Could Machine Learning Break the Convection Parameterization Deadlock?
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Pierre Gentine | Michael S. Pritchard | S. Rasp | G. Reinaudi | G. Yacalis | P. Gentine | S. Rasp | M. Pritchard | G. Reinaudi | G. Yacalis | M. Pritchard
[1] Kyongmin Yeo,et al. Measurement of Convective Entrainment Using Lagrangian Particles , 2013 .
[2] Paul A. O'Gorman,et al. Influence of entrainment on the thermal stratification in simulations of radiative‐convective equilibrium , 2013 .
[3] Akio Arakawa,et al. Development of a Quasi‐3D Multiscale Modeling Framework: Motivation, Basic Algorithm and Preliminary results , 2010 .
[4] D. Randall,et al. Large‐Eddy Simulation of Maritime Deep Tropical Convection , 2009 .
[5] Filipe Aires,et al. Water, Energy, and Carbon with Artificial Neural Networks (WECANN): A statistically-based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence. , 2017, Biogeosciences.
[6] Mitchell W. Moncrieff,et al. Representing convective organization in prediction models by a hybrid strategy , 2006 .
[7] Bob Blakley,et al. Conceptual Model , 2017, Encyclopedia of GIS.
[8] Aiko Voigt,et al. Climate and climate change in a radiative‐convective equilibrium version of ECHAM6 , 2013 .
[9] Stefan N. Tulich,et al. A strategy for representing the effects of convective momentum transport in multiscale models: Evaluation using a new superparameterized version of the Weather Research and Forecast model (SP‐WRF) , 2015 .
[10] David A. Randall,et al. Global‐scale convective aggregation: Implications for the Madden‐Julian Oscillation , 2015 .
[11] Andrew J. Majda,et al. A Simple Multicloud Parameterization for Convectively Coupled Tropical Waves. Part I: Linear Analysis , 2006 .
[12] F. Aires,et al. Soil moisture retrieval from AMSR-E and ASCAT microwave observation synergy. Part 1: Satellite data analysis , 2016 .
[13] Filipe Aires,et al. Soil moisture retrieval from multi‐instrument observations: Information content analysis and retrieval methodology , 2013 .
[14] Pedro M. M. Soares,et al. Sensitivity of moist convection to environmental humidity , 2004 .
[15] S. Bony,et al. Spread in model climate sensitivity traced to atmospheric convective mixing , 2014, Nature.
[16] Sandrine Bony,et al. What favors convective aggregation and why? , 2015 .
[17] A. P. Siebesma,et al. Clouds, circulation and climate sensitivity , 2015 .
[18] G. Bellon,et al. The double ITCZ bias in CMIP5 models: interaction between SST, large-scale circulation and precipitation , 2015, Climate Dynamics.
[19] Christopher S. Bretherton,et al. Sensitivity of Coupled Tropical Pacific Model Biases to Convective Parameterization in CESM1 , 2018 .
[20] Peter A. Bogenschutz,et al. Simulation, Modeling, and Dynamically Based Parameterization of Organized Tropical Convection for Global Climate Models , 2017 .
[21] D. Randall,et al. Simulations of the Atmospheric General Circulation Using a Cloud-Resolving Model as a Superparameterization of Physical Processes , 2005 .
[22] R. Trapp. Mesoscale Convective Systems , 2013 .
[23] Andrew J. Majda,et al. Stochastic Behavior of Tropical Convection in Observations and a Multicloud Model , 2012 .
[24] William B. Rossow,et al. Increases in tropical rainfall driven by changes in frequency of organized deep convection , 2015, Nature.
[25] Bjorn Stevens,et al. Coupled radiative convective equilibrium simulations with explicit and parameterized convection , 2016 .
[26] David M. Romps,et al. Convective self‐aggregation, cold pools, and domain size , 2013 .
[27] Aneesh C. Subramanian,et al. Ensemble superparameterization versus stochastic parameterization: A comparison of model uncertainty representation in tropical weather prediction , 2017 .
[28] C. Hohenegger,et al. Coupling of convection and circulation at various resolutions , 2015 .
[29] Bjorn Stevens,et al. What Are Climate Models Missing? , 2013, Science.
[30] Tara N. Sainath,et al. Deep Neural Networks for Acoustic Modeling in Speech Recognition , 2012 .
[31] Simon P. de Szoeke,et al. Mechanisms of convective cloud organization by cold pools over tropical warm ocean during the AMIE/DYNAMO field campaign , 2015 .
[32] Andrew J. Majda,et al. A stochastic multicloud model for tropical convection , 2010 .
[33] C. Bretherton,et al. The Soil Moisture–Precipitation Feedback in Simulations with Explicit and Parameterized Convection , 2009 .
[34] Steven J. Woolnough,et al. The Effects of Explicit versus Parameterized Convection on the MJO in a Large-Domain High-Resolution Tropical Case Study. Part II: Processes Leading to Differences in MJO Development* , 2015 .
[35] David A. Randall,et al. Structure of the Madden-Julian Oscillation in the Superparameterized CAM , 2009 .
[36] Yann Kerr,et al. Soil Moisture , 1922, Botanical Gazette.
[37] S. Bony,et al. Using aquaplanets to understand the robust responses of comprehensive climate models to forcing , 2015, Climate Dynamics.
[38] S. Sorooshian,et al. Evaluation of the PERSIANN-CDR Daily Rainfall Estimates in Capturing the Behavior of Extreme Precipitation Events over China , 2014 .
[39] Khan Fa,et al. The Net is many people's only chance of access , 2001 .
[40] Juan Pedro Mellado,et al. A conceptual model of a shallow circulation induced by prescribed low-level radiative cooling , 2017 .
[41] A. Genio,et al. The Role of Entrainment in the Diurnal Cycle of Continental Convection , 2010 .
[42] A. Ihler,et al. A Deep Neural Network Modeling Framework to Reduce Bias in Satellite Precipitation Products , 2016 .
[43] Jian Sun,et al. Effects of explicit convection on global land‐atmosphere coupling in the superparameterized CAM , 2015 .
[44] Guang J. Zhang,et al. Role of Vertical Structure of Convective Heating in MJO Simulation in NCAR CAM5.3 , 2017 .
[45] Kuan-Man Xu,et al. An explicit representation of vertical momentum transport in a multiscale modeling framework through its 2‐D cloud‐resolving model component , 2014 .
[46] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[47] John C. H. Chiang,et al. Tropical tropospheric temperature variations caused by ENSO and their influence on the remote tropical climate , 2002 .
[48] M. J. Herman,et al. Intercomparison of methods of coupling between convection and large‐scale circulation: 1. Comparison over uniform surface conditions , 2015, Journal of advances in modeling earth systems.
[49] Pierre Gentine,et al. Representation of daytime moist convection over the semi‐arid Tropics by parametrizations used in climate and meteorological models , 2015 .
[50] Z. Kuang,et al. Moist Static Energy Budget of MJO-like Disturbances in the Atmosphere of a Zonally Symmetric Aquaplanet , 2012 .
[51] Dorian S. Abbot,et al. Effects of explicit atmospheric convection at high CO2 , 2014, Proceedings of the National Academy of Sciences.
[52] Richard C. J. Somerville,et al. Robustness and sensitivities of central U.S. summer convection in the super‐parameterized CAM: Multi‐model intercomparison with a new regional EOF index , 2013 .
[53] A. Pier Siebesma,et al. Entrainment and detrainment in cumulus convection: an overview , 2013 .
[54] Sandrine Bony,et al. Physical mechanisms controlling the initiation of convective self‐aggregation in a General Circulation Model , 2015 .
[55] Masaki Satoh,et al. Nonhydrostatic icosahedral atmospheric model (NICAM) for global cloud resolving simulations , 2008, J. Comput. Phys..
[56] David A. Randall,et al. Robust effects of cloud superparameterization on simulated daily rainfall intensity statistics across multiple versions of the Community Earth System Model , 2016 .
[57] Mitchell W. Moncrieff,et al. The Multiscale Organization of Moist Convection and the Intersection of Weather and Climate , 2013 .
[58] A. Arneth,et al. Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations , 2011 .
[59] Shahram Jafari,et al. An Expert System for Detection of Breast Cancer Using Data Preprocessing and Bayesian Network , 2011 .
[60] C. Bretherton,et al. Convective self‐aggregation feedbacks in near‐global cloud‐resolving simulations of an aquaplanet , 2015 .
[61] A. P. Siebesma,et al. Climate goals and computing the future of clouds , 2017 .
[62] J. Kolassaa,et al. Soil moisture retrieval from AMSR-E and ASCAT microwave observation synergy . Part 2 : Product evaluation , 2017 .
[63] C. Taylor,et al. Modeling soil moisture‐precipitation feedback in the Sahel: Importance of spatial scale versus convective parameterization , 2013 .
[64] Akio Arakawa,et al. Development of a Quasi-3 D Multiscale Modeling Framework : Motivation , Basic Algorithm and Preliminary results , 2010 .
[65] Catherine Rio,et al. Deep Convection Triggering by Boundary Layer Thermals. Part I: LES Analysis and Stochastic Triggering Formulation , 2014 .
[66] Pierre Gentine,et al. Role of surface heat fluxes underneath cold pools , 2016, Geophysical research letters.
[67] A. Pier Siebesma,et al. Analytical expressions for entrainment and detrainment in cumulus convection , 2010 .
[68] Steven C. Sherwood,et al. Slippery Thermals and the Cumulus Entrainment Paradox , 2013 .
[69] Yang Tian,et al. Dependence of entrainment in shallow cumulus convection on vertical velocity and distance to cloud edge , 2016 .
[70] Yu-Bin Yang,et al. Lung cancer cell identification based on artificial neural network ensembles , 2002, Artif. Intell. Medicine.
[71] Charles Cohen. A Quantitative Investigation of Entrainment and Detrainment in Numerically Simulated Convective Clouds, PT 1: Model Development , 2013 .
[72] Richard C. J. Somerville,et al. Orogenic Propagating Precipitation Systems over the United States in a Global Climate Model with Embedded Explicit Convection , 2011 .
[73] Andrew J Majda,et al. Coarse-grained stochastic models for tropical convection and climate , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[74] M. Pritchard,et al. The response of US summer rainfall to quadrupled CO2 climate change in conventional and superparameterized versions of the NCAR community atmosphere model , 2014 .
[75] Johan Nilsson,et al. The Weak Temperature Gradient Approximation and Balanced Tropical Moisture Waves , 2001 .
[76] D. Randall,et al. Cloud resolving modeling of the ARM summer 1997 IOP: Model formulation, results, uncertainties, and sensitivities , 2003 .
[77] Pierre Gentine,et al. Triggering Deep Convection with a Probabilistic Plume Model , 2014 .
[78] C. Bretherton,et al. Restricting 32–128 km horizontal scales hardly affects the MJO in the Superparameterized Community Atmosphere Model v.3.0 but the number of cloud‐resolving grid columns constrains vertical mixing , 2014 .
[79] M. J. Herman,et al. Intercomparison of methods of coupling between convection and large‐scale circulation: 2. Comparison over nonuniform surface conditions , 2016, Journal of advances in modeling earth systems.
[80] J. T. Dawe,et al. Direct entrainment and detrainment rate distributions of individual shallow cumulus clouds in an LES , 2013 .
[81] Catherine Rio,et al. Deep Convection Triggering by Boundary Layer Thermals. Part II: Stochastic Triggering Parameterization for the LMDZ GCM , 2014 .
[82] Richard C. J. Somerville,et al. Assessing the Diurnal Cycle of Precipitation in a Multi‐Scale Climate Model , 2009 .
[83] Steven J. Woolnough,et al. Precipitation distributions for explicit versus parametrized convection in a large‐domain high‐resolution tropical case study , 2012 .
[84] Tara N. Sainath,et al. FUNDAMENTAL TECHNOLOGIES IN MODERN SPEECH RECOGNITION Digital Object Identifier 10.1109/MSP.2012.2205597 , 2012 .
[85] Christopher M. Taylor,et al. Impact of soil moisture on the development of a Sahelian mesoscale convective system: a case‐study from the AMMA Special Observing Period , 2010 .
[86] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[87] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[88] William D. Collins,et al. Sensitivity of MJO propagation to a robust positive Indian Ocean dipole event in the superparameterized CAM , 2015 .
[89] Yang Hong,et al. Comparison of PERSIANN and V7 TRMM Multi-satellite Precipitation Analysis (TMPA) products with rain gauge data over Iran , 2013 .
[90] David M. Romps,et al. A Direct Measure of Entrainment , 2010 .
[91] W. Grabowski. Coupling Cloud Processes with the Large-Scale Dynamics Using the Cloud-Resolving Convection Parameterization (CRCP) , 2001 .
[92] David M. Romps,et al. The Stochastic Parcel Model: A deterministic parameterization of stochastically entraining convection , 2016 .
[93] Wojciech W. Grabowski. A parameterization of cloud microphysics for long-term cloud-resolving modeling of tropical convection , 1999 .
[94] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[95] Martin Köhler,et al. Modelling the diurnal cycle of deep precipitating convection over land with cloud‐resolving models and single‐column models , 2004 .
[96] A. Arakawa,et al. Toward unification of the multiscale modeling of the atmosphere , 2011 .
[97] Duane E. Waliser,et al. Multiscale Convective Organization and the YOTC Virtual Global Field Campaign , 2012 .
[98] M. Cevdet Ince,et al. An expert system for detection of breast cancer based on association rules and neural network , 2009, Expert Syst. Appl..
[99] David A. Randall,et al. High-Resolution Simulation of Shallow-to-Deep Convection Transition over Land , 2006 .
[100] A. Sobel,et al. Response of convection to relative sea surface temperature: Cloud‐resolving simulations in two and three dimensions , 2011 .
[101] Lorenzo Tomassini,et al. On the connection between tropical circulation, convective mixing, and climate sensitivity , 2015 .
[102] M. Ringnér,et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks , 2001, Nature Medicine.
[103] Hossein Parishani,et al. Global Effects of Superparameterization on Hydrothermal Land‐Atmosphere Coupling on Multiple Timescales , 2018 .
[104] Kerry A. Emanuel,et al. Physical mechanisms controlling self‐aggregation of convection in idealized numerical modeling simulations , 2013 .
[105] C. Bretherton,et al. Toward low‐cloud‐permitting cloud superparameterization with explicit boundary layer turbulence , 2017 .
[106] Filipe Aires,et al. Toward an estimation of global land surface heat fluxes from multisatellite observations , 2009 .
[107] Dorian S. Abbot,et al. The Effects of Explicit Atmospheric Convection at High CO 2 , 2014 .
[108] Daehyun Kim,et al. The Tropical Subseasonal Variability Simulated in the NASA GISS General Circulation Model , 2012 .
[109] D. Randall,et al. Beyond deadlock , 2013 .
[110] Charles Cohen,et al. A Quantitative Investigation of Entrainment and Detrainment in Numerically Simulated Cumulonimbus Clouds , 2000 .
[111] Eli Tziperman,et al. MJO Intensification with Warming in the Superparameterized CESM , 2015 .
[112] Dong Yu,et al. Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition , 2012, IEEE Transactions on Audio, Speech, and Language Processing.
[113] Jana Kolassa,et al. Merging active and passive microwave observations in soil moisture data assimilation. , 2017 .
[114] A. Sobel,et al. Forcings and feedbacks on convection in the 2010 Pakistan flood: Modeling extreme precipitation with interactive large‐scale ascent , 2016, 1603.01218.
[115] Richard Neale,et al. Parameterizing Convective Organization to Escape the Entrainment Dilemma , 2011 .
[116] David A. Randall,et al. Impacts of cloud superparameterization on projected daily rainfall intensity climate changes in multiple versions of the Community Earth System Model , 2016 .