数值模式的预报策略和方法研究进展

数值预报经历了半个多世纪的发展,已成为当前主要的客观预报工具。在模式和资料状况给定的情况下,预报效果的改善很大程度上依赖于所采用的预报策略和方法。为此,全面回顾了国内外基于数值模式的预报策略和方法研究进展,认为采取统计—动力相结合、从历史资料中提炼信息的预报策略是提高数值预报水平的可行之路。最后在总结前人工作基础上,着重介绍了动力相似预报策略和方法的相关研究,特别是实际预报中的试验情况。     

海洋生态预报的复杂性与研究方法的讨论

海洋生态动态预报预测研究已经成为海洋科学乃至地球系统科学领域中的热点问题。比较深入地分析了海洋生态预报的复杂性、不确定性和实况监测资料严重不足等问题,为促进海洋生态预报研究的快速发展,借鉴中期数值天气预报的一些有效方法,提出以下建议：①加深理解海洋生态系统的非线性动力学特征,深入开展随机-动力耦合的海洋生态系统模型研究;②加强海洋生态集合（ensemble）预报和综合预报方法研究;③大力促进卫星遥感信息的海洋生态应用研究,加强资料同化研究和反问题研究方法的应用,努力发掘各种信息资料的预报应用。     

全天候卫星微波观测资料变分同化研究进展

云和降水区微波观测包含大量与天气系统,特别是台风、暴雨等灾害性天气系统发生发展密切相关的大气信息,因此微波资料的全天候同化应用成为当前数值预报领域的热点研究问题。过去20年间,全球几大数值预报中心逐步开展了全天候同化技术的研究和业务应用,证实了全天候卫星微波观测资料能够改进模式中的质量、风场、湿度以及云和降水场的初始信息,从而改进数值预报模式的预报效果。通过梳理和评述全天候卫星微波观测资料同化方法,分析其中的关键技术问题和目前存在的困难和挑战,为未来在我国数值天气预报领域开展全天候同化研究提供依据。随着我国新一代数值天气预报模式的发展应用,加强我国全天候资料同化技术的研究将会在业务中发挥更大的科学效益和应用效益。     

海洋可预报性和集合预报研究综述

海洋是高度复杂的非线性动力系统,由于海洋初值和数值模式本身存在无法避免的误差,海洋数值预报具有不确定性。通过理解和认识海洋不同时空尺度运动的特征和规律,定量估计和预测海洋动力系统的可预报性,研究预报误差产生的原因及其增长和传播机制,探讨减小预报误差的方法和延长可预报时限的途径,对于改进海洋预报系统、提高预报技巧,具有重要意义。系统回顾了海洋可预报性及其应用的研究进展,论述了海洋可预报性的概念、分类以及国内外的研究现状,其中重点介绍了常用的奇异向量法、李亚普诺夫指数法和繁殖向量法等3种动力学方法以及海洋集合预报研究现状,最后对海洋可预报性的未来发展方向和应用前景给以展望。     

无资料地区水文预报研究的方法与出路

无资料地区的水文研究是最近国际上水资源水环境研究的热点.总结了中国过去在流域洪水预报和设计洪水计算中针对无资料地区研究所取得的相关成果,指出现代水文学应该充分运用新的系统理论和方法,依靠计算机技术和遥感手段,在水文数值模拟方向上寻找无资料地区水文预报的新出路.对无资料地区的水文研究要从有充分实测资料的流域开始,人为隐去部分站点的实测数据以检验依靠剩余少量数据而建成的水文模型的适用性和精度.最后分析了流域分布式水文模型、空间插值、四维同化技术等方法在无资料地区水文预报中的意义和作用.     

集合—变分数据同化方法的发展与应用

近年来,集合—变分数据同化方法已成为大气数据同化领域研究的热点问题.该方法能够综合利用集合卡尔曼滤波和变分同化的优势,是实现“集合预报和数据同化一体化”的有效途径.在分析变分同化和集合卡尔曼滤波优缺点的基础上引出集合—变分数据同化的概念;按照不同实现方式,将集合—变分同化分为协方差线性组合和增加控制变量2类,介绍了相应的研究进展,并将集合—变分同化概念拓展;然后介绍了集合—变分同化在英美两国的应用;最后回顾了集合—变分同化研究的主要问题,展望了未来的发展趋势.     

基于DEM的集总式水文预报模型产流结构生成

降水数值预报和水文预报的耦合是水文研究的热点问题之一,在避免水文预报模型开发的前提下,本文探讨了如何利用现有的集总式水文预报模型,实现两个系统耦合的问题。提出了在DEM资料提取的数字流域基础上,人工干预子流域和泰森多边形的生成方法,使生成的子流域、泰森多边形与原集总式的水文预报方案划分情况基本一致,在产流层面解决了降水数值预报和集总式水文预报方案耦合的技术问题,为降水数值预报成果在水文预报中广泛应用创造了条件。该方法在潘家口水库的水文预报中应用,取得了较好的效果。     

论地震数值预报——关于我国地震预报研究发展战略的思考

随着中国工业化和社会现代化进程的加快 ,中国城市化进程必将进一步加速。如何减轻地震灾害的问题正变得日益严峻。尽管地震预测是一个举世公认的国际性科学难题 ,但在强化各种减轻地震灾害措施的同时 ,仍须大力推进地震预测研究。为此 ,需要打破长期徘徊在以地震前兆异常监测为基础的经验性预测局面 ,把注意力尽快转向研究以动力学为基础的数值预报。以GPS为代表的空间对地观测技术 ,岩石圈巨型高分辨率宽频带流动地震台阵观测技术以及数值模拟技术已经为地震数值预报研究提供了前所未有的技术基础。以地震数值预报为目标的GPS阵列地壳形变连续观测 ,高分辨率地壳上地幔结构探测 ,地壳动力学 ,地震孕育和破裂过程的理论、模拟试验和实际观测 ,数据同化和计算软件的开发应成为今后研究的重点。现在的问题是 ,需要积极借助数值天气预报的经验 ,强化多学科 ,多部门的组织协调 ,尽早在有条件的地区开展地震动力学数值预报的科学试验。地震数值预报研究必将极大地促进中国地震科学基础研究和地震预报的进一步发展。     

中国水文预报技术的发展与展望

介绍了我国水文预报技术在预报方法、预报系统建设和新技术应用等方面取得的进展，客观评价了我国水文预报技术的水平．展望了我国水文预报技术的发展方向，指出了干旱、半干旱及无资料地区或资料缺乏地区的水文模拟将是我国进一步提高水文预报技术的研究重点。     

滑坡稳定性评价和监测预报常用方法综述

本文简要评述了滑坡稳定性评价常用的极限平衡法、数值法和概率法,介绍了用于滑坡监测的常用仪器与技术,以及典型的滑坡预报模型;并就滑坡研究的发展趋势做了初步展望.     

云分析预报方法研究进展

云作为地球大气系统的重要组成部分,不仅影响着气候变化和天气系统的发展演变,还与航空活动密切相关,一直以来是空军和民航部门非常关注的气象要素之一。在云探测、资料同化和反演方法发展的基础上,从实际业务保障和数值模式发展需求出发,综述国内外云分析、预报方法和云分析预报系统开发的研究成果,分析各类方法的优势和不足,明确国内外研究的主要差距,并探讨国内未来研究的方向。云分析方法中,探空对云廓线识别较好,卫星可见光和红外资料在云顶信息反演方面优势明显,多普勒雷达能够获取对流层中层和底层的云信息,而毫米波雷达能够很好地反映云三维结构信息,发展潜力巨大。云预报方法中,传统的统计和诊断方法发展较为成熟,而考虑了大气温湿和云微物理状况的大气辐射传输模式正演模拟云顶亮温的方法是未来的发展趋势。加强云探测技术,综合利用云分析预报方法,借鉴国外先进云分析预报系统的设计理念,积极开发我国自主的云分析预报系统,推动天气预报、航空气象保障和数值预报模式的发展将会是我国云研究的重要方面。     

海上大气波导研究进展

大气波导是对流层中具有异常大气折射率梯度的大气层,对于评估和预测电磁波传播和海上探测通信系统等具有重要的科学意义和应用价值.以海上发生的大气波导类型为线索总结了与大气波导相关的研究方法.在蒸发波导研究中以相似理论为基础,开发蒸发波导诊断模型为重点,开展区域海域适应性研究;海上悬空波导和表面波导从早期的定性分析到目前精确定量研究过程中,中尺度数值模式逐渐成为极其重要的研究手段,不仅提高了特定天气过程中大气波导模拟预测精度,而且在此基础上开展区域大气波导环境研究,分析其出现规律、气候原因等.针对海上大气波导研究现状,借鉴气象上的手段和技术,开展海上水文气象调查和电波传播实验,结合中尺度数值模式和海气耦合模式,采用同化技术和集合预报等手段,提高海上低空大气波导量化精度.     

Assimilation of Doppler weather radar observations in a mesoscale model for the prediction of rainfall associated with mesoscale convective systems

Obtaining an accurate initial state is recognized as one of the biggest challenges in accurate model prediction of convective events. This work is the first attempt in utilizing the India Meteorological Department (IMD) Doppler radar data in a numerical model for the prediction of mesoscale convective complexes around Chennai and Kolkata. Three strong convective events both over Chennai and Kolkata have been considered for the present study. The simulation experiments have been carried out using fifth-generation Pennsylvania State University-National Center for Atmospheric Research (PSU-NCAR) mesoscale model (MM5) version 3.5.6. The variational data assimilation approach is one of the most promising tools available for directly assimilating the mesoscale observations in order to improve the initial state. The horizontal wind derived from the DWR has been used alongwith other conventional and non-conventional data in the assimilation system. The preliminary results from the three dimensional variational (3DVAR) experiments are encouraging. The simulated rainfall has also been compared with that derived from the Tropical Rainfall Measuring Mission (TRMM) satellite. The encouraging result from this study can be the basis for further investigation of the direct assimilation of radar reflectivity data in 3DVAR system. The present study indicates that Doppler radar data assimilation improves the initial field and enhances the Quantitative Precipitation Forecasting (QPF) skill.     

Improvement in predictability of waves over the Indian Ocean

Accurate prediction of ocean surface waves is a challenging task with many associated difficulties. Availability of good quality wind and wave information from satellite platforms inspired the scientific community to assimilate such data in various spectral wave models for enhancing the accuracy of prediction. Over the Indian Ocean, which is the region of interest for the present study, wave heights in extreme situation can go up to 12–14 m, thereby increasing the probability of coastal hazards. This region is further governed by the southern ocean swells that propagate thousands of kilometers. These are, in general, not well captured by the spectral wave models. Therefore, assimilation of altimeter data in open ocean wave model WAM has been attempted with the aim of enhancing the quality of prediction of significant wave height. Further, simulated wave spectra have been assimilated in a coastal wave model SWAN. This assimilation has been found to significantly improve the prediction of the height of wind waves as well as swell waves. V. Bhatt and S. Surendran are former students of Meteorology and Oceanography Group, Space Applications Centre, ISRO, Ahmedabad.     

Research Progress of Quality Control for AIRS Data

Satellite data assimilation can provide accurate initial field for Numerical Weather Prediction (NWP) models. So far, data variational assimilation is based on the theory where error obeys Gaussian distribution, so as to apply the least square method. During classical variational assimilation, if the data contain outliers, the results of optimal parameter estimation is meaningless. Therefore, quality control is quite necessary for Atmospheric Infrared Sounder (AIRS) data before data assimilation. This paper made a comment of the advances in the quality control using AIRS data, which analyzed and discussed the research status from five aspects: channel selection, outliers elimination, bias correction, cloud detection and data sparseness. Three methods for channel selection were summarized, which are stepwise iterative method based on information entropy, the cumulative effect coefficient of principal component and principal components—Stepwise regression, respectively. Comparatively, stepwise iterative method based on information entropy is more widely used, but the selected channels are weak related; Channel combination with large amount of information can be obtained through the method of principal components—stepwise regression, but the implementation process is time-consuming due to the algorithm. Both the lane of law and the double weight method were used in outliers elimination, and the result shows that the latter one is better. Two kinds of bias correction method including off-line and on-line, were introduced, which contain static, adaptive, regression method, variational, method based on the radiative transfer model, bias correction with Kalman filter and dynamic update of bias correction technique. It is found that the timeliness of static method is better; while variational method could solve the problems of data drift and so on. The result is better when using bias correction based on the model and Kalman methods, but it is more time-consuming and not suitable for business application. Generally, the effect and timeliness of dynamic update one is the best among them. In this paper four kinds of cloud detection method are discussed here, including the sky field-of-view, sky channel, cloud radiation correction and different instrument cloud products matching. The first two methods are more feasible from the perspective of timeliness for numerical prediction, but the data quantity using could detection method of sky field-of-view is less than sky channel, leading to discarding of lots of channel data in climate sensitive area such as upper channel, and thus affecting the quality of analysis field. Further on, the methods of hops jumper, box and principal component applied to AIRS data sparseness were analyzed. From assimilation timeliness and operability, box method is feasible; although there is high complexity with algorithm of principal component analysis, which has a certain application prospect. After reviewing the quality control section, some further research directions in these fields were given respectively.     

An efficient matrix-free algorithm for the ensemble Kalman filter

In this work, we present an efficient matrix-free ensemble Kalman filter (EnKF) algorithm for the assimilation of large data sets. The EnKF has increasingly become an essential tool for data assimilation of numerical models. It is an attractive assimilation method because it can evolve the model covariance matrix for a non-linear model, through the use of an ensemble of model states, and it is easy to implement for any numerical model. Nevertheless, the computational cost of the EnKF can increase significantly for cases involving the assimilation of large data sets. As more data become available for assimilation, a potential bottleneck in most EnKF algorithms involves the operation of the Kalman gain matrix. To reduce the complexity and cost of assimilating large data sets, a matrix-free EnKF algorithm is proposed. The algorithm uses an efficient matrix-free linear solver, based on the Sherman–Morrison formulas, to solve the implicit linear system within the Kalman gain matrix and compute the analysis. Numerical experiments with a two-dimensional shallow water model on the sphere are presented, where results show the matrix-free implementation outperforming an singular value decomposition-based implementation in computational time.     

Assimilation of satellite observations of the atmosphere

Measurements of the atmosphere by satellite were first collected in the 1960s. However, it was not until the mid-1990s that these observations were translated into systematic improvements of numerical weather forecasts. We present here the data and methodology of data assimilation that enabled this achievement. Data assimilation is essentially a filtering processing that exploits the (assumed) known error statistical properties of the observations and of the underlying numerical model in which data are assimilated. It is also a process which corrects the state of the numerical model with physical observations of the real world. This part relies on (assumed) known physical laws to relate meteorological quantities (such as temperature, humidity, pressure, and wind) to observables. Atmospheric data collected by satellite all come from the interaction of electromagnetic waves with the atmosphere. Satellite data assimilation has greatly supported the progress in numerical weather prediction and holds promises for climate studies, for example via reanalysis.     

“深部综合信息矿产资源预测评价”专辑特邀主编寄语

深部综合信息矿产资源预测评价研究是地球系统科学研究中最具有交叉学科性质的领域。随着地表矿、浅部矿、易识别矿的日益减少,地质找矿工作逐步向第二深度空间发展。深部矿产资源三维预测已成为当前成矿预测研究的重点领域,地质学家们经过长期的持续研究,在该领域取得了一系列重大成果与重要认识。《地球学报》集中在2020年第2期刊发15篇文章作为“深部综合信息矿产资源预测评价”专辑,专辑涵盖了三个方向的研究成果:(1)矿产资源三维预测;(2)地质调查与研究;(3)地质、地化数据处理方法等。这些工作主要涉及矿产资源预测评价的两个方面,即预测方法和成矿规律,具体包括深部构造三维重建技术方法、同位素定年及示踪在成矿规律研究中的应用以及地球化学异常信息机器学习提取方法等方面的内容。本文将简要介绍收入本专辑论文的研究工作,对深入研究和认识深部综合信息矿产资源预测评价提供一定参考价值。