Abstract.Interplanetary magnetic clouds (MCs) have been identified for the first 8.6 years of the WIND mission, and their magnetic field structures have been parameter-fitted by a static, force free, cylindrically-symmetric model ) with various levels of success. This paper summarizes various aspects of the results of the model fitting by providing: seven estimated model fit-parameter values for each of the 82 MCs found, their objectively determined quality estimates, closest approach vectors (in two coordinate frames), fit-parameter errors for the cases of acceptable quality (50 cases, or 61%), axial magnetic fluxes, axial current densities, and total axial current -as well as some examples of MC profiles for various conditions and "categories" for each case (e.g. B z : N→S or S→N, etc.). MC quality is estimated from a quantitative consideration of a large set of parameters, such as the chi-squared of the model fit, degree of asymmetry of the B profile, and a comparison of two means of estimating radius. This set of MCs was initially identified by visual inspection of relevant field and plasma data. Each resulting MC candidate is then tested through the use of the MC parameter model, for various adjusted durations to determine the best fit, which helps to refine the boundarytimes. The resulting MC set is called Set 1. Another, larger, set (Set 2) of MCs is identified through an automated program whose criteria are based on general MC plasma and field characteristics at 1 AU determined through past experience. Set 1 is almost fully contained within Set 2, whose frequency of occurrence better matches that of the sunspot cycle than Set 1. The difference-set (Set 2-Set 1) is referred to as the magnetic cloud-like (MCL) set, whose members do not very well represent good flux ropes through modeling. We present a discussion of how a MC's front boundary isCorrespondence to: R. P. Lepping (ronald.p.lepping@nasa.gov) specifically identified in terms of multi-parameter considerations (i.e. any one or more of: increase in B, directional discontinuity, magnetic hole in B, drop in proton plasma beta, B-fluctuation level change, proton temperature drop, etc.), as well as through the application of the flux rope model. Also presented are examples of unusual MCs, as well as some commonly occurring relationships, such as the existence and frequency (approx. 1/2 the time) of upstream interplanetary shocks, and less frequent internal shocks.

[1] Over 12 years of IMP 8, data was searched for observed bow shock crossings. Out of the total 4562 crossings found, we used the 2293 unambiguous bow shocks for which upstream interplanetary magnetic field and solar wind parameters were available to study selected bow shock models under normal and unusual solar wind conditions. The chosen models were F79, NS91, FR94, FR94c, CL95, and P95 [Formisano, 1979; Němeček and Šafránková, 1991;Farris and Russell, 1994;Cairns and Lyon, 1995;Peredo et al., 1995]. This statistical study investigates these models' reliability not only for average solar wind plasma and interplanetary magnetic field (IMF) conditions but also for unusual conditions and as a result points out some deficiencies of these models. Statistically, the predictions of F79 and the phenomenological and MHD models FR94, FR94c, and CL95 are the most accurate, with F79 giving a slightly better result. The P95 model predicts standoff distances which are too large by $20%. For large values of the IMF and its components, all models except NS91 underestimate the bow shock distance. Furthermore, the models underestimate the bow shock distance when the upstream Mach numbers are low (]5). The models also do not properly reflect changes in the relative orientation of the IMF and solar wind velocity vectors. An independent evaluation of the dawn and dusk sectors suggests an asymmetry in the bow shock shape and/or a different reaction of the flanks to solar wind deviations from a radial flow. Taking the upstream parameters from a distant solar wind monitor (the Wind spacecraft) resulted in the models predicting the shock farther away from the Earth, which is likely a result of the spacecraft separation perpendicular to the solar wind flow, or of calibrational differences of the plasma density measurements by the spacecraft.

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GeoLink has leveraged linked data principles to create a dataset that allows users to seamlessly query and reason over some of the most prominent geoscience metadata repositories in the United States. The GeoLink dataset includes such diverse information as port calls made by oceanographic cruises, physical sample metadata, research project funding and staffing, and authorship of technical reports. The data has been published according to best practices for linked data and is publicly available via a SPARQL Protocol and RDF Query Language (SPARQL) end point that at present contains more than 45 million Resource Description Framework (RDF) triples together with a collection of ontologies and geo-visualization tools. This article describes the geoscience datasets, the modeling and publication process, and current uses of the dataset. The focus is on providing enough detail to enable researchers, application developers and others who wish to leverage the GeoLink data in their own work to do so.

A digital map of the built environment is useful for a range of economic, emergency response, and urban planning exercises such as helping find places in app driven interfaces, helping emergency managers know what locations might be impacted by a flood or fire, and helping city planners proactively identify vulnerabilities and plan for how a city is growing. Since its inception in 2004, OpenStreetMap (OSM) sets the benchmark for open geospatial data and has become a key player in the public, research, and corporate realms. Following the foundations laid by OSM, several open geospatial products describing the built environment have blossomed including the Microsoft USA building footprint layer and the OpenAddress project. Each of these products use different data collection methods ranging from public contributions to artificial intelligence, and if taken together, could provide a comprehensive description of the built environment. Yet, these projects are still siloed, and their variety makes integration and interoperability a major challenge. Here, we document an approach for merging data from these three major open building datasets and outline a workflow that is scalable to the continental United States (CONUS). We show how the results can be structured as a knowledge graph over which machine learning models are built. These models can help propagate and complete unknown quantities that can then be leveraged in disaster management.

The advent of the Virtual Observatory has begun an evolution in the space physics data environment. A number of nascent and discipline specific Virtual Observatories have started to emerge with an emphasis on data search and retrieval. As this new data environment takes shape an emphasis will be placed on interdisciplinary communication in attempts to address large scale and global problems. To this end we formulate the development of a query language to facilitate Virtual Observatory to Virtual Observatory communication. Furthermore, we outline the goals of such a language, how it would work and how existing community efforts can be leveraged to speed the development of this query language.