Spatial and spatial-temporal processes in the physical, environmental, and biological sciences often exhibit complicated and diverse patterns across different spacetime scales. Both scientific understanding, often reflected in computer models, and observational data vary in form and content across scales. We develop and examine a Bayesian hierarchical framework by which the combination of such ...

Most of the existing models for large scale arterial networks are not realistic and appropriate to deal with crowded conditions. As an alternative, we propose observation-based models that circumvent the fragility problems of traditional models. Monitoring replaces prediction, and the system is repeatedly modified based on observations. To succeed this goal a city is modeled in an aggregated ma...

Satellite estimates and weather forecast models have made it possible to observe and predict precipitation over large spatial scales. Despite substantial progress in observing patterns of precipitation, characterization of spatial patterns is still a challenge. Quantitative assessment methods for spatial patterns are essential for future developments in prediction of the spatial extent and patt...

NUMERICAL weather prediction has been operational since 1955 (Thompson, 1961; Fawcett, 1962). However, insufficient data and computing power have precluded a similar capability for the ocean despite the potential for numerous military and civilian applications, e.g.: antisubmarine warfare, tactical planning, optimum-track ship routing, search and rescue, long-range weather and climate predictio...

Recently, an object-oriented verification scheme was developed for assessing errors in forecasts of spatial fields. The main goal of the scheme was to allow the automatic and objective evaluation of a large number of forecasts. However, processing speed was an obstacle. Here, it is shown that the methodology can be revised to increase efficiency, allowing for the evaluation of 32 days of reflec...