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Leveraging Modern Artificial Intelligence for Remote Sensing and NWP: Benefits and Challenges


Machine learning can improve handling large volumes of observations, modeling, analysis, and forecasting of the environment by increasing the speed and accuracy of computations, but success requires great care in design and training.

Artificial Intelligence (AI) techniques have had significant recent successes in multiple fields. These fields and the fields of satellite remote sensing and NWP share the same fundamental underlying needs, including signal and image processing; quality control mechanisms; pattern recognition; data fusion; forward and inverse problems; and prediction. Thus, modern AI in general and machine learning (ML) in particular, can be positively disruptive and transformational change agents in the fields of satellite remote sensing and NWP by augmenting, and in some cases replacing, elements of the traditional remote sensing, assimilation and modeling tools. And change is needed to meet the increasing challenges of Big Data, advanced models and applications, and user demands. Future developments, for example, SmallSats and the Internet of Things, will continue the explosion of new environmental data. ML models are highly efficient and in some cases more accurate because of their flexibility to accommodate nonlinearity and/or non-Gaussianity. With that efficiency, ML can help to address the demands put on environmental products for higher accuracy, for higher resolution—spatial, temporal and vertical, for enhanced conventional medium-range forecasts, for outlooks and predictions on sub-seasonal to seasonal time scales, and for improvements in the process of issuing advisories and warnings. Using examples from satellite remote sensing and NWP, it is illustrated how ML can accelerate the pace of improvement in environmental data exploitation and weather prediction—first, by complementing existing systems, and second, where appropriate, as an alternative to some components of the NWP processing chain from observations to forecasts.

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