Numerical Model Metrics Tools in Support of Navy Operations

Increasing demands of accurate ocean forecasts that are relevant to the Navy mission decision makers demand tools that quickly provide relevant numerical model metrics to the forecasters. Increasing modelling capabilities with ever-higher resolution domains including coupled and ensemble systems as well as the increasing volume of observations and other data sources to which to compare the model output requires more tools for the forecaster to enable doing more with less. These data can be appropriately handled in a geographic information system (GIS) fused together to provide useful information and analyses, and ultimately a better understanding how the pertinent model performs based on ground truth.. Oceanographic measurements like surface elevation, profiles of temperature and salinity, and wave height can all be incorporated into a set of layers correlated to geographic information such as bathymetry and topography. In addition, an automated system that runs concurrently with the models on high performance machines matches routinely available observations to modelled values to form a database of matchups with which statistics can be calculated and displayed, to facilitate validation of forecast state and derived variables. ArcMAP, developed by Environmental Systems Research Institute, is a GIS application used by the Naval Research Laboratory (NRL) and naval operational meteorological and oceanographic centers to analyse the environment in support of a range of Navy missions. For example, acoustic propagation in the ocean is described with a three-dimensional analysis of sound speed that depends on profiles of temperature, pressure and salinity predicted by the Navy Coastal Ocean Model. The data and model output must include geo-referencing information suitable for accurately placing the data within the ArcMAP framework. NRL has developed tools that facilitate merging these geophysical data and their analyses, including intercomparisons between model predictions as well as comparison to validation data. This methodology produces new insights and facilitates identification of potential problems in ocean prediction.