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  • The 1st NOAA Workshop on Leveraging AI in the Exploitation of Satellite Earth Observations & Numerical Weather Prediction x
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John L. Cintineo, Michael J. Pavolonis, Justin M. Sieglaff, Anthony Wimmers, Jason Brunner, and Willard Bellon

. The model of this paper uses ReLU for the activations of the first two fully connected layers and uses the sigmoid function for the activation of the final fully connected layer with a single output, forcing the final prediction to be a probability between [0, 1]. We used the Keras Python API with TensorFlow backend to perform the training and evaluation of CNNs ( Chollet 2015 ). This is a binary classification problem (“intense” or “ordinary” convection are the classes), so the loss function

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Eric D. Loken, Adam J. Clark, Amy McGovern, Montgomery Flora, and Kent Knopfmeier

with the proper value of σ , spatially smoothing ensemble probabilities reduces sharpness (e.g., Sobash et al. 2011 , 2016 ; Loken et al. 2017 , 2019 ) and potentially sacrifices resolution if too much smoothing is required. Moreover, the “best” value of σ may vary based on geographic location and time of year (e.g., Fig. 3 ), as precipitation uncertainty is reduced where stronger and/or more predictable forcing is present, such as near high terrain (e.g., Blake et al. 2018 ) or during the

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