联合卷积神经网络与集成学习的遥感影像场景分类

针对人工设计的中、低层特征难以实现复杂场景影像的高精度分类以及卷积神经网络依赖大量训练数据等问题，结合迁移学习与集成学习，提出了一种联合卷积神经网络与集成学习的遥感影像场景分类算法。首先基于迁移学习的思想，利用在自然影像数据集上训练好的多个深层卷积神经网络模型作为特征提取器，提取图像多个高度抽象的语义特征；然后构建由Logistic回归和支持向量机组成的Stacking集成模型，对同一图像的多个特征分别训练Logistic模型，将预测概率结果融合构建概率特征；最后利用支持向量机对概率特征训练和预测，得到场景影像的分类结果。利用UCMerced_LandUse和NWPU-RESISC 45两种不同规模的遥感影像数据集进行试验，即使在只有10%的数据作为训练样本情况下，本文方法能够分别达到90.74%和87.21%的分类精度。     

SWCWARMS模式小时降水量时间滞后集合预报研究

基于SWCWARMS模式9km×9km分辨率逐小时降水预报,通过时间滞后集合预报方法构建多个集合成员,使用SAL评分值计算相应集合成员的权重系数,进行不等权集合平均,从而得到新的逐小时降水预报。利用SAL、TS和BIAS检验方法对四川省2019年8月的逐小时降水量时间滞后集合预报及相应的SWCWARMS模式最新时次预报进行对比分析,结果表明:(1)时间滞后集合预报SAL检验的L值和A值都较模式预报更接近于0,较好地改善了降水位置和强度的整体预报水平;(2)时间滞后集合预报对逐小时降水晴雨TS评分提升明显,评分提高百分率在10%左右,有效地减小了模式在晴雨方面的空报;(3)临近预报时效,时间滞后集合预报方法对于大量级降水预报也有较好的订正效果。     

基于集成学习的阶跃型滑坡阶跃点判别分析

针对阶跃型滑坡阶跃点识别和预测难的问题,提出了一种基于聚类分析和集成学习的阶跃型滑坡阶跃点识别和判别模型。以三峡库区八字门滑坡ZG110钻孔2010年4月至2016年12月80个滑坡位移、库水位和降雨数据为例,通过聚类分析方法识别滑坡累积位移-时间曲线中的阶跃点和平稳点,并利用K均值聚类分析检验分类结果的准确性。基于灰色关联确定了滑坡位移的最佳诱发因素,结合随机森林模型建立阶跃型滑坡阶跃点判别模型并利用八字门滑坡ZG111钻孔验证该模型的准确性。模型阶跃点和平稳点的识别准确率均达90%以上,表明该方法在阶跃型滑坡识别中具有较好的适用性,可为阶跃型滑坡的预测提供参考。     

Sub-seasonal to Seasonal Hindcasts of Stratospheric Sudden Warming by BCC_CSM1.1(m):A Comparison with ECMWF

This study focuses on model predictive skill with respect to stratospheric sudden warming(SSW) events by comparing the hindcast results of BCC_CSM1.1(m) with those of the ECMWF's model under the sub-seasonal to seasonal prediction project of the World Weather Research Program and World Climate Research Program. When the hindcasts are initiated less than two weeks before SSW onset, BCC_CSM and ECMWF show comparable predictive skill in terms of the temporal evolution of the stratospheric circumpolar westerlies and polar temperature up to 30 days after SSW onset. However, with earlier hindcast initialization, the predictive skill of BCC_CSM gradually decreases, and the reproduced maximum circulation anomalies in the hindcasts initiated four weeks before SSW onset replicate only 10% of the circulation anomaly intensities in observations. The earliest successful prediction of the breakdown of the stratospheric polar vortex accompanying SSW onset for BCC_CSM(ECMWF) is the hindcast initiated two(three) weeks earlier. The predictive skills of both models during SSW winters are always higher than that during non-SSW winters, in relation to the successfully captured tropospheric precursors and the associated upward propagation of planetary waves by the model initializations. To narrow the gap in SSW predictive skill between BCC_CSM and ECMWF, ensemble forecasts and error corrections are performed with BCC_CSM. The SSW predictive skill in the ensemble hindcasts and the error corrections are improved compared with the previous control forecasts.     

应用IAP9L-AGCM对2002年中国夏季气候的预测及效果检验

利用中科院大气所9层大气环流格点模式(IAP9L—AGCM)和IAP—ENSO预测系统对2002年中国夏季气候进行实时集合预测及其检验。结果显示，IAP9L—AGCM较好地预测出了2002年夏季我国大范围旱涝的分布形势，如华南、我国西部多雨，黄河和长江流域之间大范围干旱等；850hPa减弱的夏季风、青藏高原辐散中心以及北太平洋上空的异常气旋性环流中心亦被较好地预报出来；不足的是，模式对降水异常细致分布的预测能力有限。预测结果还表明，该模式对夏季(6—8月)平均降水的预报技巧要高于月平均状况，且月平均预报的准确度从6—8月依次递减。     

Ensemble simulation of land evapotranspiration in China based on a multi-forcing and multi-model approach

In order to reduce the uncertainty of offline land surface model(LSM) simulations of land evapotranspiration(ET), we used ensemble simulations based on three meteorological forcing datasets [Princeton, ITPCAS(Institute of Tibetan Plateau Research, Chinese Academy of Sciences), Qian] and four LSMs(BATS, VIC, CLM3.0 and CLM3.5), to explore the trends and spatiotemporal characteristics of ET, as well as the spatiotemporal pattern of ET in response to climate factors over mainland China during 1982–2007. The results showed that various simulations of each member and their arithmetic mean(Ens Mean) could capture the spatial distribution and seasonal pattern of ET sufficiently well, where they exhibited more significant spatial and seasonal variation in the ET compared with observation-based ET estimates(Obs MTE). For the mean annual ET, we found that the BATS forced by Princeton forcing overestimated the annual mean ET compared with Obs MTE for most of the basins in China, whereas the VIC forced by Princeton forcing showed underestimations. By contrast, the Ens Mean was closer to Obs MTE, although the results were underestimated over Southeast China. Furthermore, both the Obs MTE and Ens Mean exhibited a significant increasing trend during 1982–98; whereas after 1998, when the last big EI Ni ?no event occurred, the Ens Mean tended to decrease significantly between 1999 and 2007, although the change was not significant for Obs MTE. Changes in air temperature and shortwave radiation played key roles in the long-term variation in ET over the humid area of China, but precipitation mainly controlled the long-term variation in ET in arid and semi-arid areas of China.     

Impact of Assimilating Radiances with the WRFDA ETKF/3DVAR Hybrid System on Prediction of Two Typhoons in 2012

The impacts of AMSU-A and IASI (Infrared Atmospheric Sounding Interferometer) radiances assimila-tion on the prediction of typhoons Vicente and Saola (2012) are studied by using the ensemble transform ...     

基于TIGGE资料的地面气温多模式超级集合预报

基于TIGGE资料, 采用均方根误差分别对欧洲中期天气预报中心、日本气象厅、美国国家环境预报中心和英国气象局4个中心集合预报的地面气温场集合平均结果进行检验评估, 比较各中心地面气温的预报效果。并利用超级集合、多模式集合平均和消除偏差集合平均3种方法对4个中心的地面气温预报进行集成, 同时对预报结果进行分析。结果表明: 2007年夏季日本气象厅与欧洲中期天气预报中心在北半球大部分地区预报效果最好, 各中心在不同地区预报效果不同。超级集合与消除偏差集合平均降低了预报误差, 预报效果优于最好的单个中心预报和多模式集合平均。对于较长的预报时效, 消除偏差集合平均表现出了更好的预报性能。     

GRAPES区域集合预报模式的初值扰动增长特征

基于GRAPES-REPS（Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System）区域集合预报模式和集合变换卡尔曼滤波（Ensemble Transform Kalman Filter,ETKF）初值扰动方法,对2015年6月1~15日10 km与15 km水平分辨率分别进行集合预报试验,通过分析ETKF初值扰动分量、初值扰动方差准确率、动能谱、扰动能量演变、日变化及集合离散度、均方根误差等特征,揭示GRAPES-REPS区域集合预报ETKF初值扰动结构及增长特征。结果表明:（1）ETKF初值扰动方案产生的扰动能够保持所有正交、不相关方向的误差方差,且ETKF分量α参数值及放大因子具有较好的稳定性。（2）ETKF初值扰动方法生成的扰动场以大尺度扰动为主,扰动结构及能量具有随流型依赖特征,低层以内能扰动为主,高层以动能扰动为主,且集合扰动可以有效捕捉预报误差的结构。（3）GRAPES区域集合预报初值扰动总能量和集合离散度随预报时效的延长均呈发展趋势,但离散度增长率小于均方根误差增长率,即集合预报总体存在集合离散度不足的问题。（4）水平分辨率提高可以增加中高层大尺度扰动波谱能量,明显改进等压面及近地面风场及温度场的集合预报效果。值得指出的是,GRAPES-REPS区域集合预报低层内能扰动能量存在明显的日变化特征,特别是青藏高原地区更加显著,需要进一步研究青藏高原初值扰动结构的合理性。     

Impact of Coastal Radar Observability on the Forecast of the Track and Rainfall of Typhoon Morakot(2009)Using WRF-based Ensemble Kalman Filter Data Assimilation

This study explored the impact of coastal radar observability on the forecast of the track and rainfall of Typhoon Morakot(2009)using a WRF-based ensemble Kalman filter(EnKF)data assimilation(DA)system.The results showed that the performance of radar EnKF DA was quite sensitive to the number of radars being assimilated and the DA timing relative to the landfall of the tropical cyclone(TC).It was found that assimilating radial velocity(Vr)data from all the four operational radars during the 6 h immediately before TC landfall was quite important for the track and rainfall forecasts after the TC made landfall.The TC track forecast error could be decreased by about 43% and the 24-h rainfall forecast skill could be almost tripled.Assimilating Vr data from a single radar outperformed the experiment without DA, though with less improvement compared to the multiple-radar DA experiment.Different forecast performances were obtained by assimilating different radars, which was closely related to the first-time wind analysis increment, the location of moisture transport, the quasi-stationary rainband, and the local convergence line.However, only assimilating Vr data when the TC was farther away from making landfall might worsen TC track and rainfall forecasts.Besides, this work also demonstrated that Vr data from multiple radars, instead of a single radar, should be used for verification to obtain a more reliable assessment of the EnKF performance.