Sea ice concentration is an important parameter for polar sea ice monitoring. Based on 89 GHz AMSR-E （Advanced Microwave Scanning Radiometer for Earth Observing System） data, a gridded high-resolution passive microw...Sea ice concentration is an important parameter for polar sea ice monitoring. Based on 89 GHz AMSR-E （Advanced Microwave Scanning Radiometer for Earth Observing System） data, a gridded high-resolution passive microwave sea ice concentration product can be obtained using the ASI （the Arctic Radiation And Turbulence Interaction Study （ARTIST） Sea Ice） retrieval algorithm. Instead of using fixed-point values, we developed ASi algorithm based on daily changed tie points, called as the dynamic tie point ASI algorithm in this study. Here the tie points are expressed as the brightness temperature polarization difference of open water and 100% sea ice. In 2010, the yearly-averaged tie points of open water and sea ice in Arctic are estimated to be 50.8 K and 7.8 K, respectively. It is confirmed that the sea ice concentrations retrieved by the dynamic tie point ASI algorithm can increase （decrease） the sea ice concentrations in low-value （high-value） areas. This improved the sea ice concentrations by present retrieval algorithm from microwave data to some extent. Comparing with the products using fixed tie points, the sea ice concentrations retrieved from AMSR-E data by using the dynamic tie point ASI algorithm are closer to those obtained from MODIS （Moderate-resolution Imaging Spectroradiometer） data. In 40 selected cloud-free sample regions, 95% of our results have smaller mean differences and 75% of our results have lower root mean square （RMS） differences compare with those by the fixed tie points.展开更多
In recent years, the rapid decline of Arctic sea ice area （SIA） and sea ice extent （SIE）, especially for the multiyear （MY） ice, has led to significant effect on climate change. The accurate retrieval of MY ice ...In recent years, the rapid decline of Arctic sea ice area （SIA） and sea ice extent （SIE）, especially for the multiyear （MY） ice, has led to significant effect on climate change. The accurate retrieval of MY ice concentration retrieval is very important and challenging to understand the ongoing changes. Three MY ice concentration retrieval algorithms were systematically evaluated. A similar total ice concentration was yielded by these algorithms, while the retrieved MY sea ice concentrations differs from each other. The MY SIA derived from NASA TEAM algorithm is relatively stable. Other two algorithms created seasonal fluctuations of MY SIA, particularly in autumn and winter. In this paper, we proposed an ice concentration retrieval algorithm, which developed the NASA TEAM algorithm by adding to use AMSR-E 6.9 GHz brightness temperature data and sea ice concentration using 89.0 GHz data. Comparison with the reference MY SIA from reference MY ice, indicates that the mean difference and root mean square （rms） difference of MY SIA derived from the algorithm of this study are 0.65×10^6 km^2 and 0.69×10^6 km^2 during January to March, -0.06×10^6 km^2 and 0.14×10^6 km^2during September to December respectively. Comparison with MY SIE obtained from weekly ice age data provided by University of Colorado show that, the mean difference and rms difference are 0.69×10^6 km^2 and 0.84×10^6 km^2, respectively. The developed algorithm proposed in this study has smaller difference compared with the reference MY ice and MY SIE from ice age data than the Wang＇s, Lomax＇ and NASA TEAM algorithms.展开更多
An enhanced ARTSIST Sea Ice(ASI)algorithm is presented based on a data fusion method of calculating total sea ice concentration from high-frequency microwave data.Algorithms that use low-frequency data to calculate to...An enhanced ARTSIST Sea Ice(ASI)algorithm is presented based on a data fusion method of calculating total sea ice concentration from high-frequency microwave data.Algorithms that use low-frequency data to calculate total sea ice concentration are less affected by atmosphere,but their spatial resolutions tend to be lower.In contrast,algorithms using high-frequency data have higher spatial resolution but are significantly influenced by atmosphere.Although errors can be eliminated using weather filters,the concentration of mixed pixels cannot be modified.Here,an enhanced ASI algorithm uses the 19 GHz polarization difference to modify the 91 GHz polarization difference,which is substituted into the ASI algorithm to calculate total sea ice concentration.Arctic total sea ice concentration results are obtained based on Special Sensor Microwave Imager Sounder(SSMIS)data on January 3,from 2008 to 2017.Total sea ice area and average concentration using the enhanced ASI algorithm are compared to traditional ASI and NASA Team results.In the Marginal Ice Zone,there is a considerable difference between the enhanced and traditional ASI algorithm results,with the former much closer to the NASA Team results.The proposed algorithm effectively modifies the concentration of the mixed pixels in the marginal zone.展开更多
The dual-polarized ratio algorithm(DPR)for the retrieval of Arctic sea ice concentration from Advanced Microwave Scanning Radiometer-EOS(AMSR-E)data was improved using a contrast ratio(CR)parameter.In contrast to thre...The dual-polarized ratio algorithm(DPR)for the retrieval of Arctic sea ice concentration from Advanced Microwave Scanning Radiometer-EOS(AMSR-E)data was improved using a contrast ratio(CR)parameter.In contrast to three other algorithms(Artist Sea Ice algorithm,ASI;NASA-Team 2 algorithm,NT2;and AMSR-E Bootstrap algorithm,ABA),this algorithm does not use a series of tie-points or a priori values of brightness temperature of sea-ice constituents,such as open water and 100% sea ice.Instead,it is based on a ratio(a)of horizontally and vertically polarized sea ice emissivity at 36.5 GHz,which can be automatically determined by the CR.aexhibited a clear seasonal cycle:changing slowly during winter,rapidly at other times,and reaching a minimum during summer.The DPR was improved using a seasonala.The systematic diff erences in the Arctic sea ice area over the complete AMSR-E period(2002–2011)were-0.8% ±2.0% between the improved DPR and ASI;-1.3%±1.7% between the improved DPR and ABA;and-0.7% ±1.9% between the improved DPR and NT2.The improved DPR and ASI(or ABA)had small seasonal diff erences.The seasonal diff erences between the improved DPR and NT2 decreased,except in summer.The improved DPR identified extremely low ice concentration regions in the Pacific sector of the central Arctic(north of 83°N)around August 12,2010,which was confirmed by the Chinese National Arctic Research Expedition.A series of high-resolution MODIS images(250 m×250 m)of the Beaufort Sea during summer were used to assess the four algorithms.According to mean bias and standard deviations,the improved DPR algorithm performed equally well with the other three sea ice concentration algorithms.The improved DPR can provide reasonable sea ice concentration data,especially during summer.展开更多
[Objective] The research aimed to study forecast models for frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River based on SVR optimized by particle swarm optimization algori...[Objective] The research aimed to study forecast models for frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River based on SVR optimized by particle swarm optimization algorithm. [Method] Correlation analysis and cause analysis were used to select suitable forecast factor combination of the ice regime. Particle swarm optimization algorithm was used to determine the optimal parameter to construct forecast model. The model was used to forecast frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River. [Result] The model had high prediction accuracy and short running time. Average forecast error was 3.51 d, and average running time was 10.464 s. Its forecast effect was better than that of the support vector regression optimized by genetic algorithm (GA) and back propagation type neural network (BPNN). It could accurately forecast frozen and melted dates of the river water. [Conclusion] SVR based on particle swarm optimization algorithm could be used for ice regime forecast.展开更多
基金The Global Change Research Program of China under contract No.2015CB953901the National Natural Science Foundation of China under contract Nos 41330960 and 41276193
文摘Sea ice concentration is an important parameter for polar sea ice monitoring. Based on 89 GHz AMSR-E （Advanced Microwave Scanning Radiometer for Earth Observing System） data, a gridded high-resolution passive microwave sea ice concentration product can be obtained using the ASI （the Arctic Radiation And Turbulence Interaction Study （ARTIST） Sea Ice） retrieval algorithm. Instead of using fixed-point values, we developed ASi algorithm based on daily changed tie points, called as the dynamic tie point ASI algorithm in this study. Here the tie points are expressed as the brightness temperature polarization difference of open water and 100% sea ice. In 2010, the yearly-averaged tie points of open water and sea ice in Arctic are estimated to be 50.8 K and 7.8 K, respectively. It is confirmed that the sea ice concentrations retrieved by the dynamic tie point ASI algorithm can increase （decrease） the sea ice concentrations in low-value （high-value） areas. This improved the sea ice concentrations by present retrieval algorithm from microwave data to some extent. Comparing with the products using fixed tie points, the sea ice concentrations retrieved from AMSR-E data by using the dynamic tie point ASI algorithm are closer to those obtained from MODIS （Moderate-resolution Imaging Spectroradiometer） data. In 40 selected cloud-free sample regions, 95% of our results have smaller mean differences and 75% of our results have lower root mean square （RMS） differences compare with those by the fixed tie points.
基金The National Natural Science Foundation of China under contract Nos 41330960 and 41276193 and 41206184
文摘In recent years, the rapid decline of Arctic sea ice area （SIA） and sea ice extent （SIE）, especially for the multiyear （MY） ice, has led to significant effect on climate change. The accurate retrieval of MY ice concentration retrieval is very important and challenging to understand the ongoing changes. Three MY ice concentration retrieval algorithms were systematically evaluated. A similar total ice concentration was yielded by these algorithms, while the retrieved MY sea ice concentrations differs from each other. The MY SIA derived from NASA TEAM algorithm is relatively stable. Other two algorithms created seasonal fluctuations of MY SIA, particularly in autumn and winter. In this paper, we proposed an ice concentration retrieval algorithm, which developed the NASA TEAM algorithm by adding to use AMSR-E 6.9 GHz brightness temperature data and sea ice concentration using 89.0 GHz data. Comparison with the reference MY SIA from reference MY ice, indicates that the mean difference and root mean square （rms） difference of MY SIA derived from the algorithm of this study are 0.65×10^6 km^2 and 0.69×10^6 km^2 during January to March, -0.06×10^6 km^2 and 0.14×10^6 km^2during September to December respectively. Comparison with MY SIE obtained from weekly ice age data provided by University of Colorado show that, the mean difference and rms difference are 0.69×10^6 km^2 and 0.84×10^6 km^2, respectively. The developed algorithm proposed in this study has smaller difference compared with the reference MY ice and MY SIE from ice age data than the Wang＇s, Lomax＇ and NASA TEAM algorithms.
基金The National Natural Science Foundation of China under contract No.41606209the Open Fund from Key Laboratory of Global Change and Marine-Atmospheric Chemistry under contract No.GCMAC1605+1 种基金the Natural Science Project of Henan Education Department under contract No.15A120007the Key Laboratory of Ocean Circulation and Waves,Institute of Oceanology,Chinese Academy of Sciences under contract No.KLOCW1805
文摘An enhanced ARTSIST Sea Ice(ASI)algorithm is presented based on a data fusion method of calculating total sea ice concentration from high-frequency microwave data.Algorithms that use low-frequency data to calculate total sea ice concentration are less affected by atmosphere,but their spatial resolutions tend to be lower.In contrast,algorithms using high-frequency data have higher spatial resolution but are significantly influenced by atmosphere.Although errors can be eliminated using weather filters,the concentration of mixed pixels cannot be modified.Here,an enhanced ASI algorithm uses the 19 GHz polarization difference to modify the 91 GHz polarization difference,which is substituted into the ASI algorithm to calculate total sea ice concentration.Arctic total sea ice concentration results are obtained based on Special Sensor Microwave Imager Sounder(SSMIS)data on January 3,from 2008 to 2017.Total sea ice area and average concentration using the enhanced ASI algorithm are compared to traditional ASI and NASA Team results.In the Marginal Ice Zone,there is a considerable difference between the enhanced and traditional ASI algorithm results,with the former much closer to the NASA Team results.The proposed algorithm effectively modifies the concentration of the mixed pixels in the marginal zone.
基金Supported by the National Natural Science Foundation of China(No.41406208)the Global Change Research of National Important Research Project on Science(No.2015CB953900)+1 种基金the Scientific and Youth Science Funds of Shandong Academy of Sciences,China(No.2013QN042)the Open Research Fund of the State Oceanic Administration of the People’s Republic of China Key Laboratory for Polar Science(No.3KP201203)
文摘The dual-polarized ratio algorithm(DPR)for the retrieval of Arctic sea ice concentration from Advanced Microwave Scanning Radiometer-EOS(AMSR-E)data was improved using a contrast ratio(CR)parameter.In contrast to three other algorithms(Artist Sea Ice algorithm,ASI;NASA-Team 2 algorithm,NT2;and AMSR-E Bootstrap algorithm,ABA),this algorithm does not use a series of tie-points or a priori values of brightness temperature of sea-ice constituents,such as open water and 100% sea ice.Instead,it is based on a ratio(a)of horizontally and vertically polarized sea ice emissivity at 36.5 GHz,which can be automatically determined by the CR.aexhibited a clear seasonal cycle:changing slowly during winter,rapidly at other times,and reaching a minimum during summer.The DPR was improved using a seasonala.The systematic diff erences in the Arctic sea ice area over the complete AMSR-E period(2002–2011)were-0.8% ±2.0% between the improved DPR and ASI;-1.3%±1.7% between the improved DPR and ABA;and-0.7% ±1.9% between the improved DPR and NT2.The improved DPR and ASI(or ABA)had small seasonal diff erences.The seasonal diff erences between the improved DPR and NT2 decreased,except in summer.The improved DPR identified extremely low ice concentration regions in the Pacific sector of the central Arctic(north of 83°N)around August 12,2010,which was confirmed by the Chinese National Arctic Research Expedition.A series of high-resolution MODIS images(250 m×250 m)of the Beaufort Sea during summer were used to assess the four algorithms.According to mean bias and standard deviations,the improved DPR algorithm performed equally well with the other three sea ice concentration algorithms.The improved DPR can provide reasonable sea ice concentration data,especially during summer.
基金Supported by National Nature Science Fund Item,China (51009065)Key Science and Technology Research Plan Program in Henan Province,China(112102110033)
文摘[Objective] The research aimed to study forecast models for frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River based on SVR optimized by particle swarm optimization algorithm. [Method] Correlation analysis and cause analysis were used to select suitable forecast factor combination of the ice regime. Particle swarm optimization algorithm was used to determine the optimal parameter to construct forecast model. The model was used to forecast frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River. [Result] The model had high prediction accuracy and short running time. Average forecast error was 3.51 d, and average running time was 10.464 s. Its forecast effect was better than that of the support vector regression optimized by genetic algorithm (GA) and back propagation type neural network (BPNN). It could accurately forecast frozen and melted dates of the river water. [Conclusion] SVR based on particle swarm optimization algorithm could be used for ice regime forecast.
文摘南极数字高程模型(DEM)是从事南极地学和环境变化研究的基础.内插是建立数字高程模型的重要技术点,插值方法有多种,根据不同的适用情况,不同的插值方法各有优劣.利用克里格、距离反权、三角网剖分、最小曲率以及移动平均5种插值方法分别建立南极冰盖小范围区域的DEM,通过抽取部分观测数据作为验证值对各插值方法进行了比较.结果表明:克里格插值方法的可靠性最好,稳定性最高.然后,利用克里格插值方法,基于ICESat测高卫星的GLA12数据建立了南极冰盖的DEM.由于南极大陆实测数据有限,缺乏对DEM的检核.为了分析所建DEM的可靠性,利用中国南极内陆冰盖考察所采集的GPS实测数据,对所建立的DEM进行了验证分析.结果显示,DEM在坡度较缓的南极内陆冰盖区域精度较高,符合度在3 m以内;距离卫星轨道越近的区域精度越高,可达到1 m以内.在坡度较大,高程变化较为显著的区域如沿海地区,精度较低,差距最大的点超过40 m.
