The circulation of the Indian Ocean at 32°S

Using inverse methods a circulation for a new section along 32°S in the Indian Ocean is derived with a maximum in the overturning stream function (or deep overturning) of 10.3 Sv at 3310 m. Shipboard and Lowered Acoustic Doppler Current Profiler (ADCP) data are used to inform the choice of reference level velocity for the initial geostrophic field. Our preferred solution includes a silicate constraint (−312 ± 380 kmol s⁻¹) consistent with an Indonesian throughflow of 12 Sv. The overturning changes from 12.3 Sv at 3270 m when the silicate constraint is omitted to 10.3 Sv when it is included. The deep overturning varies by only ±0.7 Sv as the silicate constraint varies from +68 to −692 kmol s⁻¹, and by ±0.3 Sv as the net flux across the section, driven by the Indonesian throughflow, varies from −7 to −17 Sv with an appropriately scaled silicate flux constraint. Thus, the overturning is insensitive to the size of the Indonesian throughflow and silicate constraint within their apriori uncertainties. We find that the use of the ADCP data adds significant detail to the horizontal circulation. These resolved circulations include the Agulhas Undercurrent, deep cyclonic gyres and deep fronts, features evidenced by long term integrators of the flow such as current meter and float measurements as well as water properties.     

“Trends” and variations of global oceanic evaporation data sets from remote sensing

The variability in global oceanic evaporation data sets was examined for the period 1988-2000. These data sets are satellite estimates based on bulk aerodynamic formulations and include the NASA/Goddard Space Flight Center Satellite-based Surface Turbulent Flux version 2 （ GSSTF2）, the Japanese-ocean flux using remote sensing observations （J-OFURO）, and the Hamburg Ocean-Atmosphere Parameters and Fluxes from Satellite version 2 （HOAPS2）. The National Center for Environmental Prediction （NCEP） reanalysis is also included for comparison. An increase in global average surface latent heat flux （SLHF） can be observed in all the data sets. Empirical mode decomposition （EMD） shows long-term increases that started around 1990 for all remote sensing data sets. The effect of Mt. Pinatubo eruption in 1991 is clearly evident in HOAPS2 but is independent of the longterm increase. Linear regression analyses show increases of 9.4%, 13.0%, 7. 3%, and 3.9% for GSSTF2, J-OFURO, HOAPS2 and NCEP, for the periods of the data sets. Empirical orthogonal function （EOF） analyses show that the pattern of the first EOF of all data sets is consistent with a decadal variation associated with the enhancement of the tropical Hadley circulation, which is supported by other satellite observations. The second EOF of all four data sets is an ENSO mode, and the correlations between their time series and an SO1 are 0.74, 0.71,0.59, and 0.61 for GSSTF2, J-OFURO, HOAPS2, and NCEP in that order. When the Hadley modes are removed from the remote sensing data, the residue global increases are reduced to 2.2% , 7. 3%, and 〈 1% for GSSTF2, J-OFURO and HOAPS, respectively. If the ENSO mode is used as a calibration standard for the data sets, the Hadley mode is at least comparable to, if not larger than, the ENSO mode during our study period.     

44 years hindcast of sea level in the Atlantic Coast of Europe

The sea level of Northeast Atlantic Ocean is calculated for the period between 1958 and 2001 using a state-of-the-art barotropic model with a grid size of 10′ × 15′ (long × lat). The model includes astronomic effects, considering seven components of the tide, and the meteorological effects of wind and atmospheric pressure, allowing obtaining the astronomic tide, the atmospheric residuals and the non-linear addition of both components of sea level.     

Dimethylsulfide production in Sargasso Sea eddies

Lagrangian time series of dimethylsulfide (DMS) concentrations from a cyclonic and an anticyclonic eddy in the Sargasso Sea were used in conjunction with measured DMS loss rates and a model of vertical mixing to estimate gross DMS production in the upper 60 m during summer 2004. Loss terms included biological consumption, photolysis, and ventilation to the atmosphere. The time- and depth (0–60 m)-averaged gross DMS production was estimated to be 0.73±0.09 nM d⁻¹ in the cyclonic eddy and 0.90±0.15 nM d⁻¹ in the anticyclonic eddy, with respective DMS replacement times of 5±1 and 6±1 d. The higher estimated rate of gross production and lower measured loss rate constants in the anticyclonic eddy were equally responsible for this eddy's 50% higher DMS inventory (0–60 m). When normalized to chlorophyll and total dimethylsulfoniopropionate (DMSP), estimated gross production in the anticyclonic eddy was about twice that in the cyclonic eddy, consistent with the greater fraction of phytoplankton that were DMSP producers in the anticyclonic eddy. Higher rates of gross production were estimated below the mixed layer, contributing to the subsurface DMS maximum found in both eddies. In both eddies, gas exchange, microbial consumption, and photolysis were roughly equal DMS loss terms in the surface mixed layer (0.2–0.4 nM d⁻¹). Vertical mixing was a substantial source of DMS to the surface mixed layer in both eddies (0.2–0.3 nM d⁻¹) owing to the relatively high DMS concentrations below the mixed layer. Estimated net biological DMS production rates (gross production minus microbial consumption) in the mixed layer were substantially lower (by almost a factor of 3) than those estimated in a previous study of the Sargasso Sea, which may explain the relatively low mixed-layer DMS concentrations found here during July 2004 (3 nM) compared to previous summers (4–6 nM).     

基于机理的合成孔径雷达系统成像误差理论分析

目前在遥感和GIS中,关于误差和不确定性研究的主要方法有两种: 概率统计和机理模拟。本文论述了二者的特点,详细讨论了影响合成孔径雷达成像的诸多因素,提出利用机理的方法分析SAR系统成像误差。本方法是从合成孔径雷达成像机理的研究出发,利用控制论的观点来解释、分析影响合成孔径雷达成像的诸多误差因素并建立误差传播模型。这不仅对研制合成孔径雷达的设计具有指导意义,而且对遥感数据的质量控制也有重要意义。     

基于EEMD和ARIMA的海温预测模型研究

类型丰富、时空分辨率高的海洋探测数据,为信号分解和机器学习算法的应用提供了可能。本文针对如何建立有效的海温预测模型这一问题,使用高时空分辨率的海表温度(SST)融合产品,引入信号处理领域的集合经验模态分解(EEMD)和机器学习领域的自回归积分滑动平均模型(ARIMA)。首先利用最适于分解自然信号的EEMD方法,将海温数据分解成多个确定频率的序列;再利用ARIMA分别对各个频率的序列进行预测,最后将各个序列的预测结果进行组合。该方法在丰富数据的支撑下,比以往直接使用海温数据所建立的预测模型精度更高,为更好地进行海温预测提供了新方法。     

基于无人机多源遥感数据海岛植被叶面积指数协同反演研究

无人机多源遥感数据的获取、融合以及应用是当今研究的热点和难点。文中以城洲岛为例,针对海岛特殊的地理生态环境,获取无人机多源遥感数据。结合无人机多光谱遥感数据定量分析各遥感植被指数与植被叶面积指数(Leaf Area Index, LAI)的响应关系,构建单因子遥感反演模型;基于无人机激光LiDAR点云提取海岛植被冠层高度模型(Canopy Height Model,CHM),并将其作为自变量引入到多源统计回归分析中,从而构建多源遥感数据协同反演模型,对区域尺度下海岛叶面积指数(LAI)进行估算,开展验证和精度评价。结果显示,加入植被冠层高度因子的协同反演模型的判定系数R2为0.92,绝对平均误差系数为12.29%,预测精度要优于单因子反演模型(判定次数R2为0.86,绝对平均误差系数19.95%)。研究表明,加入了植被冠层高度因子的协同反演模型能在一定程度上提高乔木植被LAI的预测精度。实践证明,无人机多源遥感技术在生态学定量研究中具有巨大的潜力和广阔的应用前景。     

长江口外潮汐精细化模型研究与精度分析

长江口外海上测量除受风浪影响较大外,最重要的问题是潮位控制非常困难。文中简要阐述了开展长江口外潮汐精细化模型研究的方法,介绍了利用潮汐精细化模型对长江口外航路任意点进行潮汐预报的方法,并通过实测数据进行了精度分析,提出了建议。     

不等时距灰色模型在深基坑变形预测中的应用

传统的灰色模型多适用于等间距序列监测数据的模拟预测,而实际上由于各种原因往往使所获得的监测数据是不等时距的。研究了基于不等时距灰色预测方法的深基坑变形预测模型,应用深基坑工程变形的实际监测资料,对其预测精度及可行性进行了充分的分析比较与论证。结果表明,不等时距灰色模型预测深基坑变形的精度及可信度较高。