Publications

Julia is a new language for writing data analysis programs that are easy to implement and run at high performance. Similarly, the Dynamic Distributed Dimensional Data Model (D4M) aims to clarify data analysis operations while retaining strong performance. D4M accomplishes these goals through a composable, unified data model on associative arrays. In this work, we present an implementation of D4M in Julia and describe how it enables and facilitates data analysis. Several experiments showcase scalable performance in our new Julia version as compared to the original Matlab implementation.

San Francisco International Airport (SFO) is unable to use independent parallel approaches to its closely-spaced parallel runways when marine stratus is present in the approach. Delay programs are imposed to regulate the flow of traffic to match the true arrival capacity of the airport. Failure to forecast accurately the times of onset and dissipation of stratus in the approach results in unnecessary delays, costly airborne holding and diversions, or in wasted capacity as the traffic management planners fail to match the arrival rate to the actual airport capacity. Previous studies have shown that accurate 1-2 hour forecasts of the times of clearing in the approach could provide substantial reductions in the delays and inefficiencies associated with the marine stratus impacts on air traffic at SFO. The San Francisco Marine Stratus Initiative has provided a four-year focus on this problem and has resulted in the development of several forecast algorithms that will aid, the operational forecasting of the dissipation of marine stratus in the approach to SFO (Clark and Wilson, 1997). These algorithms involve new techniques for the analysis of observational data and statistical and dynamical prognosis of the behavior of the marine stratus. This discussion of the design and the performance of these algorithms provides an overview of the status of this project.