SBIR-STTR Award

Computer forecasts of weather and climate variability created by the U.S. and other nations on the subseasonal scale (weeks) and on the seasonal scale (months) are increasingly used by private and public activities to mitigate risk and seize opportunity. These forecasts are produced as ensembles of tens of individual computer forecasts and are most useful when calibrated by comparing histories of both forecasts and observations and then presented as probabilities of future events. This project will explore several techniques for creating new forecast systems in two phases: first by using superensembles assembled from forecasts by U.S. agencies and then by using broader collections that include forecasts from some of the most prominent national and international forecast centers. The goal is: Improve the skill and statistical reliability of subseasonal to seasonal (S2S) forecasts in ways that lead to more effective decisions and actions by the users. First, the project will explore how the performance of superensembles varies with the number and skill of contributed forecast systems. The second effort will attempt to create hybrid forecasts that combine the conventional computer forecasts with statistical forecasts. The third effort will examine improvements in forecast performance achieved with innovative new forecast calibration strategies.

The importance of weather and climate variability forecasts in the U.S. is amply demonstrated by the billions of dollars appropriated annually for the NWS and for surface, air, and satellite observations and by the existence of a vibrant private weather enterprise delivering value-added forms of NWS information to a wide range of public and private users. We have loyal customers in the energy sector, here and abroad, and are developing a strong reputation and following with our crop yield and production forecasts. Phase II accomplishments will include: ? Development and implementation of an optimally skillful global multi-model for probability forecasts ? Model calibration conditioned on climate indices ? A new focus on predicting extreme events ? Creation of ensembles combining both dynamical and statistical forecasts ? Exploration of promising new forecast opportunities with statistical methods and machine learning ? Collaboration with selected energy and agriculture customers to sharpen understanding of user needs ? Development of products focused on user decision-making processes Thus we will strengthen our assistance to NOAA in meeting its mission to share its information and will better serve our customers in business and industry, thereby assisting NOAA in meeting the mandates of the Weather Act (PL 115-25,2017).