Accelerated Prediction of the Polar Ice and Global Ocean (APPIGO)

• Main Author: Chassignet, Eric
• Other Authors: FLORIDA STATE UNIV TALLAHASSEE CENTER FOR OCEAN-ATMOSPHERIC PREDICTION STUDIES
• Format: Text
• Language: English
• Published: 2014
• Subjects: Snow; Ice and Permafrost; Computer Hardware; *FORECASTING; *MODELS; ARCTIC OCEAN; COMPUTATIONS; HIGH PERFORMANCE COMPUTING; PREDICTIONS; SEA ICE; Arctic; Arctic Ocean; Ice; permafrost; Sea ice
• Online Access: http://www.dtic.mil/docs/citations/ADA617675; http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA617675

Arctic change and reductions in sea ice are impacting Arctic communities and are leading to increased commercial activity in the Arctic. Improved forecasts will be needed at a variety of timescales to support Arctic operations and infrastructure decisions. Increased resolution and ensemble forecasts will require significant computational capability. At the same time, high performance computing architectures are changing in response to power and cooling limitations, adding more cores per chip and using Graphics Processing Units (GPUs) as computational accelerators. This project will improve Arctic forecast capability by modifying component models to better utilize new computational architectures. Specifically, we will focus on the Los Alamos Sea Ice Model (CICE), the HYbrid Coordinate Ocean Model (HYCOM) and the Wavewatch III models and optimize each model on both GPU-accelerated and MIC-based architectures. These codes form the ocean and sea ice components of the Navy s Arctic Cap Nowcast/Forecast System (ACNFS) and the Navy Global Ocean Forecasting System (GOFS), with the latter scheduled to include a coupled Wavewatch III by 2016. This work will contribute to improved Arctic forecasts and the Arctic ice prediction demonstration project for the Earth System Prediction Capability (ESPC).