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<p>Nowcasting is a trending subset of numerical weather prediction that aims to produce a highly accurate analysis of current conditions along with a short-term forecast. One of the greatest challenges to a nowcast system operating in data-sparse regions is that of accurately forecasting clouds. Clouds significantly impact a variety of operations, particularly intelligence, surveillance and reconnaissance. A prototype nowcast system is developed and tested on a case of summertime stratus clouds over the Monterey Bay in California. This system ingests high-resolution geostationary satellite data and mesoscale model fields to produce gridded 06-h forecasts of cloud reflectance and probability of cloud. A statistical post-processing technique is applied using Bayesian estimation to train the system from a set of past predictor variables and observed imagery. This approach demonstrates skill over a climatology-based approach and shows an ability to accurately forecast non-typical cloud patterns. It proves to be very computationally feasible for nowcasting. This study lays down the initial framework for a highly accurate nowcast system that can operate anywhere in the world to enable mission success while reducing costs.
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<br>Subjects: operational nowcasting; low cloud forecasting; cloud reflectance; ISR; Bayesian estimation; statistical post-processing; machine learning</div>
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Zoufaly, Sean L.</div>
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