More "normal" than normal: scaling distributions and complex systems

One feature of many naturally occurring or engineered complex systems is tremendous variability in event sizes. To account for it, the behavior of these systems is often described using power law relationships or scaling distributions, which tend to be viewed as "exotic" because of their unusual properties (e.g., infinite moments). An alternate view is based on mathematical, statistical, and data-analytic arguments and suggests that scaling distributions should be viewed as "more normal than normal". In support of this latter view that has been advocated by Mandelbrot for the last 40 years, we review in this paper some relevant results from probability theory and illustrate a powerful statistical approach for deciding whether the variability associated with observed event sizes is consistent with an underlying Gaussian-type (finite variance) or scaling-type (infinite variance) distribution. We contrast this approach with traditional model fitting techniques and discuss its implications for future modeling of complex systems

Understanding Internet topology: principles, models, and validation

Building on a recent effort that combines a first-principles approach to modeling router-level connectivity with a more pragmatic use of statistics and graph theory, we show in this paper that for the Internet, an improved understanding of its physical infrastructure is possible by viewing the physical connectivity as an annotated graph that delivers raw connectivity and bandwidth to the upper layers in the TCP/IP protocol stack, subject to practical constraints (e.g., router technology) and economic considerations (e.g., link costs). More importantly, by relying on data from Abilene, a Tier-1 ISP, and the Rocketfuel project, we provide empirical evidence in support of the proposed approach and its consistency with networking reality. To illustrate its utility, we: 1) show that our approach provides insight into the origin of high variability in measured or inferred router-level maps; 2) demonstrate that it easily accommodates the incorporation of additional objectives of network design (e.g., robustness to router failure); and 3) discuss how it complements ongoing community efforts to reverse-engineer the Internet

A contrasting look at self-organization in the Internet and next-generation communication networks

This article examines contrasting notions of self-organization in the Internet and next-generation communication networks, by reviewing in some detail recent evidence regarding several of the more popular attempts to explain prominent features of Internet structure and behavior as "emergent phenomena." In these examples, what might appear to the nonexpert as "emergent self-organization" in the Internet actually results from well conceived (albeit perhaps ad hoc) design, with explanations that are mathematically rigorous, in agreement with engineering reality, and fully consistent with network measurements. These examples serve as concrete starting points from which networking researchers can assess whether or not explanations involving self-organization are relevant or appropriate in the context of next-generation communication networks, while also highlighting the main differences between approaches to self-organization that are rooted in engineering design vs. those inspired by statistical physics

The state of Florida's estuaries and future needs in estuarine research: Part 2. an academic research agenda (review draft)

As a program supporting academic research that addresses recognized societal needs, the Florida Sea Grant Program is developing a research theme area on estuaries to provide a uniquely academic product that will augment mission-oriented research undertaken by government and by the private sector. This report is not a call for proposals. It does not prescribe a specific research plan. Rather, it is a concept paper designed to focus research on two broad "organizing themes": (1) the hydrology of Florida's estuaries, and (2) the impact of cyclic environmental variability on estuarine function. (46pp.

Analysis of North Sea offshore wind power variability

This paper evaluates, for a 2030 scenario, the impact on onshore power systems in terms of the variability of the power generated by 81 GW of offshore wind farms installed in the North Sea. Meso-scale reanalysis data are used as input for computing the hourly power production for offshore wind farms, and this total production is analyzed to identify the largest aggregated hourly power variations. Based on publicly available information, a simplified representation of the coastal power grid is built for the countries bordering the North Sea. Wind farms less than 60 km from shore are connected radially to the mainland, while the rest are connected to a hypothetical offshore HVDC (High-Voltage Direct Current) power grid, designed such that wind curtailment does not exceed 1% of production. Loads and conventional power plants by technology and associated cost curves are computed for the various national power systems, based on 2030 projections. Using the MATLAB-based MATPOWER toolbox, the hourly optimal power flow for this regional hybrid AC/DC grid is computed for high, low and medium years from the meso-scale database. The largest net load variations are evaluated per market area and related to the extra load-following reserves that may be needed from conventional generators.Parts of this work were funded by Agentschap.NL, the Netherlands, now RVO.nl (Rijksdienst voor Ondernemend Nederland [25], under the project North Sea Transnational Grid (NSTG). The NSTG project is a cooperation between Delft University of Technology and the Energy Research Center of the Netherlands

PICES Press, Vol. 9, No. 2, July 2001

Cover [pdf, 0.2 Mb] Climate, biodiversity and ecosystems of the North Pacific [pp. 1-2] [pdf, 0.2 Mb] The state of the western North Pacific in the second half of 2000 [pp. 3-5] [pdf, 0.8 Mb] The status of the Bering Sea: June – December 2000 [pp. 6-7] [pdf, 1.5 Mb] The state of the eastern North Pacific since autumn 2000 [p. 8] [pdf, 0.3 Mb] Korean Yellow Sea Large Marine Ecosystem Program [pp. 9-12] [pdf, 0.5 Mb] Past and ongoing Mexican ecosystem research in the northeast Pacific Ocean [pp. 13-15] [pdf, 0.3 Mb] Vera Alexander [pp. 16-19] [pdf, 1.0 Mb] North Pacific CO2 data for the new millennium [pp. 20-21] [pdf, 0.3 Mb] PICES Higher Trophic Level Modelling Workshop [pp. 22-23] [pdf, 0.4 Mb] Argo Science Team 3rd Meeting (AST-3) [pp. 24-25] [pdf, 0.3 Mb] 2001 coast ocean / salmon ecosystem event [p. 26-27] [pdf, 0.3 Mb] Shifts in zooplankton abundance and species composition off central Oregon and southwestern British Columbia [pp. 28-29] [pdf, 0.3 Mb] The CLIVAR - Pacific Workshop [p. 30] [pdf, 0.2 Mb] PICES dialogue with Mexican scientists [p. 31] [pdf, 0.2 Mb] Announcements [p. 32] [pdf, 0.2 Mb

Collinsville solar thermal project: yield forecasting (draft report)

The final report has been published and is available here. Executive Summary 1        Introduction This report’s primary aim is to provide yield projections for the proposed Linear Fresnel Reflector (LFR) technology plant at Collinsville, Queensland, Australia.  However, the techniques developed in this report to overcome inadequate datasets at Collinsville to produce the yield projections are of interest to a wider audience because inadequate datasets for renewable energy projects are commonplace.  The subsequent report called ‘Energy economics and dispatch forecasting’ (Bell, Wild & Foster 2014a) uses the yield projections from this report to produce long-term wholesale market price and dispatch forecasts for the plant.  2        Literature review The literature review discusses the four drivers for yield for LFR technology: DNI (Direct Normal Irradiance) Temperature Humidity Pressure Collinsville lacks complete historical datasets of the four drivers to develop yield projects but its three nearby neighbours do possess complete datasets, so could act as proxies for Collinsville.  However, analysing the four drivers for Collinsville and its three nearby sites shows that there is considerable difference in their climates.  This difference makes them unsuitable to act as proxies for yield calculations.  Therefore, the review investigates modelling the four drivers for Collinsville. We introduce the term “effective” DNI to help clarify and ameliorate concerns over the dust and dew effects on terrestrial DNI measurement and LFR technology. We also introduce a modified TMY technique to overcome technology specific Typical Metrological Year (TMY).  We discuss the effect of climate change and the El Nino Southern Oscillation (ENSO) on yield and their implications for a TMY. 2.1     Research questions Research question arising from the literature review include: The overarching research question: Can modelling the weather with limited datasets produce greater yield predictive power than using the historically more complete datasets from nearby sites? This overarching question has a number of smaller supporting research questions: Is BoM’s DNI satellite dataset adequately adjusted for cloud cover at Collinsville? Given the dust and dew effects, is using raw satellite data sufficient to model yield? Does elevation between Collinsville and nearby sites affect yield? How does the ENSO affect yield? Given the 2007-2012 constraint, will the TMY process provide a “Typical” year over the ENSO cycle? How does climate change affect yield? A further research question arises in the methodology but is included here for completeness. What is the expected frequency of oversupply from the Linear Fresnel Novatec Solar Boiler? 3        Methodology In the methodology section, we discuss the data preparation and the model selection process for the four drivers of yield. 4        Results and analysis In the results section we present the four driver models selected and the process that was undertaken to arrive at the models. 5        Discussion We analyse the extent to which the research questions are informed by the results. 6        Conclusion In this report, we have identified the key research questions and established a methodology to address these questions.  The models for the four drivers have been established allowing the calculation of the yield projections for Collinsville