In the marine environment, the task of accurate measurement and forecasting has many challenges, since most of the world’s oceans are located in data sparse areas. In-situ buoy measurements and shipboard observations provide data; however, there is not a sufficient volume of spatial observations, and ships rarely take measurements within storms. These challenges make it necessary to rely heavily on remotely-sensed satellite data to replace in-situ data that is collected over land. Although forecasting over the ocean has become much more accurate in recent years, it still has its limitations, due to the lack of data and intensity of the weather systems. These shortcomings can lead to devastating consequences, such as loss of life, property and income.
To overcome some of these issues, it becomes increasingly necessary to verify both model guidance and the forecasts that are issued based on that guidance. Therefore, a verification system was designed and implemented in real-time to diagnose deficiencies in the predicted ocean surface wind field. This system utilizes real-time remotely sensed ASCAT scatterometer data to verify analyses and predictions from both the Global Forecast System (GFS) and the forecasts produced by the OPC. Statistics, such as Biases, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), time series analysis and other widely used verification statistics are generated for every six hour forecast cycle, for the analysis and forecast fields, and disseminated via a Real-Time Dashboard which is accessible through the in-house Intranet. These statistics enable a forecaster to a) monitor recent trends in past model bias, b) evaluate skill in current model predictions, and c) gather feedback on accuracy of his/her recent forecasts. Preliminary results from the new real-time verification system will be shown.