SBIR Phase I: Operational Seasonal Forecasting of Environmental Data using Machine Learning and Statistical Methods

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The broader impact/commercial potential of this SBIR Phase I project is to extend weather forecasting capabilities beyond the current 14-day window. The project will leverage publicly available information from global gridded seasonal forecasting numerical models and in-situ observations from sensors for the Internet of Things (IoT) for a machine-learning based forecasting system that delivers seasonal and sub-seasonal forecasts. This asset-specific information is needed as weather, especially extreme weather events, continue to affect various industries and a wide range of individual assets. Accurate forecasting can improve logistics, supply-chain, labor scheduling and affect seed selection of a variety of crops important in the US economy, including stone-fruits, apples and pears. In addition, the technical innovation has the potential to improve local estimates of environmental variables relevant to the agricultural, energy and insurance industries.This project will bring into operation an in-situ seasonal and sub-seasonal forecasting system of environmental variables. The proposed innovation includes systems and methods for environmental forecasting using data-driven analog forecasting methods based on machine learning approximation of Koopman operators, governing the evolution of observables in nonlinear dynamical systems. The project will improve the forecasting accuracy beyond that of coarse resolution dynamical models by exploring sub-monthly timescales and a by expanding the predictor set beyond the traditional sea surface temperature used by most statistical approaches. Specifically, the project will create transfer functions that link the coarse resolution predictors from the dynamical models with local information (predictands) from IoT enabled environmental sensors. Furthermore, the project will apply and assess this framework for seasonal and regional predictions of the U.S. Pacific Northwest and Hawaii’s climate such as cumulative precipitation or onset of the given season, and probabilistic forecast of extreme events such as frosty days or drought, across a range of sectors.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.