{"id":4381,"date":"2016-02-02T09:00:11","date_gmt":"2016-02-02T17:00:11","guid":{"rendered":"https:\/\/blogs.msdn.microsoft.com\/msr_er\/?p=4381"},"modified":"2017-02-14T21:40:43","modified_gmt":"2017-02-15T05:40:43","slug":"predicting-ocean-chemistry-using-microsoft-azure","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/predicting-ocean-chemistry-using-microsoft-azure\/","title":{"rendered":"Predicting ocean chemistry using Microsoft Azure"},"content":{"rendered":"

By\u00a0Daron Green<\/a>, Deputy Director, Microsoft Research<\/em><\/p>\n

\"LiveOcean_WebGraphics_MRCBLOG_900x300\"<\/p>\n

Shellfish farmer Bill Dewey remembers the first year he heard of ocean acidification, a phrase that means a change in chemistry for ocean water. It was around 2008, and Dewey worked for Taylor Shellfish (opens in new tab)<\/span><\/a>, a company that farms oysters in ocean waters off the coast of Washington. That year, thousands of tiny \u201cseed\u201d oysters died off suddenly. Today, a cloud-based predictive system from the University of Washington (UW) and Microsoft Research may help the shellfish industry survive changing conditions by providing forecasts about ocean water.<\/p>\n

Dewey, director of Public Affairs for Taylor Shellfish, vividly remembers walking into a conference room where an audience of shellfish farmers first heard that ocean acidification might threaten their industry profoundly. They learned that increased carbon dioxide in the atmosphere is making ocean water more acidic. In 2013, the Washington legislature stepped in and asked the UW to study and build a predictive forecast model, aptly named, LiveOcean (opens in new tab)<\/span><\/a>.<\/p>\n

Just like a numerical weather forecast model, LiveOcean will soon provide a forecast that predicts the acidity of water in a specific bay, part of Puget Sound or other coastal regions, days in advance.<\/p>\n

Parker MacCready (opens in new tab)<\/span><\/a>, a professor of physical oceanography at UW, is the scientist leading the LiveOcean team and used Microsoft Azure (opens in new tab)<\/span><\/a> to create the cloud-based storage system. The system \u00a0holds enormous amounts of data from his remote ocean model, the Regional Ocean Modeling System (opens in new tab)<\/span><\/a> (ROMS), which helps feed the LiveOcean models. The Azure component uses Python and the Django web framework to provide these forecasts in an easy-to-consume format. To produce these forecasts, the LiveOcean system relies on other sources: US Geological Survey data (for river flow), atmospheric forecasts, and another ocean model called HYbrid Coordinate Ocean Model (opens in new tab)<\/span><\/a> (HYCOM)<\/span>.<\/p>\n