The long-term goal of this RTP project is to provide the warfighter with superior battlespace environmental awareness in terms of high fidelity four-dimensional (4D) depiction of the global atmospheric state. This situational awareness is a key aspect of information superiority in the DoD’s strategic plan to ensure battlespace dominance in the 21 century. This goal is to be accomplished by prov...

Ensemble Kalman Filtering is a sequential Monte Carlo method commonly used in meteorology to track atmospheric states and make numerical weather predictions (NWP). With the intent to introduce statisticians to this important area of application we address some of the practical aspects of the ensemble Kalman Filter in dynamic systems. We focus on three topics related to NWP: extending the ensemb...

The Supercomputer Toolkit constructs parallel computation networks by connecting processor modules. These connections are set by the user prior to a run and are static during the run. The Technion’s Toolkit prototype was used to run a simplified version of the PSU/NCAR MM5 mesoscale model [9]. Each processor is assigned columns of the grid points of a square in the (x,y) space. When n × n colum...

The coarse grid of numerical weather prediction and climate models requires parametrization models to resolve atmospheric processes that are smaller than the grid size. For parametrization development, these processes are simulated by a high resolution model. At the Royal Netherlands Meteorological Institute, the Dutch Atmospheric Large-Eddy Simulation (DALES) is used [15]. This three-dimension...

Complex numerical weather prediction models incorporate a variety of physical processes, each described by multiple alternative physical schemes with specific parameters. The selection of the physical schemes and the choice of the corresponding physical parameters during model configuration can significantly impact the accuracy of model forecasts. There is no combination of physical schemes tha...

The verification of a gridded forecast field, for example, one produced by numerical weather prediction (NWP) models, cannot be performed on a gridpoint-by-gridpoint basis; that type of approach would ignore the spatial structures present in both forecast and observation fields, leading to misinformative or noninformative verification results. A variety of methods have been proposed to acknowle...

High-resolution synthetic aperture radar (SAR) wind observations provide fine structural information for tropical cycles and could be assimilated into numerical weather prediction (NWP) models. However, in the conventional method assimilating the u and v components for SAR wind observations (SAR_uv), the wind direction is not a state vector and its observational error is not considered during t...

A large number of recent observational data strongly suggest that we live in a flat, accelerating Universe composed of ∼ 1/3 of matter (baryonic + dark) and ∼ 2/3 of an exotic component with large negative pressure, usually named Dark Energy or Quintessence. The basic set of experiments includes: observations from SNe Ia, CMB anisotropies, large scale structure, X-ray data from galaxy clusters,...

AbstrAct The spatio-temporal prediction problem requires that one or more future values be predicted for time series input data obtained from sensors at multiple physical locations. Examples of this type of problem include weather prediction, flood prediction, network traffic flow, and so forth. In this chapter we provide an overview of this problem, highlighting the principles and issues that ...

A comprehensive review of the propagation prediction models for terrestrial wireless communication systems is presented in this paper. The classic empirical models are briefly described and the focus is placed on the application of ray-tracing techniques to the development of deterministic propagation models. Schemes to increase the computational efficiency and accuracy are discussed. Tradition...