根据海洋中的盐度预测降雨

伍兹霍尔海洋研究所的科学家雷·施密特和他的两个双胞胎儿子在降雨预报竞赛中获得最高奖。7~10天的天气预测,能够帮助我们规划我们的日常生活,但是当涉及季节或者数周的天气预测时.     

夏季降雨过程对南海上层盐度的可能影响

根据对1998年南海中部、北部航次的海水盐度观测资料以及GPCP-1DD(Global Precipitation Climatology Project,1 Degree Daily Precipitation Estimate)降雨资料,研究了夏季风期间降雨过程对南海上层盐度的可能影响。个例和合成分析表明,当有降雨过程发生时,降雨对南海上层盐度的影响深度为85—110m,上层盐度恢复所需要的时间为14—42d左右。此外还利用NCEP/NCAR(National Center for Environmental Prediction/National Center for Atmospheric Research)再分析资料的逐月风场资料以及基于法国AVISO(Archiving,Validation and Interpretation of Satellite Oceanographic Data)提供的融合海平面高度异常资料计算的地转流场分析平流效应对其可能产生的影响。     

降雨导致的海面粗糙度对Aquarius卫星盐度反演的影响研究

Rainfall has two significant effects on the sea surface, including salinity decreasing and surface becoming rougher,which have further influence on L-band sea surface emissivity. Investigations using the Aquarius and TRMM 3B42 matchup dataset indicate that the retrieved sea surface salinity(SSS) is underestimated by the present Aquarius algorithm compared to numerical model outputs, especially in cases of a high rain rate. For example, the bias between satellite-observed SSS and numerical model SSS is approximately 2 when the rain rate is 25 mm/h. The bias can be eliminated by accounting for rain-induced roughness, which is usually modeled by rain-generated ring-wave spectrum. The rain spectrum will be input into the Small Slope Approximation(SSA) model for the simulation of sea surface emissivity influenced by rain. The comparison with theoretical model indicated that the empirical model of rain spectrumis more suitable to be used in the simulation. Further, the coefficients of the rain spectrum are modified by fitting the simulations with the observations of the 2–year Aquarius and TRMM matchup dataset. The calculations confirm that the sea surface emissivity increases with the wind speed and rain rate. The increase induced by the rain rate is rapid in the case of low rain rate and low wind speed. Finally, a modified model of sea surface emissivity including the rain spectrum is proposed and validated by using the matchup dataset in May 2014. Compared with observations, the bias of the rain-induced sea surface emissivity simulated by the modified modelis approximately 1e–4, and the RMSE is slightly larger than 1e–3. With using more matchup data, thebias between model retrieved sea surface salinities and observationsmay be further corrected,and the RMSE may be reduced to less than 1 in the cases of low rain rate and low wind speed.     

RBF网络和BP网络在海水盐度建模中的比较研究

介绍了RBF神经网络模型结构、特点及原理,并针对海水盐度参数具有受诸多因素影响的复杂的非线性输入输出特性,训练并建立了海水盐度的RBF(Radial Basis Function)神经网络模型,为海水盐度的预测提供了一种新的方法.与BP神经网络模型相比.该模型具有收敛速度快,精度高的优点.比较结果表明,该方法在海水盐度...     

海洋环境预测中的关键科学问题

国家海洋环境预报中心经过40年的发展,海洋环境预报事业初具规模.本文主要分别从海洋环境预报多个预报要素出发,结合当前的国内外发展趋势,介绍各种海洋环境预报所面临的关键科学技术问题和挑战.     

海洋溢油污染预测预警技术

本项目对溢油应急体系建设与溢油污染预测预警关键技术进行攻关,为海上溢油应急快速反应、指挥决策和控制处理提供技术支持。自主研发渤海海域溢油漂移轨迹动态快速预报模型和溢油漂移轨迹及归宿模型;     

集合预报及其在季节尺度气候预测中的应用

系统回顾了集合预报技术的发展,介绍了4种比较常用的集合构建方法,重点关注这些方法在季节尺度气候预测领域的研究进展及应用,特别是在ENSO集合预报领域。此外,还从季节尺度气候预测系统的发展、集合设计方案和ENSO集合预报效果等方面介绍了国内外3个应用比较广泛的季节尺度气候预测系统,可为集合预报技术和季节尺度气候预测系统的发展和应用提供参考。