The NOAA CPO Modeling, Analysis, Predictions, and Projections (MAPP) program hosted a webinar on the topic of Transitioning Prediction and Social Science Research into NOAA Operations on Monday, October 19, 2015. The announcement is provided below. This Webinar was co-sponsored by the OAR Office of Weather and Air Quality and the NWS Office of Science and Technology Integration.
Jim Wilczak - A PM2.5 bias correction algorithm for the Community Multi-scale Air Quality (CMAQ) forecast model has been transitioned for “developmental testing” at NCEP, with the plan to make the results public in September, 2016, dependent on the next year’s test results. The bias correction algorithm is based on model prediction analogs, and is referred to as AN. The algorithm searches through the history of past model predictions for those that are similar to the current prediction, weights those analogous predictions by their degree of similarity to the present prediction, and then bias corrects the current model prediction based on the observed errors of the weighted previous analogous predictions. In addition, an option exists to apply a Kalman filter to the set of analog model predictions, referred to as the KFAN bias correction. Corrections calculated at individual observation sites across the CONUS are then spread to the entire model grid, allowing for display of forecast guidance maps similar in appearance to those of the original raw forecasts. We will present results that demonstrate significant improvements in model skill provided by this Research to Operations application.
Nat Johnson and Dan Harnos - In September 2015 the NOAA Climate Prediction Center (CPC) initiated experimental temperature and precipitation forecasts for weeks 3 and 4. This initiative has heightened the need to identify sources of skill at these lead times and to transition this knowledge into operational forecast guidance to yield skillful forecasts. Recent work suggests that the combined influence of the El Niño-Southern Oscillation (ENSO), Madden-Julian Oscillation (MJO), and long-term trend are important sources of predictability that can yield skillful forecasts in weeks 3-4 under certain initial tropical states. In this presentation, we discuss the prospects of skillful forecasts in weeks 3-4 with a simple statistical model based on the initial state of ENSO, MJO, and long-term trend. Our analysis reveals potentially skillful forecasts for both temperature and precipitation at weeks 3 and 4 throughout the year for varied regions and initial states. We then discuss the transition of this statistical tool into operational forecast guidance at CPC and its current role in weeks 3-4 forecasting.
Rachel Hogan Carr - National Weather Service has a tremendous suite of flood forecast and warning products that offer timely, accurate data about the potential for riverine flooding. Despite this information, people at risk for flooding still often fail to take protective actions to reduce their losses. Nurture Nature Center, a non-profit in Easton, Pennsylvania with a focus on flood issues, did a study including focus groups and surveys of flood-prone communities in PA and NJ to understand how the public uses and understands these products (including precipitation forecasts, hydrographs, ensemble forecasts and more). The study report includes a series of recommendations for graphical and other revisions to the products to help make them easier to understand and more likely to make audiences take protective actions.