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
The temporal and spatial variations in the wind and wave fields in the Pacific Ocean between 2002 and 2011 are analyzed using a third-generation wave model（WAVEWATCH III）. The model performance for a significant wav...The temporal and spatial variations in the wind and wave fields in the Pacific Ocean between 2002 and 2011 are analyzed using a third-generation wave model（WAVEWATCH III）. The model performance for a significant wave height is validated using in situ buoy data. The results show that the wave model effectively hindcasts the significant wave height in the Pacific Ocean, but the errors are relatively large in the mid- and low-latitude regions. The spatial distributions and temporal variations in a wind speed and the significant wave height in the Pacific Ocean are then considered after dividing the Pacific Ocean into five regions, which show meridional differences and seasonal cycles. Regional mean values are used to give yearly average time series for each separate zone. The high latitude region in the Southern Hemisphere had a stronger significant wave height trend in the model results than regions at other latitudes. The sources and sinks of wave energy are then investigated. Their regional mean values are used to quantify variations in surface waves. Finally, the spectral analyses of the daily mean wind speeds and the significant wave heights are obtained. The significant wave height and the wind speed spectra are found to be connected in some ways but also show certain differences.展开更多
One-dimensional synthetic aperture microwave radiometers have higher spatial resolution and record measurements at multiple incidence angles.In this paper,we propose a multiple linear regression method to retrieve sea...One-dimensional synthetic aperture microwave radiometers have higher spatial resolution and record measurements at multiple incidence angles.In this paper,we propose a multiple linear regression method to retrieve sea surface wind speed at an incidence angle between 0°65°.We assume that a one-dimensional synthetic aperture microwave radiometer operates at frequencies of 6.9,10.65,18.7,23.8 and 36.5 GHz.Then,the microwave radiative transfer forward model is used to simulate the measured brightness temperatures.The sensitivity of the brightness temperatures at 0°65°to the sea surface wind speed is calculated.Then,vertical polarization channels(VR),horizontal polarization channels(HR)and all channels(AR)are used to retrieve the sea surface wind speed via a multiple linear regression algorithm at 0°65°,and the relationship between the retrieval error and incidence angle is obtained.The results are as follows:(1)The sensitivity of the vertical polarization brightness temperature to the sea surface wind speed is smaller than that of the horizontal polarization.(2)The retrieval error increases with Gaussian noise.The retrieval error of VR first increases and then decreases with increasing incidence angle,the retrieval error of HR gradually decreases with increasing incidence angle,and the retrieval error of AR first decreases and then increases with increasing incidence angle.(3)The retrieval error of AR is the lowest and it is necessary to retrieve the sea surface wind speed at a larger incidence angle for AR.展开更多
Big wind farms must be integrated to power system.Wind power from big wind farms,with randomness,volatility and intermittent,will bring adverse impacts on the connected power system.High precision wind power forecasti...Big wind farms must be integrated to power system.Wind power from big wind farms,with randomness,volatility and intermittent,will bring adverse impacts on the connected power system.High precision wind power forecasting is helpful to reduce above adverse impacts.There are two kinds of wind power forecasting.One is to forecast wind power only based on its time series data.The other is to forecast wind power based on wind speeds from weather forecast.For a big wind farm,due to its spatial scale and dynamics of wind,wind speeds at different wind turbines are obviously different,that is called wind speed spatial dispersion.Spatial dispersion of wind speeds and its influence on the wind power forecasting errors have been studied in this paper.An error evaluation framework has been established to account for the errors caused by wind speed spatial dispersion.A case study of several wind farms has demonstrated that even ifthe forecasting average wind speed is accurate,the error caused by wind speed spatial dispersion cannot be ignored for the wind power forecasting of a wind farm.展开更多
Spatial interpolation(SI)is currently one of the most common ways to estimate wind speed(Ws).However,classic SI models either ignore the complex geography[e.g.inverse distance weighting(IDW)],or demand high computatio...Spatial interpolation(SI)is currently one of the most common ways to estimate wind speed(Ws).However,classic SI models either ignore the complex geography[e.g.inverse distance weighting(IDW)],or demand high computational resources(e.g.cokriging).This study aimed to develop a simple yet effective SI model for estimating Ws in Eastern Thrace of Turkey.This new method,named MIDW(Ws),is a modified IDW through the integration of IDW with wind profile model,power law(PL),representing the influence of land cover and topography on Ws.Terrain features and elevation data of PL were obtained using normalized difference vegetation index(NDVI)and digital elevation model(DEM),respectively.Results showed superior and comparable performance of MIDW(Ws)to standard IDW and ordinary kriging(OK)across all months of year.Compared to ordinary cokriging(OCK)using DEM as covariate,MIDW(Ws)generated better results in the arid–semiarid seasons(around summer).Local complex atmospheric conditions during rainy seasons(around winter)may have affected the performance of incorporating PL with MIDW(Ws).Generally,the proposed MIDW(Ws)is simpler and easier to implement compared to OCK.For landscape-scale projects,its high computational efficiency and relatively robust performance show potential to deal with large volumes of datasets.展开更多
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The National High Technology Research and Development Program(863 Program)of China under contract No.2013AA122803the National Natural Science Foundation of China under contract Nos 41506033,41576013 and 41476021
文摘The temporal and spatial variations in the wind and wave fields in the Pacific Ocean between 2002 and 2011 are analyzed using a third-generation wave model（WAVEWATCH III）. The model performance for a significant wave height is validated using in situ buoy data. The results show that the wave model effectively hindcasts the significant wave height in the Pacific Ocean, but the errors are relatively large in the mid- and low-latitude regions. The spatial distributions and temporal variations in a wind speed and the significant wave height in the Pacific Ocean are then considered after dividing the Pacific Ocean into five regions, which show meridional differences and seasonal cycles. Regional mean values are used to give yearly average time series for each separate zone. The high latitude region in the Southern Hemisphere had a stronger significant wave height trend in the model results than regions at other latitudes. The sources and sinks of wave energy are then investigated. Their regional mean values are used to quantify variations in surface waves. Finally, the spectral analyses of the daily mean wind speeds and the significant wave heights are obtained. The significant wave height and the wind speed spectra are found to be connected in some ways but also show certain differences.
基金National Natural Science Foundation of China(41475019,41631072)
文摘One-dimensional synthetic aperture microwave radiometers have higher spatial resolution and record measurements at multiple incidence angles.In this paper,we propose a multiple linear regression method to retrieve sea surface wind speed at an incidence angle between 0°65°.We assume that a one-dimensional synthetic aperture microwave radiometer operates at frequencies of 6.9,10.65,18.7,23.8 and 36.5 GHz.Then,the microwave radiative transfer forward model is used to simulate the measured brightness temperatures.The sensitivity of the brightness temperatures at 0°65°to the sea surface wind speed is calculated.Then,vertical polarization channels(VR),horizontal polarization channels(HR)and all channels(AR)are used to retrieve the sea surface wind speed via a multiple linear regression algorithm at 0°65°,and the relationship between the retrieval error and incidence angle is obtained.The results are as follows:(1)The sensitivity of the vertical polarization brightness temperature to the sea surface wind speed is smaller than that of the horizontal polarization.(2)The retrieval error increases with Gaussian noise.The retrieval error of VR first increases and then decreases with increasing incidence angle,the retrieval error of HR gradually decreases with increasing incidence angle,and the retrieval error of AR first decreases and then increases with increasing incidence angle.(3)The retrieval error of AR is the lowest and it is necessary to retrieve the sea surface wind speed at a larger incidence angle for AR.
基金funded by National Basic Research Program of China(973 Program)(No.2013CB228201)National Natural Science Foundation of China(No.51307017)
文摘Big wind farms must be integrated to power system.Wind power from big wind farms,with randomness,volatility and intermittent,will bring adverse impacts on the connected power system.High precision wind power forecasting is helpful to reduce above adverse impacts.There are two kinds of wind power forecasting.One is to forecast wind power only based on its time series data.The other is to forecast wind power based on wind speeds from weather forecast.For a big wind farm,due to its spatial scale and dynamics of wind,wind speeds at different wind turbines are obviously different,that is called wind speed spatial dispersion.Spatial dispersion of wind speeds and its influence on the wind power forecasting errors have been studied in this paper.An error evaluation framework has been established to account for the errors caused by wind speed spatial dispersion.A case study of several wind farms has demonstrated that even ifthe forecasting average wind speed is accurate,the error caused by wind speed spatial dispersion cannot be ignored for the wind power forecasting of a wind farm.
文摘Spatial interpolation(SI)is currently one of the most common ways to estimate wind speed(Ws).However,classic SI models either ignore the complex geography[e.g.inverse distance weighting(IDW)],or demand high computational resources(e.g.cokriging).This study aimed to develop a simple yet effective SI model for estimating Ws in Eastern Thrace of Turkey.This new method,named MIDW(Ws),is a modified IDW through the integration of IDW with wind profile model,power law(PL),representing the influence of land cover and topography on Ws.Terrain features and elevation data of PL were obtained using normalized difference vegetation index(NDVI)and digital elevation model(DEM),respectively.Results showed superior and comparable performance of MIDW(Ws)to standard IDW and ordinary kriging(OK)across all months of year.Compared to ordinary cokriging(OCK)using DEM as covariate,MIDW(Ws)generated better results in the arid–semiarid seasons(around summer).Local complex atmospheric conditions during rainy seasons(around winter)may have affected the performance of incorporating PL with MIDW(Ws).Generally,the proposed MIDW(Ws)is simpler and easier to implement compared to OCK.For landscape-scale projects,its high computational efficiency and relatively robust performance show potential to deal with large volumes of datasets.
