Regional accuracy of QuikSCAT gridded winds

The quality of gridded 00 UTC and 12 UTC QuikSCAT wind speed fields provided by the Florida State University (FSU) and NASA Jet Propulsion Laboratory (JPL) are analysed over the Bay of Bengal during May–August 2001. Additionally, an examination of these fields is performed over the Gulf of Mexico for the May–August period from 2001 to 2003. Both 00 UTC and 12 UTC time almost coincide with QuikSCAT sampling times (twice a day) and correspond to either early morning or late evening local time over these regions. The primary restriction for generating accurate maps with a temporal resolution of 12 hours and less is a lack of adequate sampling. Due to non‐uniform spatial‐temporal sampling of the scatterometer, interpolation procedures cannot provide proper estimates in data gaps over the regions not covered by a swath. Wind speed autocorrelation coefficients for gridded datasets have been compared with that of original QuikSCAT data and buoy winds. It is shown that the spatial and temporal interpolation used to obtain these datasets results in smoothing of the QuikSCAT wind speeds, reducing their original variance. This smoothing is amplified where substantial diurnal wind variability occurs. A comparison with buoy data shows that FSU and JPL gridded fields are unable to reproduce correctly observed low correlations in wind speed between morning and evening breeze flows and diurnal variability of winds, leading to a reduced difference between 00 UTC and 12 UTC values in comparison with buoys and swath QuikSCAT data. Rather, the FSU and JPL maps describe daily mean fields. Another consequence of the spatial‐temporal interpolation is that the winds are distorted at a frequency matching the dominant sampling interval (3–4 days) of QuikSCAT measurements over the Bay of Bengal.     

降雨影响散射计测风的初步研究

给出了降雨影响C、Ku波段微波散射计测量海面风速的初步结果。研究结果显示,海面风速为25～30 m/s时,雨速为15 mm/hr的降雨会使这两个波段的微波散射计测量的风速偏低10 m/s。
     

Recent progress on high wind-speed retrieval from multi-polarization SAR imagery: a review

Synthetic aperture radar (SAR) is a useful instrument for monitoring typhoons, hurricanes, and tropical cyclones from space. In this article, we summarize high wind-speed retrieval and validation methods for multi-polarization SAR. Compared to co-polarized geophysical model functions, we show that a cross-polarized wind-speed retrieval model is potentially able to estimate wind speed under extreme weather conditions. The resulting wind speeds are validated using in situ buoy observations, and airborne Stepped-Frequency Microwave Radiometer (SFMR) and spaceborne scatterometer measurements, as well as the National Oceanic and Atmospheric Administration (NOAA) Hurricane Research Division’s Hurricane Wind Analysis System (H*Wind) data. We also analyse the effect of intense rainfall on SAR-derived winds. The wind-speed retrieval accuracy can be improved in cases where the normalized radar cross sections (NRCSs) are suitably modified in rainfall areas.     

Analysis of 1.4 GHz Radiometric measurements from Skylab

Results from the 1.4 GHz, S-194, microwave radiometer located on the SKYLAB satellite are presented. The objective of the investigation is to establish the degree to which quantitative measurements of sea surface temperature, salinity and related marine wind fields can be made using the S-194 radiometer. The analysis of the S-194 data demonstrates that it is possible to make measurements of the brightness temperature of open ocean areas with an RMS absolute accuracy of ±1.3 K over a wide range of environmental conditions. The S-194 is relatively insensitive to sea surface temperature changes and, even though measurements are available over a range in sea surface temperature of 30°C, no significant change in the measured antenna temperature was detected. However, S-194 is relatively sensitive to changes in surface salinity; and the analysis of data taken over the open ocean shows that salinity can be determined to an accuracy of ±2 parts per thousand. Further, it is shown that is it possible to accurately model complex regions such as lakes and bays, where a significant portion of the antenna beam is filled by land, and obtain excellent agreement between calculated and measured antenna temperatures. Open ocean measurements, obtained over the wide range in winds speeds of from 0 to 48 knots, show the antenna temperature to be weakly, but unambiguously, correlated with wind speed. The wind speed dependence determined, of 0.16 K/knot, indicates that surface wind speed can be measured to an accuracy of ±8 knots using the S-194.     

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.     

Assessment of the offshore wind resource in Japan with the ASCAT microwave scatterometer

We analysed wind speed and direction off the coast of Japan using data from the satellite-borne Advanced Scatterometer (ASCAT) and the Weather Research and Forecasting model (WRF), validated these data using in situ wind measurements from 20 buoys, and evaluated the effect of the long time intervals from ASCAT observations on wind resource assessment. More than 25 km from the coast, and at heights of 10 m, the ASCAT wind speed has negative biases of up to 3.4% and root mean square errors of up to 18.5%; its wind direction has 11° to 27° of mean absolute error compared to buoy measurements at a height of 10 m. These accuracies are better than either the expected accuracies reported in the technical manual or those simulated with WRF with its spatial resolution of 10 km. We also evaluated long-term average ASCAT wind speeds in comparison to 4- and 5-year averages of in situ buoy wind speeds measured at three buoys, with resulting differences of –0.3%, –6.3%, and – 1.6%. Furthermore, wind roses show that appearance frequencies of the ASCAT wind direction for the long term are in a good agreement with those of the measurements at the three buoys. Our results show that the ASCAT-derived wind speed and direction are appropriate more than 25 km from the coast, and that the long time interval between ASCAT observations has an insignificant effect on wind resource assessment, if at least 4 or 5 years of averaged ASCAT data are used.     

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.     

Evaluation of hurricane wind speed retrieval from cross-dual-pol SAR

Understanding and forecasting hurricanes are important components of weather prediction and climate studies. A critical concern is accuracy in hurricane wind speed estimates, especially in areas over the ocean where in situ measurements are sparse. Moreover, for very intense hurricanes, remotely sensed ocean surface winds generally lack accuracy. Recent studies on cross-polarization RADARSAT-2 synthetic aperture radar (SAR) data show very promising capability for high wind speed retrieval. The monotonic increase of cross-pol radar backscattered intensity with wind speed, and less sensitive dependence on wind direction, makes it superior to co-pol SAR measurements for operational application. Before further application of the methodology, it is important to evaluate the capability of hurricane forced wind speed retrieval from the promising, simple cross-pol SAR data. In this study, we apply a newly developed wind retrieval model to hurricanes using VH polarization dual-pol mode (VH dual-pol) RADARSAT-2 ScanSAR images. Validation of SAR wind retrievals is via surface wind analysis data from the Hurricane Research Division (HRD) of the National Oceanic and Atmospheric Administration (NOAA) and airborne stepped frequency microwave radiometer (SFMR) measurements. We found that compared to the co-polarization RADARSAT-2 SAR measurements, retrieved wind speeds from cross-polarization mode SAR have better overall accuracy and are more consistent with expected hurricane structures. Moreover, the cross-polarization model for VH dual-pol-retrieved winds does not appear to exhibit the speed ambiguity problem, which is one of the main obstacles for co-polarization retrieval of hurricane winds. SAR-retrieved winds contain detailed wind structures of the hurricane eyewall which are potentially of value for ongoing improvements in numerical models of hurricanes, air–sea interactions, and climate change. Special attention must be paid to biases caused by precipitation in order to reduce remaining errors in SAR retrieval of hurricane winds; precipitation has not been explicitly considered by current geophysical model functions.     

Global structure of extreme wind and wave climate derived from TOPEX altimeter data

Sea surface wind speed and significant wave height (SWH) are two basic parameters, in addition to sea surface height, which can be inferred from satellite altimeter measurements. Traditionally, altimeter-derived wind and wave data are less extensively used compared to sea surface height, as they are sometimes considered as by-products of satellite altimetry (in contrast to, for example, the dedicated scatterometer missions for marine wind observations). However, it is clear that altimeter-based wind and wave data have the unique advantage of being concurrent and collocated with each other. Using eight years (1993–2000) of TOPEX altimeter data with unprecedented accuracy and continuity, the 10-, 50- and 100-year return values of global wind speed and SWH are derived, their characteristics are discussed in relation to wind climatology and wind variability. Validations against in situ observations indicate that the uncertainties of altimeter-derived extreme winds and waves are at the levels of 10% and 5%, respectively. These results suggest that satellite altimeter data, with present quality and duration, can be very useful in many aspects of coastal engineering and marine technology such as design of offshore facilities, ship routing, and preparation of other sea-going activities.     

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.     

Satellite observations of the circulation east of the Mississippi Delta: Cold-air outbreak conditions

Examination of 12 years of Landsat multispectral scanner images shows a recurrent pattern of westward flow immediately south of the Mississippi-Alabama barrier islands under northerly winds. Such flow patterns are also seen under similar conditions in imagery from the Advanced Very High Resolution Radiometer (AVHRR) of the NOAA-series satellites. The flow enters Chandeleur Sound between Ship Island and the northern end of the Chandeleur Islands. It appears to be driven by northerly winds, which force water south through the ChandeleurBreton Sound, drawing water in from the shelf region south of the Mississippi-Alabama barrier islands. These observations on circulation can be simply explained assuming linear dynamics. These two operational satellite systems are accumulating valuable records of coastal circulation patterns under clear-sky conditions.     

Microwave sensing from satellites

Microwave data from the ESMR sensor of the NIMBUS-6 satellite is used to obtain (i) rainfall rates during a tropical cyclone over the northern part of Western Australia, (ii) wind speed over the surface of the waters of the coastal regions of the southern part of Western Australia, and (iii) the microwave emissivity over the land surface of the southern part of Western Australia.     

Short-term wind speed forecasting based on a hybrid model

Wind power is currently one of the types of renewable energy with a large generation capacity. However, operation of wind power generation is very challenging because of the intermittent and stochastic nature of the wind speed. Wind speed forecasting is a very important part of wind parks management and the integration of wind power into electricity grids. As an artificial intelligence algorithm, radial basis function neural network (RBFNN) has been successfully applied into solving forecasting problems. In this paper, a novel approach named WTT–SAM–RBFNN for short-term wind speed forecasting is proposed by applying wavelet transform technique (WTT) into hybrid model which hybrids the seasonal adjustment method (SAM) and the RBFNN. Real data sets of wind speed in Northwest China are used to evaluate the forecasting accuracy of the proposed approach. To avoid the randomness caused by the RBFNN model or the RBFNN part of the hybrid model, all simulations in this study are repeated 30 times to get the average. Numerical results show that the WTT–SAM–RBFNN outperforms the persistence method (PM), multilayer perceptron neural network (MLP), RBFNN, hybrid SAM and RBFNN (SAM–RBFNN), and hybrid WTT and RBFNN (WTT–RBFNN). It is concluded that the proposed approach is an effective way to improve the prediction accuracy.     

Assessment of an analytical model for sea surface wind speed retrieval from spaceborne SAR

An analytical model based on radar backscatter theory was utilized to retrieve sea surface wind speeds from C-band satellite synthetic aperture radar (SAR) data at either vertical (VV) or horizontal (HH) polarization in transmission and reception. The wind speeds were estimated from several ENVISAT Advanced SAR (ASAR) images in Hong Kong coastal waters and from Radarsat-1 SAR images along the west coast of North America. To evaluate the accuracy of the analytical model, the estimated wind speeds were compared to coincident buoy measurements, as well as winds retrieved by C-band empirical algorithms (CMOD4, CMOD_IRF2 and CMOD5). The comparison shows that the accuracy of the analytical model is comparable to that of the C-band empirical algorithms. The results indicate the capability of the analytical model for sea surface wind speed retrieval from SAR images at both VV and HH polarization.     

Variation in aerosol properties over Hyderabad,India during intense cyclonic conditions

In this study, we examine the changes in aerosol properties associated with an intense tropical cyclone, the so‐called ‘Mala’, that occurred during April 2006, over the Bay of Bengal. This cyclone, accompanied by very strong surface winds reaching 240 km h^?1, caused extensive disasters in houses and beach resorts in the coastal areas of Myanmar. Ground‐based measurements of aerosol optical depth (AOD), particle‐size distribution and erythemal UV radiation in the neighbouring urban environment of Hyderabad, India, showed significant variations due to changes in wind velocity and direction associated with the cyclone event. The results show an increase in ground‐measured PM_1.0, PM_2.5, and PM₁₀ concentrations, probably associated with the strong surface winds on 28 April, the day on which the cyclone affected the study region. In contrast, the AOD on that day exhibited a significant decrease, since the winds probably acted as a ventilation mechanism for the atmosphere. The Terra‐MODIS satellite images showed a prevalence of dust particles over the study region on the next day of the cyclone. Results from ground‐based AOD sun‐photometer observations matched well with satellite AOD retrievals. Aerosol index obtained from Ozone Monitoring Instrument (OMI) during the cyclone events suggested increasing trend, indicating the presence of an elevated dust‐aerosol layer during and after the cyclone. Results on the effects of wind and air mass fields in affecting the AOD during cyclone events are also presented.     

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.     

基于机载多普勒雷达数据的飓风内部三维风场反演算法

利用2002年NOAA海洋飓风实验中三维风雨微波成像仪(IWRAP,UMass Imaging Wind and Rain Airborne Profiler)数据,提出了两种自适应卡尔曼滤波算法进行飓风三维风矢量的反演。为验证不同自适应滤波算法的准确性,通过对实际风场以及相应的机载雷达观测数据进行仿真,得到一种最优自适应滤波;文中对滤波结果误差进行了初步分析,探讨了误差来源。根据仿真结果,利用最优自适应滤波对2002年10月2日飓风莉莉多普勒雷达测量数据进行处理,滤波得到的三维风场与本次实验中GPS下投式探空仪的三维风场同步测量数据,多波段微波辐射计的海面风场测量数据,以及飞机高度风场数据进行了比较。
     

Monitoring snowpack evolution using interferometric synthetic aperture radar on the North Slope of Alaska,USA

This research investigates the use of Interferometric Synthetic Aperture Radar (InSAR) to generate a time-series of snow water equivalent (SWE) for dry snow within the Kuparuk watershed, North Slope, Alaska, during the winter of 1993/1994. Maps depicting relative change in phase and the theoretical relative change in SWE between satellite acquisitions are created for 3-day periods at the end of March 1994 using both ascending and descending ERS-1 overpasses. The theoretical coefficient relating relative change in phase and relative change in SWE for C-band is found to be at least twice as large as what is expected when using a simple single-layer snow model for this study area and time period. Without any direct measurements of SWE on the ground, station measurements of snow depth and hourly wind are linked to each 3-day relative change in phase map. Along with a qualitative assessment, quantitative measures of the rate and magnitude of phase change around these stations are directly compared to the hourly wind data for a given 3-day period. InSAR-derived maps acquired around a measured precipitation event show a considerable relationship to the predominant direction of strong winds over each 3-day period while maps acquired around no measureable precipitation depict much less correlation between phase change and predominant direction of strong winds. Despite limited ground measurements to infer snowpack conditions, these results show continued promise for the InSAR technique to measure changes in snowpack conditions (e.g. SWE) at much higher resolutions than manual sampling methods or passive microwave remote sensing. The extension of this technique to current L-band InSAR satellite platforms is also discussed.     

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.