The Scientific Foundations of Forecasting Magnetospheric Space Weather

The magnetosphere is the lens through which solar space weather phenomena are focused and directed towards the Earth. In particular, the non-linear interaction of the solar wind with the Earth's magnetic field leads to the formation of highly inhomogenous electrical currents in the ionosphere which can ultimately result in damage to and problems with the operation of power distribution networks. Since electric power is the fundamental cornerstone of modern life, the interruption of power is the primary pathway by which space weather has impact on human activity and technology. Consequently, in the context of space weather, it is the ability to predict geomagnetic activity that is of key importance. This is usually stated in terms of geomagnetic storms, but we argue that in fact it is the substorm phenomenon which contains the crucial physics, and therefore prediction of substorm occurrence, severity and duration, either within the context of a longer-lasting geomagnetic storm, but potentially also as an isolated event, is of critical importance. Here we review the physics of the magnetosphere in the frame of space weather forecasting, focusing on recent results, current understanding, and an assessment of probable future developments.Peer reviewe

Comparison of three maps at multiple resolutions: a case study of land change simulation in Cho Don District, Vietnam

Geographic modelers frequently compare maps of observed land transitions to maps of simulated land transitions to relate the patterns in reference maps to the output from a simulation model. Pixel-by-pixel analysis of raster maps at a single resolution is useful for this task at a single scale, but scientists often need to consider additional scales. This article presents methods to satisfy this need by proposing a multiple-resolution method to compare land categories in three maps: a reference map of time 1, a reference map of time 2, and a simulation map of time 2. The method generates a three-dimensional table that gives the percentage of the study area for each combination of categories at the maps' native resolution and at several nested sets of coarser squares. The method differentiates allocation disagreement within coarse squares, allocation disagreement among coarse squares, quantity disagreement, and agreement. We illustrate the method with output from a run of the SAMBA agent-based model from 1990 to 2001 using 32-m resolution pixels for Cho Don District, Vietnam. Results show that half of the overall disagreement is attributable to allocation disagreement within squares that are 506 m 506 m, which is about the average size of a village. Much of the remaining disagreement is misallocation of forest and shrub between the northern and southern parts of the district, which is caused by differences between the data and the simulation concerning transitions from the crop and shrub categories

Object-based classification with features extracted by a semi-automatic feature extraction algorithm -SEaTH

Object-based image analysis (OBIA) uses object features (or attributes) that relate to the pixels contained by the image object to assist in image classification. These object features include spectral, shape, texture and context features. With hundreds of available features, the identification of those that can improve separability between classes is critical for OBIA. The Separability and Thresholds (SEaTH) algorithm calculates the SEaTH of object-classes for the given features. The SEaTH algorithm avoids time-consuming trial-and-error practice for seeking important features and thresholds. This article tests the SEaTH algorithm on Landsat-7 Enhanced Thematic Mapper (ETM+) imagery in a heterogeneous landscape with multiple land cover classes. The results suggest SEaTH is a strong alternative to other automated approaches, yielding an agreement of 79% with reference data. In comparison, an object-based nearest neighbour classifier yielded 66% agreement and a pixel-based maximum likelihood classifier yielded 69% agreement. © 2011 Taylor & Francis

A Framework of Map Comparison Methods to Evaluate Geosimulation Models from a Geospatial Perspective

Geosimulation is a form of microsimulation that seeks to understand geographical patterns and dynamics as the outcome of micro level geographical processes. Geosimulation has been applied to understand such diverse systems as lake ecology, traffic congestion and urban growth. A crucial task common to these applications is to express the agreement between model and reality and hence the confidence one can have in the model results. Such evaluation requires a geospatial perspective; it is not sufficient if the micro-level interactions are realistic. Importantly the interactions should be such that the meso and macro level patterns that emerge from the model are realistic. In recent years, a host of map comparison methods have been developed that address different aspects of the agreement between model and reality. This paper places such methods in a framework to systematically assess the breadth and width of model performance. The framework expresses agreement at the continuum of spatial scales ranging from local to the whole landscape and separately addresses agreement in structure and presence. A common reference level makes different performance metrics mutually comparable and guides the interpretation of results. The framework is applied for the evaluation of a constrained cellular automata model of the Netherlands. The case demonstrates that a performance assessment lacking either a multi-criteria and multi-scale perspective or a reference level would result in an unbalanced account and ultimately false conclusions