind analyses issues. In this study, SAR wind speeds and directions retrieved from backscatter coefficients acquired in interferometric wide (IW) swath mode are used. Their accuracy is determined through comprehensive comparisons with moored buoy wind measurements. SAR and buoy winds agree well at offshore and nearshore locations. The statistics characterizing the comparison of SAR and buoy wind speeds and directions are of the same order as those obtained from scatterometer (Advanced SCATterometer (ASCAT) and RapidScat) and buoy wind comparisons. The main discrepancy between SAR and buoy data are found for high wind speeds. SAR wind speeds exceeding 10 m s^–1 tend to be underestimated. A similar conclusion is drawn from SAR and scatterometer wind speed comparisons. It is based on the underestimation of SAR backscatter coefficient (σ°) with respect to σ° estimated from scatterometer winds and the geophysical model function (GMF) named CMOD-IFR2 (Ifremer C band MODel). New SAR wind speeds are retrieved using CMOD-IFR2. The corrected SAR retrievals allow better determination of the spatial characteristics of surface wind speeds and of the related wind components in near-coast areas. They are used for enhancing the determination of the spatial structure function required for the estimation of wind fields gridded in space and time at the regional scale. The resulting wind fields are only determined from scatterometer wind observations in combination with radiometer retrievals. Their qualities are determined through comparisons with SAR wind speeds and directions, and through their application for determination of wind power off Brittany coasts.     

Use of an optimum interpolation method to construct a high-resolution global ocean surface vector wind dataset from active scatterometers and passive radiometers

This study presents a new 0.25° gridded 6-hourly global ocean surface wind vector dataset from 2000 to 2015 produced by blending satellite wind retrievals from five active scatterometers (QuikSCAT, ASCAT-A, ASCAT-B, OSCAT, and HY-2A), nine passive radiometers (four SSM/I sensors, two SSMIS sensors, TMI, AMSR-E, and AMSR2) and one polarimetric radiometer (WindSat) with reanalysis from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) employing an optimum interpolation method (OIM). The accuracy of this wind product is determined through various comparisons with buoy measurements, NCEP/NCAR reanalysis and the cross-calibrated multi-platform (CCMP) winds. The comparisons indicate that OIM winds agree well with buoys, showing a root-mean-squared difference of 1.32 m s^?1 for wind speed and 24.73° for wind direction over 0–30 m s^?1 wind speed range. And the quality of OIM winds is improved significantly relative to NCEP/NCAR reanalysis and can be comparable with CCMP winds. Furthermore, OIM winds can reveal abundant small-scale features that are not visible in reanalysis data. In addition, the wind speed and direction retrievals of most satellites are proved to play an important role in generating the high-quality product, but the procedure for including HY-2A winds and WindSat wind directions should be further explored.     

Gridded surface wind fields from Metop/ASCAT measurements

This article deals with the calculation and validation of daily surface wind vector fields from wind speed and direction observations derived from Advanced SCATterometer (ASCAT) scatterometer measurements over the global ocean. According to the ASCAT sampling scheme, the objective method allowing for the determination of regular in space and time wind speed and direction fields uses ASCAT observations as well as European Centre for Medium Weather Forecasts (ECMWF) analyses. The latter are considered as external drift for the kriging method and as the temporal interpolation basis for ASCAT retrievals. This study focuses on the investigation of the capability of the method to add valuable wind information to the operational atmospheric analyses and on the quality of the resulting wind fields. The accuracy of the former is determined through comprehensive comparisons with daily winds calculated from moored buoy data. At global and regional scales, comparisons are performed with surface wind patterns derived from the ECMWF analysis and from ECMWF Re-analysis project (ERA-Interim) re-analyses.     

Winds in the Gulf of Riga from QuikSCAT and ground-based measurements

This article presents an overview of marine winds in the Gulf of Riga, measured by the SeaWinds instrument on the Quick Scatterometer (QuikSCAT) satellite during the whole lifetime period of the satellite, i.e. 1999–2009. The data were collected with a resolution of 12.5 km during the satellite overflights at 02–04 UTC and 16–18 UTC and referenced to the height of 10 m. The quality of the data was carefully checked, and necessary adjustment was applied to remove the contaminated recordings. Wind speed and direction were compared with those registered on the islands of Kihnu and Ruhnu. It has been shown that allowing lenient filtering of rain-contaminated data derives larger wind speed estimates but increases considerably the quantity of data, allowing separate analysis of the northern and southern parts of the gulf. Wind speed in the northern part is slightly higher, the wind roses for the early morning measurements are similar, but those for the evening measurements show that in spring and summer, the most frequent winds in the southern part are northwesterly and in the northern part are westerly. Wind speed measured on the islands is less than that estimated from the satellite even in the case when rain contamination is removed through application of strict criteria. Wind roses measured at Kihnu are practically similar to those estimated from satellites for the northern part of the gulf in the evening but show some differences during the early morning. In winter, ground-based measurements show maximal frequency of southerly winds, and satellite measurements show southwesterly winds. In spring, the secondary maximum in the wind rose shows northwesterly winds in ground-based records and easterly winds in satellite measurements. Ground-based wind directions are well correlated with those measured by the satellite showing correlation coefficients of over 0.9. For wind speed, this quantity is somewhat lower, i.e. around 0.6.     

卫星遥感反演海面风矢量数据真实性检验与质量评价

微波散射计(QuikSCAT)和微波辐射计(WindSAT)的10m风矢量数据是目前覆盖范围最大、持续时间最长的海面风场卫星数据产品。利用QuikSCAT和WindSAT运行时间重叠的风矢量原始轨道资料,分别与同步的全球海洋浮标实测风矢量资料进行比较。结果显示:QuikSCAT平均风速略大于浮标,平均绝对误差约为1m/s;风向平均绝对误差在8°以下。WindSAT平均风速略大于浮标,平均绝对误差不超过0.5m/s,均方根误差约为1m/s;风向平均绝对误差在10°以下。在全球海域,QuikSCAT和WindSAT风矢量数据质量高,可信度极好。QuikSCAT和WindSAT在同一点上空的过境时间为准同步,并且二者的风矢量数据相关性极好,可以互相替代。     

Multisatellite observations and numerical simulation of an along‐coast cumulus cloud line induced by sea‐breeze circulation

A coastal cumulus cloud‐line formation along the east coast of the USA was observed on a National Oceanic and Atmospheric Administration (NOAA) Polar Orbiting Environmental Satellite (POES) Advanced Very High Resolution Radiometer (AVHRR) satellite image from 17 August 2001. The cloud line starts to form at about 16:00 UTC (local 12:00 noon) and follows the coastline from Florida to North Carolina. The length and width of the cloud line are about 850 km and 8.5 km, respectively. A 15‐min interval sequence of NOAA Geostationary Operational Environmental Satellite (GOES) images shows that the cloud line maintains the shape of the coastline and penetrates inland for more than 20 km over the next 6‐h timespan. Model simulation with actual atmospheric conditions as inputs shows that the cloud line is formed near the land–sea surface temperature (SST) gradient. The synoptic flow at all model levels is in the offshore direction prior to 16:00 UTC whereas low‐level winds (below 980 hPa) reverse direction to blow inland after 16:00 UTC. This reversal is due to the fact that local diurnal heating over the land takes place on shorter time‐scales than over the ocean. The vertical wind at these levels becomes stronger as the land–SST increases during the summer afternoon, and the leading edge of the head of the inland wind ascends from 920 hPa to about 850 hPa in the 3 h after 16:00 UTC. Model simulation and satellite observations show that the cloud line becomes very weak after 21:00 UTC when the diurnal heating decreases.     

Comparison of wind data from QuikSCAT and buoys in the Indian Ocean

The performance of QuikSCAT‐derived wind vectors is evaluated using in‐situ data from moored buoys over the Indian Ocean. The results show that the mean differences for wind speed and wind direction are 0.37 ms^?1 and 5.8°, root mean square deviations are 1.57 ms^?1 and 44.1° and corresponding coefficients of correlation are 0.87 and 0.75, respectively. The matching between in‐situ and satellite estimates seems to be better in the North Indian Ocean than in the Equatorial Indian Ocean. The effects of sea surface temperature and air–sea temperature difference on wind residuals were also investigated. In general, QuikSCAT is found to overestimate the winds. It is speculated that low wind speed during rain‐free conditions and high wind speed, normally associated with rain, may be the reason for the less accurate estimation of the wind vector from QuikSCAT over the Indian Ocean.     

船测资料与智利外海QuikSCAT风场比较分析

利用大洋渔船在智利外海观测的风场资料与QuikSCAT 10 m散射风原始轨道资料L3产品进行了比较分析。两种资料的偏差统计特征显示:①智利外海船测风速总体上高于QuikSCAT风速,船测风向总体上偏于QuikSCAT风向的左侧;②智利外海船测风场资料与QuikSCAT散射风的风速偏差集中分布在-1~1 m/s之间;风向偏差主要集中分布于-60°~-10°之间,其次为10°~60° 和-10°~10°段;③智利外海白天的风速偏差特征值均小于夜晚,昼、夜风向平均偏差数值差别很大,但昼、夜风向平均绝对偏差、均方根偏差数值相差不大;④2008年智利外海船测风场资料与QuikSCAT散射风的偏差大于其他年份的整体平均值,在高风速段风速偏差尤为明显。     

Long-term variation in the North Pacific using satellite-derived wind data set/J-OFURO over the last decade and other data sets over a longer record

Time series of gridded data sets of surface winds (from Qscat (J-OFURO)) constructed by satellite microwave sensors covering almost a decade (1999–2009) are used to examine long-term change in surface wind fields over the world’s oceans. Evidence has been provided by most previous studies that wind speeds have a tendency to increase over time in many area, and we verify whether or not this tendency persists. Results reveal that zonal winds tended to be weaker over the study period in the region of the North Pacific where westerly winds prevail. Time series of different types of data sets based on numerical model products and voluntary ship measurements present similar features of weakening westerly winds, even allowing for discrepancies among the data sets. These time series also exhibit a tendency of enhanced westerly winds in periods prior to the start of the twenty-first century, which means that the long-term trend in wind speed has changed from positive in the 1980s/1990s to negative in the 2000s. Examinations of time series for each season reveal that the above feature is found in winter, suggesting that it is related to the strength of the Aleutian Low.     

Interpretation of scatterometer ocean surface wind vector EOFs over the Northwestern Pacific

Satellite scatterometer winds over the northwestern Pacific were analyzed with the vector empirical orthogonal function (VEOF) method. The Hilbert-Huang transform (HHT), a newly developed non-linear and non-stationary time series data processing method, was also employed in the analysis. A combination of European Remote Sensing Satellite (ERS) −1/2 scatterometer, NASA Scatterometer (NSCAT) and NASA's Quick Scatterometer (QuikSCAT) winds covering the period from January 1992 to April 2000 and the area of 0-50°N, 100-148°E constitutes the baseline for this study. The results indicate that annual cycles dominate the two leading VEOF modes. The first VEOF shows the East Asian monsoon features and the second represents a spring-autumn oscillation. We removed the annual signal from the data set and calculated the interannual VEOFs. The first interannual VEOF represents the interannual variability existing in the spring-autumn oscillation. The temporal mode is correlated with the Southern Oscillation Index (SOI), but has a half-year lag with respect to the SOI. The spatial mode of the first interannual VEOF reflects the response of the tropical and extratropical winds to ENSO events. The second interannual VEOF is another ENSO related mode, and the temporal VEOF mode is correlated with the SOI with a correlation coefficient of 0.78, revealing the wind variability over mid-latitudes, which is associated with ENSO events. Further analysis indicated that the wind variability over the coast of East Asia represents anomalies of a Hadley cell. The quasi-biennial oscillation (QBO) was found in the temporal mode, indicating and verifying that the QBO in the wind fields is related to ENSO events. The third VEOF shows the interannaul variability in the winter-summer mode and displays the interannual variability of the East Asian monsoon. The three leading interannual VEOFs are statistically meaningful as confirmed by a significance test.     

Comparison of Oceansat-2 scatterometer- to buoy-recorded winds and spatial distribution over the Arabian Sea during the monsoon period

For this wind resource assessment (WRA) study, wind speed and direction are the fundamental inputs. Also, these studies are data driven and require large historical wind speed data sets available on the site. This work explores the application of space-based scatterometer winds for assimilation into WRA studies towards the development of offshore wind energy. This article focuses on estimating the performance of Oceansat-2 scatterometer (OSCAT)-derived wind vector using in situ data from buoys at different locations in the Arabian Sea. A comparative study between three methods for estimating the equivalent neutral winds (ENW) for buoys is carried out. OSCAT winds were closest to ENW estimated by the Liu–Katsaros–Businger (LKB) method. The spatial and temporal windows for comparison were 0.5° and ±60 minutes, respectively. The monsoon months (June–September) of 2011 were selected for study. The root mean square deviation for wind speed is less than 2.5 m s^?1 and wind direction is less than 20°, and a small positive bias is observed in the OSCAT wind values. From the analysis, the OSCAT wind values are consistent with in situ-observed values. Furthermore, wind atlas maps were developed with OSCAT winds, representing the spatial distribution of winds at a height of 10 m over the Arabian Sea.     

星载散射计再分析风场在海浪模拟中的应用研究

基于第三代海浪(谱)模式WAVEWATCHⅢ,采用NCEP/QuikSCAT混合风场作为模式输入,进行了东中国海、太平洋东岸以及太平洋中部夏威夷附近海域的海浪数值模拟。在东中国海,模拟有效波高与浮标的参考值有较高的相关系数,波高的均方根误差约为0.5 m;在太平洋东岸和夏威夷附近海域,模拟有效波高比浮标的参考值普遍偏低,不同月份波高的均方根误差在0.4~1.2m之间,但模拟波高与浮标的参考值仍有较强的相关性。结果表明利用WAVEWATCHⅢ结合NCEP/QuikSCAT混合风场模拟东中国海的波高是可行的,但要模拟太平洋中、东部开阔深水海域的波高仍需考虑如涌浪、海流等非风场因素。     

A satellite-derived climatology of global ocean winds

An interactive Climatology of Global Ocean Winds (COGOW) is presented based on 5 years (August 1999-July 2004) of QuikSCAT satellite measurements of wind speed and direction 10 m above the sea surface. This climatology provides the first high spatial resolution, observationally based, online atlas of ocean winds. Users can retrieve climatological wind maps and wind statistics, both in tabular and graphical form, from the COGOW web-based atlas. The global coverage of these data provides highly accurate information about the wind statistics in regions of the world ocean that are sparsely sampled by ships and buoys. A case study of the recovery of the vessel Ehime Maru off the Hawaiian Island of Oahu is presented to demonstrate the usage and value of COGOW. Evidence of air-sea interactions, one of many wind phenomena visible within COGOW, is discussed to further familiarize users with COGOW. Finally, the utility of COGOW with regard to various operational and research communities is summarized.     

Visualizing Time‐Specific Hurricane Predictions,with Uncertainty,from Storm Path Ensembles

The U.S. National Hurricane Center (NHC) issues advisories every six hours during the life of a hurricane. These advisories describe the current state of the storm, and its predicted path, size, and wind speed over the next five days. However, from these data alone, the question “What is the likelihood that the storm will hit Houston with hurricane strength winds between 12:00 and 14:00 on Saturday?” cannot be directly answered. To address this issue, the NHC has recently begun making an ensemble of potential storm paths available as part of each storm advisory. Since each path is parameterized by time, predicted values such as wind speed associated with the path can be inferred for a specific time period by analyzing the statistics of the ensemble. This paper proposes an approach for generating smooth scalar fields from such a predicted storm path ensemble, allowing the user to examine the predicted state of the storm at any chosen time. As a demonstration task, we show how our approach can be used to support a visualization tool, allowing the user to display predicted storm position – including its uncertainty – at any time in the forecast. In our approach, we estimate the likelihood of hurricane risk for a fixed time at any geospatial location by interpolating simplicial depth values in the path ensemble. Adaptivelysized radial basis functions are used to carry out the interpolation. Finally, geometric fitting is used to produce a simple graphical visualization of this likelihood. We also employ a non‐linear filter, in time, to assure frame‐to‐frame coherency in the visualization as the prediction time is advanced. We explain the underlying algorithm and definitions, and give a number of examples of how our algorithm performs for several different storm predictions, and for two different sources of predicted path ensembles.     

Sampling error of daily mean surface wind speed and air specific humidity due to Sun-synchronous satellite sampling and its reduction by multi-satellite sampling

To quantify the sampling error of wind speed (W) and the surface air specific humidity (Q _a) resulting from Sun-synchronous polar-orbit satellite sampling and the effect of single- and multi-satellite sampling, we compared satellite-simulated data with true daily mean data using buoy data. True daily mean data were obtained by averaging buoy data at all available times over 24 h, while satellite-simulated data were the averages of buoy data sampled at satellite passing times (once or twice each day). The difference between true and satellite-simulated data was defined as the sampling error. The sampling error of the daily mean data of W and Q _a depends considerably on the satellite observation time and location. Although the sampling error is fairly reduced if multi-satellite sampling is employed, a noticeable sampling error remains in some cases if a wrong sampling combination is employed. Therefore, multi-satellite data should be carefully used to obtain more accurate global data.     

一种基于海面背景的WindSat极化通道替代定标方法研究

凭借能够测量海面辐射全部4个Stokes参数的能力,全极化微波辐射计成为测量海面风场的一种新手段。由于风向信号仅仅只有几K甚至更小的振幅,故对全极化微波辐射计极化通道的定标精度要求颇高。使用WindSat实测亮温及其海面参数匹配数据集,基于海面风向信号的谐波特征,对WindSat极化通道亮温的定标偏差和仪器噪声进行了计算和分析。研究结果表明:WindSat的10.7、18.7和37.0 GHz这3个频率的极化通道均存在明显的定标偏差,并且该定标偏差会随着升轨和降轨、前向刈幅和后向刈幅等不同观测模态发生变化。另外,WindSat极化通道的仪器噪声较小,其中T3的仪器噪声在0.2 K以内,T4的仪器噪声在0.1 K以内。     

Fusion of sea surface wind vector data acquired by multi-source active and passive sensors in China sea

This work is the first to analyse the sea surface wind vector (SSWV) data acquisition capabilities of eight satellites carrying microwave scatterometer (scanning scatterometer carried by Haiyang satellite 2A, advanced scatterometer carried by Metop satellite A, advanced scatterometer carried by Metop satellite B and scanning scatterometer carried by Oceansat satellite 2) or radiometers (Special Sensor Microwave Imager carried by Meteorological Satellite Program satellites F15 and F17, advanced microwave scanning radiometer 2 carried by GCOM-W1 satellite, and windsat polarimetric radiometer carried by Coriolis satellite) and investigate a SSWV fusion algorithm for active and passive remote-sensing data. We found that combining observations of the eight satellites can provide an SSWV data product with spatial resolution of 25 km × 25 km and temporal resolution of 3 h. Sea surface wind speed and direction data were obtained from multi-source active and passive sensors using a spatiotemporally weighted fusion algorithm. An adaptive sliding window was introduced for calculating effective observation data within spatial/temporal radii, which can effectively improve calculation efficiency for wind field fusion. Comparing the fused and buoy observation results, the root-mean-square errors of the wind direction and speed were 20.6° and 1.2 m s^–1, respectively, indicating that the fusion results can meet most application requirements for wind vector. Meanwhile, the space coverage, accuracy of merged wind speed and wind direction can be improved comparing to a single sensor.     

Multivehicle coverage control for a nonstationary spatiotemporal field

This paper presents a multivehicle sampling algorithm to generate trajectories for nonuniform coverage of a nonstationary spatiotemporal field characterized by spatial and temporal decorrelation scales that vary in space and time, respectively. The sampling algorithm described in this paper uses a nonlinear coordinate transformation that renders the field locally stationary so that existing multivehicle control algorithms can be used to provide uniform coverage. When transformed back to the original coordinates, the sampling trajectories are concentrated in regions of short spatial and temporal decorrelation scales. For fields with coupled spatial statistics, i.e., the spatial decorrelation scales are functions of both spatial dimensions, the coordinate transformation is implemented numerically, whereas for decoupled spatial statistics, the transformation is expressed analytically. We show that the analytical transformation results in vehicle motion that preserves the vehicle sampling speed (which is a measure of vehicle speed scaled by the ratio of the spatial and temporal decorrelation scales), in the original domain; the sampling speed determines the minimum number of vehicles needed to cover a spatiotemporal domain. Theoretical results are illustrated by numerical simulations.     

Impact of sub-daily wind forcing on mixed-layer depth and sea surface temperature: study using observations and a one-dimensional upper ocean model

The impact of sub-daily wind sampling on the diurnal cycle of oceanic mixed-layer depth (MLD) and sea surface temperature (SST) is investigated using a one-dimensional upper ocean model and observations at two locations: the Central Arabian Sea (CAS) and Eastern Equatorial Indian Ocean (EEIO). Motivation to carry out this study is twofold: first, it will help in understanding the possible error in model-simulated MLD and SST due to the non-inclusion of high-temporal wind sampling; and second, it will also emphasize the requirements of temporal sampling from space-based measurements of surface winds. Temporal decorrelation analysis suggests that over a 24-hour period, auto-correlation falls rapidly in the EEIO region, whereas the fall is less even at a lag of 24 hours in CAS. Time series analysis with different sub-daily sampling rates suggests that the optimum sampling rate is three hours for MLD and SST. A suite of one-dimensional model simulations performed at the CAS and EEIO locations with sub-daily wind suggests that once-daily synoptic measurements of wind, which is the most likely scenario with one scatterometer, results in small biases but large standard deviations in MLD. In the case of SST, there is a small positive bias in the order of 0.1°C at the CAS buoy location while at the EEIO location, no such bias is observed. With two scatterometers in a constellation resulting in two observations per day, one can obtain a small standard deviation in MLD, but the bias is greater in this case. For SST, except for a small bias (about 0.1°C) at the CAS location, the distribution is mostly well-behaved Gaussian in all cases. The present study suggests the advisability of acquiring more frequent wind measurements from space-borne scatterometers. A well-coordinated satellite scatterometer constellation will help in resolving the diurnal variability and associated feedback mechanism of air–sea exchange processes, enhancing the understanding of large-scale phenomena such as the Indian summer monsoon, El Niño-southern oscillations, and the Madden–Julian oscillation.     

Using spatial interpolation to construct a comprehensive archive of Australian climate data

A comprehensive archive of Australian rainfall and climate data has been constructed from ground-based observational data. Continuous, daily time step records have been constructed using spatial interpolation algorithms to estimate missing data. Datasets have been constructed for daily rainfall, maximum and minimum temperatures, evaporation, solar radiation and vapour pressure. Datasets are available for approximately 4600 locations across Australia, commencing in 1890 for rainfall and 1957 for climate variables. The datasets can be accessed on the Internet at http://www.dnr.qld.gov.au/silo. Interpolated surfaces have been computed on a regular 0.05° grid extending from latitude 10°S to 44°S and longitude 112°E to 154°E. A thin plate smoothing spline was used to interpolate daily climate variables, and ordinary kriging was used to interpolate daily and monthly rainfall. Independent cross validation has been used to analyse the temporal and spatial error of the interpolated data. An Internet based facility has been developed which allows database clients to interrogate the gridded surfaces at any desired location.