Tyndall Working Paper No. 146, Oct 2010
by Abiy S. Kebede, Robert J. Nicholls, Susan Hanson, and Mustafa Mokrech
“This GIS-based study provides a first quantitative estimate, both now and through the 21st Century, of the number of people and associated economic assets potentially exposed to coastal flooding due to sea-level rise and storm surges in Mombasa. The methods used here could be applied more widely to other coastal cities in Africa and elsewhere to better understand present and future exposure and worst-case risks due to climate change and rising sea levels.”
American Geophysical Union, Fall Meeting 2010
Aulov, O.; Lary, D. J.
“There is great utility in having a flexible and automated objective observation direction system for the decadal survey missions and beyond. Such a system allows us to optimize the observations made by suite of sensors to address specific goals from long term monitoring to rapid response. We have developed such a prototype using a network of communicating software elements to control a heterogeneous network of sensor systems, which can have multiple modes and flexible viewing geometries. Our system makes sensor systems intelligent and situationally aware. Together they form a sensor web of multiple sensors working together and capable of automated target selection, i.e. the sensors “know” where they are, what they are able to observe, what targets and with what priorities they should observe. This system is implemented in three components. The first component is a Sensor Web simulator. The Sensor Web simulator describes the capabilities and locations of each sensor as a function of time, whether they are orbital, sub-orbital, or ground based. The simulator has been implemented using AGIs Satellite Tool Kit (STK). STK makes it easy to analyze and visualize optimal solutions for complex space scenarios, and perform complex analysis of land, sea, air, space assets, and shares results in one integrated solution. The second component is target scheduler that was implemented with STK Scheduler. STK Scheduler is powered by a scheduling engine that finds better solutions in a shorter amount of time than traditional heuristic algorithms. The global search algorithm within this engine is based on neural network technology that is capable of finding solutions to larger and more complex problems and maximizing the value of limited resources. The third component is a modeling and data assimilation system. It provides situational awareness by supplying the time evolution of uncertainty and information content metrics that are used to tell us what we need to observe and the priority we should give to the observations. A prototype of this component was implemented with AutoChem. AutoChem is NASA release software constituting an automatic code generation, symbolic differentiator, analysis, documentation, and web site creation tool for atmospheric chemical modeling and data assimilation. Its model is explicit and uses an adaptive time-step, error monitoring time integration scheme for stiff systems of equations. AutoChem was the first model to ever have the facility to perform 4D-Var data assimilation and Kalman filter. The project developed a control system with three main accomplishments. First, fully multivariate observational and theoretical information with associated uncertainties was combined using a full Kalman filter data assimilation system. Second, an optimal distribution of the computations and of data queries was achieved by utilizing high performance computers/load balancing and a set of automatically mirrored databases. Third, inter-instrument bias correction was performed using machine learning. The PI for this project was Dr. David Lary of the UMBC Joint Center for Earth Systems Technology at NASA/Goddard Space Flight Center.”
ASHEcon 3rd Biennial Conference, June 2010
“The purpose of this study is to estimate structural elements of consumers’ demand functions for healthcare facilities, particularly hospitals and ambulatory surgery centers (ASCs), towards the goal of answering questions about welfare gains earned from the introduction of ASCs. For identification I use spatial variation across patients and facilities. In line with the existing literature, I show that there is a strong spatial component to demand. Developing a discrete choice model of demand for healthcare facilities, I estimate structural parameters from consumers’ demand functions from nested logit and mixed logit (also called random coefficient) specifications. Travel costs are found to be the best predictor of consumers’ healthcare facility purchase. Insurance variables are found to be a significant choice in the nest decision of ASC or hospital. The estimation methodology enables calculation of a cross-time substitution matrix to explain how consumers substitute between facilities over space. Finally, I measure how consumer welfare would change if a subset of facilities (ASCs) were removed from consumers’ choice sets. All of this is done without explicitly including a price variable, but instead using time and travel costs to give meaning to welfare numbers. Welfare loss from the elimination of ASCs is found to be small, less than five minutes of welfare loss per patient for a given procedure.”