在NCEPGDAS中同化MSG和GOES资料

首次将MSG-2（Meteosat Second Generation-2）卫星上的旋转增强可见光及红外成像仪（Spin—ning Enhanced Visibleand Infrared Imager,SEVIRI）的观测资料同化到美国国家环境预报中心（Na—tional Centers for Environmental Prediction,NCEP）全球资料同化系统（globaldataassimilationsys—tern,GDAS）中。对当前的地球静止业务环境卫星（Geostationary Operational Environmental Satel—lite,GOES）成像仪资料的同化问题也进行了进一步探讨。利用CRTM（The Community Radiative Transfer Model）模式,对SEVIRI辐射率观测资料进行了模拟。为了对红外辐射率资料进行模拟,CRTM模式中的几个关键部分得到改进,例如：动态更新地面发射率资料以及采用了快速精确的气体吸收模块。为了改进对SEVIRI和GOES成像仪辐射率资料的模拟效果,采用了GSICS（The Global Space—Based Inter—Calibration System）标定订正。初步研究结果表明,包含对SEVIRI辐射率资料的水汽通道（6．25和7．35μm）和二氧化碳通道（13．40μm）的同化对GFS（Global Forecast System）6d预报具有显著的正影响;而对其他5个SEVIRI红外窗口通道资料的同化则减小了这种正影响。通过应用GSICS标定算法,订正了SEVIRI和GOES-12成像仪观测资料的偏差,提高了对GFS预报的影响。此外,还需作进一步研究来提高对SEVIRI红外窗口通道辐射率资料同化的有效性。     

Further evidences of deep convective vertical transport of water vapor through the tropopause

A few years ago, we identified a deep convective transport mechanism, of water vapor through the tropopause, namely, storm top gravity wave breaking, such that tropospheric water substance can be injected into the lower stratosphere via this pathway. The main evidence presented previously was taken from the lower resolution AVHRR images of the storm anvil top cirrus plumes obtained by polar orbiting satellites. Recent observations have provided further supporting evidence for this important cross-tropopause transport mechanism. There are now many higher resolution satellite images, mainly from MODIS instrument, that show more definitely the existence of these plumes, many of which would probably be unseen by lower resolution images.Furthermore, a thunderstorm movie taken in Denver (USA) area during STEPS2000 field campaign and another thunderstorm movie taken by a building top webcam in Zurich also demonstrate that the jumping cirrus phenomenon, first identified by T. Fujita in 1980s, may be quite common in active thunderstorm cells, quite contrary to previous belief that it is rare. We have used a cloud model to demonstrate that the jumping cirrus is exactly the gravity wave breaking phenomenon that transports water vapor through the tropopause.These additional evidences provide increasing support that deep convection contributes substantially to the troposphere-to-stratosphere transport of water substance. This corroborates well with recent studies of the stratospheric HDO/H₂O ratio which is much highly than it would be if the transport is via slow ascent. The only explanation that can be used to interpret this observation at present is that water substance is transported through the tropopause via rapid vertical motion, i.e., deep convection.     

基于深度学习的FY3D/MERSI和EOS/MODIS云检测模型研究

针对FY3D/MERSI和EOS/MODIS的云检测问题,提出了一种基于深度学习技术的全自动云检测算法,首次将深度学习引入到卫星影像云检测领域。本算法使用深度全卷积神经网络(Deep Convolutional Neural Networks)作为核心结构,基于EOS/MODIS基本云检测原理选择合适的通道作为特性向量参数,针对不同的场景进行分类和网络模型的训练,最终得到基于深度学习的云检测模型。经过EOS/MODIS数据和FY3D/MERSI数据的测试,云检测的精度达到98%以上,可以看出基于深度学习的云检测算法能够用于云检测,该算法具有效率高、精度高等特点,云检测效果理想。