The North Atlantic Oscillation (NAO) is one of the major causes of many recent changes in the Arctic Ocean. Generally, it is related to wind speed, sea surface temperature (SST), and sea ice cover. In this study, ...The North Atlantic Oscillation (NAO) is one of the major causes of many recent changes in the Arctic Ocean. Generally, it is related to wind speed, sea surface temperature (SST), and sea ice cover. In this study, we analyzed the distributions of and correlations between SST, wind speed, NAO, and sea ice cover from 2003 to 2009 in the Greenland Sea at 10°W to 10°E, 65°N to 80°N. SST reached its peak in July, while wind speed reached its minimum in July. Seasonal variability of SST and wind speed was different for different regions. SST and wind speed mainly had negative correlations. Detailed correlation research was focused on the 75~N to 80~N band. Regression analysis shows that in this band, the variation of SST lagged three months behind that of wind speed Ice cover and NAO had a positive correlation, and the correlation coefficient between ice cover and NAO in the year 2007 was 0.61 SST and NAO also had a positive correlation, and SST influenced NAO one month in advance. The correlation coefficients between SST and NAO reached 0.944 for the year 2005, 0.7 for the year 2008, and 0.74 for the year 2009 after shifting SST one month later. NAO also had a positive correlation with wind speed, and it also influenced wind speed one month in advance. The correlation coefficients between NAO and wind speed reached 0.783, 0.813, and 0.818 for the years 2004, 2005, and 2008, respectively, after shifting wind speed one month earlier.展开更多
Wind and wave data are essential in climatological and engineering design applications.In this study,data from 15 buoys located throughout the South China Sea(SCS)were used to evaluate the ERA5 wind and wave data.Appl...Wind and wave data are essential in climatological and engineering design applications.In this study,data from 15 buoys located throughout the South China Sea(SCS)were used to evaluate the ERA5 wind and wave data.Applicability assessment are beneficial for gaining insight into the reliability of the ERA5 data in the SCS.The bias range between the ERA5 and observed wind-speed data was-0.78-0.99 m/s.The result indicates that,while the ERA5 wind-speed data underestimation was dominate,the overestimation of such data existed as well.Additionally,the ERA5 data underestimated annual maximum wind-speed by up to 38%,with a correlation coefficient>0.87.The bias between the ERA5 and observed significant wave height(SWH)data varied from-0.24 to 0.28 m.And the ERA5 data showed positive SWH bias,which implied a general underestimation at all locations,except those in the Beibu Gulf and centralwestern SCS,where overestimation was observed.Under extreme conditions,annual maximum SWH in the ERA5 data was underestimated by up to 30%.The correlation coefficients between the ERA5 and observed SWH data at all locations were greater than 0.92,except in the central-western SCS(0.84).The bias between the ERA5 and observed mean wave period(MWP)data varied from-0.74 to 0.57 s.The ERA5 data showed negative MWP biases implying a general overestimation at all locations,except for B1(the Beibu Gulf)and B7(the northeastern SCS),where underestimation was observed.The correlation coefficient between the ERA5 and observed MWP data in the Beibu Gulf was the smallest(0.56),and those of other locations fluctuated within a narrow range from 0.82 to 0.90.The intercomparison indicates that during the analyzed time-span,the ERA5 data generally underestimated wind-speed and SWH,but overestimated MWP.Under non-extreme conditions,the ERA5 wind-speed and SWH data can be used with confidence in most regions of the SCS,except in the central-western SCS.展开更多
Field and laboratory observations indicate that the variation of drag coefficient with wind speed at high winds is different from that under low-to-moderate winds.By taking the effects of wave development and sea spra...Field and laboratory observations indicate that the variation of drag coefficient with wind speed at high winds is different from that under low-to-moderate winds.By taking the effects of wave development and sea spray into account,a new parameterization of drag coefficient applicable from low to extreme winds is proposed.It is shown that,under low-to-moderate wind conditions so that the sea spray effects could be neglected,the nondimensional aerodynamic roughness first increases and then decreases with the increasing wave age;whereas under high wind conditions,the drag coefficient decreases with the increasing wind speed due to the modification of the logarithmic wind profile by the effect of sea spray droplets produced by bursting bubbles or wind tearing breaking wave crests.The drag coefficients and sea surface aerodynamic roughnesses reach their maximum values vary under different wave developments.Correspondingly,the reduction of drag coefficient under high winds reduces the increasing rate of friction velocity with increasing wind speed.展开更多
In this study, the statistical characterization of sea conditions in the East China Sea(ECS) is investigated by analyzing a significant wave height and wind speed data at a 6-hour interval for 30 years(1980–2009), wh...In this study, the statistical characterization of sea conditions in the East China Sea(ECS) is investigated by analyzing a significant wave height and wind speed data at a 6-hour interval for 30 years(1980–2009), which was simulated and computed using the WAVEWATCH Ⅲ(WW3) model. The monthly variations of these parameters showed that the significant wave height and wind speed have minimum values of 0.73 m and 5.15 ms^(-1) and 1.73 m and 8.24 ms^(-1) in the month of May and December, respectively. The annual, seasonal, and monthly mean sea state characterizations showed that the slight sea generally prevailed in the ECS and had nearly the highest occurrence in all seasons and months. Additionally, the moderate sea prevailed in the winter months of December and January, while the smooth(wavelets) sea prevailed in May. Furthermore, the spatial variation of sea states showed that the calm and smooth sea had the largest occurrences in the northern ECS. The slight sea occurred mostly(above 30%) in parts of the ECS and the surrounding locations, while higher occurrences of the rough and very rough seas were distributed in waters between the southwest ECS and the northeast South China Sea(SCS). The occurrences of the phenomenal sea conditions are insignificant and are distributed in the northwest Pacific and its upper region, which includes the Southern Kyushu-Palau Ridge and Ryukyu Trench.展开更多
Long-term variations in a sea surface wind speed (WS) and a significant wave height (SWH) are associated with the global climate change, the prevention and mitigation of natural disasters, and an ocean resource ex...Long-term variations in a sea surface wind speed (WS) and a significant wave height (SWH) are associated with the global climate change, the prevention and mitigation of natural disasters, and an ocean resource exploitation, and other activities. The seasonal characteristics of the long-term trends in China's seas WS and SWH are determined based on 24 a (1988-2011) cross-calibrated, multi-platform (CCMP) wind data and 24 a hindcast wave data obtained with the WAVEWATCH-III (WW3) wave model forced by CCMP wind data. The results show the following. (1) For the past 24 a, the China's WS and SWH exhibit a significant increasing trend as a whole, of 3.38 cm/(s.a) in the WS, 1.3 cm/a in the SWH. (2) As a whole, the increasing trend of the China's seas WS and SWH is strongest in March-April-May (MAM) and December-January-February (DJF), followed by June-July-August (JJA), and smallest in September-October-November (SON). (3) The areal extent of significant increases in the WS was largest in MAM, while the area decreased in JJA and DJF; the smallest area was apparent in SON. In contrast to the WS, almost all of China's seas exhibited a significant increase in SWH in MAM and DJF; the range was slightly smaller in JJA and SON. The WS and SWH in the Bohai Sea, the Yellow Sea, East China Sea, the Tsushima Strait, the Taiwan Strait, the northern South China Sea, the Beibu Gull and the Gulf of Thailand exhibited a significant increase in all seasons. (4) The variations in China's seas SWH and WS depended on the season. The areas with a strong increase usually appeared in DJF.展开更多
The surface wind speed(SWS)is affected by both large-scale circulation and land use and cover change(LUCC).In China,most studies have considered the effect of large-scale circulation rather than LUCC on SWS.In this st...The surface wind speed(SWS)is affected by both large-scale circulation and land use and cover change(LUCC).In China,most studies have considered the effect of large-scale circulation rather than LUCC on SWS.In this study,we evaluated the effects of LUCC on the SWS decrease during 1979-2015 over China using the observation minus reanalysis(OMR)method.There were two key findings:(1)Observed wind speed declined significantly at a rate of 0.0112 m/(s·a),whereas ERA-Interim,which can only capture the inter-annual variation of observed data,indicated a gentle downward trend.The effects of LUCC on SWS were distinct and caused a decrease of 0.0124 m/(s·a)in SWS;(2)Due to variations in the characteristics of land use types across different regions,the influence of LUCC on SWS also varied.The observed wind speed showed a rapid decline over cultivated land in Northwest China,as well as a decrease in China’s northeastern and eastern plain regions due to the urbanization.However,in the Tibetan Plateau,the impact of LUCC on wind speed was only slight and can thus be ignored.展开更多
Conventional retrieval and neural network methods are used simultaneously to retrieve sea surface wind speed(SSWS)from HH-polarized Sentinel-1(S1)SAR images.The Polarization Ratio(PR)models combined with the CMOD5.N G...Conventional retrieval and neural network methods are used simultaneously to retrieve sea surface wind speed(SSWS)from HH-polarized Sentinel-1(S1)SAR images.The Polarization Ratio(PR)models combined with the CMOD5.N Geophysical Model Function(GMF)is used for SSWS retrieval from the HH-polarized SAR data.We compared different PR models developed based on previous C-band SAR data in HH-polarization for their applications to the S1 SAR data.The recently proposed CMODH,i.e.,retrieving SSWS directly from the HHpolarized S1 data is also validated.The results indicate that the CMODH model performs better than results achieved using the PR models.We proposed a neural network method based on the backward propagation(BP)neural network to retrieve SSWS from the S1 HH-polarized data.The SSWS retrieved using the BP neural network model agrees better with the buoy measurements and ASCAT dataset than the results achieved using the conventional methods.Compared to the buoy measurements,the bias,root mean square error(RMSE)and scatter index(SI)of wind speed retrieved by the BP neural network model are 0.10 m/s,1.38 m/s and 19.85%,respectively,while compared to the ASCAT dataset the three parameters of training set are–0.01 m/s,1.33 m/s and 15.10%,respectively.It is suggested that the BP neural network model has a potential application in retrieving SSWS from Sentinel-1 images acquired at HH-polarization.展开更多
The co-variation of surface wind speed and sea surface temperature (SST) over the Gulf Stream frontal region is investigated using high-resolution satellite measurements and atmospheric reanalysis data. Results show t...The co-variation of surface wind speed and sea surface temperature (SST) over the Gulf Stream frontal region is investigated using high-resolution satellite measurements and atmospheric reanalysis data. Results show that the pattern of positive SST-surface wind speed correlations is anchored by strong SST gradient and marine atmospheric boundary layer (MABL) height front, with active warm and cold-ocean eddies around. The MABL has an obvious transitional structure along the strong SST front, with greater (lesser) heights over the north (south) side. The significant positive SST-surface wind-speed perturbation correlations are mostly found over both strong warm and cold eddies. The surface wind speed increases (decreases) about 0.32 (0.41) m/s and the MABL elevates (drops) approximate 55 (54) m per 1℃ of SST perturbation induced by warm (cold) eddies. The response of the surface wind speed to SST perturbations over the mesoscale eddies is mainly attributed to the momentum vertical mixing in the MABL, which is confirmed by the linear relationships between the downwind (crosswind) SST gradient and wind divergence (curl).展开更多
利用全国40个地面台站的观测资料对ERA5及ERA5-Land两种不同空间分辨率的再分析资料开展了地面风速误差评估研究,结果表明:ERA5和ERA5-Land资料多年平均风速偏差的平均值分别为0.08 m s^(−1)、-0.06 m s^(−1),偏差的最大值分别为0.46 m ...利用全国40个地面台站的观测资料对ERA5及ERA5-Land两种不同空间分辨率的再分析资料开展了地面风速误差评估研究,结果表明:ERA5和ERA5-Land资料多年平均风速偏差的平均值分别为0.08 m s^(−1)、-0.06 m s^(−1),偏差的最大值分别为0.46 m s^(−1)、-0.19 m s^(−1),相对偏差的平均值为4.4%、-2.0%,相对偏差的最大值分别为33.0%、-10.1%;月平均风速线性拟合方程的斜率分别为0.93、0.97,截距分别为0.29 m s^(−1)、0.02 m s^(−1),相关系数分别为0.98、0.99;月平均风速均方根误差的平均值分别为0.17 m s^(−1)、0.14 m s^(−1),均方根误差的最大值分别为0.49 m s^(−1)、0.22 m s^(−1),相对均方根误差的平均值为7.4%、5.7%,相对均方根误差的最大值分别为35.2%、13.3%。ERA5-Land高分辨率资料地面风速误差相对较低,有利于提高风能资源评估的准确性。展开更多
For open sea conditions the sea surface roughness is described as a function of surface stress and wind speed over sea surface by Charnock relation. The sea surface roughnessn in the North-west Pacific Ocean is derive...For open sea conditions the sea surface roughness is described as a function of surface stress and wind speed over sea surface by Charnock relation. The sea surface roughnessn in the North-west Pacific Ocean is derived successfully using wind speed data estimated by the TOPEX satellite altimeter. From the results we find that: (1) the mean sea surface roughness in winter is greater than in summer; (2) compared with other sea areas, the sea surface roughness in the sea area east of Japan ( N30°- 40°, E135°- 150°) is larger than in other sea areas; (3) sea surface roughness in the South China Sea changes more greatly than that in the Bohai Sea, Yellow Sea and East China Sea.展开更多
Recent research has shown that winter warmings are phenomenally high compared to summer warmings over the poles,especially over the Arctic.Taking the current scenario into account,this paper attempts to understand the...Recent research has shown that winter warmings are phenomenally high compared to summer warmings over the poles,especially over the Arctic.Taking the current scenario into account,this paper attempts to understand the atmospheric variables causing sea ice variability over and around the region of Svalbard for seasons;winter,spring,summer and autumn for the span of 42 years(1979-2021).The variability in atmospheric and oceanic parameters namely temperature,precipitation,wind speed,and sea surface salinity are analysed over inter-spatial,inter-seasonal and inter-annual domains.Winters are characterized by inter-annual increasing trend in temperature.During 1981-1990 the rise from the decadal mean is found to be 0.39 K·a^(-1),during 1991-2000 it is 0.20 K·a^(-1),during 2001-2010 it is 0.04 K·a^(-1) and during 2011-2020 it is 0.23 K·a^(-1).Interestingly while considering inter-spatial domains,the region southwest to Svalbard seems to be wetter(0.05 mm·(10 a)-1)compared to its northeast(-0.03 mm·(10 a)-1).Across all the three domains,wind speeds are highest during autumn and then decrease subsequently through summer,spring and are least during winter.Wind is predominantly from the south,and hence it is suspected to carry hot Atlantic air.Additionally,the significant role of salinity in the ocean also plays a key role in governing the fate of sea ice conditions.The long-term forecasts of temperature over seaice of Svalbard are alarming especially for the winter ice(r=-0.84).Correlation matrices between atmospheric and sea ice parameters are shown to gain a better understanding on their inter relation.展开更多
Daily observations of wind speed at 12 stations in the Greater Beijing Area during 1960–2008 were homogenized using the Multiple Analysis of Series for Homogenization method. The linear trends in the regional mean an...Daily observations of wind speed at 12 stations in the Greater Beijing Area during 1960–2008 were homogenized using the Multiple Analysis of Series for Homogenization method. The linear trends in the regional mean annual and seasonal (winter, spring, summer and autumn) wind speed series were-0.26,-0.39,-0.30,-0.12 and-0.22 m s-1 (10 yr)-1 , respectively. Winter showed the greatest magnitude in declining wind speed, followed by spring, autumn and summer. The annual and seasonal frequencies of wind speed extremes (days) also decreased, more prominently for winter than for the other seasons. The declining trends in wind speed and extremes were formed mainly by some rapid declines during the 1970s and 1980s. The maximum declining trend in wind speed occurred at Chaoyang (CY), a station within the central business district (CBD) of Beijing with the highest level of urbanization. The declining trends were in general smaller in magnitude away from the city center, except for the winter case in which the maximum declining trend shifted northeastward to rural Miyun (MY). The influence of urbanization on the annual wind speed was estimated to be about-0.05 m s-1 (10 yr)-1 during 1960–2008, accounting for around one fifth of the regional mean declining trend. The annual and seasonal geostrophic wind speeds around Beijing, based on daily mean sea level pressure (MSLP) from the ERA-40 reanalysis dataset, also exhibited decreasing trends, coincident with the results from site observations. A comparative analysis of the MSLP fields between 1966–1975 and 1992–2001 suggested that the influences of both the winter and summer monsoons on Beijing were weaker in the more recent of the two decades. It is suggested that the bulk of wind in Beijing is influenced considerably by urbanization, while changes in strong winds or wind speed extremes are prone to large-scale climate change in the region.展开更多
This study investigates the long-term changes of monthly sea surface wind speeds over the China seas from 1988 to 2015. The 10-meter wind speeds products from four major global reanalysis datasets with high resolution...This study investigates the long-term changes of monthly sea surface wind speeds over the China seas from 1988 to 2015. The 10-meter wind speeds products from four major global reanalysis datasets with high resolution are used: Cross-Calibrated Multi-Platform data set(CCMP), NCEP climate forecast system reanalysis data set(CFSR),ERA-interim reanalysis data set(ERA-int) and Japanese 55-year reanalysis data set(JRA55). The monthly sea surface wind speeds of four major reanalysis data sets have been investigated through comparisons with the longterm and homogeneous observation wind speeds data recorded at ten stations. The results reveal that(1) the wind speeds bias of CCMP, CFSR, ERA-int and JRA55 are 0.91 m/s, 1.22 m/s, 0.62 m/s and 0.22 m/s, respectively.The wind speeds RMSE of CCMP, CFSR, ERA-int and JRA55 are 1.38 m/s, 1.59 m/s, 1.01 m/s and 0.96 m/s,respectively;(2) JRA55 and ERA-int provides a realistic representation of monthly wind speeds, while CCMP and CFSR tend to overestimate observed wind speeds. And all the four data sets tend to underestimate observed wind speeds in Bohai Sea and Yellow Sea;(3) Comparing the annual wind speeds trends between observation and the four data sets at ten stations for 1988-1997, 1988–2007 and 1988–2015, the result show that ERA-int is superior to represent homogeneity monthly wind speeds over the China seaes.展开更多
A scanning microwave radiometer(RM) was launched on August 16,2011,on board HY-2 satellite.The six-month long global sea surface wind speeds observed by the HY-2 scanning microwave radiometer are preliminarily valid...A scanning microwave radiometer(RM) was launched on August 16,2011,on board HY-2 satellite.The six-month long global sea surface wind speeds observed by the HY-2 scanning microwave radiometer are preliminarily validated using in-situ measurements and WindSat observations,respectively,from January to June 2012.The wind speed root-mean-square(RMS) difference of the comparisons with in-situ data is 1.89 m/s for the measurements of NDBC and 1.72 m/s for the recent four-month data measured by PY30-1 oil platform,respectively.On a global scale,the wind speeds of HY-2 RM are compared with the sea surface wind speeds derived from WindSat,the RMS difference of 1.85 m/s for HY-2 RM collocated observations data set is calculated in the same period as above.With analyzing the global map of a mean difference between HY-2 RM and WindSat,it appears that the bias of the sea surface wind speed is obviously higher in the inshore regions.In the open sea,there is a relatively higher positive bias in the mid-latitude regions due to the overestimation of wind speed observations,while the wind speeds are underestimated in the Southern Ocean by HY-2 RM relative to WindSat observations.展开更多
Mean sea level rise and climatological wind speed changes occur as part of the ongoing climate change and future projections of both variables are still highly uncertain. Here the Baltic Sea’s response in extreme sea...Mean sea level rise and climatological wind speed changes occur as part of the ongoing climate change and future projections of both variables are still highly uncertain. Here the Baltic Sea’s response in extreme sea levels to perturbations in mean sea level and wind speeds is investigated in a series of simulations with a newly developed storm surge model based on the nucleus for European modeling of the ocean(NEMO)-Nordic. A simple linear model with only two tunable parameters is found to capture the changes in the return levels extremely well. The response to mean sea level rise is linear and nearly spatially uniform, meaning that a mean sea level rise of 1 m increases the return levels by a equal amount everywhere. The response to wind speed perturbations is more complicated and return levels are found to increase more where they are already high. This behaviour is alarming as it suggests that already flooding prone regions like the Gulf of Finland will be disproportionally adversely affected in a future windier climate.展开更多
In offshore engineering design, it is considerably significant to have an adequately accurate estimation of marine environmental parameters, in particular, the extreme wind speed of tropical cyclone (TC) with differ...In offshore engineering design, it is considerably significant to have an adequately accurate estimation of marine environmental parameters, in particular, the extreme wind speed of tropical cyclone (TC) with different return periods to guarantee the safety in projected operating life period. Based on the 71-year (1945-2015) TC data in the Northwest Pacific (NWP) by the Joint Typhoon Warning Center (JTWC) of US, a notable growth of the TC intensity is observed in the context of climate change. The fact implies that the traditional stationary model might be incapable of predicting parameters in the extreme events. Therefore, a non-stationary model is proposed in this study to estimate extreme wind speed in the South China Sea (SCS) and NWP. We find that the extreme wind speeds of different return periods exhibit an evident enhancement trend, for instance, the extreme wind speeds with different return periods by non- stationary model are 4.1%-4.4% higher than stationary ones in SCS. Also, the spatial distribution of extreme wind speed in NWP has been examined with the same methodology by dividing the west sea areas of the NWP 0°-45°N, 105°E-130°E into 45 subareas of 5° × 5°, where oil and gas resources are abundant. Similarly, remarkable spacial in-homogeneity in the extreme wind speed is seen in this area: the extreme wind speed with 50-year return period in the subarea (15°N-20°N, 115°E-120°E) of Zhongsha and Dongsha Islands is 73.8 m/s, while that in the subarea of Yellow Sea (30°N-35°N, 120°E-125°E) is only 47.1 m/s. As a result, the present study demonstrates that non-stationary and in-homogeneous effects should be taken into consideration in the estimation of extreme wind speed.展开更多
An increasing number of marine structures have been built for coastal protection and marine development in recent years,and wind,which is crucial to marine structures,should be analyzed.Therefore,typhoon frequency,win...An increasing number of marine structures have been built for coastal protection and marine development in recent years,and wind,which is crucial to marine structures,should be analyzed.Therefore,typhoon frequency,wind climate,wind energy assess-ment,and extreme wind speed in the South China Sea(SCS)are investigated in detail in this study.The data are obtained from the China Meteorological Administration,the European Centre for Medium-range Weather Forecasts,and the National Centers for Envi-ronmental Prediction.The offshore wind energy potential is analyzed at five sites near the coast.The spatial and monthly frequencies of tropical cyclones for different intensity categories are analyzed.The extreme wind speed is fitted by five distribution models,and the generalized extreme value(GEV)distribution is selected as the most suitable function according to the goodness of fit.The spa-tial distributions of extreme wind speeds in the SCS are plotted on the basis of the GEV distribution and ERA5 data sets.The influ-ences of the distribution models and data sets on the calculated results are discussed.Moreover,the monthly extreme wind speed and comparison with the results of previous studies are analyzed.This study provides a reference for the design of wind turbines.展开更多
Land-sea breeze(LSB)is an atmospheric mesoscale circulation that occurs in the vicinity of the coast and is caused by uneven heating resulting from the difference in specific heat capacity between the sea and land sur...Land-sea breeze(LSB)is an atmospheric mesoscale circulation that occurs in the vicinity of the coast and is caused by uneven heating resulting from the difference in specific heat capacity between the sea and land surfaces.The circulation structure of LSB was quantitatively investigated with a Doppler wind lidar Windcube100s on the west coast of the Yellow Sea for the first time.The time of observation was 31 August to 28 September 2018.It was found that the height of LSB development was 700 m to 1300 m.The duration of conversion of LSB was between 6 h and 8 h.The biggest average horizontal sea-breeze wind speed at 425 m was 5.6 m s^(-1),and at 375 m it was 4.5 m s^(-1).During the conversion process from sea breeze to land breeze,the maximum wind shear exponent was 2.84 at 1300 m altitude.During the conversion process from land breeze to sea breeze,the maximum wind shear exponent was 1.28 at 700 m altitude.The differences in wind shear exponents between sea-breeze and landbreeze systems were between 0.2 and 3.6 at the same altitude.The maximum value of the wind shear exponent can reflect the height of LSB development.展开更多
Considering about the effect of whitecaps and foams on pulse-limited Radar Altimeters, an improved algorithm of retrieving sea surface wind speed is proposed in this paper. Firstly, a four-layer dielectric model is es...Considering about the effect of whitecaps and foams on pulse-limited Radar Altimeters, an improved algorithm of retrieving sea surface wind speed is proposed in this paper. Firstly, a four-layer dielectric model is established in order to simulate an air-sea interface. Secondly, the microwave reflectivity of a sea surface covered by spray droplets and foams at 13.5 GHz is computed based on the established model. These computed results show that the effect of spray droplets and foams in high sea state conditions shall not be negligible on retrieving sea surface wind speed. Finally, compared with the analytical algorithms proposed by Zhao and some calculated results based on a three-layer dielectric model, an improved algorithm of retrieving sea surface wind speed is presented. At a high wind speed, the improved algorithm is in a better accord with some empirical algorithms such as Brown, Young ones and et al., and also in a good agreement with ZT and other algorithms at low wind speed. This new improved algorithm will be suitable not only for low wind speed retrieval, but also for high wind speed retrieval. Better accuracy and effectiveness of wind speed retrieval can also be obtained.展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 41276097)
文摘The North Atlantic Oscillation (NAO) is one of the major causes of many recent changes in the Arctic Ocean. Generally, it is related to wind speed, sea surface temperature (SST), and sea ice cover. In this study, we analyzed the distributions of and correlations between SST, wind speed, NAO, and sea ice cover from 2003 to 2009 in the Greenland Sea at 10°W to 10°E, 65°N to 80°N. SST reached its peak in July, while wind speed reached its minimum in July. Seasonal variability of SST and wind speed was different for different regions. SST and wind speed mainly had negative correlations. Detailed correlation research was focused on the 75~N to 80~N band. Regression analysis shows that in this band, the variation of SST lagged three months behind that of wind speed Ice cover and NAO had a positive correlation, and the correlation coefficient between ice cover and NAO in the year 2007 was 0.61 SST and NAO also had a positive correlation, and SST influenced NAO one month in advance. The correlation coefficients between SST and NAO reached 0.944 for the year 2005, 0.7 for the year 2008, and 0.74 for the year 2009 after shifting SST one month later. NAO also had a positive correlation with wind speed, and it also influenced wind speed one month in advance. The correlation coefficients between NAO and wind speed reached 0.783, 0.813, and 0.818 for the years 2004, 2005, and 2008, respectively, after shifting wind speed one month earlier.
基金Supported by the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(No.SML2021SP102)the Key Laboratory of Marine Environmental Survey Technology and Application+2 种基金Ministry of Natural Resources(Nos.MESTA-2020-C003,MESTA-2020-C004)the Key Research and Development Project of Guangdong Province(No.2020B1111020003)the Science and Technology Research Project of Jiangxi Provincial Department of Education(No.GJJ200330)。
文摘Wind and wave data are essential in climatological and engineering design applications.In this study,data from 15 buoys located throughout the South China Sea(SCS)were used to evaluate the ERA5 wind and wave data.Applicability assessment are beneficial for gaining insight into the reliability of the ERA5 data in the SCS.The bias range between the ERA5 and observed wind-speed data was-0.78-0.99 m/s.The result indicates that,while the ERA5 wind-speed data underestimation was dominate,the overestimation of such data existed as well.Additionally,the ERA5 data underestimated annual maximum wind-speed by up to 38%,with a correlation coefficient>0.87.The bias between the ERA5 and observed significant wave height(SWH)data varied from-0.24 to 0.28 m.And the ERA5 data showed positive SWH bias,which implied a general underestimation at all locations,except those in the Beibu Gulf and centralwestern SCS,where overestimation was observed.Under extreme conditions,annual maximum SWH in the ERA5 data was underestimated by up to 30%.The correlation coefficients between the ERA5 and observed SWH data at all locations were greater than 0.92,except in the central-western SCS(0.84).The bias between the ERA5 and observed mean wave period(MWP)data varied from-0.74 to 0.57 s.The ERA5 data showed negative MWP biases implying a general overestimation at all locations,except for B1(the Beibu Gulf)and B7(the northeastern SCS),where underestimation was observed.The correlation coefficient between the ERA5 and observed MWP data in the Beibu Gulf was the smallest(0.56),and those of other locations fluctuated within a narrow range from 0.82 to 0.90.The intercomparison indicates that during the analyzed time-span,the ERA5 data generally underestimated wind-speed and SWH,but overestimated MWP.Under non-extreme conditions,the ERA5 wind-speed and SWH data can be used with confidence in most regions of the SCS,except in the central-western SCS.
基金supported by the National Key R&D Program of China(No.2018YFB1501901)the National Natural Science Foundation of China(Nos.51909114,U1806227 and U1906231)the Guangxi Key Laboratory of Marine Environmental Science,Guangxi Academy of Sciences(No.GXKLHY21-04).
文摘Field and laboratory observations indicate that the variation of drag coefficient with wind speed at high winds is different from that under low-to-moderate winds.By taking the effects of wave development and sea spray into account,a new parameterization of drag coefficient applicable from low to extreme winds is proposed.It is shown that,under low-to-moderate wind conditions so that the sea spray effects could be neglected,the nondimensional aerodynamic roughness first increases and then decreases with the increasing wave age;whereas under high wind conditions,the drag coefficient decreases with the increasing wind speed due to the modification of the logarithmic wind profile by the effect of sea spray droplets produced by bursting bubbles or wind tearing breaking wave crests.The drag coefficients and sea surface aerodynamic roughnesses reach their maximum values vary under different wave developments.Correspondingly,the reduction of drag coefficient under high winds reduces the increasing rate of friction velocity with increasing wind speed.
基金supported by the National Key Research and Development Program of China(No.2016YFC1401405)the National Natural Science Foundation of China(No.41376010)
文摘In this study, the statistical characterization of sea conditions in the East China Sea(ECS) is investigated by analyzing a significant wave height and wind speed data at a 6-hour interval for 30 years(1980–2009), which was simulated and computed using the WAVEWATCH Ⅲ(WW3) model. The monthly variations of these parameters showed that the significant wave height and wind speed have minimum values of 0.73 m and 5.15 ms^(-1) and 1.73 m and 8.24 ms^(-1) in the month of May and December, respectively. The annual, seasonal, and monthly mean sea state characterizations showed that the slight sea generally prevailed in the ECS and had nearly the highest occurrence in all seasons and months. Additionally, the moderate sea prevailed in the winter months of December and January, while the smooth(wavelets) sea prevailed in May. Furthermore, the spatial variation of sea states showed that the calm and smooth sea had the largest occurrences in the northern ECS. The slight sea occurred mostly(above 30%) in parts of the ECS and the surrounding locations, while higher occurrences of the rough and very rough seas were distributed in waters between the southwest ECS and the northeast South China Sea(SCS). The occurrences of the phenomenal sea conditions are insignificant and are distributed in the northwest Pacific and its upper region, which includes the Southern Kyushu-Palau Ridge and Ryukyu Trench.
基金The National Basic Research Program of China under contract Nos 2015CB453200,2013CB956200,2012CB957803 and2010CB950400the National Natural Science Foundation of China under contract Nos 41275086 and 41475070
文摘Long-term variations in a sea surface wind speed (WS) and a significant wave height (SWH) are associated with the global climate change, the prevention and mitigation of natural disasters, and an ocean resource exploitation, and other activities. The seasonal characteristics of the long-term trends in China's seas WS and SWH are determined based on 24 a (1988-2011) cross-calibrated, multi-platform (CCMP) wind data and 24 a hindcast wave data obtained with the WAVEWATCH-III (WW3) wave model forced by CCMP wind data. The results show the following. (1) For the past 24 a, the China's WS and SWH exhibit a significant increasing trend as a whole, of 3.38 cm/(s.a) in the WS, 1.3 cm/a in the SWH. (2) As a whole, the increasing trend of the China's seas WS and SWH is strongest in March-April-May (MAM) and December-January-February (DJF), followed by June-July-August (JJA), and smallest in September-October-November (SON). (3) The areal extent of significant increases in the WS was largest in MAM, while the area decreased in JJA and DJF; the smallest area was apparent in SON. In contrast to the WS, almost all of China's seas exhibited a significant increase in SWH in MAM and DJF; the range was slightly smaller in JJA and SON. The WS and SWH in the Bohai Sea, the Yellow Sea, East China Sea, the Tsushima Strait, the Taiwan Strait, the northern South China Sea, the Beibu Gull and the Gulf of Thailand exhibited a significant increase in all seasons. (4) The variations in China's seas SWH and WS depended on the season. The areas with a strong increase usually appeared in DJF.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA19030204)the CAS"Light of West China"Program(2015-XBQNB-17)
文摘The surface wind speed(SWS)is affected by both large-scale circulation and land use and cover change(LUCC).In China,most studies have considered the effect of large-scale circulation rather than LUCC on SWS.In this study,we evaluated the effects of LUCC on the SWS decrease during 1979-2015 over China using the observation minus reanalysis(OMR)method.There were two key findings:(1)Observed wind speed declined significantly at a rate of 0.0112 m/(s·a),whereas ERA-Interim,which can only capture the inter-annual variation of observed data,indicated a gentle downward trend.The effects of LUCC on SWS were distinct and caused a decrease of 0.0124 m/(s·a)in SWS;(2)Due to variations in the characteristics of land use types across different regions,the influence of LUCC on SWS also varied.The observed wind speed showed a rapid decline over cultivated land in Northwest China,as well as a decrease in China’s northeastern and eastern plain regions due to the urbanization.However,in the Tibetan Plateau,the impact of LUCC on wind speed was only slight and can thus be ignored.
基金The National Key Research and Development Program under contract Nos 2016YFC1402703 and 2018YFC1407100
文摘Conventional retrieval and neural network methods are used simultaneously to retrieve sea surface wind speed(SSWS)from HH-polarized Sentinel-1(S1)SAR images.The Polarization Ratio(PR)models combined with the CMOD5.N Geophysical Model Function(GMF)is used for SSWS retrieval from the HH-polarized SAR data.We compared different PR models developed based on previous C-band SAR data in HH-polarization for their applications to the S1 SAR data.The recently proposed CMODH,i.e.,retrieving SSWS directly from the HHpolarized S1 data is also validated.The results indicate that the CMODH model performs better than results achieved using the PR models.We proposed a neural network method based on the backward propagation(BP)neural network to retrieve SSWS from the S1 HH-polarized data.The SSWS retrieved using the BP neural network model agrees better with the buoy measurements and ASCAT dataset than the results achieved using the conventional methods.Compared to the buoy measurements,the bias,root mean square error(RMSE)and scatter index(SI)of wind speed retrieved by the BP neural network model are 0.10 m/s,1.38 m/s and 19.85%,respectively,while compared to the ASCAT dataset the three parameters of training set are–0.01 m/s,1.33 m/s and 15.10%,respectively.It is suggested that the BP neural network model has a potential application in retrieving SSWS from Sentinel-1 images acquired at HH-polarization.
基金Supported by the China’s National Key Research and Development Projects(No.2016YFA0601803)the National Natural Science Foundation of China(Nos.41490641,41521091,U1606402)the Qingdao National Laboratory for Marine Science and Technology(No.2017ASKJ01)
文摘The co-variation of surface wind speed and sea surface temperature (SST) over the Gulf Stream frontal region is investigated using high-resolution satellite measurements and atmospheric reanalysis data. Results show that the pattern of positive SST-surface wind speed correlations is anchored by strong SST gradient and marine atmospheric boundary layer (MABL) height front, with active warm and cold-ocean eddies around. The MABL has an obvious transitional structure along the strong SST front, with greater (lesser) heights over the north (south) side. The significant positive SST-surface wind-speed perturbation correlations are mostly found over both strong warm and cold eddies. The surface wind speed increases (decreases) about 0.32 (0.41) m/s and the MABL elevates (drops) approximate 55 (54) m per 1℃ of SST perturbation induced by warm (cold) eddies. The response of the surface wind speed to SST perturbations over the mesoscale eddies is mainly attributed to the momentum vertical mixing in the MABL, which is confirmed by the linear relationships between the downwind (crosswind) SST gradient and wind divergence (curl).
文摘利用全国40个地面台站的观测资料对ERA5及ERA5-Land两种不同空间分辨率的再分析资料开展了地面风速误差评估研究,结果表明:ERA5和ERA5-Land资料多年平均风速偏差的平均值分别为0.08 m s^(−1)、-0.06 m s^(−1),偏差的最大值分别为0.46 m s^(−1)、-0.19 m s^(−1),相对偏差的平均值为4.4%、-2.0%,相对偏差的最大值分别为33.0%、-10.1%;月平均风速线性拟合方程的斜率分别为0.93、0.97,截距分别为0.29 m s^(−1)、0.02 m s^(−1),相关系数分别为0.98、0.99;月平均风速均方根误差的平均值分别为0.17 m s^(−1)、0.14 m s^(−1),均方根误差的最大值分别为0.49 m s^(−1)、0.22 m s^(−1),相对均方根误差的平均值为7.4%、5.7%,相对均方根误差的最大值分别为35.2%、13.3%。ERA5-Land高分辨率资料地面风速误差相对较低,有利于提高风能资源评估的准确性。
文摘For open sea conditions the sea surface roughness is described as a function of surface stress and wind speed over sea surface by Charnock relation. The sea surface roughnessn in the North-west Pacific Ocean is derived successfully using wind speed data estimated by the TOPEX satellite altimeter. From the results we find that: (1) the mean sea surface roughness in winter is greater than in summer; (2) compared with other sea areas, the sea surface roughness in the sea area east of Japan ( N30°- 40°, E135°- 150°) is larger than in other sea areas; (3) sea surface roughness in the South China Sea changes more greatly than that in the Bohai Sea, Yellow Sea and East China Sea.
文摘Recent research has shown that winter warmings are phenomenally high compared to summer warmings over the poles,especially over the Arctic.Taking the current scenario into account,this paper attempts to understand the atmospheric variables causing sea ice variability over and around the region of Svalbard for seasons;winter,spring,summer and autumn for the span of 42 years(1979-2021).The variability in atmospheric and oceanic parameters namely temperature,precipitation,wind speed,and sea surface salinity are analysed over inter-spatial,inter-seasonal and inter-annual domains.Winters are characterized by inter-annual increasing trend in temperature.During 1981-1990 the rise from the decadal mean is found to be 0.39 K·a^(-1),during 1991-2000 it is 0.20 K·a^(-1),during 2001-2010 it is 0.04 K·a^(-1) and during 2011-2020 it is 0.23 K·a^(-1).Interestingly while considering inter-spatial domains,the region southwest to Svalbard seems to be wetter(0.05 mm·(10 a)-1)compared to its northeast(-0.03 mm·(10 a)-1).Across all the three domains,wind speeds are highest during autumn and then decrease subsequently through summer,spring and are least during winter.Wind is predominantly from the south,and hence it is suspected to carry hot Atlantic air.Additionally,the significant role of salinity in the ocean also plays a key role in governing the fate of sea ice conditions.The long-term forecasts of temperature over seaice of Svalbard are alarming especially for the winter ice(r=-0.84).Correlation matrices between atmospheric and sea ice parameters are shown to gain a better understanding on their inter relation.
基金supported by grants from the MOST NBRPC(2009CB421401)CNNSF(41075063) and the CMA Institute of Urban Meteorology
文摘Daily observations of wind speed at 12 stations in the Greater Beijing Area during 1960–2008 were homogenized using the Multiple Analysis of Series for Homogenization method. The linear trends in the regional mean annual and seasonal (winter, spring, summer and autumn) wind speed series were-0.26,-0.39,-0.30,-0.12 and-0.22 m s-1 (10 yr)-1 , respectively. Winter showed the greatest magnitude in declining wind speed, followed by spring, autumn and summer. The annual and seasonal frequencies of wind speed extremes (days) also decreased, more prominently for winter than for the other seasons. The declining trends in wind speed and extremes were formed mainly by some rapid declines during the 1970s and 1980s. The maximum declining trend in wind speed occurred at Chaoyang (CY), a station within the central business district (CBD) of Beijing with the highest level of urbanization. The declining trends were in general smaller in magnitude away from the city center, except for the winter case in which the maximum declining trend shifted northeastward to rural Miyun (MY). The influence of urbanization on the annual wind speed was estimated to be about-0.05 m s-1 (10 yr)-1 during 1960–2008, accounting for around one fifth of the regional mean declining trend. The annual and seasonal geostrophic wind speeds around Beijing, based on daily mean sea level pressure (MSLP) from the ERA-40 reanalysis dataset, also exhibited decreasing trends, coincident with the results from site observations. A comparative analysis of the MSLP fields between 1966–1975 and 1992–2001 suggested that the influences of both the winter and summer monsoons on Beijing were weaker in the more recent of the two decades. It is suggested that the bulk of wind in Beijing is influenced considerably by urbanization, while changes in strong winds or wind speed extremes are prone to large-scale climate change in the region.
基金The National Key R&D Program of China under contract No.2016YFC1401905the National Natural Science Foundation of China under contract No.41776004the Fundamental Research Funds for the Central Universities under contract No.2016B12514
文摘This study investigates the long-term changes of monthly sea surface wind speeds over the China seas from 1988 to 2015. The 10-meter wind speeds products from four major global reanalysis datasets with high resolution are used: Cross-Calibrated Multi-Platform data set(CCMP), NCEP climate forecast system reanalysis data set(CFSR),ERA-interim reanalysis data set(ERA-int) and Japanese 55-year reanalysis data set(JRA55). The monthly sea surface wind speeds of four major reanalysis data sets have been investigated through comparisons with the longterm and homogeneous observation wind speeds data recorded at ten stations. The results reveal that(1) the wind speeds bias of CCMP, CFSR, ERA-int and JRA55 are 0.91 m/s, 1.22 m/s, 0.62 m/s and 0.22 m/s, respectively.The wind speeds RMSE of CCMP, CFSR, ERA-int and JRA55 are 1.38 m/s, 1.59 m/s, 1.01 m/s and 0.96 m/s,respectively;(2) JRA55 and ERA-int provides a realistic representation of monthly wind speeds, while CCMP and CFSR tend to overestimate observed wind speeds. And all the four data sets tend to underestimate observed wind speeds in Bohai Sea and Yellow Sea;(3) Comparing the annual wind speeds trends between observation and the four data sets at ten stations for 1988-1997, 1988–2007 and 1988–2015, the result show that ERA-int is superior to represent homogeneity monthly wind speeds over the China seaes.
基金The National High-Tech Project of China under contract No.2008AA09A403the Marine Public Welfare Project of China under contract No.201105032
文摘A scanning microwave radiometer(RM) was launched on August 16,2011,on board HY-2 satellite.The six-month long global sea surface wind speeds observed by the HY-2 scanning microwave radiometer are preliminarily validated using in-situ measurements and WindSat observations,respectively,from January to June 2012.The wind speed root-mean-square(RMS) difference of the comparisons with in-situ data is 1.89 m/s for the measurements of NDBC and 1.72 m/s for the recent four-month data measured by PY30-1 oil platform,respectively.On a global scale,the wind speeds of HY-2 RM are compared with the sea surface wind speeds derived from WindSat,the RMS difference of 1.85 m/s for HY-2 RM collocated observations data set is calculated in the same period as above.With analyzing the global map of a mean difference between HY-2 RM and WindSat,it appears that the bias of the sea surface wind speed is obviously higher in the inshore regions.In the open sea,there is a relatively higher positive bias in the mid-latitude regions due to the overestimation of wind speed observations,while the wind speeds are underestimated in the Southern Ocean by HY-2 RM relative to WindSat observations.
基金funding from the project “Future flooding risks at the Swedish Coast: Extreme situations in present and future climat”, Ref. No. P02/12 by Lansforsakringsbolagens Forskningsfondthrough the Swedish Civil Contingencies Agency (MSB) through the project “Hazard Support: Risk-based decision support for adaptation to future natural hazards”
文摘Mean sea level rise and climatological wind speed changes occur as part of the ongoing climate change and future projections of both variables are still highly uncertain. Here the Baltic Sea’s response in extreme sea levels to perturbations in mean sea level and wind speeds is investigated in a series of simulations with a newly developed storm surge model based on the nucleus for European modeling of the ocean(NEMO)-Nordic. A simple linear model with only two tunable parameters is found to capture the changes in the return levels extremely well. The response to mean sea level rise is linear and nearly spatially uniform, meaning that a mean sea level rise of 1 m increases the return levels by a equal amount everywhere. The response to wind speed perturbations is more complicated and return levels are found to increase more where they are already high. This behaviour is alarming as it suggests that already flooding prone regions like the Gulf of Finland will be disproportionally adversely affected in a future windier climate.
基金financially supported by the Ministry of Science and Technology(863 program)(2006AA09A103-4)the National Natural Science Foundation of China(11232012)the Chinese Academy of Sciences(CAS)knowledge innovation program(KJCXYW-L02)
文摘In offshore engineering design, it is considerably significant to have an adequately accurate estimation of marine environmental parameters, in particular, the extreme wind speed of tropical cyclone (TC) with different return periods to guarantee the safety in projected operating life period. Based on the 71-year (1945-2015) TC data in the Northwest Pacific (NWP) by the Joint Typhoon Warning Center (JTWC) of US, a notable growth of the TC intensity is observed in the context of climate change. The fact implies that the traditional stationary model might be incapable of predicting parameters in the extreme events. Therefore, a non-stationary model is proposed in this study to estimate extreme wind speed in the South China Sea (SCS) and NWP. We find that the extreme wind speeds of different return periods exhibit an evident enhancement trend, for instance, the extreme wind speeds with different return periods by non- stationary model are 4.1%-4.4% higher than stationary ones in SCS. Also, the spatial distribution of extreme wind speed in NWP has been examined with the same methodology by dividing the west sea areas of the NWP 0°-45°N, 105°E-130°E into 45 subareas of 5° × 5°, where oil and gas resources are abundant. Similarly, remarkable spacial in-homogeneity in the extreme wind speed is seen in this area: the extreme wind speed with 50-year return period in the subarea (15°N-20°N, 115°E-120°E) of Zhongsha and Dongsha Islands is 73.8 m/s, while that in the subarea of Yellow Sea (30°N-35°N, 120°E-125°E) is only 47.1 m/s. As a result, the present study demonstrates that non-stationary and in-homogeneous effects should be taken into consideration in the estimation of extreme wind speed.
基金by the NSFC-Shandong Joint Fund(No.U1706226)the Fundamental Research Funds for the Central Universities.
文摘An increasing number of marine structures have been built for coastal protection and marine development in recent years,and wind,which is crucial to marine structures,should be analyzed.Therefore,typhoon frequency,wind climate,wind energy assess-ment,and extreme wind speed in the South China Sea(SCS)are investigated in detail in this study.The data are obtained from the China Meteorological Administration,the European Centre for Medium-range Weather Forecasts,and the National Centers for Envi-ronmental Prediction.The offshore wind energy potential is analyzed at five sites near the coast.The spatial and monthly frequencies of tropical cyclones for different intensity categories are analyzed.The extreme wind speed is fitted by five distribution models,and the generalized extreme value(GEV)distribution is selected as the most suitable function according to the goodness of fit.The spa-tial distributions of extreme wind speeds in the SCS are plotted on the basis of the GEV distribution and ERA5 data sets.The influ-ences of the distribution models and data sets on the calculated results are discussed.Moreover,the monthly extreme wind speed and comparison with the results of previous studies are analyzed.This study provides a reference for the design of wind turbines.
基金supported by the National Key Research and Development Program of China[Grant number 2016YFC0202001]the Chinese Academy of Sciences Strategic Priority Research Program[Grant number XDA23020301]the National Natural Science Foundation of China[Grant number 41375036]。
文摘Land-sea breeze(LSB)is an atmospheric mesoscale circulation that occurs in the vicinity of the coast and is caused by uneven heating resulting from the difference in specific heat capacity between the sea and land surfaces.The circulation structure of LSB was quantitatively investigated with a Doppler wind lidar Windcube100s on the west coast of the Yellow Sea for the first time.The time of observation was 31 August to 28 September 2018.It was found that the height of LSB development was 700 m to 1300 m.The duration of conversion of LSB was between 6 h and 8 h.The biggest average horizontal sea-breeze wind speed at 425 m was 5.6 m s^(-1),and at 375 m it was 4.5 m s^(-1).During the conversion process from sea breeze to land breeze,the maximum wind shear exponent was 2.84 at 1300 m altitude.During the conversion process from land breeze to sea breeze,the maximum wind shear exponent was 1.28 at 700 m altitude.The differences in wind shear exponents between sea-breeze and landbreeze systems were between 0.2 and 3.6 at the same altitude.The maximum value of the wind shear exponent can reflect the height of LSB development.
文摘Considering about the effect of whitecaps and foams on pulse-limited Radar Altimeters, an improved algorithm of retrieving sea surface wind speed is proposed in this paper. Firstly, a four-layer dielectric model is established in order to simulate an air-sea interface. Secondly, the microwave reflectivity of a sea surface covered by spray droplets and foams at 13.5 GHz is computed based on the established model. These computed results show that the effect of spray droplets and foams in high sea state conditions shall not be negligible on retrieving sea surface wind speed. Finally, compared with the analytical algorithms proposed by Zhao and some calculated results based on a three-layer dielectric model, an improved algorithm of retrieving sea surface wind speed is presented. At a high wind speed, the improved algorithm is in a better accord with some empirical algorithms such as Brown, Young ones and et al., and also in a good agreement with ZT and other algorithms at low wind speed. This new improved algorithm will be suitable not only for low wind speed retrieval, but also for high wind speed retrieval. Better accuracy and effectiveness of wind speed retrieval can also be obtained.