Could Machine Learning Break the Convection Parameterization Deadlock?
Open accessAbstract
Representing unresolved moist convection in coarse‐scale climate models remains one of the main bottlenecks of current climate simulations. Many of the biases present with parameterized convection are strongly reduced when convection is explicitly resolved (i.e., in cloud resolving models at high spatial resolution approximately a kilometer or so). We here present a novel approach to convective parameterization based on machine learning, using an aquaplanet with prescribed sea surface temperatures as a proof of concept. A deep neural network is trained with a superparameterized version of a climate model in which convection is resolved by thousands of embedded 2‐D cloud resolving models. The machine learning representation of convection, which we call the Cloud Brain (CBRAIN), can skillfully predict many of the convective heating, moistening, and radiative features of superparameterization that are most important to climate simulation, although an unintended side effect is to reduce some of the superparameterization's inherent variance. Since as few as three months' high‐frequency global training data prove sufficient to provide this skill, the approach presented here opens up a new possibility for a future class of convection parameterizations in climate models that are built “top‐down,” that is, by learning salient features of convection from unusually explicit simulations.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Gentine et al. trained a deep neural network to emulate the behavior of a high-resolution cloud simulation, and found that the network gave similar results for a fraction of the cost and was stable in a simplified global model.
“Gentine et al. trained a deep neural network to emulate the behavior of a high-resolution cloud simulation, and found that the network gave similar results for a fraction of the cost [275] and was stable in a simplified global model [670].”
From Tackling climate change with machine learning (2023) · cites this paper as [275]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · see citing works
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260 · see citing works
Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition
Dahl, G. E., Yu, D., Deng, L., & Acero, A. (2012) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2134090 · Cited by 3,011 · see citing works
Houze, R. A. (2004) · Reviews of Geophysics · doi:10.1029/2004rg000150 · Cited by 1,561 · see citing works
Jung, M., Reichstein, M., Margolis, H. A., Cescatti, A., Richardson, A. D., Arain, M. A., Arneth, A., Bernhofer, C., Bonal, D., Chen, J., et al. (2011) · Journal of Geophysical Research Atmospheres · doi:10.1029/2010jg001566 · Cited by 1,393 · see citing works
Clouds, circulation and climate sensitivity
Bony, S., Stevens, B., Frierson, D. M. W., Jakob, C., Kageyama, M., Pincus, R. L., Shepherd, T. G., Sherwood, S. C., Siebesma, A. P., Sobel, A. H., et al. (2015) · Nature Geoscience · doi:10.1038/ngeo2398 · Cited by 987 · see citing works
Spread in model climate sensitivity traced to atmospheric convective mixing
Sherwood, S. C., Bony, S., & Dufresne, J.-L. (2014) · Nature · doi:10.1038/nature12829 · Cited by 762 · see citing works
Nonhydrostatic icosahedral atmospheric model (NICAM) for global cloud resolving simulations
Satoh, M., Matsuno, T., Tomita, H., Miura, H., Nasuno, T., & Iga, S. (2007) · Journal of Computational Physics · doi:10.1016/j.jcp.2007.02.006 · Cited by 651 · see citing works
What Are Climate Models Missing?
Stevens, B., & Bony, S. (2013) · Science · doi:10.1126/science.1237554 · Cited by 559 · see citing works
An expert system for detection of breast cancer based on association rules and neural network
Karabatak, M., & İnce, M. C. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.02.064 · Cited by 496 · see citing works
A Comparison of Breeding and Ensemble Transform Kalman Filter Ensemble Forecast Schemes
Wang, X., & Bishop, C. (2003) · Journal of the Atmospheric Sciences · doi:10.1175/1520-0469(2003)060 · Cited by 437 · see citing works
Khairoutdinov, M. F., Randall, D. A., & DeMott, C. A. (2005) · Journal of the Atmospheric Sciences · doi:10.1175/jas3453.1 · Cited by 416 · see citing works
High-Resolution Simulation of Shallow-to-Deep Convection Transition over Land
Khairoutdinov, M. F., & Randall, D. A. (2006) · Journal of the Atmospheric Sciences · doi:10.1175/jas3810.1 · Cited by 393 · see citing works
Lung cancer cell identification based on artificial neural network ensembles
Zhou, Z., Jiang, Y. Y., Yang, Y.-B., & Chen, S. (2002) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(01)00094-x · Cited by 352 · see citing works
Wing, A. A., & Emanuel, K. (2013) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2013ms000269 · Cited by 337 · see citing works
Climate goals and computing the future of clouds
Schneider, T., Teixeira, J., Bretherton, C. S., Brient, F., Pressel, K. G., Schär, C., & Siebesma, A. P. (2017) · Nature Climate Change · doi:10.1038/nclimate3190 · Cited by 294 · see citing works
Guichard, F., Petch, J. C., Redelsperger, J., et al. (2004) · Quarterly Journal of the Royal Meteorological Society · doi:10.1256/qj.03.145 · Cited by 289 · see citing works
Miao, C., Ashouri, H., Hsu, K., Sorooshian, S., & Duan, Q. (2015) · Journal of Hydrometeorology · doi:10.1175/jhm-d-14-0174.1 · Cited by 284 · see citing works
Khouider, B., & Majda, A. J. (2006) · Journal of the Atmospheric Sciences · doi:10.1175/jas3677.1 · Cited by 281 · see citing works
Andersen, J. A., & Kuang, Z. (2011) · Journal of Climate · doi:10.1175/jcli-d-11-00168.1 · Cited by 275 · see citing works
Parameterizing Convective Organization to Escape the Entrainment Dilemma
Mapes, B. E., & Neale, R. (2011) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2011ms000042 · Cited by 226 · see citing works
What favors convective aggregation and why?
Muller, C. J., & Bony, S. (2015) · Geophysical Research Letters · doi:10.1002/2015gl064260 · Cited by 225 · see citing works
Large‐Eddy Simulation of Maritime Deep Tropical Convection
Khairoutdinov, M. F., Krueger, S. K., Moeng, C., et al. (2009) · Journal of Advances in Modeling Earth Systems · doi:10.3894/james.2009.1.15 · Cited by 211 · see citing works
A stochastic multicloud model for tropical convection
Biello, J. A., Khouider, B., & Majda, A. J. (2010) · Communications in Mathematical Sciences · doi:10.4310/cms.2010.v8.n1.a10 · Cited by 210 · see citing works
Structure of the Madden–Julian Oscillation in the Superparameterized CAM
Benedict, J. J., & Randall, D. A. (2009) · Journal of the Atmospheric Sciences · doi:10.1175/2009jas3030.1 · Cited by 202 · see citing works
Increases in tropical rainfall driven by changes in frequency of organized deep convection
Tan, J., Jakob, C., Rossow, W. B., & Tselioudis, G. (2015) · Nature · doi:10.1038/nature14339 · Cited by 201 · see citing works
Moazami, S., Golian, S., Kavianpour, M. R., et al. (2013) · International Journal of Remote Sensing · doi:10.1080/01431161.2013.833360 · Cited by 196 · see citing works
Toward unification of the multiscale modeling of the atmosphere
Arakawa, A., Jung, J.-H., & Wu, C. (2011) · Atmospheric chemistry and physics · doi:10.5194/acp-11-3731-2011 · Cited by 192 · see citing works
Alemohammad, H., Fang, B., Konings, A. G., et al. (2017) · Biogeosciences · doi:10.5194/bg-14-4101-2017 · Cited by 176 · see citing works
Global‐scale convective aggregation: Implications for the Madden‐Julian Oscillation
Arnold, N., & Randall, D. A. (2015) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2015ms000498 · Cited by 167 · see citing works
A Deep Neural Network Modeling Framework to Reduce Bias in Satellite Precipitation Products
Tao, Y., Gao, X., Hsu, K., Sorooshian, S., & Ihler, A. (2016) · Journal of Hydrometeorology · doi:10.1175/jhm-d-15-0075.1 · Cited by 160 · see citing works
Coppin, D., & Bony, S. (2015) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2015ms000571 · Cited by 155 · see citing works
Convective self‐aggregation, cold pools, and domain size
Jeevanjee, N., & Romps, D. M. (2013) · Geophysical Research Letters · doi:10.1002/grl.50204 · Cited by 153 · see citing works
Multiscale Convective Organization and the YOTC Virtual Global Field Campaign
Moncrieff, M. W., Waliser, D. E., Miller, M. J., Shapiro, M. A., Asrar, G., & Caughey, J. (2012) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-11-00233.1 · Cited by 122 · see citing works
Coarse-grained stochastic models for tropical convection and climate
Khouider, B., Majda, A. J., & Katsoulakis, M. A. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1634951100 · Cited by 117 · see citing works
Convective self‐aggregation feedbacks in near‐global cloud‐resolving simulations of an aquaplanet
Bretherton, C. S., & Khairoutdinov, M. F. (2015) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2015ms000499 · Cited by 115 · see citing works
Moncrieff, M. W., Liu, C., & Bogenschutz, P. (2017) · Journal of the Atmospheric Sciences · doi:10.1175/jas-d-16-0166.1 · Cited by 112 · see citing works
Pritchard, M. S., Moncrieff, M. W., & Somerville, R. C. J. (2011) · Journal of the Atmospheric Sciences · doi:10.1175/2011jas3699.1 · Cited by 111 · see citing works
Self-citationMJO Intensification with Warming in the Superparameterized CESM
Arnold, N., Branson, M., Kuang, Z., Randall, D. A., & Tziperman, E. (2015) · Journal of Climate · doi:10.1175/jcli-d-14-00494.1 · Cited by 110 · see citing works
Holloway, C. E., Woolnough, S. J., & Lister, G. M. S. (2012) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.1903 · Cited by 105 · see citing works
Using aquaplanets to understand the robust responses of comprehensive climate models to forcing
Medeiros, B., Stevens, B., & Bony, S. (2014) · Climate Dynamics · doi:10.1007/s00382-014-2138-0 · Cited by 101 · see citing works
The multiscale organization of moist convection and the intersection of weather and climate
Moncrieff, M. W. (2010) · Geophysical monograph · doi:10.1029/2008gm000838 · Cited by 99 · see citing works
Coupled radiative convective equilibrium simulations with explicit and parameterized convection
Hohenegger, C., & Stevens, B. (2016) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2016ms000666 · Cited by 93 · see citing works
Randall, D. A. (2013) · Geophysical Research Letters · doi:10.1002/2013gl057998 · Cited by 89 · see citing works
Representing Convective Organization in Prediction Models by a Hybrid Strategy
Moncrieff, M. W., & Liu, C. (2006) · Journal of the Atmospheric Sciences · doi:10.1175/jas3812.1 · Cited by 85 · see citing works
Taylor, C. M., Harris, P. P., & Parker, D. J. (2009) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.465 · Cited by 81 · see citing works
Kooperman, G. J., Pritchard, M. S., Burt, M., Branson, M., & Randall, D. A. (2016) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2015ms000574 · Cited by 79 · see citing works
Self-citationStochastic Behavior of Tropical Convection in Observations and a Multicloud Model
Peters, K., Jakob, C., Davies, L., Khouider, B., & Majda, A. J. (2013) · Journal of the Atmospheric Sciences · doi:10.1175/jas-d-13-031.1 · Cited by 78 · see citing works
Assessing the Diurnal Cycle of Precipitation in a Multi‐Scale Climate Model
Pritchard, M. S., & Somerville, R. C. J. (2009) · Journal of Advances in Modeling Earth Systems · doi:10.3894/james.2009.1.12 · Cited by 76 · see citing works
Self-citationToward an estimation of global land surface heat fluxes from multisatellite observations
Jiménez, C., Prigent, C., & Aires, F. (2009) · Journal of Geophysical Research Atmospheres · doi:10.1029/2008jd011392 · Cited by 71 · see citing works
Merging active and passive microwave observations in soil moisture data assimilation
Kolassa, J., Reichle, R. H., & Draper, C. S. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.01.015 · Cited by 70 · see citing works
Kolassa, J., Gentine, P., Prigent, C., et al. (2015) · Remote Sensing of Environment · doi:10.1016/j.rse.2015.11.011 · Cited by 68 · see citing works
Self-citationKolassa, J., Gentine, P., Prigent, C., et al. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.04.020 · Cited by 67 · see citing works
Self-citationDaleu, C. L., Plant, R. S., Woolnough, S. J., et al. (2015) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2015ms000468 · Cited by 66 · see citing works
Kolassa, J., Aires, F., Polcher, J., et al. (2012) · Journal of Geophysical Research Atmospheres · doi:10.1029/2012jd018150 · Cited by 55 · see citing works
Grabowski, W. W. (1999) · Atmospheric Research · doi:10.1016/s0169-8095(99)00029-0 · Cited by 55 · see citing works
Toward low‐cloud‐permitting cloud superparameterization with explicit boundary layer turbulence
Parishani, H., Pritchard, M. S., Bretherton, C. S., Wyant, M. C., & Khairoutdinov, M. F. (2017) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2017ms000968 · Cited by 54 · see citing works
Self-citationRochetin, N., Grandpeix, J., Rio, C., et al. (2013) · Journal of the Atmospheric Sciences · doi:10.1175/jas-d-12-0337.1 · Cited by 52 · see citing works
Rochetin, N., Couvreux, F., Grandpeix, J., & Rio, C. (2013) · Journal of the Atmospheric Sciences · doi:10.1175/jas-d-12-0336.1 · Cited by 50 · see citing works
Couvreux, F., Roehrig, R., Rio, C., et al. (2015) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.2517 · Cited by 46 · see citing works
Pritchard, M. S., Bretherton, C. S., & DeMott, C. A. (2014) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2014ms000340 · Cited by 43 · see citing works
Self-citationEffects of explicit atmospheric convection at high CO 2
Arnold, N., Branson, M., Burt, M., Abbot, D. S., Kuang, Z., Randall, D. A., & Tziperman, E. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1407175111 · Cited by 39 · see citing works
Role of Vertical Structure of Convective Heating in MJO Simulation in NCAR CAM5.3
Cao, G., & Zhang, G. J. (2017) · Journal of Climate · doi:10.1175/jcli-d-16-0913.1 · Cited by 39 · see citing works
Triggering Deep Convection with a Probabilistic Plume Model
D’Andrea, F., Gentine, P., Betts, A. K., & Lintner, B. R. (2014) · Journal of the Atmospheric Sciences · doi:10.1175/jas-d-13-0340.1 · Cited by 37 · see citing works
Self-citationEffects of explicit convection on global land‐atmosphere coupling in the superparameterized CAM
Sun, J., & Pritchard, M. S. (2016) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2016ms000689 · Cited by 37 · see citing works
Self-citationTulich, S. N. (2015) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2014ms000417 · Cited by 36 · see citing works
Kooperman, G. J., Pritchard, M. S., Burt, M., Branson, M., & Randall, D. A. (2016) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2016ms000715 · Cited by 35 · see citing works
Self-citationSensitivity of Coupled Tropical Pacific Model Biases to Convective Parameterization in CESM1
Woelfle, M. D., Yu, S., Bretherton, C. S., & Pritchard, M. S. (2017) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2017ms001176 · Cited by 35 · see citing works
Self-citationCoupling of convection and circulation at various resolutions
Hohenegger, C., Schlemmer, L., & Silvers, L. G. (2015) · Tellus A Dynamic Meteorology and Oceanography · doi:10.3402/tellusa.v67.26678 · Cited by 32 · see citing works
Jung, J., & Arakawa, A. (2010) · Journal of Advances in Modeling Earth Systems · doi:10.3894/james.2010.2.11 · Cited by 30 · see citing works
Nie, J., Shaevitz, D. A., & Sobel, A. H. (2016) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2016ms000663 · Cited by 26 · see citing works
Daleu, C. L., Plant, R. S., Woolnough, S. J., et al. (2016) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2015ms000570 · Cited by 26 · see citing works
Holloway, C. E., Woolnough, S. J., & Lister, G. M. S. (2015) · Journal of the Atmospheric Sciences · doi:10.1175/jas-d-14-0308.1 · Cited by 21 · see citing works
Subramanian, A. C., & Palmer, T. N. (2017) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2016ms000857 · Cited by 21 · see citing works
The Net is many people's only chance of access
Khan, F. A. (2001) · Nature · doi:10.1038/35079160 · Cited by 8 · see citing works
Qin, H., Pritchard, M. S., Kooperman, G. J., et al. (2018) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2017ms001185 · Cited by 7 · see citing works
Self-citationSobel (2001) · doi:10.1175/1520-0469(2001)058
Flagged first, then by citations
Research works printed but not linked: 2
- Khairoutdinov, M. F., & Randall, D. A. (2003). Cloud resolving modeling of the ARM summer 1997 IOP: Model formulation, results, uncer- tainties, and sensitivities. Journal of the Atmospheric Sciences, 60(4), 607-625.
- Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization, arXiv, cs. LG.
Cited by
Sorted by citations
Deep learning and process understanding for data-driven Earth system science
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat (2019) · Nature · doi:10.1038/s41586-019-0912-1 · Cited by 5,673 · see citing works
Deep learning to represent subgrid processes in climate models
Rasp, S., Pritchard, M. S., & Gentine, P. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1810286115 · Cited by 1,044 · see citing works
Self-citationWhat Role Does Hydrological Science Play in the Age of Machine Learning?
Nearing, G. S., Kratzert, F., Sampson, A. K., Pelissier, C. S., Klotz, D., Frame, J. M., Prieto, C., & Gupta, H. V. (2021) · Water Resources Research · doi:10.1029/2020wr028091 · Cited by 894 · see citing works
Machine learning and artificial intelligence to aid climate change research and preparedness
Huntingford, C., Jeffers, E. S., Bonsall, M. B., Christensen, H. M., Lees, T., & Yang, H. (2019) · Environmental Research Letters · doi:10.1088/1748-9326/ab4e55 · Cited by 469 · see citing works
LMDZ6A: The Atmospheric Component of the IPSL Climate Model With Improved and Better Tuned Physics
Hourdin, F., Rio, C., Grandpeix, J., Madeleine, J., Cheruy, F., Rochetin, N., Jam, A., Musat, I., Idelkadi, A., Fairhead, L., et al. (2020) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2019ms001892 · Cited by 268 · see citing works
Data‐Driven Equation Discovery of Ocean Mesoscale Closures
Zanna, L., & Bolton, T. (2020) · Geophysical Research Letters · doi:10.1029/2020gl088376 · Cited by 259 · see citing works
Analog forecasting of extreme-causing weather patterns using deep learning
Chattopadhyay, A., Nabizadeh, E., & Hassanzadeh, P. (2020) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2019ms001958 · Cited by 224 · see citing works
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
Materia, S., García, L. P., van Straaten, C., O, S., Mamalakis, A., Cavicchia, L., Coumou, D., de Luca, P., Kretschmer, M., & Donat, M. (2024) · WIREs Climate Change · doi:10.1002/wcc.914 · Cited by 130 · see citing works
Couvreux, F., Hourdin, F., Williamson, D., Roehrig, R., Volodina, V., Villefranque, N., Rio, C., Audouin, O., Salter, J., Bazile, E., et al. (2021) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2020ms002217 · Cited by 121 · see citing works
Deep Learning-Based Extreme Heatwave Forecast
Jacques-Dumas, V., Ragone, F., Borgnat, P., Abry, P., & Bouchet, F. (2022) · Frontiers in Climate · doi:10.3389/fclim.2022.789641 · Cited by 92 · see citing works
“…(Weyn et al., 2019), they prove useful to improve physics models (Schneider et al., 2017) or their parameter tuning (Brenowitz and Bretherton, 2018; Gentine et al., 2018), to complementing them for analysis or pattern recognition (Liu et al., 2016), or to performing tasks not achievable with…”
BackgroundDeep learning for bias correction of MJO prediction
Kim, H., Ham, Y. G., Joo, Y. S., & Son, S. W. (2021) · Nature Communications · doi:10.1038/s41467-021-23406-3 · Cited by 92 · see citing works
Hourdin, F., Williamson, D., Rio, C., Couvreux, F., Roehrig, R., Villefranque, N., Musat, I., Fairhead, L., Diallo, F. B., & Volodina, V. (2021) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2020ms002225 · Cited by 83 · see citing works
Machine learning for the physics of climate
Bracco, A., Brajard, J., Dijkstra, H. A., Hassanzadeh, P., Lessig, C., & Monteleoni, C. (2024) · Nature Reviews Physics · doi:10.1038/s42254-024-00776-3 · Cited by 79 · see citing works
Yang, P., Zhao, Q., & Cai, X. (2020) · Environmental Research Letters · doi:10.1088/1748-9326/ab865f · Cited by 39 · see citing works
Arcomano, T., Szunyogh, I., Wikner, A., Hunt, B. R., & Ott, E. (2023) · Geophysical Research Letters · doi:10.1029/2022gl102649 · Cited by 28 · see citing works
Comparative analysis of machine learning approaches for heatwave event prediction in India
V, R. C., Johnvictor, A. C., & N, P. S. (2025) · Scientific Reports · doi:10.1038/s41598-025-04634-9 · Cited by 9 · see citing works
- Authors
- Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G., & Yacalis, G.
- Affiliations
- Columbia University (US)
- University of California, Irvine (US)
- Ludwig-Maximilians-Universität München (DE)
- Corresponding author
- Gentine, P.
- Venue
- Geophysical Research Letters
- Publisher
- American Geophysical Union (AGU)
- Published
- 2018-06-12
- Volume
- 45
- Issue
- 11
- Pages
- 5742-5751
- ISSN
- 0094-8276 (ISSN-L)
- 1944-8007
- OpenAlex
- W2804943168
- Cited by
- 497
- References
- 79
- Citations by year
- 2026: 40 · 2025: 63 · 2024: 69 · 2023: 67 · 2022: 79 · 2021: 91 · 2020: 53 · 2019: 25 · 2018: 10
- FWCI
- 21.93
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Geophysical Research Letters (publishedVersion, cc-by-nc-nd)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- National Science Foundation (1552304, 1649770, TG‐ATM120034, AGS‐1734164)
- U.S. Department of Energy (DE‐SC00‐12548, DE‐SC0012152)
- Topics
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Environmental Science › Global and Planetary Change › Atmospheric aerosols and clouds
- Keywords
- convection parameterization, machine learning, deep neural network, superparameterization, cloud-resolving model, aquaplanet, convective heating, climate model bias
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
