James A. Hansen

Ensemble-Based Adaptive Targeting of Mobile Sensor Networks

This work presents an efficient algorithm for an observation targeting problem that is complicated by the combinatorial number of targeting choices. The approach explicitly incorporates an ensemble forecast to ensure that the measurements are chosen based on their expected improvement in the forecast at a separate verification time and location. The primary improvements in the efficiency are obtained by computing the impact of each possible measurement on the uncertainty reduction over this verification site backwards. In particular, the approach determines the impact of a series of fictitious observations taken at the verification site back on the search space (and time), which provides all of the information needed to optimize the set of measurements to take and significantly reduces the number of times that the computationally expensive ensemble updates must be performed. A computation time analysis and numerical performance simulations using the two-dimensional Lorenz-95 chaos model are presented to validate the computational advantage of the proposed algorithm over conventional search strategies.

Simulating pirate behavior to exploit environmental information

Recent years have seen an upsurge in piracy, particularly off the Horn of Africa. Piracy differs from other asymmetric threats, such as terrorism, in that it is economically motivated. Pirates operating off East Africa have threatened maritime safety and cost commercial shipping billions of dollars paid in ransom. Piracy in this region is conducted from small boats which can only survive for a few days away from their base of operations, have limited survival in severe weather, and cannot perform boarding operations in high wind or sea state conditions. In this study we use agent models and statistical design of experiments to gain insight into how meteorological and oceanographic forecasts can used to dynamically predict relative risks for commercial shipping.

Assimilation of AERONET and MODIS AOT observations using variational and ensemble data assimilation methods and its impact on aerosol forecasting skill

Data assimilation of AERONET and MODIS Aerosol Optical Thickness (AOT) for aerosol forecasting was tested within the Navy Aerosol Analysis Prediction System (NAAPS) framework, using variational and ensemble data assimilation methods. Navy aerosol forecasting is currently comprised of a deterministic NAAPS simulation coupled to NAVDAS-AOD, a 2-dimensional variational data assimilation system, for MODIS AOT assimilation. An ensemble version of NAAPS (ENAAPS) coupled to an Ensemble Adjustment Kalman Filter (EAKF) from DART was recently developed, allowing for a range of data assimilation and forecasting experiments to be run with deterministic NAAPS and ENAAPS. The main findings are that the EAKF, with its flow dependent error covariances, makes better use of sparse observations such as AERONET AOT. Assimilating individual AERONET observations in the 2DVar can increase the analysis errors when observations are located in high AOT gradient regions. By including AERONET with MODIS AOT assimilation, the magnitudes of peak aerosol events (AOT > 1) were better captured with improved temporal variability, especially in India and Asia where aerosol prediction is a challenge. Assimilating AERONET AOT with MODIS had little impact on the 24-hour forecast skill compared to MODIS assimilation only, but differences were found downwind of AERONET sites. The 24-hour forecast skill was approximately the same for forecasts initialized with analyses from AERONET AOT assimilation alone compared to MODIS assimilation, particularly in regions where the AERONET network is dense; including the United States and Europe, indicating AERONET could serve as a backup observation network for over-land synoptic scale aerosol events.

Wave Probabilities Consistent with Official Tropical Cyclone Forecasts

AbstractDevelopment of a 12-ft-seas significant wave height ensemble consistent with the official tropical cyclone intensity, track, and wind structure forecasts and their errors from the operational U.S. tropical cyclone forecast centers is described. To generate the significant wave height ensemble, a Monte Carlo wind speed probability algorithm that produces forecast ensemble members is used. These forecast ensemble members, each created from the official forecast and randomly sampled errors from historical official forecast errors, are then created immediately after the official forecast is completed. Of 1000 forecast ensemble members produced by the wind speed algorithm, 128 of them are selected and processed to produce wind input for an ocean surface wave model. The wave model is then run once per realization to produce 128 possible forecasts of significant wave height. Probabilities of significant wave height at critical thresholds can then be computed from the ocean surface wave model–generated si...

Optimal asset network planning for counter piracy operation support, part 1: Under the hood

This work proposes a system to allocate surveillance resources in areas of high piracy risk when the satellite AIS coverage is low, resulting in an improved maritime traffic situational awareness. The system is based on multiobjective optimization algorithms providing solutions that are on the so-called Pareto optimal front. The solutions are a trade-off among different objectives, including surveillance risk, area coverage, and mission costs. In general, the inclusion of an objective in the multiobjective vector function is application dependent.

GPCE-AX: An Anisotropic Extension to the Goerss Predicted Consensus Error in Tropical Cyclone Track Forecasts

AbstractA method to predict an anisotropic expected forecast error distribution for consensus forecasts of tropical cyclone (TC) tracks is presented. The method builds upon the Goerss predicted consensus error (GPCE), which predicts the isotropic radius of the 70% isopleth of expected TC track error. Consensus TC track forecasts are computed as the mean of a collection of TC track forecasts from different models and are basin dependent. A novel aspect of GPCE is that it uses not only the uncertainty in the collection of constituent models to predict expected error, but also other features of the predicted storm, including initial intensity, forecast intensity, and storm speed. The new method, called GPCE along–across (GPCE-AX), takes a similar approach but separates the predicted error into across-track and along-track components. GPCE-AX has been applied to consensus TC track forecasts in the Atlantic (CONU/TVCN, where CONU is consensus version U and TVCN is the track variable consensus) and in the weste...

Optimal asset network planning for counter piracy operation support, part 2: Results

A companion article (SYSTEMS Magazine, May 2014, 29 5, pages 4-11.) proposed a system to allocate surveillance resources in areas of high piracy risk when the satellite AIS coverage is low, resulting in an improved maritime traffic situational awareness. Here, the methodology has been applied to realistic scenarios in the Indian Ocean using real-world data evaluated by comparing extreme and trade-off solutions to assess the effectiveness of the proposed scheme for timely delivery of the solutions and achievement of mission objectives. The proposed asset planning system has a highly flexible architecture that can be expanded to account for different application-dependent risks and performance metrics. Possible directions for future studies include developing models for adaptive (adversarial) pirate behavior, as recently shown to exist and to be expected. A game theoretical approach would be suitable to model not only the optimal asset network planning but also the adaptive response of pirate activities.

Adaptive Observation Strategies for Forecast Error Minimization

Using a scenario of multiple mobile observing platforms (UAVs) measuring weather variables in distributed regions of the Pacific, we are developing algorithms that will lead to improved forecasting of high-impact weather events. We combine technologies from the nonlinear weather prediction and planning/control communities to create a close link between model predictions and observed measurements, choosing future measurements that minimize the expected forecast error under time-varying conditions. We have approached the problem on three fronts. We have developed an information-theoretic algorithm for selecting environment measurements in a computationally effective way. This algorithm determines the best discrete locations and times to take additional measurement for reducing the forecast uncertainty in the region of interest while considering the mobility of the sensor platforms. Our second algorithm learns to use past experience in predicting good routes to travel between measurements. Experiments show that these approaches work well on idealized models of weather patterns.

A Multiobjective Path-Planning Algorithm With Time Windows for Asset Routing in a Dynamic Weather-Impacted Environment

This paper presents a mixed-initiative tool for multiobjective planning and asset routing (TMPLAR) in dynamic and uncertain environments. TMPLAR is built upon multiobjective dynamic programming algorithms to route assets in a timely fashion, while considering fuel efficiency, voyage time, distance, and adherence to real world constraints (asset vehicle limits, navigator-specified deadlines, etc.). TMPLAR has the potential to be applied in a variety of contexts, including ship, helicopter, or unmanned aerial vehicle routing. The tool provides recommended schedules, consisting of waypoints, associated arrival and departure times, asset speed and bearing, that are optimized with respect to several objectives. The ship navigation is exacerbated by the need to address multiple conflicting objectives, spatial and temporal uncertainty associated with the weather, multiple constraints on asset operation, and the added capability of waiting at a waypoint with the intent to avoid bad weather, conduct opportunistic training drills, or both. The key algorithmic contribution is a multiobjective shortest path algorithm for networks with stochastic nonconvex edge costs and the following problem features: 1) time windows on nodes; 2) ability to choose vessel speed to next node subject to (minimum and/or maximum) speed constraints; 3) ability to select the power plant configuration at each node; and 4) ability to wait at a node. The algorithm is demonstrated on six real world routing scenarios by comparing its performance against an existing operational routing algorithm.

Dynamic asset allocation for counter-smuggling operations under disconnected, intermittent and low-bandwidth environment

Counter-smuggling operations constitute a high priority national security mission since drug-trafficking not only involves many criminals, but can also be a source of financing for many illicit activities such as narco-terrorism and arms trafficking. The counter-smuggling mission involves surveillance operations (to search, detect, track and identify potential threats) and interdiction operations (to intercept, investigate and potentially apprehend suspects). Potential smuggling activity is represented in the form of color-coded heat maps built using intelligence and meteorological and oceanographic information, which are interpreted in the form of probability of activity (PoA) surfaces. The PoA surfaces constitute the “sufficient statistics” for the asset allocation and scheduling processes. However, in the case of disconnected, intermittent, and low-bandwidth environments, the problem of allocating resources becomes very challenging as PoA information is unavailable or is not up to date. In this paper, we propose to utilize flow (historic PoA)-based surfaces, which provide cues on where the smugglers may have traversed in the past. Using the flow surfaces, we allocate the surveillance and interdiction assets to best thwart potential smuggling activities. We further evaluate the quality of our solution in terms of the number of targets interdicted and the amount of contraband seized.