Sai Ravela

A STATISTICAL DETERMINISTIC APPROACH TO HURRICANE RISK ASSESSMENT

Hurricanes are lethal and costly phenomena, and it is therefore of great importance to assess the long-term risk they pose to society. Among the greatest threats are those associated with high winds and related phenomena, such as storm surges. Here we assess the probability that hurricane winds will affect any given point in space by combining an estimate of the probability that a hurricane will pass within some given radius of the point in question with an estimate of the spatial probability density of storm winds. To assess the probability that storms will pass close enough to a point of interest to affect it, we apply two largely independent techniques for generating large numbers of synthetic hurricane tracks. The first treats each track as a Markov chain, using statistics derived from observed hurricane-track data. The second technique begins by generating a large class of synthetic, time-varying wind fields at 850 and 250 hPa whose variance, covariance, and monthly means match NCEP–NCAR reanalysis d...

Model-based monitoring for early warning flood detection

Predictive environmental sensor networks provide complex engineering and systems challenges. These systems must withstand the event of interest, remain functional over long time periods when no events occur, cover large geographical regions of interest to the event, and support the variety of sensor types needed to detect the phenomenon. Prediction of the phenomenon on the network complicates the system further, requiring additional computation on themicrocontrollers and utilizing prediction models that are not typically designed for sensor networks. This paper describes a system architecture and deployment to meet the design requirements and to allow model-driven control, thereby optimizing the prediction capability of the system. We explore the application of river flood prediction using this architecture, describing our work on a centralized form of the prediction model, network implementation, component testing and infrastructure development in Honduras, deployment on a river in Massachusetts, and results of the field experiments. Our system uses only a small number of nodes to cover basins of 1000-10000 square km2 using an unique heterogeneous communication structure to provide real-time sensed data, incorporating self-monitoring for failure, and adapting measurement schedules to capture events of interest.

Retrieving images by similarity of visual appearance

A system to retrieve images using a description of visual appearance is presented. A multi-scale invariant vector representation is obtained by first filtering images in the database with Gaussian derivative filters at several scales and then computing low-order differential invariants. The multi-scale representation is indexed for rapid retrieval. Queries are designed by the users from an example image by selecting appropriate regions. The invariant vectors corresponding to these regions are matched with those in the database both in feature space as well as in coordinate space, and a match score is obtained for each image. The results are then displayed to the user sorted by the match score. From experiments conducted with over 1500 images of objects embedded in arbitrary backgrounds, it is shown that images which are similar in appearance and whose viewpoint is within 25 degrees of the query image can be retrieved with an average precision of 57.4%

Joint effects of storm surge and sea-level rise on US Coasts: new economic estimates of impacts, adaptation, and benefits of mitigation policy

Recent literature, the US Global Change Research Program’s National Climate Assessment, and recent events, such as Hurricane Sandy, highlight the need to take better account of both storm surge and sea-level rise (SLR) in assessing coastal risks of climate change. This study combines three models—a tropical cyclone simulation model; a storm surge model; and a model for economic impact and adaptation—to estimate the joint effects of storm surge and SLR for the US coast through 2100. The model is tested using multiple SLR scenarios, including those incorporating estimates of dynamic ice-sheet melting, two global greenhouse gas (GHG) mitigation policy scenarios, and multiple general circulation model climate sensitivities. The results illustrate that a large area of coastal land and property is at risk of damage from storm surge today; that land area and economic value at risk expands over time as seas rise and as storms become more intense; that adaptation is a cost-effective response to this risk, but residual impacts remain after adaptation measures are in place; that incorporating site-specific episodic storm surge increases national damage estimates by a factor of two relative to SLR-only estimates, with greater impact on the East and Gulf coasts; and that mitigation of GHGs contributes to significant lessening of damages. For a mid-range climate-sensitivity scenario that incorporates dynamic ice sheet melting, the approach yields national estimates of the impacts of storm surge and SLR of

Fast ensemble smoothing

990 billion through 2100 (net of adaptation, cumulative undiscounted 2005

Sloop: A pattern retrieval engine for individual animal identification

); GHG mitigation policy reduces the impacts of the mid-range climate-sensitivity estimates by

On viewpoint control

84 to

Appearance-Based Global Similarity Retrieval of Images

100 billion.

Mixture Ensembles for Data Assimilation in Dynamic Data-driven Environmental Systems

Smoothing is essential to many oceanographic, meteorological, and hydrological applications. There are two predominant classes of smoothing problems. The first is fixed-interval smoothing, where the objective is to estimate model states within a time interval using all available observations in the interval. The second is fixed-lag smoothing, where the objective is to sequentially estimate model states over a fixed or indefinitely growing interval by restricting the influence of observations within a fixed window of time ahead of the evolving estimation time. In this paper, we use an ensemble-based approach to fixed-interval and fixed-lag smoothing, and synthesize two algorithms. The first algorithm is a fixed-interval smoother whose computation time is linear in the interval. The second algorithm is a fixed-lag smoother whose computation time is independent of the lag length. The complexity of these algorithms is presented, shown to improve upon existing implementations and verified with identical-twin experiments conducted with the Lorenz-95 system. Results suggest that ensemble methods yield efficient fixed-interval and fixed-lag smoothing solutions in the sense that the additional increment for smoothing is a small fraction of either filtering or model propagation costs in a practical ensemble application. We also show that fixed-interval smoothing can perform as fast as fixed-lag smoothing, and it may not be necessary to use a fixed-lag approximation for computational savings alone.

Deformation invariant image matching by spectrally controlled diffeomorphic alignment

Abstract Identifying individuals in photographs of animals collected over time is a non-invasive approach for ecological monitoring and conservation. This paper describes the design and use of Sloop, the first image retrieval system for individual animal identification incorporating crowd-sourced relevance feedback. Sloop׳s iterative retrieval strategy using hierarchical and aggregated matching and relevance feedback consistently improves deformation and correspondence-based approaches for individual identification across several species. Its crowdsourcing strategy is successful in utilizing relevance feedback on a large scale. Sloop is in operational use. The user experience and results are presented here to facilitate the creation of a community-based individual identification system for conservation planning.