The National Center for Atmospheric Research (NCAR) recently updated the comprehensive wind power forecasting system in collaboration with Xcel Energy addressing users’ needs and requirements by enhancing and expanding integration between numerical weather prediction and machine-learning methods. While the original system was designed with the primary focus on day-ahead power prediction in support of power trading, the enhanced system provides short-term forecasting for unit commitment and economic dispatch, uncertainty quantification in wind speed prediction with probabilistic forecasting, and prediction of extreme events such as icing. Furthermore, the empirical power conversion machine-learning algorithms now use a quantile approach to data quality control that has improved the accuracy of the methods. Forecast uncertainty is quantified using an analog ensemble approach. Two methods of providing short-range ramp forecasts are blended: the variational doppler radar analysis system and an observation-based expert system. Extreme events, specifically changes in wind power due to high winds and icing, are now forecasted by combining numerical weather prediction and a fuzzy logic artificial intelligence system. These systems and their recent advances are described and assessed.

The High-Resolution Rapid Refresh (HRRR) model with its hourly updating cycles provides multiple weather forecasts valid at any given time. A logical combination of these individual deterministic forecasts is postulated to show more skill than any single forecast for predicting clouds containing supercooled liquid water (SLW), an aircraft icing threat. To examine the potential value of using multiple HRRR forecasts for icing prediction, a time-lag-ensemble (TLE) averaging method of combining a number of HRRR forecasts was implemented for a multiple month real-time test during the winter of 2016/17. The skills of individual HRRR and HRRR-TLE aircraft icing predictions were evaluated using icing pilot reports (PIREPs) and surface weather observations and compared with the operational Forecast Icing Product (FIP) using the Rapid Refresh (RAP) model. The HRRR-TLE was found to produce a higher capture rate of icing PIREPs and surface icing conditions of freezing drizzle or freezing rain than single deterministic HRRR forecasts. As a trade-off, the volume of airspace warned in HRRR-TLE increased, resulting in a higher false detection rate than in the deterministic HRRR forecasts. Overall, the HRRR-TLE had similar probability of detection and volume of airspace warned for icing as the operational FIP prediction for the icing probability of 25% or greater. Alternative techniques for composing TLE from multiple HRRR forecasts were tested in postseason rerun experiments. The rerun tests also included a comparison of the skills of HRRR and HRRR-TLE to the skills of RAP and RAP-TLE.

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Weather Research Program (AWRP) is to provide timely and accurate forecasts of inflight icing conditions. The flying public wants to know not only where icing conditions are likely to reside, but also the probability of their occurrence and expected severity. Automated diagnosis and forecast icing products have been developed at NCAR and deployed at the Aviation Weather Center (AWC), where they provide this information to pilots, forecasters, and dispatchers. These products, known as the Current and Forecast Icing Products (CIP and FIP, respectively), have been approved for operational decision making by these groups. Recently, changes were made to both algorithms to accommodate the transition in numerical weather prediction (NWP) models, from the Rapid Update Cycle (RUC) to the Weather Research and Forecasting Rapid Refresh (WRF-RAP). This transition required some changes to the algorithms to handle updated model information and a verification of the results. The verification study confirmed that the new model had the desired effects on the icing products and also brought to light some interesting information on the handling of convection and supercooled large drops (SLD). During this transition other upgrades and changes were also made to the algorithms dealing with icing severity at night, the use of radar data, and the development of an algorithm testbed.

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