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Rui M. Ponte

DECEMBER 1989 RUI M. PONTE 1881A Simple Model for Deep Equatorial Zonal Currents Forced at Lateral Boundaries* RUI M. PONTE* *Department of Physical Oceanography, Woods Hold Oceanographic Institution, Woods Hole, Massachusetts(Manuscript received 13 June 1988, in final form 8 August 1989) ABSTRACT Deep lateral boundary processes (e

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Malarvizhi Arulraj and Ana P. Barros

1. Introduction The space–time variability of hydrometeors in convective precipitation shows high variability in vertical and horizontal structures typically with distinct deep cores of heavy rainfall and broad drop size distributions (DSD) in contrast with narrow DSDs for stratiform rainfall ( Houze 1993 ; Zafar and Chandrasekar 2004 ). Independently of precipitation regimes, DSDs can change significantly with height over time because of changes in the surrounding environment and drop

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Kirstin Dow

of Climate Change” ( National Research Council 2010 ) highlights the need to take a risk management approach to adaptation. The “Progress Report of the Interagency Climate Change Adaptation Task Force” ( White House Council on Environmental Quality 2010 ) states as a guiding principle that priority be placed on protecting the most vulnerable people as well as places and infrastructure. To take these recommendations seriously and act wisely requires learning more about who and what is vulnerable

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Jordane A. Mathieu and Filipe Aires

end of the introduction. 3) Neural network (NN) In this section, the possibility of using a highly nonlinear function for the impact model is considered. Neural networks are good candidates. We only consider here feed-forward neural networks trained using a supervised-learning approach ( Bishop 1995 ). This type of NN has only forward connections from the input to the output layers. An artificial neuron η ( Fig. 6 ) is a model characterized by 1) n inputs inputs, 2) a vector of weights W , 3

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Yaling Liu, Dongdong Chen, Soukayna Mouatadid, Xiaoliang Lu, Min Chen, Yu Cheng, Zhenghui Xie, Binghao Jia, Huan Wu, and Pierre Gentine

learning (deep neural network), to develop a daily multilayer soil moisture product for the cropland regions of China during 1981–2013, accounting for cropping patterns. The neural network presents good performance ( R 2 value of 0.64–0.7 between predictions and observations during training and testing) across regions and cropping patterns. This product is open access and provides the research community an alternative opportunity to explore a variety of research topics pertaining to soil moisture

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Daniel J. Kirshbaum and Dale R. Durran

in the orographic cloud. As discussed by Banta (1990) , one form of moist instability termed “latent” instability is characterized by the existence of convective available potential energy (CAPE) in the orographically modified flow. As with conditionally unstable flow over flat terrain, air parcels lifted to the level of free convection in a latently unstable atmosphere can develop into deep convective storms. This type of instability was present in the Big Thompson flash-flood of 1976

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Dmitry Mukhin, Dmitri Kondrashov, Evgeny Loskutov, Andrey Gavrilov, Alexander Feigin, and Michael Ghil

model by data-driven empirical model reduction (EMR) from the multivariate time series of the SST field. Moreover, the real-time ENSO prediction by this leading EOF-based EMR model has proved to be highly competitive among other dynamical and statistical ENSO forecasts ( Barnston et al. 2012 ). Still, preparation of the learning sample by projection onto the leading EOFs has an important drawback: it uses only the instantaneous correlations between points of the spatial grid, and it does not take

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Seth P. Tuler, Thomas Webler, and Jason L. Rhoades

Water Management Model (SWMM)] that can help stormwater managers understand how existing infrastructure will perform under possible future conditions ( EPA 2015 ). SWMM models hydrological systems to estimate the impacts on water quantity and quality of different engineered stormwater solutions. Tools such as these provide detailed information about the specific aspects of broader systems producing stormwater and managing stormwater. While decision support tools can enable deeper understandings of

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Aaron J. Hill, Gregory R. Herman, and Russ S. Schumacher

, 7088 , 708804, https://doi.org/10.1117/12.795570 . 10.1117/12.795570 Wimmers , A. , C. Velden , and J. H. Cossuth , 2019 : Using deep learning to estimate tropical cyclone intensity from satellite passive microwave imagery . Mon. Wea. Rev. , 147 , 2261 – 2282 , https://doi.org/10.1175/MWR-D-18-0391.1 . 10.1175/MWR-D-18-0391.1

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Michael Scheuerer, Matthew B. Switanek, Rochelle P. Worsnop, and Thomas M. Hamill

, 2015 : Keras: The Python Deep Learning library. Accessed 2019, https://keras.io . Clevert , D. A. , T. Unterthiner , and S. Hochreiter , 2015 : Fast and accurate deep network learning by exponential linear units (ELUs). Int. Conf. on Learning Representations , San Juan, Puerto Rico, ICLR, 1–14, https://arxiv.org/abs/1511.07289 . Cloud , K. A. , B. J. Reich , C. M. Rozoff , S. Alessandrini , W. E. Lewis , and L. Delle Monache , 2019 : A feed forward neural network

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