Constantinos Evangelinos

Path Planning of Autonomous Underwater Vehicles for Adaptive Sampling Using Mixed Integer Linear Programming

The goal of adaptive sampling in the ocean is to predict the types and locations of additional ocean measurements that would be most useful to collect. Quantitatively, what is most useful is defined by an objective function and the goal is then to optimize this objective under the constraints of the available observing network. Examples of objectives are better oceanic understanding, to improve forecast quality, or to sample regions of high interest. This work provides a new path-planning scheme for the adaptive sampling problem. We define the path-planning problem in terms of an optimization framework and propose a method based on mixed integer linear programming (MILP). The mathematical goal is to find the vehicle path that maximizes the line integral of the uncertainty of field estimates along this path. Sampling this path can improve the accuracy of the field estimates the most. While achieving this objective, several constraints must be satisfied and are implemented. They relate to vehicle motion, intervehicle coordination, communication, collision avoidance, etc. The MILP formulation is quite powerful to handle different problem constraints and flexible enough to allow easy extensions of the problem. The formulation covers single- and multiple-vehicle cases as well as single- and multiple-day formulations. The need for a multiple-day formulation arises when the ocean sampling mission is optimized for several days ahead. We first introduce the details of the formulation, then elaborate on the objective function and constraints, and finally, present a varied set of examples to illustrate the applicability of the proposed method.

Adaptive Coupled Physical and Biogeochemical Ocean Predictions: A Conceptual Basis

Physical and biogeochemical ocean dynamics can be intermittent and highly variable, and involve interactions on multiple scales. In general, the oceanic fields, processes and interactions that matter thus vary in time and space. For efficient forecasting, the structures and parameters of models must evolve and respond dynamically to new data injected into the executing prediction system. The conceptual basis of this adaptive modeling and corresponding computational scheme is the subject of this presentation. Specifically, we discuss the process of adaptive modeling for coupled physical and biogeochemical ocean models. The adaptivity is introduced within an interdisciplinary prediction system. Model-data misfits and data assimilation schemes are used to provide feedback from measurements to applications and modify the runtime behavior of the prediction system. Illustrative examples in Massachusetts Bay and Monterey Bay are presented to highlight ongoing progress.

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

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.

Many task computing for multidisciplinary ocean sciences: real-time uncertainty prediction and data assimilation

Error Subspace Statistical Estimation (ESSE), an uncertainty prediction and data assimilation methodology employed for real-time ocean forecasts, is based on a characterization and prediction of the largest uncertainties. This is carried out by evolving an error subspace of variable size. We use an ensemble of stochastic model simulations, initialized based on an estimate of the dominant initial uncertainties, to predict the error subspace of the model fields. The dominant error covariance (generated via an SVD of the ensemble-generated error covariance matrix) is used for data assimilation. The resulting ocean fields are provided as the input to acoustic modeling, allowing for the prediction and study of the spatiotemporal variations in acoustic propagation and their uncertainties. The ESSE procedure is a classic case of Many Task Computing: These codes are managed based on dynamic workflows for the: (i) perturbation of the initial mean state, (ii) subsequent ensemble of stochastic PE model runs, (iii) continuous generation of the covariance matrix, (iv) successive computations of the SVD of the ensemble spread until a convergence criterion is satisfied, and (v) data assimilation. Its ensemble nature makes it a many task data intensive application and its dynamic workflow gives it heterogeneity. Subsequent acoustics propagation modeling involves a very large ensemble of short-in-duration acoustics runs. We study the execution characteristics and challenges of a distributed ESSE workflow on a large dedicated cluster and the usability of enhancing this with runs on Amazon EC2 and the Teragrid and the I/O challenges faced.

Flow feature extraction in oceanographic visualization

This paper presents a novel method to detect an important flow feature, vortices, in the ocean. Our method can detect closed streamlines around vortex cores. Coupled with existing vortex core detection, the entire vortex area, which is the combination of the vortex core and surrounding streamlines, can be detected. A variety of feature extraction methods are presented, and those more pertinent to this study are implemented. Detection results are evaluated in terms of accuracy, clarity and usability

Path Planning Methods for Adaptive Sampling of Environmental and Acoustical Ocean Fields

Adaptive sampling aims to predict the types and locations of additional observations that are most useful for specific objectives, under the constraints of the available observing network. Path planning refers to the computation of the routes of the assets that are part of the adaptive component of the observing network. In this paper, we present two path planning methods based on Mixed Integer Linear Programming (MILP). The methods are illustrated with some examples based on environmental ocean fields and compared to highlight their strengths and weaknesses. The stronger method is further demonstrated on a number of examples covering multi-vehicle and multi-day path planning, based on simulations for the Monterey Bay region. The framework presented is powerful and flexible enough to accommodate changes in scenarios. To demonstrate this feature, acoustical path planning is also discussed

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

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.

A schema based paradigm for facile description and control of amulti-component parallel, coupled atmosphere-ocean model

Multidisciplinary Earth System science applications involve coupling interacting components over differing time scales and varying spatial locations. Each individual Earth System model component is, in itself, a complex high-performance application that involves considerable user expertise in setting up both at build-time and at runtime. The added complexity of running in a coupled fashion presents domain science experts with additional hurdles as they end up handling the configuration of codes they are not intimately familiar with. Seeing the need to free the domain scientists from a lot of this complexity at the programming level and encourage interdisciplinary code development, component frameworks (e.g. ESMF, CCA) have emerged over the past few years with considerable success. But in order to facilitate the build and runtime configuration of the existing and emerging coupled component applications we need to extend the individual component definitions beyond interface syntax to an encapsulation of the build- and runtime configurations and augment that with the respective coupler component configuration. We have previously successfully employed an XML Schema based language to comprehensively describe modifiable build- and runtime options of complex ocean modeling systems. In this paper we examine extending our approach to cover a coupled ocean-atmosphere model. The result is that we can present the end-user with an automatically generated, semantically rich and validating, GUI to setup and run the coupled system in parallel on different architectures and in the future on Grid systems as well. This paradigm allows scientists using the coupled model to more rapidly comprehend a coupled system, avoid technical and physical configuration errors and concentrate more on coupled model results and analysis.