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Christina Kumler-Bonfanti, Jebb Stewart, David Hall, and Mark Govett

popular and widely used CNNs, were trained with millions of images and are highly accurate ( Krizhevsky et al. 2017 ; Simonyan and Zisserman 2015 ). Deep learning (DL) is a term used to describe the optimization of NNs with multiple layers. Deep networks tend to be large relative to other ML models and their training typically requires powerful graphics processing unit (GPU) acceleration in order to make training practical. In comparison with other approaches, deep CNNs are particularly effective for

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Ryan Lagerquist, Amy McGovern, and David John Gagne II

induced by wind shear and convergence between two anticyclones), the thermal method is better at detecting warm fronts (which are almost never detected by the wind-shift method). Machine learning (ML) is a process whereby computers learn autonomously from data, as opposed to an expert system like NFA, which is based on human-derived rules. Deep learning (DL) is a subset of ML, which offers the ability to encode the input data at various levels of abstraction. These abstractions are called features

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Yang Liu, Laurens Bogaardt, Jisk Attema, and Wilco Hazeleger

to forecast Arctic sea ice with a statistical model. This brings contemporary machine learning techniques into scope. Machine learning approaches, especially deep learning, are widely embraced by many fields and are increasingly used to deal with problems like clustering, classification, and regression ( LeCun et al. 2015 ). Benefiting from large volumes of data of Earth system ( Knüsel et al. 2019 ), those deep learning methods may be appropriate for the weather and climate domain ( Reichstein

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Stephan Rasp, Hauke Schulz, Sandrine Bony, and Bjorn Stevens

identification. In these situations, machine learning techniques, particularly deep learning (see “Deep learning for vision tasks in the geosciences” sidebar), have demonstrated their ability to mimic the human capacity for identifying patterns, also from satellite cloud imagery (e.g., Wood and Hartmann 2006 ). However, the application and assessment of such techniques is often limited by the tedious task of obtaining sufficient training data, so much so that (in cloud studies at least) these approaches

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Anthony Wimmers, Christopher Velden, and Joshua H. Cossuth

1. Introduction Deep learning (DL) is a newly popular, powerful and often confounding computational tool for developing predictive models in the sciences. It builds on a long legacy of neural network modeling, with a key feature being the organization of neural connections into multiple layers of nonlinear operations, enabling models to apply high levels of abstraction in their tasks. New hardware innovations, particularly in accessing graphical processing units (GPUs), have enabled DL

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Veljko Petković, Marko Orescanin, Pierre Kirstetter, Christian Kummerow, and Ralph Ferraro

observations and storm morphology, little, if any, room has been left for a potentially novel physically based approach to emerge. However, recent advances in deep learning methods with neural networks may offer perhaps not new but for the first time fully applicable models that could better exploit the information content in PMW observations. This study seeks to investigate such a possibility through the use of deep learning for both retrieving precipitation types and improving the performance of PMW

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Kuai Fang and Chaopeng Shen

function can be regarded as piecewise linear, to be estimated locally at each pixel. However, it is not clear if such a linear model could fully describe the process of soil moisture change, and, especially, provide strong performance for forecasts with a few days of latency. Deep learning (DL) is well known for its ability to learn nonlinear mapping relationships and model dynamical systems ( Shen 2018 ; LeCun et al. 2015 ; Schmidhuber 2015 ). In our previous work ( Fang et al. 2017 ), we employed

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Imme Ebert-Uphoff and Kyle Hilburn

) for deeper study. From simple neurons to powerful neural networks. An artificial neural network (ANN; or NN for short) is a machine learning method loosely inspired by the human brain. An NN consists of a set of neurons (aka nodes ) that are connected by synapses which pass signals between the neurons. In sequential NNs , which are the primary type considered here, all neurons are arranged in a sequence of layers, and signals pass in a one-directional manner from the input layer through

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Tao Song, Zihe Wang, Pengfei Xie, Nisheng Han, Jingyu Jiang, and Danya Xu

optimization of machine-learning methods may be hard, and overfitting is really a tough problem to be solved ( Jiang et al. 2018 ; Siahkoohi et al. 2019 ). In recent decades, deep-learning methods, especially recurrent neural network (RNN), have been widely used for time series data processing and value prediction. RNN introduces the recurrent unit structure and allows the internal connection between the hidden units, so it is suitable for analyzing and processing time series data ( Krizhevsky et al. 2012

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Xu Wenwei, Balaguru Karthik, August Andrew, Lalo Nicholas, Hodas Nathan, DeMaria Mark, and Judi David


Reducing tropical cyclone (TC) intensity forecast errors is a challenging task that has interested the operational forecasting and research community for decades. To address this, we developed a deep learning (DL)-based Multilayer Perceptron (MLP) TC intensity prediction model. The model was trained using the global Statistical Hurricane Intensity Prediction Scheme (SHIPS) predictors to forecast the change in TC maximum wind speed for the Atlantic Basin. In the first experiment, a 24-hour forecast period was considered. To overcome sample size limitations, we adopted a Leave One Year Out (LOYO) testing scheme, where a model is trained using data from all years except one and then evaluated on the year that is left out. When tested on 2010–2018 operational data using the LOYO scheme, the MLP outperformed other statistical-dynamical models by 9-20%. Additional independent tests in 2019 and 2020 were conducted to simulate real-time operational forecasts, where the MLP model again outperformed the statistical-dynamical models by 5-22% and achieved comparable results as HWFI. The MLP model also correctly predicted more rapid intensification events than all the four operational TC intensity models compared. In the second experiment, we developed a lightweight MLP for 6-hour intensity predictions. When coupled with a synthetic TC track model, the lightweight MLP generated realistic TC intensity distribution in the Atlantic Basin. Therefore, the MLP-based approach has the potential to improve operational TC intensity forecasts, and will also be a viable option for generating synthetic TCs for climate studies.

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