Nonparametric Stochastic Generation of Daily Precipitation and Other Weather Variables

Traditional stochastic approaches for synthetic generation of weather variables often assume a prior functional form for the stochastic process, are often not capable of reproducing the probabilistic structure present in the data, and may not be uniformly applicable across sites. In an attempt to find a general framework for stochastic generation of weather variables, this study marks a unique departure from the traditional approaches, and ushers in the use of data-driven nonparametric techniques and demonstrates their utility. Precipitation is one of the key variables that drive hydrologic systems and hence warrants more focus . In this regard, two major aspects of precipitation modeling were considered: (I) resampling traces under the assumption of stationarity in the process, or with some treatment of the seasonality, and (2) investigations into interannual and secular trends in precipitation and their likely implications. A nonparametric seasonal wet/dry spell model was developed for the generation of daily precipitation. In this the probability density functions of interest are estimated using non parametric kernel density estimators. In the course of development of this model, various nonparametric density estimators for discrete and continuous data were reviewed, tested, and documented, which resulted in the development of a nonparametric estimator for discrete probability estimation. Variations in seasonality of precipitation as a function of latitude and topographic factors were seen through the non parametric estimation of the time-varying occurrence frequency. Nonparametric spectral analysis, performed on monthly precipitation, revealed significant interannual frequencies and coherence with known atmospheric oscillations. Consequently, a non parametric, nonhomogeneous Markov chain for modeling daily precipitation was developed that obviated the need to divide the year into seasons. Multivariate nonparametric resampling technique from the nonparametrically fitted probability density functions, which can be likened to a smoothed bootstrap approach, was developed for the simulation of other weather variables (solar radiation, maximum and minimum temperature, average dew point temperature, and average wind speed). In this technique the vector of variables on a day is generated by conditioning on the vector of these variables on the preceding day and the precipitation amount on the current day generated from the wet/dry spell model

Daily minimum and maximum temperature simulation over complex terrain

Spatiotemporal simulation of minimum and maximum temperature is a fundamental requirement for climate impact studies and hydrological or agricultural models. Particularly over regions with variable orography, these simulations are difficult to produce due to terrain driven nonstationarity. We develop a bivariate stochastic model for the spatiotemporal field of minimum and maximum temperature. The proposed framework splits the bivariate field into two components of "local climate" and "weather." The local climate component is a linear model with spatially varying process coefficients capturing the annual cycle and yielding local climate estimates at all locations, not only those within the observation network. The weather component spatially correlates the bivariate simulations, whose matrix-valued covariance function we estimate using a nonparametric kernel smoother that retains nonnegative definiteness and allows for substantial nonstationarity across the simulation domain. The statistical model is augmented with a spatially varying nugget effect to allow for locally varying small scale variability. Our model is applied to a daily temperature data set covering the complex terrain of Colorado, USA, and successfully accommodates substantial temporally varying nonstationarity in both the direct-covariance and cross-covariance functions.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS602 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Open Source Software Capability Maturity Model: A Conceptual Framework

Use of Open Source Software (OSS) projects is increasing in the corporate environment, thereby creating a need for an evaluation framework for these projects. Owing to the significant differences in the development process of open source from the traditional software development model, the Capability Maturity Model framework cannot be directly applied to the OSS development environment. For organizations evaluating OSS for adoption, a framework that provides a barometer of process maturity in the open source development environment can be valuable. To this end, we propose a framework of Open Source Maturity Model and describe the key process areas for different levels of maturity as relevant to OSS domain

Flow Turbulence And Information Quality

A crucial aspect of managing the corporate information resource is the ability to assess and maintain information quality.  This paper proposes criteria for defining and measuring information quality based on established parameters of information flow.  We develop a conceptual model linking information flow metrics and information qualities.  The basic premise of the model is that changes in flow metrics affect the usefulness criteria of information, which in turn impact information quality.  The usefulness criteria of information are based on established accounting standards.  This mapping of flow metrics to information quality is a necessary and critical step towards the development of a robust instrument for quality assessment.  The implications of the proposed model for managing information flows resulting from business processes are discussed

Risk Visualization: A Mechanism for Supporting Unstructured Decision Making Processes

The premise of this paper is that risk visualization has the potential to reduce the seemingly irrational behavior of decision makers. In this context, we present a model that enhances our understanding of visualization and how it can be used to support risk based decision making. The contribution of our research stems from the fact that decision making scenarios in business are characterized by uncertainty and a lack of structure. The complexity inherent in such scenarios is manifested in the form of unavailability of information, too many alternatives, inability to quantify alternatives, or lack of knowledge of the payoff matrix. This is particularly prevalent in domains such as investment decision making. Rational decision making in such domains requires a careful assessment of the risk reward payoff matrix. However, individuals cope with such uncertainty by resorting to a variety of heuristics. Prior decision support models have been unsuccessful in dealing with complexity and nuances that have come to typify such heuristic based decision making

Data Mining for Decision Support

The amount of data collected by businesses today is phenomenal. The analysis of this data is critical as more and more businesses are using this data to analyze their competition, product or market. Data mining is the process of digging through this mass of data to discover information (patterns or new knowledge) that can be critical to decision making in organizations. Data mining has added importance as organizations begin to rely more heavily on this information to make critical decisions. The need for using the right data mining tools effectively to support decision making cannot be overemphasized