Multi-year Simulations and Experimental Seasonal Predictions for Rainy Seasons in China by Using a Nested Regional Climate Model (RegCM_NCC). Part Ⅰ: Sensitivity Study

A modified version of the NCAR/RegCM2 has been developed at the National Climate Center (NCC), China Meteorological Administration, through a series of sensitivity experiments and multi-year simulations and hindcasts, with a special emphasis on the adequate choice of physical parameterization schemes suitable for the East Asian monsoon climate. This regional climate model is nested with the NCC/IAP (Institute of Atmospheric Physics) T63 coupled GCM to make an experimental seasonal prediction for China and East Asia. The four-year (2001 to 2004) prediction results are encouraging. This paper is the first part of a two-part paper, and it mainly describes the sensitivity study of the physical process parameterization represented in the model. The systematic errors produced by the different physical parameterization schemes such as the land surface processes, convective precipitation, cloud-radiation transfer process, boundary layer process and large-scale terrain features have been identified based on multi-year and extreme flooding event simulations. A number of comparative experiments has shown that the mass flux scheme (MFS) and Betts-Miller scheme (BM) for convective precipitation, the LPMI (land surface process model I) and LPMII (land surface process model Ⅱ) for the land surface process, the CCM3 radiation transfer scheme for cloud-radiation transfer processes, the TKE (turbulent kinetic energy) scheme for the boundary layer processes and the topography treatment schemes for the Tibetan Plateau are suitable for simulations and prediction of the East Asia monsoon climate in rainy seasons. Based on the above sensitivity study, a modified version of the RegCM2 (RegCM_NCC) has been set up for climate simulations and seasonal predictions.     

Applications of conditional nonlinear optimal perturbation to the study of the stability and sensitivity of the Jovian atmosphere

A two-layer quasi-geostrophic model is used to study the stability and sensitivity of motions on smallscale vortices in Jupiter’s atmosphere. Conditional nonlinear optimal perturbations (CNOPs) and linear singular vectors (LSVs) are both obtained numerically and compared in this paper. The results show that CNOPs can capture the nonlinear characteristics of motions in small-scale vortices in Jupiter’s atmosphere and show great difference from LSVs under the condition that the initial constraint condition is large or the optimization time is not very short or both. Besides, in some basic states, local CNOPs are found. The pattern of LSV is more similar to local CNOP than global CNOP in some cases. The elementary application of the method of CNOP to the Jovian atmosphere helps us to explore the stability of variousscale motions of Jupiter’s atmosphere and to compare the stability of motions in Jupiter’s atmosphere and Earth’s atmosphere further.     

基于卡尔曼滤波的中国区域气温和降水的多模式集成预报

利用TIGGE资料集下欧洲中期天气预报中心(ECMWF)、日本气象厅(JMA)、美国国家环境预报中心(NCEP)、中国气象局(CMA)和英国气象局(UKMO)5个模式预报的结果,对基于卡尔曼滤波的气温和降水的多模式集成预报进行研究。结果表明,卡尔曼滤波方法的预报效果优于消除偏差集合平均(BREM)和单模式的预报,但是对于地面气温和降水,其预报效果也存在一定的差异。在中国区域2 m气温的预报中,卡尔曼滤波的预报结果最优。而对于24 h累积降水预报,尽管卡尔曼滤波在所有量级下的TS评分均优于BREM,但随着预报时效增加,其在大雨及以上量级的TS评分跟最佳单模式UKMO预报相当,改进效果不明显。卡尔曼滤波在地面气温和24 h累积降水每个预报时效下的均方根误差均最优,预报效果更佳且稳定。     

KK型管节点应力集中系数几何敏感性分析

KK型管节点是自升式平台桁架式桩腿中的一种管节点,其应力集中系数是影响桩腿疲劳寿命的重要参数。应力集中系数与管节点的几何形式密切相关,为分析KK型管节点应力集中系数对几何参数的敏感性,利用ANSYS软件对某KK型管节点进行几何参数化建模,利用有限元数值模拟方法对各工况下的热点应力进行分析,并分别计算各相应工况下的名义应力,然后将热点应力与名义应力相比得到不同几何参数下的热点应力集中系数。对计算结果进行整理分析,得到了KK型管节点应力集中系数对无量纲几何参数的敏感性规律。结果表明,应力集中系数与撑杆受力状态、管节点结构形式有关,在满足结构布置、建造工艺和其他安全性指标的前提下,分析结果能够为KK型管节点的结构设计和疲劳分析提供技术支撑。     

雷达-雨量计联合估测区域降水量方法研究Ⅰ——模式集成法的提出

天气雷达估测区域降水量是其应用研究的重要领域,不断研究改进雷达估测区域降水量精度和稳定度是雷达气象工作的重要目标。文章回顾了集合预报理论基础及其发展概况,总结分析集合预报方法、产品表现形式等。对前期雷达估测区域降水量的8种方法,从基本原理和特点等方面进行了系统分析。对该理论在雷达估测区域降水领域的适用性进行了分析,阐述了集合预报理论在该领域应用的概念模型,并提出了两种集成估测区域降水量的新方法,为下一步的应用研究提供了理论支撑。     

Fostering Environmental Sensitivity by Observing Everyday Environments

This study examined the effects of observing and reflecting on everyday environments in the development of environmental sensitivity, defined as a holistic capability composed of predisposition, knowledge, and behavior subdimensions. We encouraged participants to use diverse sources, such as texts, photographs, and animation, to record their observations and thinking. As a result, the activity in this study was beneficial in enhancing students’ environmental sensitivity. Students’ mean scores on the environmental sensitivity questionnaire improved, and their reflective journals reported increased interest in everyday environments, enhanced knowledge about diverse environmental elements, and strengthened will to act pro-environmentally. The usefulness of the geospatial platform was also reported.     

Water quality modeling and sensitivity analysis using Water Quality Analysis Simulation Program (WASP) in the Shenandoah River watershed

This study used the Water Quality Analysis Simulation Program (WASP) to simulate nutrients, dissolved oxygen (DO), and chlorophyll-a dynamics in the Shenandoah River basin and performed an uncertainty analysis to examine the complexity of these variables in water quality estimation and their influence on the Shenandoah River. Significant progress has been made; however, nutrient loads emitted into the Shenandoah River from nonpoint sources remain high. Modeling of three points on the Shenandoah River in Virginia and West Virginia provides an ideal case study since the river is classified by the Virginia Department of Environmental Quality as being impaired. The results of a sensitivity test show that model error decreases with increasing model complexity and sensitivity. The model predicted DO values that tended to be close to the measured data, while total nitrogen and phosphorus tended to be overemphasized. Our results examine the importance of temperature, stream flow, and velocity in influencing water quality between seasons and levels on the different sections of the watershed.     

Case Study of Fog Predictability for an Event with Cold-Front Synoptic Pattern

Fog has recently become a frequent high-impact weather phenomenon along the coastal regions of North China. Accurate fog forecasting remains challenging due to limited understanding of the predictability and mechanism of fog formation associated with synoptic-scale circulation. One frequent synoptic pattern of fog formation in this area is associated with cold front passage(cold-front synoptic pattern, CFSP). This paper explored the predictability of a typical CFSP fog event from the perspective of analyzing key characteristics of synoptic-scale circulation determining fog forecasting performance and the possible mechanism. The event was ensemble forecasted with the Weather Research and Forecasting model. Two groups of ensemble members with good and bad forecasting performance were selected and composited. Results showed that the predictability of this case was largely determined by the simulated strengths of the cold-front circulation(i.e., trough and ridge and the associated surface high). The bad-performing members tended to have a weaker ridge behind a stronger trough, and associated higher pressure over land and a weaker surface high over the sea, leading to an adverse impact on strength and direction of steering flows that inhibit warm moist advection and enhance cold dry advection transported to the focus region. Associated with this cold dry advection, adverse synoptic conditions of stratification and moisture for fog formation were produced, consequently causing failure of fog forecasting in the focus region. This study highlights the importance of accurate synoptic-scale information for improved CFSP fog forecasting, and enhances understanding of fog predictability from perspective of synoptic-scale circulation.     

Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change

Understanding the spatial scale sensitivity of cellular automata is crucial for improving the accuracy of land use change simulation. We propose a framework based on a response surface method to comprehensively explore spatial scale sensitivity of the cellular automata Markov chain (CA-Markov) model, and present a hybrid evaluation model for expressing simulation accuracy that merges the strengths of the Kappa coefficient and of Contagion index. Three Landsat-Thematic Mapper remote sensing images of Wuhan in 1987, 1996, and 2005 were used to extract land use information. The results demonstrate that the spatial scale sensitivity of the CA-Markov model resulting from individual components and their combinations are both worthy of attention. The utility of our proposed hybrid evaluation model and response surface method to investigate the sensitivity has proven to be more accurate than the single Kappa coefficient method and more efficient than traditional methods. The findings also show that the CA-Markov model is more sensitive to neighborhood size than to cell size or neighborhood type considering individual component effects. Particularly, the bilateral and trilateral interactions between neighborhood and cell size result in a more remarkable scale effect than that of a single cell size.     

一维综合孔径微波辐射计遥感海面温度的敏感性分析

一维综合孔径微波辐射计能够有效提高观测的空间分辨率,其观测入射角通常在0°~55°范围内变化。为了开发适用于一维综合孔径微波辐射计的海面温度反演算法,需要评估其观测亮温对海洋大气环境要素的敏感性。利用海面发射率模型和大气辐射传输模型,构建了适用于一维综合孔径微波辐射计的微波海洋大气辐射传输模式,研究了C波段垂直和水平极化微波辐射亮温在不同入射角下对海洋大气环境要素的敏感性变化情况,并定量计算了相应的敏感系数。结果表明:垂直和水平极化亮温对海洋大气环境要素的敏感性表现出不同的特性。随着入射角的增大,垂直极化亮温对海面温度的敏感性增强,对海面风场的敏感性相对减弱;水平极化亮温则相反。由大气水汽含量和云液态水含量误差引入的垂直和水平极化亮温误差随入射角增大而增大,但是,即使在55°的大入射角下垂直和水平极化亮温误差仍小于0.12 K。对于海面温度反演精度优于1 K的要求,一维综合孔径微波辐射计的测温精度需优于0.6 K。研究结果对于一维综合孔径微波辐射计海面温度反演算法的研究和载荷设计具有一定的理论指导意义。