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1.
基于CORA资料的中国海海表面温度季节和年际变化特征分析 总被引：1，自引：0，他引：1
基于1986-2008年的中国近海及邻近海域再分析产品(CORA),采用经验正交函数分解方法(EOF)分析了海表面温度(SST)的季节及年际变化特征,并用相应的SODA、AVHRR以及Levitus资料对CORA做了对比评估。相比于AVHRR而言,CORA资料SST的偏差和均方根误差均小于SODA,相比Levitus资料而言CORA资料温度盐度的均方根误差随深度的变化皆小于SODA。 CORA与SODA资料相比,两者前3个模态的时空分布大体一致,区别在于CORA资料能更好地反映参量的一些细微特征。结果表明,CORA资料能很好的刻画中国近海SST的季节、年际变化特征,尤其是黑潮流经区域SST的局地变化特征。季节EOF第二模态显示的是SST对由风引起的潜热释放的响应特征。第三模态刻画了冬夏转换季的分布特征,主要揭示了东北-西南走向的锋面特征。SST年际变化与ENSO密切相关,区域平均的南海SSTA与Nino指数的吻合程度CORA优于SODA。 相似文献
2.
海面高度异常 （SSHA） 作为重要的海洋要素，对研究海洋温盐剖面、海洋涡旋等海洋动力现象具有重要意义。然而，传统的海洋预测技术存在着预测时效过短、预测过程复杂等诸多问题，现有的机器学习预测方法也只针对几个点或区域
进行平均，忽略了很多重要信息。因此，本文提出了一种基于经验正交函数和 BP 神经网络 （一种机器学习方法） 的 SSHA预测模型 （EOF-BPNN） 来实现对起报时刻后 30 天的南海 SSHA 预测。首先，对 1993 年 1 月 1 日—2013 年 12 月 31 日的逐日 SSHA 数据进行距平归一化预处理，构建相关系数矩阵，并对该矩阵进行 EOF 分解，获取主成分。然后将主成分输入 BP神经网络进行训练，实现对主成分的预测。最后将主成分预测值与相应的空间模态结合，获取 SSHA 预测值。结果表明，相较于惯性预报和气候态预报，EOF-BPNN 模型不仅能够提供提前 30 天的较为精确的 SSHA 和相应的涡旋演化过程预报，且在整个南海区域拥有更高的 SSHA 相关系数，证明了 EOF-BPNN 模型具有较好的预测性能。 相似文献
3.
尝试利用卫星遥感高分辨率海表温度资料GHRSST (Group for High Resolution Sea Surface Temperature) 与海表温度(sea surface temperature, SST)数值预报产品之间的误差, 建立一种南海SST模式预报订正方法。首先, 利用南海的Argo浮标上层海温数据对GHRSST 海温数据进行验证, 结果表明两者之间均方根误差约为0.3℃, 相关系数为0.98, GHRSST 海温数据可用于南海业务化数值预报SST的订正。预报订正后的SST与Argo浮标海温数据相比, 24h、48h和72h的均方根误差均由0.8℃左右下降到0.5℃以内。与GHRSST 海温数据相比, 南海北部海域(110°E—121°E, 13°N—23°N)订正后的24h、48h和72h的SST预报空间误差均显著减小, 在冷空气影响南海期间或中尺度涡存在的过程中, SST预报订正效果也较为显著。因此, 该方法可考虑在南海业务化SST数值预报系统中应用。 相似文献
4.
华北地区夏季降雨量与南海海温长期变化的关系 总被引：6，自引：0，他引：6
比较了华北地区7个站与17个站1951-1997年夏季（6，7，8月）降雨量与气候随时间的变化特征，并对其成因作了探讨。结果表明，用北京、天津、邢台、烟台、郑州、太原和济南等7个站可代表该地区夏季降雨量与气候的多尺度变化特征，过去47a该地区依次经历了湿凉、湿热、湿凉、干热、湿热几个时期，降雨量的长期变化与南海前冬（1-2月）海温成负相关。前冬南海海温偏高，意味着初夏南海地区大气对流低频振动偏弱，南海夏季风爆发较晚，西南季风较弱，夏季西太平洋副高位置偏南，华北地区大气低层北风加强，华北地区夏季少雨，前冬南海海温偏低时情况则相反，考虑冬季（1-2月）南海南温和7-8月西太平洋副高脊线位置（纬度）的影响用均生函数建模，试验结果与用子波变换重构方法考虑华北地区夏季降雨量的变化趋势比较，二者相吻合，预测试验结果与过去3a的实况基本一致。 相似文献
5.
Ang Li Xueming Zhu Yunfei Zhang Shihe Ren Miaoyin Zhang Ziqing Zu Hui Wang 《海洋学报(英文版)》2021,40(11):87-103
In order to satisfy the increasing demand for the marine forecasting capacity, the Bohai Sea, the Yellow Sea and the East China Sea Operational Oceanography Forecasting System (BYEOFS) has been upgraded and improved to Version 2.0. Based on the Regional Ocean Modeling System (ROMS), a series of comparative experiments were conducted during the improvement process, including correcting topography, changing sea surface atmospheric forcing mode, adjusting open boundary conditions, and considering atmospheric pressure correction. (1) After the topography correction, the volume transport and meridional velocity maximum of Yellow Sea Warm Current increase obviously and the unreasonable bending of its axis around 36.1°N, 123.5°E disappears. (2) After the change of sea surface forcing mode, an effective negative feedback mechanism is formed between predicted sea surface temperature (SST) by the ocean model and sea surface radiation fluxes fields. The simulation errors of SST decreased significantly, and the annual average of root-mean-square error (RMSE) decreased by about 18％. (3) The change of the eastern lateral boundary condition of baroclinic velocity from mixed Radiation-Nudging to Clamped makes the unreasonable westward current in Tsushima Strait disappear. (4) The adding of mean sea level pressure correction option which forms the mean sea level gradient from the Bohai Sea and the Yellow Sea to the western Pacific in winter and autumn is helpful to increasing the fluctuation of SLA and outflow of the Yellow Sea when the cold high air pressure system controls the Yellow Sea area. 相似文献
6.
Based on the analysis of the quality level in a Pathfinder 4km daily nighttime Sea Surface Temperature product (PFSST) in the East China Seas (ECS) from 1985 to 2004, the proportion of high-quality data was lower than that in the global level. Additionally, the PFSST maps showed clearly the void and anomaly data impacted by atmospheric contamination. In order to solve the above problem, an optimal algorithm was established through introducing the structure function, setting up the daily first-guess sea surface temperature (SST) field and taking PFSST product of the highest quality as reference points. Comparisons were done between this optimally interpolated SST and the selected original PFSST and the simultaneous in situ measurements. It illustrated it was possible to exactly estimate the SST values in the ECS during the recent two decades. The mean bias error and the root mean square error between data sets optimally evaluated and in situ observed were lower than those between the previous global estimations and in situ measurnments. 相似文献
7.
In this study, sea surface salinity(SSS) Level 3(L3) daily product derived from soil moisture active passive(SMAP)during the year 2016, was validated and compared with SSS daily products derived from soil Moisture and ocean salinity(SMOS) and in-situ measurements. Generally, the root mean square error(RMSE) of the daily SSS products is larger along the coastal areas and at high latitudes and is smaller in the tropical regions and open oceans. Comparisons between the two types of daily satellite SSS product revealed that the RMSE was higher in the daily SMOS product than in the SMAP, whereas the bias of the daily SMOS was observed to be less than that of the SMAP when compared with Argo floats data. In addition, the latitude-dependent bias and RMSE of the SMAP SSS were found to be primarily influenced by the precipitation and the sea surface temperature(SST). Then, a regression analysis method which has adopted the precipitation and SST data was used to correct the larger bias of the daily SMAP product. It was confirmed that the corrected daily SMAP product could be used for assimilation in high-resolution forecast models, due to the fact that it was demonstrated to be unbiased and much closer to the in-situ measurements than the original uncorrected SMAP product. 相似文献
8.
3套不同的SST再分析数据与中国近海浮标观测的对比研究 总被引：1，自引：0，他引：1
基于自然资源部浮标数据，通过分析均值差、均方根误差、相关系数和标准差偏差4个统计量，检验了2018年7月1日至8月6日全时段及该时段内3个台风（1808号台风“玛利亚”、1810号台风“安比”、1812号台风“云雀”）过境期间，3套海表面温度（Sea Surface Temperature，SST）再分析资料（OISST、OSTIA SST、RTG SST）在中国近海区域的可靠性。对比结果表明，在全时段内，3套SST再分析资料都能在一定程度上反映中国近海SST的基本状况，其中OSTIA SST资料同浮标实测SST数据的均值差为0.12℃、相关系数为0.94，均优于OISST资料（均值差为–0.85℃、相关系数为0.90）和RTG SST资料（均值差为–0.17℃、相关系数为0.86）。通过对比单个浮标数据发现，相较约80%的MF浮标实测SST数据，OSTIA SST资料都显著优于RTG SST资料和OISST资料，具有较高的可信度。在台风过境期间，较之RTG SST资料和OISST资料，OSTIA SST资料同大部分浮标实测数据的均值差绝对值及均方根误差更小、相关系数更大，表明在高海况条件下，OSTIA SST资料能更真实地反映中国近海SST的基本状况。 相似文献
9.
基于人工神经网络方法,利用海面水温、海面风速以及海面气压反演南海近海面气温,采用的基础数据集是国际综合海洋-大气数据集(International Comprehensive Ocean-Atmosphere Data Set,2.4 Release,ICOADS2.4)1981—2008年的观测资料,其中1981—2000年的观测资料用来建立模型,2001—2008年的观测资料用来进行模型检验。采用的人工神经网络方法是引入动量因子并采用批处理梯度下降法的BP(Back propagation)算法。试验结果表明,基于人工神经网络建立的近海面气温反演方法明显优于多元线性回归方法,尤其是在春季和冬季,海面水温、海面风速以及海面气压与近海面气温之间存在较强的非线性关系,人工神经网络的优势更加明显。总体而言,人工神经网络在各月的反演效果较均衡,均方根误差介于1.5—1.8℃之间,平均绝对误差为1.1—1.3℃。 相似文献
10.
通过卫星遥感获取的海表温度（SST）产品已经成为海洋和大气研究中的重要数据源，我国海洋水色遥感卫星（HY1C和HY1D）的海洋水色水温扫描仪（COCTS）具有两个热红外通道，可反演全球SST遥感产品。对比Terra和Aqua卫星的中分辨率成像光谱仪（MODIS）的SST产品，分析COCTS海表温度产品对MODIS相应产品的可替代性。比较了两种卫星的全球SST单日和月平均融合产品的图像空间结构，分析了匹配像元SST值的离散度，统计了HY1C/1D的误差结果，讨论了HY1C与HY1D产品的一致性、不同质量控制方案对SST产品影响以及遥感产品质量对昼夜SST变化研究影响等问题。结果表明，以2020年6月SST(Terra)为真值，HY1C白天SST的单日全球遥感产品的平均偏差、绝对偏差、均方根误差和相关系数分别为0.04℃、0.60℃、0.78℃和0.98，夜晚SST的单日全球遥感产品的平均偏差、绝对偏差、均方根误差和相关系数分别为-0.16℃、0.78℃、0.95℃和0.86。以2020年6月SST(Aqua)为真值，HY1D白天SST的单日全球遥感产品的平均偏差、绝对偏差、均方根误差和相... 相似文献
11.
Sea surface temperature(SST) measurements from 26 coastal hydrological stations of China during 1960–2015 were homogenized and analyzed in this study. The homogenous surface air temperature(SAT) series from meteorological stations which were highly correlated to SST series was used to construct the reference series.Monthly mean SST series were then derived and subjected to a statistical homogeneity test, called penalized maximal t test. Homogenized monthly mean SST series were obtained by adjusting all significant change points which were supported by historic metadata information. Results show that the majority of break points are caused by instrument change and station relocation, which accounts for about 61.3% and 24.2% of the total break points,respectively. The regionally averaged annual homogeneous SST series from the 26 stations shows a warming trend(0.19°C per decade). This result is consistent with that based on the homogenized annual mean SAT at the same region(0.22°C per decade), while the regionally averaged mean original SST series from the same stations shows a much weaker warming of 0.09°C per decade for 1960–2015. This finding suggests that the effects of artificial change points on the result of trend analysis are remarkable, and the warming rate from original SST observations since 1960 may be underestimated. Thus a high quality homogenized observation is crucial for robust detection and assessment of regional climate change. Furthermore, the trends of the seasonal mean homogenized SST were also analyzed. This work confirmed that there was an asymmetric seasonal temperature trends in the Chinese coastal water in the past decades, with the largest warming rate occurring in winter. At last,the significant warming in winter and its relationships to the variability of three large-scale atmospheric modes were investigated. 相似文献
12.
OSTIA数据在中国近海业务化环流模型中的同化应用 总被引：3，自引：0，他引：3
The prediction of sea surface temperature(SST) is an essential task for an operational ocean circulation model. A sea surface heat flux, an initial temperature field, and boundary conditions directly affect the accuracy of a SST simulation. Here two quick and convenient data assimilation methods are employed to improve the SST simulation in the domain of the Bohai Sea, the Yellow Sea and the East China Sea(BYECS). One is based on a surface net heat flux correction, named as Qcorrection(QC), which nudges the flux correction to the model equation; the other is ensemble optimal interpolation(En OI), which optimizes the model initial field. Based on such two methods, the SST data obtained from the operational SST and sea ice analysis(OSTIA) system are assimilated into an operational circulation model for the coastal seas of China. The results of the simulated SST based on four experiments, in 2011, have been analyzed. By comparing with the OSTIA SST, the domain averaged root mean square error(RMSE) of the four experiments is 1.74, 1.16, 1.30 and 0.91°C, respectively; the improvements of assimilation experiments Exps 2, 3 and 4 are about 33.3%, 25.3%, and 47.7%, respectively.Although both two methods are effective in assimilating the SST, the En OI shows more advantages than the QC,and the best result is achieved when the two methods are combined. Comparing with the observational data from coastal buoy stations, show that assimilating the high-resolution satellite SST products can effectively improve the SST prediction skill in coastal regions. 相似文献
13.
基于一个一维湍能海洋混合层模式,发展了一个易于与大气或海洋模式耦合的,可应用于水平二维空间的模块化海洋混合层模式。并加入垂直上翻参数化方案,引入日平均海面温度及气候态松弛项,进行二维海洋模拟理想气旋实验和飓风实例模拟实验。理想气旋强迫实验表明,垂直上翻过程的参数化方案可有效地弥补原有一维海洋混合层模式无法形成气旋中心动力上翻的不足,消除了虚假暖水核心。卡特里娜飓风个例实验的结果表明,加入垂直上翻后,飓风中心附近的海表面温度误差明显减小。海洋混合层模式对海洋表面温度日变化模拟能力在二维应用中依然表现良好。经过上述改进,发展的海洋混合层模式可以较为真实地模拟平常海表温度高频日变化的同时,还对剧烈天气过程也有一定的模拟能力,具有广泛的应用前景。 相似文献
14.
Measured and remotely sensed estimates of primary production in the Atlantic Ocean from 1998 to 2005
《Deep Sea Research Part II: Topical Studies in Oceanography》2009,56(15):918-930
Primary production (PP) was determined using 14C uptake at 117 stations in the Atlantic Ocean to validate three PP satellite algorithms of varying complexity. An empirical satellite algorithm based on log chlorophyll-a had the highest bias and root-mean square error compared with measured 14C PP and tended to under-estimate PP. The vertical generalised production model improved PP estimates and was the most accurate algorithm in the Eastern Tropical Atlantic (ETRA) and Western Tropical Atlantic (WTRA), but tended to over-estimate PP in eutrophic provinces. A photosynthesis-light wavelength-resolved model was the most accurate over the Atlantic basin, having the lowest mean log-difference error, root-mean square error and bias, and exhibited a superior performance in six out of the nine ecological provinces surveyed. Using this algorithm and mean monthly SeaWiFS fields, a PP time series was generated for the Atlantic Ocean from 1998 to 2005 which was compared with Advanced Very High Resolution Radiometer (AVHRR) sea-surface temperature (SST) data. There was a significant negative correlation between SST and PP in the North Atlantic Subtropical Gyre Province (NAST), North Atlantic Tropical Gyre (NATR), and WTRA suggesting that recent warming trends in these provinces are coupled with a decrease in phytoplankton production. 相似文献
15.
CMIP5模式对南海SST的模拟和预估 总被引：4，自引：1，他引：3
分析了32个CMIP5模式对南海历史海表温度(SST)的模拟能力和不同排放情景下未来SST变化的预估。通过检验各气候模式对南海历史SST增温趋势和均方差的模拟,发现大部分模式都能较好地模拟出南海20世纪历史SST的基本特征和变化规律,但也有部分模式的模拟存在较大偏差。尽管这些模拟偏差较大的模式对SST多模式集合平均的影响不大,但会增加未来情景预估的不确定性。剔除15个模式后,分析了南海SST在RCP26、RCP45和RCP85三种排放情景下的变化趋势,发现在未来百年呈明显的增温趋势,多模式集合平均的增温趋势分别为0.42、1.50和3.30℃/(100a)。这些增温趋势在空间上变化不大,但随时间并不是均匀变化的。在前两种排放情景下,21世纪前期的增温趋势明显强于后期,而在RCP85情景下,21世纪后期的增温趋势强于前期。 相似文献
16.
Beach and nearshore levels have been measured yearly along the entire Dutch North Sea coast since the mid 1960s (the ‘Jarkus’ data set). This data set has been processed to create separate time series of beach volumes at longshore intervals of about 250 m, giving over 2000 time series in total. These time series typically show a high annual variability with weak long-term trends. The present Dutch national coastal management strategy involves making year-ahead forecasts of beach volumes by extrapolating a linear least squares trend through the previous ten years' data separately for each longshore location. In this paper, these forecasts are shown to be worse than the trivial forecast in which the most recently measured beach volume persists unchanged into the future, with a mean square error (MSE) about 13.5% worse (equivalent to a root mean square error (RMSE) 6.5% worse). Improvements to these forecasts are sought by testing six different univariate forecasting methods. The two best methods improve on the persistence of the most recently measured beach volume by about 15% MSE (8% RMSE), and on the presently used linear least squares trend method by about 25% MSE (13.5% RMSE). Further comparisons are made between the forecasting methods to investigate several factors. These include varying the amount of fitting data for the forecasting methods, smoothing of the fitting data, different methods for interpolating gaps in the data, the longshore aggregation of data, making forecasts for coastal profiles with and without nourishments, and making forecasts up to five years ahead. These forecasting methods are designed as a coastal management tool to provide yearly forecasts quickly and routinely for the whole Dutch North Sea coast. 相似文献
17.
利用西北印度洋船测数据评估基于卫星的海表面温度 总被引：1，自引：1，他引：0
本文描述了一次夏季在西北印度洋进行的调查船水文测量,用船测数据评估卫星海面表温度,并寻找影响海表面温度误差的主要因素。我们考虑了两种卫星数据,第一种是微波遥感产品——热带降雨测量任务微波成像仪TMI数据,另外一种是融合了微波,红外线,以及少部分观测数据的融合数据产品——可处理海表温度和海冰分析OSTIA数据。结果表明融合数据的日平均海表面温度的平均误差和均方根误差都比微波遥感小。这一结果证明了融合红外线遥感,微波遥感以及观测数据来提高海表面温度数据质量的必要性。此外,我们分析了海表面温度误差与各项水文参数之间的相关关系,包括风速,大气温度,想对湿度,大气压力,能见度。结果表明风速与TMI海表面温度误差的相关系数最大。而大气温度是影响OSTIA海表面温度误差最重要的因素;与此同时,想对湿度与海表面温度误差的相关系数也很高。 相似文献
18.
Sea surface temperature (SST) prediction based on the multi-model seasonal forecast with numerous ensemble members have more useful skills to estimate the possibility of climate events than individual models. Hence, we assessed SST predictability in the North Pacific (NP) from multi-model seasonal forecasts. We used 23 years of hindcast data from three seasonal forecasting systems in the Copernicus Climate Change Service to estimate the prediction skill based on temporal correlation. We evaluated the predictability of the SST from the ensemble members' width spread, and co-variability between the ensemble mean and observation. Our analysis revealed that areas with low prediction skills were related to either the large spread of ensemble members or the ensemble members not capturing the observation within their spread. The large spread of ensemble members reflected the high forecast uncertainty, as exemplified in the Kuroshio–Oyashio Extension region in July. The ensemble members not capturing the observation indicates the model bias; thus, there is room for improvements in model prediction. On the other hand, the high prediction skills of the multi-model were related to the small spread of ensemble members that captures the observation, as in the central NP in January. Such high predictability is linked to El Niño Southern Oscillation (ENSO) via teleconnection.
相似文献19.
Hiroshi Kawamura Huiling Qin Futoki Sakaida Riza Yuliratno Setiawan 《Journal of Oceanography》2010,66(1):61-70
An algorithm has been developed for retrieving sea surface temperature (SST) from hourly data transmitted from the Japanese
Advanced Meteorological Imager (JAMI) aboard a Japanese geostationary satellite, Multi-functional Transport Satellite (MTSAT)-1R.
Threshold tests screening cloudy pixels are empirically adjusted to cases of daytime with/without sun glitter, and nighttime.
The Non-Linear SST (NLSST) equation, including several new additional terms, is used to calculate MTSAT SST. The estimated
SST is compared with drifting and moored buoy measurements, with the result that the bias of the MTSAT SST is nearly 0.0°K.
The root mean square (rms) error is about 0.8°K, and it is 0.7°K under the condition that the satellite zenith angle is less
than 50°. It is demonstrated that the hourly MTSAT SST produced by the algorithm developed here captures diurnal SST variations
in the equatorial sea in mid-November 2006. 相似文献
20.
In the present article, we introduce a high resolution sea surface temperature(SST) product generated daily by Korea Institute of Ocean Science and Technology(KIOST). The SST product is comprised of four sets of data including eight-hour and daily average SST data of 1 km resolution, and is based on the four infrared(IR) satellite SST data acquired by advanced very high resolution radiometer(AVHRR), Moderate Resolution Imaging Spectroradiometer(MODIS), Multifunctional Transport Satellites-2(MTSAT-2) Imager and Meteorological Imager(MI), two microwave radiometer SSTs acquired by Advanced Microwave Scanning Radiometer 2(AMSR2), and Wind SAT with in-situ temperature data. These input satellite and in-situ SST data are merged by using the optimal interpolation(OI) algorithm. The root-mean-square-errors(RMSEs) of satellite and in-situ data are used as a weighting value in the OI algorithm. As a pilot product, four SST data sets were generated daily from January to December 2013. In the comparison between the SSTs measured by moored buoys and the daily mean KIOST SSTs, the estimated RMSE was 0.71°C and the bias value was –0.08°C. The largest RMSE and bias were 0.86 and –0.26°C respectively, observed at a buoy site in the boundary region of warm and cold waters with increased physical variability in the Sea of Japan/East Sea. Other site near the coasts shows a lower RMSE value of 0.60°C than those at the open waters. To investigate the spatial distributions of SST, the Group for High Resolution Sea Surface Temperature(GHRSST) product was used in the comparison of temperature gradients, and it was shown that the KIOST SST product represents well the water mass structures around the Korean Peninsula. The KIOST SST product generated from both satellite and buoy data is expected to make substantial contribution to the Korea Operational Oceanographic System(KOOS) as an input parameter for data assimilation. 相似文献