Real-time Ensemble Prediction

Real-time, ensemble prediction is a central focus because it introduces some of the biggest technical challenges for the architecture, as well as some of the biggest potential benefits. The techniques being utilized are increasingly common in weather forecasting, but they remain relatively uncommon for most other disciplines of environmental prediction. Real-time, ensemble-modeling techniques are fundamental to advancing our nation’s capacity for environmental prediction. The enormous technical challenges can be overcome with a community infrastructure. An example use case is:

1. Hurricane warnings issued by the NOAA National Hurricane Center (NHC) are used to create an ensemble of forecast wind fields. Each of these wind fields represents a plausible set of forecast winds over the entire region of interest for several days into the future.
2. Each forecast wind field is used as input for numerical predictions of storm surge and wave fields. Because each individual element in this ensemble of surge and wave predictions involves a numerical calculation that could take many hours on a large supercomputer cluster, they are farmed out to the available computational resources within the distributed network.
3. Results from each of the predictions in the ensemble are then aggregated for analysis. Results include maps that show the probability of inundation with street level detail.
4. For verification, all relevant and available observations are aggregated and compared with predictions, which provides a real-time measure of accuracy and quality for the predictions.
5. The results of the analysis are visualized and disseminated in a form that can be readily incorporated into decision-support tools used by emergency response personnel (currently: http://www.openioos.org).

Retrospective Analyses
Research, development, and testing require analysis of historical events, such as comparison of two different models for storm surge. The SCOOP architecture will support these needs as well. Information relating to historical events exists in a wide variety of places and is increasingly available with dynamic mechanisms. The challenge is making these data discoverable and accessible. Various types of model inter-comparison could involve different algorithms, spatial resolution, or forcing inputs. The architecture should be capable of keeping track of such detail and document provenance of the various inputs and observations.

Katrina water level models and observations		Rita wind and visible satellite image

Continuous Forecasts
Not all predictive scenarios are event driven as with the wind-ensemble use case described above, nor do they require ensemble modeling techniques. For example, moderate winds can effect water level variations in ports and harbors. Predictions of this effect can be critically important for large ships that transit with less than a meter of clearance over the bottom. Similarly, the U.S. Coast Guard is in the process of rolling out a new computer-based search and rescue software suite that relies on predictions of coastal water velocities on a 24/7/365 basis. The model, data translation and transport infrastructure, as well as model results running within SCOOP, provide exactly the type of data streams needed by this application. The SCOOP infrastructure supports continuous forecasts that meet these operational requirements.

Multi-disciplinary Inundation Modeling
In addition to storm surge and waves that have damaging effects on the coastal zone, the inundation problem can be dominated by precipitation and terrestrial hydrology. Thus, a comprehensive inundation prediction capability includes use cases with models developed in the hydrologic and/or meteorology disciplines. Although the individual components for such disciplines will differ in detail, the service-oriented architecture will readily accommodate them by allowing addition of new system components relevant to other kinds of models, model output, and analysis, as well as new services that facilitate coordination of the components for various specific workflows. This type of extensibility is a basic feature of the architecture.