A Surrogate Weather Generator for Estimating Natural Gas Design Day Conditions

Natural gas customers rely upon utilities to provide gas for heating in the coldest parts of winter. Heating capacity is expensive, so utilities and end users (represented by commissions) must agree on the coldest day on which a utility is expected to meet demand. The return period of such a day is long relative to the amount of weather data that are typically available. This paper develops a weather resampling method called the Surrogate Weather Resampler, which creates a large dataset to support analysis of extremely infrequent events. While most current methods for generating weather data are based on simulation, this method resamples the deviations from typical weather. The paper also shows how extreme temperatures are strongly correlated to the demand for natural gas. The Surrogate Weather Resampler was compared in-sample and out-of-sample to the WeaGETS weather generator using both the Kolmogorov–Smirnov test and an exceedance-based test for cold weather generation. A naïve benchmark was also examined. These methods studied weather data from the National Oceanic and Atmospheric Administration and AccuWeather. Weather data were collected for 33 weather stations across North America, with 69 years of data from each weather station. We show that the Surrogate Weather Resampler can reproduce the cold tail of distribution better than the naïve benchmark and WeaGETS.

Structure of Small World Innovation Network and Learning Performance

This paper examines the differences of learning performance of 5 MNCs (multinational corporations) that filed the largest number of patents in China. We establish the innovation network with the patent coauthorship data by these 5 MNCs and classify the networks by the tail of distribution curve of connections. To make a comparison of the learning performance of these 5 MNCs with differing network structures, we develop an organization learning model by regarding the reality as havingmdimensions, which denotes the heterogeneous knowledge about the reality. We further setninnovative individuals that are mutually interactive and own unique knowledge about the reality. A longer (shorter) distance between the knowledge of the individual and the reality denotes a lower (higher) knowledge level of that individual. Individuals interact with and learn from each other within the small-world network. By making 1,000 numerical simulations and averaging the simulated results, we find that the differing structure of the small-world network leads to the differences of learning performance between these 5 MNCs. The network monopolization negatively impacts and network connectivity positively impacts learning performance. Policy implications in the conclusion section suggest that to improve firm learning performance, it is necessary to establish a flat and connective network.

Extreme Wave Analysis in Campos Basin (Rio de Janeiro - Brazil) Associated With Extra-Tropical Cyclones and Anticyclones

Campos Basin is a petroleum rich area located offshore of Rio de Janeiro, Brazil. The most energetic ocean waves that reach location are generated by extra-tropical weather systems. This article studies extreme waves generated by cyclones and anticyclones, using two approaches. The first is the statistical extreme analysis applying the Peaks Over Threshold technique. The second is an evaluation of metocean features of events selected by POT as the tail of distribution. The research used 42 months of directional wave buoy measurements from 1991 to 1995 and 20 years of WAVEWATCH III simulation from 1986 to 2005, forced by NCEP/NCAR reanalysis2 surface winds. From metocean evaluation, the authors conclude that the greatest swells hitting Campos Basin come from southwest direction, peak periods over 11 seconds, generated by cyclones, occurring mainly in winter and autumn. The extreme buoy data analysis of significant wave height resulted in return values for 50 and 100 year respectively 8.77 and 9.54 meters, but with considerable uncertainty due to the short duration of data collection. Despite the limitations of Wavewatch hindcast, the methodology was able to capture the characteristics of extreme events in terms of shape of the distribution tail.