Date: June 25, 2007 - June 29, 2007
Venue: 22nd Conference on Weather Analysis and Forecasting/18th Conference on Numerical Weather Prediction
Henderson J. M, T. S. Zaccheo, N. Vinogradova, D. Vandemark, J. W. Cannon and D. S. Jean. Initial Verification of the NOAA-UNH Joint Center for OCean Observing Technology real-time MM5/WRF forecasts. 22nd Conference on Weather Analysis and Forecasting/18th Conference on Numerical Weather Prediction, Park City, Utah. June 25 - 29, 2007
The NOAA-UNH Joint Center for Ocean Observing Technology is a collaborative effort whose goal is to develop prototype analyses and predictions of the ocean, atmosphere and land spheres to enhance economic productivity and quality of life for both lay and advanced users. Real-time atmospheric and oceanic products have been developed through the optimal fusion of variational data assimilation, in-situ and remotely-sensed observations, and modeling techniques. Member organizations include NOAA, the University of New Hampshire (UNH), the Gulf of Maine Ocean Observing System (GoMOOS) and Atmospheric and Environmental Research, Inc. (AER). The center's focus is the Gulf of Maine's 93,000 square kilometres of ocean, 12,500 kilometres of coastline and 180,000 square kilometres of watershed that are shared between three states and two provinces.
Initial work implemented a real-time atmospheric prediction system using the Penn State/NCAR atmospheric Mesoscale Model Version 5 (MM5) system in a triply-nested configuration centered over the southern half of the Gulf of Maine. The Advanced Research - Weather Research and Forecasting Model (ARW-WRF) has since replaced the MM5. A primary project goal is to collect and optimally assimilate in MM5/WRF 3d-VAR a diverse set of observations - currently upper-air; land and ocean surface reports; and QSCAT surface winds. Current real-time users include the US National Weather Service Forecast Offices in Gray and Caribou, ME, and the UNH terrestrial water budget modeling community. One recent example of this collaborative effort occurred in December 2006. During a mesoscale snow event, NWS Gray forecasters were able to improve their forecast by taking advantage of the mesoscale detail and skill provided by the MM5 model.