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Intelligent Systems for Geosciences: An Essential Research Agenda

Gil, Y., S.A. Pierce, H. Babaie, A. Banerjee, K. Borne, G. Bust, M. Cheatham, I. Ebert-Uphoff, C. Gomes, M. Hill, J. Horel, L. Hsu, J. Kinter, C. Knoblock, D. Krum, V. Kumar, P.F.J. Lermusiaux, Y. Liu, C. North, V. Pankratius, S. Peters, B. Plale, A. Pope, S. Ravela, J. Restrepo, A. Ridley, H. Samet, and S. Shekhar, 2019. Intelligent Systems for Geosciences: An Essential Research Agenda. Communications of the ACM, 62(1), 76–84. doi:10.1145/3192335

Many aspects of geosciences pose novel problems for intelligent systems research. Geoscience data is challenging because it tends to be uncertain, intermittent, sparse, multiresolution, and multiscale. Geosciences processes and objects often have amorphous spatiotemporal boundaries. The lack of ground truth makes model evaluation, testing, and comparison difficult. Overcoming these challenges requires breakthroughs that would significantly transform intelligent systems, while greatly benefitting the geosciences in turn. Although there have been significant and beneficial interactions between the intelligent systems and geosciences communities, the potential for synergistic research in intelligent systems for geosciences is largely untapped. A recently launched Research Coordination Network on Intelligent Systems for Geosciences followed a workshop at the National Science Foundation on this topic. This expanding network builds on the momentum of the NSF EarthCube initiative for geosciences, and is driven by practical problems in Earth, ocean, atmospheric, polar, and geospace sciences. Based on discussions and activities within this network, this article presents a research agenda for intelligent systems inspired by geosciences challenges.


Multiple-Pursuer-One-Evader Pursuit Evasion Game in Dynamic Flow Fields

Sun, W., P. Tsiotras, T. Lolla, D. N. Subramani, and P. F. J. Lermusiaux, 2017. Multiple-Pursuer-One-Evader Pursuit Evasion Game in Dynamic Flow Fields. Journal of Guidance, Control and Dynamics, 40 (7), 1627-1637. DOI: 10.2514/1.G002125

In this paper a reachability-based approach is adopted to deal with the pursuit-evasion di erential game between one evader and multiple pursuers in the presence of dynamic environmental disturbances (e.g., winds, sea currents). Conditions for the game to be terminated are given in terms of reachable set inclusions. Level set equations are defi ned and solved to generate the forward reachable sets of the pursuers and the evader. The time-optimal trajectories and the corresponding optimal strategies are sub- sequently retrieved from these level sets. The pursuers are divided into active pursuers, guards, and redundant pursuers according to their respec- tive roles in the pursuit-evasion game. The proposed scheme is implemented on problems with both simple and realistic time-dependent flow fi elds, with and without obstacles.

Many Task Computing for Real-Time Uncertainty Prediction and Data Assimilation in the Ocean

Evangelinos, C., P.F.J. Lermusiaux, J. Xu, P.J. Haley, and C.N. Hill, 2011. Many Task Computing for Real-Time Uncertainty Prediction and Data Assimilation in the Ocean. IEEE Transactions on Parallel and Distributed Systems, Special Issue on Many-Task Computing, I. Foster, I. Raicu and Y. Zhao (Guest Eds.), 22, doi: 10.1109/TPDS.2011.64.

Uncertainty prediction for ocean and climate predictions is essential for multiple applications today. Many-Task Computing can play a significant role in making such predictions feasible. In this manuscript, we focus on ocean uncertainty prediction using the Error Subspace Statistical Estimation (ESSE) approach. In ESSE, uncertainties are represented by an error subspace of variable size. To predict these uncertainties, we perturb an initial state based on the initial error subspace and integrate the corresponding ensemble of initial conditions forward in time, including stochastic forcing during each simulation. The dominant error covariance (generated via SVD of the ensemble) is used for data assimilation. The resulting ocean fields are used as inputs for predictions of underwater sound propagation. ESSE is a classic case of Many Task Computing: It uses dynamic heterogeneous workflows and ESSE ensembles are data intensive applications. We first study the execution characteristics of a distributed ESSE workflow on a medium size dedicated cluster, examine in more detail the I/O patterns exhibited and throughputs achieved by its components as well as the overall ensemble performance seen in practice. We then study the performance/usability challenges of employing Amazon EC2 and the Teragrid to augment our ESSE ensembles and provide better solutions faster.

Automated Sensor Networks to Advance Ocean Science

Schofield, O., S. Glenn, J. Orcutt, M. Arrott, M. Meisinger, A. Gangopadhyay, W. Brown, R. Signell, M. Moline, Y. Chao, S. Chien, D. Thompson, A. Balasuriya, P.F.J. Lermusiaux and M. Oliver, 2010. Automated Sensor Networks to Advance Ocean Science. EOS, Vol. 91, No. 39, 28 September 2010.

Oceanography is evolving from a ship-based expeditionary science to a distributed, observatory- based approach in which scientists continuously interact with instruments in the field. These new capabilities will facilitate the collection of long- term time series while also providing an interactive capability to conduct experiments using data streaming in real time. The U.S. National Science Foundation has funded the Ocean Observatories Initiative (OOI), which over the next 5 years will deploy infrastructure to expand scientists’ ability to remotely study the ocean. The OOI is deploying infrastructure that spans global, regional, and coastal scales. A global component will address planetary- scale problems using a new network of moored buoys linked to shore via satellite telecommunications. A regional cabled observatory will “wire” a single region in the northeastern Pacific Ocean with a high-speed optical and power grid. The coastal component will expand existing coastal observing assets to study the importance of high-frequency forcing on the coastal environment. These components will be linked by a robust cyberinfrastructure (CI) that will integrate marine observatories into a coherent system of systems. This CI infrastructure will also provide a Web- based social network enabled by real- time visualization and access to numerical model information, to provide the foundation for adaptive sampling science. Thus, oceanographers will have access to automated machine-to-machine sensor networks that can be scalable to increase in size and incorporate new technology for decades to come. A case study of this CI in action shows how a community of ocean scientists and engineers located throughout the United States at 12 different institutions used the automated ocean observatory to address daily adaptive science priorities in real time.

Towards Dynamic Data Driven Systems for Rapid Adaptive Interdisciplinary Ocean Forecasting

Patrikalakis, N.M., P.F.J. Lermusiaux, C. Evangelinos, J.J. McCarthy, A.R. Robinson, H. Schmidt, P.J. Haley, S. Lalis, R. Tian, W.G. Leslie, and W. Cho, 2009. Towards Dynamic Data Driven Systems for Rapid Adaptive Interdisciplinary Ocean Forecasting Invited paper in "Dynamic Data-Driven Application Systems''. F. Darema, Editor. Springer, 2009. In press.

The state of the ocean evolves and its dynamics involves transitions occurring at multiple scales. For efficient and rapid interdisciplinary forecasting, ocean observing and prediction systems must have the same behavior and adapt to the everchanging dynamics. The work discussed here aims to set the basis of a distributed system for real-time interdisciplinary ocean field and uncertainty forecasting with adaptive modeling and adaptive sampling. The scientific goal is to couple physical and biological oceanography with ocean acoustics. The technical goal is to build a dynamic system based on advanced infrastructures, distributed / grid computing and efficient information retrieval and visualization interfaces. Importantly, the system combines a suite of modern legacy physical models, acoustic models and ocean current monitoring data assimilation schemes with new adaptive modeling and adaptive sampling methods. The legacy systems are encapsulated at the binary level using software component methodologies. Measurement models are utilized to link the observed data to the dynamical model variables and structures. With adaptive sampling, the data acquisition is dynamic and aims to minimize the predicted uncertainties, maximize the sampling of key dynamics and maintain overall coverage. With adaptive modeling, model improvements are dynamic and aim to select the best model structures and parameters among different physical or biogeochemical parameterizations. The dynamic coupling of models and measurements discussed here represents a Dynamic Data-Driven Application System (DDDAS). Technical and scientific progress is highlighted based on examples in Massachusetts Bay, and Monterey Bay and the California Current System. Keywords: Oceanography, interdisciplinary, adaptive, sampling,

Web-Enabled Configuration and Control of Legacy Codes: An Application to Ocean Modeling

Evangelinos, C., P.F.J. Lermusiaux, S. Geiger, R.C. Chang, and N.M. Patrikalakis, 2006. Web-Enabled Configuration and Control of Legacy Codes: An Application to Ocean Modeling. Ocean Modeling, 13, 197-220.

For modern interdisciplinary ocean prediction and assimilation systems, a significant part of the complexity facing users is the very large number of possible setups and parameters, both at build-time and at run-time, especially for the core physical, biological and acoustical ocean predictive models. The configuration of these modeling systems for both local as well as remote execution can be a daunting and error-prone task in the absence of a graphical user interface (GUI) and of software that automatically controls the adequacy and compatibility of options and parameters. We propose to encapsulate the configurability and requirements of ocean prediction codes using an eXtensible Markup Language (XML) based description, thereby creating new computer-readable manuals for the executable binaries. These manuals allow us to generate a GUI, check for correctness of compilation and input parameters, and finally drive execution of the prediction system components, all in an automated and transparent manner. This web-enabled configuration and automated control software has been developed (it is currently in “beta” form) and exemplified for components of the interdisciplinary Harvard ocean prediction system (HOPS) and for the uncertainty prediction components of the error subspace statistical estimation (ESSE) system. Importantly, the approach is general and applies to other existing ocean modeling applications and to other “legacy” codes.

Rapid real-time interdisciplinary ocean forecasting using adaptive sampling and adaptive modeling and legacy codes: Component encapsulation using XML

Evangelinos C., R. Chang, P.F.J. Lermusiaux and N.M. Patrikalakis, 2003. Rapid real-time interdisciplinary ocean forecasting using adaptive sampling and adaptive modeling and legacy codes: Component encapsulation using XML. Lecture Notes in Computer Science, 2660, 375-384.

We present the high level architecture of a real-time interdisciplinary ocean forecasting system that employs adaptive elements in both modeling and sampling. We also discuss an important issue that arises in creating an integrated, web-accessible framework for such a system out of existing stand-alone components: transparent support for handling legacy binaries. Such binaries, that are most common in scientific applications, expect a standard input stream, maybe some command line options, a set of input files and generate a set of output files as well as standard output and error streams. Legacy applications of this form are encapsulated using XML. We present a method that uses XML documents to describe the parameters for executing a binary.