变分同化中使用背景场时尺度匹配的数值研究

分资料同化方法运用于中尺度系统的分析时，粗网格模式的输出经常被用作背景场，此时分析场和背景场之间存在尺度不一致的问题。为了让二者的尺度相匹配，文中提出可以在目标函数中增加一个过滤算子适当滤除分析场中的短波成分。应用浅水方程模式和模拟资料所作的数值试验表明，该方法可以明显地改善分析的效果。     

有限区域伴随模式中适定空间边界条件的理论研究

从理论上论证了借助于同伦方法构造的适定空间边界条件确保有限区域上伴随模式产生的超定边界条件问题得到解决，同时又能维持伴随模式中边界处理的优化特征。从某种意义上讲，伴随模式超定空间边界条件的存在是不可避免的，这是因为数据同化过程必须引进和采用给定的观测资料，而它们在模式空间边界上的定义往往是超定的。我们提出的空间边界条件的算法构架事实上是在数据同化过程中综合运用了张弛滤波、考虑外部强迫的辐射边界条件以及与观测相容的狄里希利边界条件。显然，对于该理论构架所涉及到的具体数值处理方法在中尺度模式中都十分成熟易行。     

海洋模式对1995年5月南海AXBT海温资料的同化分析

利用美国普林斯顿大学海洋模式 (POM )对 1995年 5月南海AXBT海洋观测资料和MISST卫星观测的海面温度进行模式四维同化分析研究。POM模式同化处理使用最优插值分析与逼近方法 ,从模式积分到第 4年开始 ,使用COADS气候平均风应力场、净热通量场强迫海洋 ,从第 5年 5月开始加入 1995年 5月的MISST和AXBT资料同化积分 30d ,然后对同化结果进行分析。结果表明 ,资料同化可以改进模式对温度场的预报 ,也可改进模式对海流的模拟。分析结果还显示 ,1995年 5月南海浅层有暖涡和冷涡形成     

数值预报/卫星反演/数据同化循环试验

文中介绍了数值预报／卫星反演／数据同化的循环试验。由于湿度反演方法的改进，使得湿度反演产品的精度有所提高，所以首先进行了湿度廓线参与同化过程的试验。结果表明，反演的水汽产品对台风中心强度有正影响，经过４次同化过程２４ｈ预报模拟的台风加深与实况比较一致。４８ｈ预报台风中心减弱趋势在低层显得不足，但比业务预报有明显改进。２４与４８ｈ预报的总降水量和暴雨中心强度比未加入反演水汽产品的预报有明显增加，且接近实况。     

ASSIMILATION METHOD FOR RETRIEVING HORIZONTAL WIND FIELD FROM SINGLE-DOPPLER DATA

Doppler weather radar can provide the wind information such as the radial velocity,and hasbecome one of the most powerful means for studies of severe mesoscale and microscale weathersystems.Usually the radar observation provides only plane position indicator(PPI)scanning dataand range height indicator(RHI)scanning data.and this makes some retrieval methods usingthree-dimensional volume scanning data impractical in operation.In order to use these data moreefficiently,a plane assimilation retrieval(PAR)method is developed using data assimilationtechnique on the PPI scanning plane.The PAR method is tested with the observation data ofChinese Academy of Meteorological Sciences(CAMS)Doppler weather radar and the result isencouraging.     

GPS ZTD资料同化对台风“利奇马”模拟的影响研究

2019年超级台风“利奇马”对我国东部地区造成了巨大灾害,针对台风“利奇马”,研究GPS ZTD资料同化对于台风“利奇马”登陆后降水预报的影响。在进行GPS ZTD资料同化试验前,采取了稳定性检查、极值检查、双权重检查、偏差订正检查等质量控制方法以改善资料的同化应用水平。模拟试验结果表明在同化探空、风廓线等常规资料基础上,增加质控GPS ZTD有效改善了初始场的水汽条件,使得低层水汽辐合更强,从而改进了台风外围雨带的预报。并且通过循环同化能够改善初始环流场,进而改进了台风路径的预报使得台风主体降水的位置得到改善。     

两种岩石地球化学图解在确定岩石同化,混染成因方面的应用

首先介绍了两种简易岩石地球化学图解，即元素，元素对比值混合方程和混染岩，混染端员元素浓度差相关性图解。进而应用这两种图解配合其它地质事实共同验证了冀东迁西上营－十八盘地区的云英闪长质片麻岩是由奥长花岩是由奥长花岗岩浆对角闪质岩石不同程度同化，混染形成的。     

Application of the distributed parameter filter to predict simulated tidal induced shallow water flow

Distributed parameter filtering theory is employed for estimating the state variables and associated error covariances of a dynamical distributed system under highly random tidal and meteorological influences. The stochastic-deterministic mathematical model of the physical system under study consists of the shallow water equations described by the momentum and continuity equations in which the external forces such as Coriolis force, wind friction, and atmospheric pressure are considered. White Gaussian noises in the system and measurement equations are used to account for the inherent stochasticity of the system. By using an optimal distributed parameter filter, the information provided by the stochastic dynamical model and the noisy measurements taken from the actual system are combined to obtain an optimal estimate of the state of the system, which in turn is used as the initial condition for the prediction procedure. The approach followed here has numerical approximation carried out at the end, which means that the numerical discretization is performed in the filtering equations, and not in the equations modelling the system. Therefore, the continuous distributed nature of the original system is maintained as long as possible and the propagation of modelling errors in the problem is minimized. The appropriateness of the distributed parameter filter is demonstrated in an application involving the prediction of storm surges in the North Sea. The results confirm excellent filter performance with considerable improvement with respect to the deterministic prediction.     

Application of the distributed parameter filter to predict simulated tidal induced shallow water flow

Distributed parameter filtering theory is employed for estimating the state variables and associated error covariances of a dynamical distributed system under highly random tidal and meteorological influences. The stochastic-deterministic mathematical model of the physical system under study consists of the shallow water equations described by the momentum and continuity equations in which the external forces such as Coriolis force, wind friction, and atmospheric pressure are considered. White Gaussian noises in the system and measurement equations are used to account for the inherent stochasticity of the system. By using an optimal distributed parameter filter, the information provided by the stochastic dynamical model and the noisy measurements taken from the actual system are combined to obtain an optimal estimate of the state of the system, which in turn is used as the initial condition for the prediction procedure. The approach followed here has numerical approximation carried out at the end, which means that the numerical discretization is performed in the filtering equations, and not in the equations modelling the system. Therefore, the continuous distributed nature of the original system is maintained as long as possible and the propagation of modelling errors in the problem is minimized. The appropriateness of the distributed parameter filter is demonstrated in an application involving the prediction of storm surges in the North Sea. The results confirm excellent filter performance with considerable improvement with respect to the deterministic prediction.     

Assimilation of the FY-4A AGRI Clear-Sky Radiance Data in a Regional Numerical Model and Its Impact on the Forecast of the “21·7” Henan Extremely Persistent Heavy Rainfall

Assimilation of the Advanced Geostationary Radiance Imager(AGRI) clear-sky radiance in a regional model is performed. The forecasting effectiveness of the assimilation of two water vapor(WV) channels with conventional observations for the “21·7” Henan extremely heavy rainfall is analyzed and compared with a baseline test that assimilates only conventional observations in this study. The results show that the 24-h cumulative precipitation forecast by the assimilation experiment with the addition ...