Analysis of the positive ionospheric response to a moderate geomagnetic storm using a global numerical model

Current theories of F-layer storms are discussed using numerical simulations with the Upper Atmosphere Model, a global self-consistent, time dependent numerical model of the thermosphere-ionosphere-plasmasphere-magnetosphere system including electrodynamical coupling effects. A case study of a moderate geomagnetic storm at low solar activity during the northern winter solstice exemplifies the complex storm phenomena. The study focuses on positive ionospheric storm effects in relation to thermospheric disturbances in general and thermospheric composition changes in particular. It investigates the dynamical effects of both neutral meridional winds and electric fields caused by the disturbance dynamo effect. The penetration of short-time electric fields of magnetospheric origin during storm intensification phases is shown for the first time in this model study. Comparisons of the calculated thermospheric composition changes with satellite observations of AE-C and ESRO-4 during storm time show a good agreement. The empirical MSISE90 model, however, is less consistent with the simulations. It does not show the equatorward propagation of the disturbances and predicts that they have a gentler latitudinal gradient. Both theoretical and experimental data reveal that although the ratio of [O]/[N₂] at high latitudes decreases significantly during the magnetic storm compared with the quiet time level, at mid to low latitudes it does not increase (at fixed altitudes) above the quiet reference level. Meanwhile, the ionospheric storm is positive there. We conclude that the positive phase of the ionospheric storm is mainly due to uplifting of ionospheric F2-region plasma at mid latitudes and its equatorward movement at low latitudes along geomagnetic field lines caused by large-scale neutral wind circulation and the passage of travelling atmospheric disturbances (TADs). The calculated zonal electric field disturbances also help to create the positive ionospheric disturbances both at middle and low latitudes. Minor contributions arise from the general density enhancement of all constituents during geomagnetic storms, which favours ion production processes above ion losses at fixed height under day-light conditions.     

Thermal evolution of an extensional detachment as constrained by organic metamorphic data and thermal modeling: Graz Paleozoic Nappe Complex (Eastern Alps)

Following Early Cretaceous nappe stacking, the Eastern Alps were affected by late-orogenic extension during the Late Cretaceous. In the eastern segment of this range, a Late Cretaceous detachment separates a very low- to low-grade metamorphic cover (Graz Paleozoic Nappe Complex, GPNC) above a low- to high-grade metamorphic basement. Synchronously, the Kainach Gosau Basin (KGB) collapsed and subsided on top of the section.Metamorphism of organic material within this section has been investigated using vitrinite reflectance data and Raman spectra of extracted carbonaceous material. In the southern part of the GPNC, vitrinite reflectance indicates a decrease in organic maturity towards the stratigraphic youngest unit. The remaining part of the GPNC is characterized by an aureole of elevated vitrinite reflectance values and Raman R2 ratios that parallels the margins of the GPNC. Vitrinite reflectance in the KGB shows a steep coalification gradient and increases significantly towards the western basin margin. The observed stratigraphic trend in the southern GPNC is a result of deep Paleozoic to Early Cretaceous burial. This maturity pattern was overprinted along the margins by advective heat and convective fluids during Late Cretaceous to Paleogene exhumation of basement rocks.During shearing, the fault zone was heated up to ca. 500 °C. This overprint is explained by a two-dimensional thermal model with a ramp-flat fault geometry and a slip rate of 1 to 1.5 cm/year during 5 Ma fault movement. The collapse basin above the detachment subsided in a thermal regime which was characterized by relaxing isotherms.     

Habitat modeling for health sovereignty: Increasing indigenous access to medicinal plants in northern Maine,USA

Medicinal plants and fungi play important roles in the health of Maliseet people of northern Maine, USA. A critical aspect of exercising choice in health care for this community is the ability to locate and have access to these plants. Habitat suitability modeling is a form of geospatial technology that can enhance health sovereignty by identifying locations in which populations of medicinal plants can be conserved or established. However, use of this technology within indigenous communities has been limited. Focusing on the medicinal plant muskrat root, Acorus americanus (Raf.) Raf., we generate a habitat suitability model for eastern Aroostook County, Maine (1,055,653.659 ha) that also takes community needs into consideration. Drawing on participatory ethnographic data as well as environmental characteristics, our model combines ecological and sociocultural parameters to identify previously unknown populations of A. americanus that are accessible to tribal elders. Our model successfully predicted 95% of A. americanus locations in our field validation data set of ∼71,000 ha. Results suggest that approximately 0.6% of our study area contains suitable habitat to plant muskrat root that could also meet tribal members' gathering needs for the future. Increasing the number of potential collection sites gives communities options for gathering, thereby enhancing health sovereignty. Broadly, our work suggests that, when done in partnership with communities, different forms of geospatial technology can be beneficial tools for efforts to promote health sovereignty.     

顾及地理-语义动态的城市热点预测框架

城市热点时空预测是城市管理和智慧城市建设的一项长期而富有挑战性的任务。准确地进行城市热点时空预测可以提高城市规划、调度和安全保障能力并降低资源消耗。现有的区域级深度时空预测方法主要利用基于地理网格的图像、给定的网络结构或额外的数据来获取时空动态。通过从原始数据中挖掘出潜在的自语义信息,并将其与基于地理空间的网格图像融合,也可以提高时空预测的性能,基于此,本文提出了一种新的深度学习方法地理语义集成神经网络(GSEN),将地理预测神经网络和语义预测神经网络相叠加。GSEN模型综合了预测递归神经网络(PredRNN)、图卷积预测递归神经网络(GCPredRNN)和集成层的结构,从不同的角度捕捉时空动态。并且该模型还可以与现实世界中一些潜在的高层动态进行关联,而不需要任何额外的数据。最终在3个不同领域的实际数据集上对本文提出的模型进行了评估,均取得了很好的预测效果,实验结果表明GSEN模型在不同城市热点时空预测任务中的推广性和有效性,利用该模型可以更好地进行城市热点时空预测,解决一系列如犯罪、火灾、网约车预订等等现代城市发展中亟需解决的相关问题。     

金融危机下国家大规模投资政策模拟

构建了一个中国多区域可计算一般均衡模型(MReCGE-C),利用该模型分析了我国应对国际金融危机的大规模投资政策。MReCGE-C根据城乡属性和年龄结构对居民进行了分组,放松了要素流动的限制,使劳动力和资本可以同时跨区域跨部门流动,并设计了一种基于居民终生效用的区域均衡机制,通过区域差距变量可以调控和测度区域差距。最后利用MReCGE-C模型针对国家大规模投资计划下的3种典型投资方案进行了模拟。结果发现在等量新增投资下,针对中部地区投资方案的GDP增长率高于针对中西部和全国投资方案的GDP增长率,同时针对中部地区投资方案对于城乡就业的拉动也要优于其余两种投资方案,而且可以实现缩小区域差距的目标。综合比较,目前国家大规模投资的优势应该指向中部地区。     

伊犁盆地北部山麓黄土粒度端元指示的粉尘堆积过程

中亚干旱区是全球重要的粉尘源区,是全球变化与区域响应研究的关键区域之一。中亚粉尘形成搬运沉积过程一直是全球变化研究的热点科学问题。本文选取位于伊犁盆地北部、北天山南麓不同地形和气候条件下的两处黄土剖面,对其沉积速率最高的层段进行了粒度测试分析,结合聚类分析和粒度分布曲线拟合两种方法,重建了黄土粉尘的堆积过程。伊犁盆地黄土主要由远源和近源物质组成,分别由高空西风和中尺度的区域风搬运而来。高空西风所携带的远源粘粒级矿物颗粒对盆地东部降雨较高地区有一定的贡献,而在盆地西部不容易沉降下来。中尺度区域风所搬运的近源物质组成了黄土沉积物的主体,而近源区沉积物的可用性在黄土的形成过程中扮演着重要角色。另外,非风暴过程中(沙尘暴过后)出现的浮尘在沙尘暴天气频率减少的时期对黄土的发育有重要贡献,而在沙尘暴天气频发的时期,较差的植被覆盖度能够使得已经沉降下来的浮尘组分重新被扬起至大气中。由此建立了一个粉尘堆积的概念模型。认识黄土粉尘的堆积过程对现代沙尘天气的治理和人类生存环境的改善具有重要作用。     

http://www.sciencedirect.com/science/article/pii/S1674987111000788

This paper presents numerical investigation on the ore-forming fluid migration driven by tectonic deformation and thermally-induced buoyancy force in the Chanziping ore district in South China. A series of numerical scenarios are considered to examine the effect of meteoric water precipitation, the dip angle of the faults, unconformity surface, and thermal input on the ore genesis. Our computations reveal that the downward basinal fluid flow driven by extensional stress mixes with the upward basal fluid driven by the thermal input from depth at the junction of two faults at a temperature of about 200 C, triggering the precipitation of the Chanziping uranium deposit.     

Assimilating data into open ocean tidal models

The problem of deriving tidal fields from observations by reason of incompleteness and imperfectness of every data set practically available has an infinitely large number of allowable solutions fitting the data within measurement errors and hence can be treated as ill-posed. Therefore, interpolating the data always relies on some a priori assumptions concerning the tides, which provide a rule of sampling or, in other words, a regularization of the ill-posed problem. Data assimilation procedures used in large scale tide modeling are viewed in a common mathematical framework as such regularizations. It is shown that they all (basis functions expansion, parameter estimation, nudging, objective analysis, general inversion, and extended general inversion), including those (objective analysis and general inversion) originally formulated in stochastic terms, may be considered as utilizations of one of the three general methods suggested by the theory of ill-posed problems. The problem of grid refinement critical for inverse methods and nudging is discussed.     

Approach to modeling and virtual-reality-based simulation for plant canopy lighting

Over the past 20 years, significant progress has been made in virtual plant modeling corresponding to the rapid advances in information technology. Virtual plant research has broad applications in agronomy, forestry, ecology and remote sensing. As many biological processes are driven by light, it is the key for virtual plant to estimate the light absorbed by each organ. This paper presents the radiance equation suitable for calculating sun and sky light intercepted by plant organs based on the principles of the interaction between light and plant canopy firstly; analyzes the process principles of plant canopy primary lighting based on ray casting and projection secondly; describes the multiple scattering of plant lighting based on Monte Carlo ray tracing method and on the radiosity method thirdly; and confirms the research with 3D visualization based on Virtual Reality Modeling Language （VRML） finally. The research is the primary work of digital agriculture, and important for monitoring and estimating corn growth in Northeast China.     

基于机器学习的格点气温预报订正方法

使用2017年9月至2021年3月国家级业务化运行的智能网格实况分析产品和欧洲中期天气预报中心全球模式(EC)产品,根据湖北省的地理分布特征构建6个分区,采用基于LightGBM机器学习算法建立的气温预报方法,生成湖北省0.05°×0.05°格点气温预报产品。利用2021年4—9月的预报产品和格点实况资料进行检验,结果表明:基于机器学习的气温预报方法(MLT)取得了较好的预报效果,其在0～72 h时效内优于中央气象台下发的气温精细化指导预报(SCMOC)和EC产品;MLT在山区的误差较平原大,但山区的订正幅度大于平原,日最高气温的订正幅度大于日最低气温的订正幅度;4—9月MLT、SCMOC、EC产品的平均绝对误差(MAE)日变化都呈现了白天偏高、夜间偏低、午后凸起的单峰特征,MLT的MAE值较SCMOC和EC产品的更低,并且在转折性天气中仍具有优势;站点检验与格点检验结论一致,基于格点建模的气温预报产品对站点预报同样得到了订正。机器学习在格点气温的模式订正方面可以作为一个行之有效的手段。