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A physics-based cloud identification scheme, originally developed for a machine-learning forecast system, was applied to verify cloud location and coverage bias errors from two years of 6-h forecasts. The routine identifies stable and unstable environments by assessing the potential for buoyant versus stable cloud formation. The efficacy of the scheme is documented by investigating its ability to identify cloud patterns and systematic forecast errors. Results showed that stable cloud forecasts contained widespread, persistent negative cloud cover biases most likely associated with turbulent, radiative, and microphysical feedback processes. In contrast, unstable clouds were better predicted despite being poorly resolved. This suggests that scale aliasing, while energetically problematic, results in less-severe short-term cloud cover errors. This study also evaluated Geostationary Operational Environmental Satellite (GOES) cloud-base retrievals for their effectiveness at identifying regions of lower-tropospheric cloud cover. Retrieved cloud-base heights were sometimes too high with respect to their actual values in regions of deep-layered clouds, resulting in underestimates of the extent of low cloud cover in these areas. Sensitivity experiments indicate that the most accurate cloud-base estimates existed in regions with cloud tops at or below 8 km. Significance Statement: Cloud forecasts are difficult to verify because the height, depth, and type of the clouds are just as important as the spatial location. Satellite imagery and retrievals are good for verifying location, but these measurements are sometimes uncertain because of obscuration from above. Despite these uncertainties, we can learn a lot about specific forecast errors by tracking general areas of clouds based on their physical forcing mechanisms. We chose to sort by atmospheric stability because buoyant and stable processes are physically very distinct. Studies of this nature exist, but they typically assess mean cloud frequencies without considering spatial and temporal displacements. Here, we address displacement error by assessing the direct overlap between the observed and predicted clouds. [ABSTRACT FROM AUTHOR]

The article focuses on a maritime surveillance problem involving the allocation of multiple heterogeneous assets over a large area of responsibility to detect multiple drug smugglers using heterogeneous types of transportation on the sea. It mentions the problem increased exponentially with the advent of narco-terrorism. It also mentions the U.S. Navy has showing interest in mixed initiative human–machine systems and mastering information dominance for effective context-driven operations.

A generalized 3-dimensional assignment problem is a decisionmaking process that involves allocating limited resources to a set of tasks over time, where the objective is to optimize a cost function subject to a set of generalized assignment constraints. The 3- dimensional (3-D) assignment problems are known to be NP-hard. In this paper, we propose a novel approach to efficiently solve an m-best 3-D assignment problem with non-unity right-hand side constraints (also referred to simply as 3-D assignment problem), where m may be large (as many as 104 solutions), by decomposing it into two sequential phases. In phase I, we partition the original problem space into a series of subproblems via Murty's m-best search space decomposition procedure. Modifications previously proposed in the literature for the 2-dimensional (2-D) assignment problem are applied to optimize the search space decomposition for the 3- D assignment problem. In phase II, we solve each subproblem by using Lagrangian relaxation and solving the 3-D assignment problem as a combination of relaxed 2-D assignment problems and 2-D transportation problems. The 2-D assignment problem is solved by the JVC or auction algorithms and the 2-D transportation problem is solved by the simplex-based transportation, Transauction or RELAX-IV algorithms. The sequence of relaxed 2-D problems are interchangeable, while adhering to the relaxed constraints. We validate and compare the performance and utility of the proposed algorithms and search space decomposition optimizations via extensive numerical experiments. Overall, the fully optimized algorithm took less than 50 seconds, on average, to obtain 104 soutions for a tensor of dimension 30X30X8. [ABSTRACT FROM AUTHOR]

The critical layer thickness hc for the onset of lattice relaxation has important implications for the design of pseudomorphic and metamorphic II-VI device structures on lattice-mismatched substrates. Several theoretical models have been developed for the critical layer thickness, including the well-known force-balance model of Matthews and Blakeslee [J. Cryst. Growth 27, 188 (1974)]. Experimentally measured critical layer thicknesses in ZnSe/GaAs (001) heterostructures are often at variance with one another as well as the Matthews and Blakeslee model. By assuming that the lattice relaxation is a fixed fraction of the equilibrium relaxation (constant γ/γeq), Fritz [Appl. Phys. Lett. 51, 1080 (1987)] has shown that the measured hc may be much larger than the equilibrium value when using a finite experimental resolution. However, the assumption of constant fractional relaxation is not applicable to any heterostructure exhibiting kinetically limited lattice relaxation. In order to reconcile the conflicting results for II-VI materials, the authors applied a general dislocation flow model to determine the apparent critical layer thickness as a function of the experimental resolution for ZnSe/GaAs (001) heterostructures. The authors show that the Matthews and Blakeslee model is consistent with several measured values of hc once the kinetically limited relaxation and finite experimental strain resolution are taken into account. [ABSTRACT FROM AUTHOR]

On October 5, 2015, El Faro, a cargo ship heading to San Juan, Puerto Rico, foundered in the Atlantic Ocean off the Bahamas due to Category 4 Hurricane Joaquin. Thirty-three crew members remain missing. The captain cut south of the storm, due to the rare occurrence of such storm systems traveling south, but ended up sailing straight into the high winds and seas in the area, a situation that had a high probability of occurrence given the forecast. This event and its outcome demonstrate the importance of using context in weather routing. Context is assumed to mean any information that can be used to characterize the situation of a person, place, physical or computational object considered relevant to providing a clear picture of the dynamics of the problem. [ABSTRACT FROM PUBLISHER]

The Kalman filter requires knowledge of the noise statistics; however, the noise covariances are generally unknown . Although this problem has a long history, reliable algorithms for their estimation are scant, and necessary and sufficient conditions for identifiability of the covariances are in dispute. We address both of these issues in this paper. We first present the necessary and sufficient condition for unknown noise covariance estimation; these conditions are related to the rank of a matrix involving the auto and cross-covariances of a weighted sum of innovations, where the weights are the coefficients of the minimal polynomial of the closed-loop system transition matrix of a stable, but not necessarily optimal, Kalman filter. We present an optimization criterion and a novel six-step approach based on a successive approximation, coupled with a gradient algorithm with adaptive step sizes, to estimate the steady-state Kalman filter gain, the unknown noise covariance matrices, as well as the state prediction (and updated) error covariance matrix. Our approach enforces the structural assumptions on unknown noise covariances and ensures symmetry and positive definiteness of the estimated covariance matrices. We provide several approaches to estimate the unknown measurement noise covariance R via post-fit residuals , an approach not yet exploited in the literature. The validation of the proposed method on five different test cases from the literature demonstrates that the proposed method significantly outperforms previous state-of-the-art methods. It also offers a number of novel machine learning motivated approaches, such as sequential (one sample at a time) and mini-batch-based methods, to speed up the computations.