{"id":16,"date":"2020-09-14T13:15:22","date_gmt":"2020-09-14T13:15:22","guid":{"rendered":"http:\/\/dowitcherdevelopment.com\/eqi\/?page_id=16"},"modified":"2025-04-03T23:09:26","modified_gmt":"2025-04-03T23:09:26","slug":"home","status":"publish","type":"page","link":"https:\/\/sites.ps.uci.edu\/pritchard\/","title":{"rendered":"Home"},"content":{"rendered":"<p><img decoding=\"async\" src=\"\/pritchard\/wp-content\/uploads\/sites\/23\/2020\/12\/ccc-logo.png\" alt=\"\"><\/p>\n<h1>\n<p>About<\/p>\n<\/h1>\n<hr>\n<div>\n<p>We study how the planetary water cycle and climate work, and how it may change in the future, focusing on cloud physics and moist convection processes. Our tools are next-gen global atmospheric simulations, ocean-coupled climate dynamics, high-performance computing, and machine learning for turbulent process emulation and neural-network assisted inquiry. <\/p>\n<\/div>\n<h1>\n<p>Research Themes<\/p>\n<\/h1>\n<hr>\n<ul>\n<li>\n<p>        <img decoding=\"async\" src=\"\/pritchard\/wp-content\/uploads\/sites\/23\/2020\/10\/climate-dynamics-icon-300.jpg\" alt=\"\"><\/p>\n<h3>Climate Dynamics <\/h3>\n<p><a href=\"\/pritchard\/climate-dynamics\/\">Read more<\/a><\/p>\n<\/li>\n<li>\n<p>        <img decoding=\"async\" src=\"\/pritchard\/wp-content\/uploads\/sites\/23\/2020\/10\/land-atmosphere-icon.jpg\" alt=\"\"><\/p>\n<h3>Land-Atmosphere Interactions <\/h3>\n<p><a href=\"\/pritchard\/land-atmosphere-interactions\/\">Read more<\/a><\/p>\n<\/li>\n<li>\n<p>        <img decoding=\"async\" src=\"\/pritchard\/wp-content\/uploads\/sites\/23\/2020\/10\/low-cloud-icon.jpg\" alt=\"\"><\/p>\n<h3>Shallow Clouds <\/h3>\n<p><a href=\"\/pritchard\/shallow-clouds\/\">Read more<\/a><\/p>\n<\/li>\n<li>\n<p>        <img decoding=\"async\" src=\"\/pritchard\/wp-content\/uploads\/sites\/23\/2020\/10\/machine-learning-icon.jpg\" alt=\"\"><\/p>\n<h3>Machine Learning<\/h3>\n<p><a href=\"\/pritchard\/machine-learning\/\">Read more<\/a><\/p>\n<\/li>\n<li>\n<p>        <img decoding=\"async\" src=\"\/pritchard\/wp-content\/uploads\/sites\/23\/2020\/10\/multiscale-modeling-icon-300.jpg\" alt=\"\"><\/p>\n<h3>Multi-Scale Modeling <\/h3>\n<p><a href=\"\/pritchard\/multi-scale-modeling\/\">Read more<\/a><\/p>\n<\/li>\n<\/ul>\n<ul>\n<li>\n<p>        <img decoding=\"async\" src=\"\/pritchard\/wp-content\/uploads\/sites\/23\/2020\/10\/climate-dynamics-icon-300.jpg\" alt=\"\"><\/p>\n<h3>Climate dynamics <\/h3>\n<p><a href=\"http:\/\/sites.ps.uci.edu\/pritchard\/climate-dynamics\/\"><\/a><\/p>\n<\/li>\n<li>\n<p>        <img decoding=\"async\" src=\"\/pritchard\/wp-content\/uploads\/sites\/23\/2020\/10\/land-atmosphere-icon.jpg\" alt=\"\"><\/p>\n<h3>Land-atmosphere interactions <\/h3>\n<p><a href=\"\/pritchard\/land-atmosphere-interactions\/\"><\/a><\/p>\n<\/li>\n<li>\n<p>        <img decoding=\"async\" src=\"\/pritchard\/wp-content\/uploads\/sites\/23\/2020\/10\/low-cloud-icon.jpg\" alt=\"\"><\/p>\n<h3>Shallow clouds <\/h3>\n<p><a href=\"\/pritchard\/shallow-clouds\/\"><\/a><\/p>\n<\/li>\n<li>\n<p>        <img decoding=\"async\" src=\"\/pritchard\/wp-content\/uploads\/sites\/23\/2020\/10\/machine-learning-icon.jpg\" alt=\"\"><\/p>\n<h3>Machine learning<\/h3>\n<p><a href=\"\/pritchard\/machine-learning\/\"><\/a><\/p>\n<\/li>\n<li>\n<p>        <img decoding=\"async\" src=\"\/pritchard\/wp-content\/uploads\/sites\/23\/2020\/10\/multiscale-modeling-icon-300.jpg\" alt=\"\"><\/p>\n<h3>Multi-scale modeling <\/h3>\n<p><a href=\"\/pritchard\/multi-scale-modeling\/\"><\/a><\/p>\n<\/li>\n<\/ul>\n<h1>\n<p>Publications<\/p>\n<\/h1>\n<hr>\n<div>\n<p>*<sup>,+<\/sup> denotes led by Pritchard\u2019s UCI* (NVIDIA<sup>+<\/sup>) group; <u>underlined<\/u>\u00a0are: PI-advised (P)<u>ostdoctoral scholar or project scientist<\/u>, (G)<u>raduate\u00a0student<\/u>, (U)<u>ndergraduate<\/u> student and\/or (N)<span style=\"text-decoration: underline;\">VIDIA scientis<\/span>t. Prior to 2023, PI tended to occupy second author slot for closely advised work, following atmospheric science conventions.<\/p>\n<\/div>\n<div>\n<h3>2025 &#038; In Review<\/h3>\n<div>\n<div>\n<p>* <span style=\"text-decoration: underline;\">Ferretti, S. L.<\/span> (G), <strong>M. S. Pritchard<\/strong>, F. Ahmed, <span style=\"text-decoration: underline;\">L. Peng<\/span> (P) and J. W. Baldwin, Stress-Testing a Process-Oriented Diagnostic Relating Tropical Rainfall to Plume Buoyancy [<a href=\"https:\/\/essopenarchive.org\/doi\/pdf\/10.22541\/essoar.174078542.28042108\/v1\">preprint<\/a>].<\/p>\n<p><span class=\"preview-author-name\" data-collab-id=\"1884371\">* Peng,<span style=\"text-decoration: underline;\"> L.<\/span> (P), P. N. Blossey, W. M. Hannah, C. S. Bretherton, C. Terai, A. M. Jenney, <span style=\"text-decoration: underline;\">S. L. Ferretti<\/span> (G) and <strong>M. S. Pritchard<\/strong>, Resolving Low Cloud Feedbacks Globally with HR-MMF: Agreement with LES but Stronger Shortwave Effects [<a href=\"https:\/\/essopenarchive.org\/doi\/pdf\/10.22541\/essoar.173939558.83159342\/v1\">preprint<\/a>].<\/span><\/p>\n<\/div>\n<div><sup>+<\/sup>Pandey, K. (N), <span style=\"text-decoration: underline;\">Pathak, J.<\/span> (N<span style=\"text-decoration: underline;\">),<\/span>\u00a0Y. Xu, S. Mandt, <strong>M. Pritchard<\/strong>, A. Vahdat and M. Mardani, Heavy-tailed diffusion models, in review [<a href=\"https:\/\/arxiv.org\/abs\/2410.14171\">preprint<\/a>].<\/div>\n<div><\/div>\n<div><span><span><br \/><sup>+<\/sup>Fotiadis, S., <span style=\"text-decoration: underline;\">N. Brenowitz<\/span> <span style=\"text-decoration: underline;\">(<\/span>N<span style=\"text-decoration: underline;\">)<\/span>, T. Geffner, <span style=\"text-decoration: underline;\">Y. Cohen<\/span>\u00a0<span style=\"text-decoration: underline;\">(<\/span>N<span style=\"text-decoration: underline;\">),<\/span> <strong>M. Pritchard<\/strong>, A. Vahdat and M. Mardani, Stochastic Flow Matching for Resolving Small-Scale Physics, in review [<a href=\"https:\/\/arxiv.org\/abs\/2410.19814\">preprint<\/a>]<\/span><\/span>.<\/p>\n<\/div>\n<div><sup>+<\/sup><span style=\"text-decoration: underline;\">P. Manshausen<\/span><span>\u00a0<span style=\"text-decoration: underline;\">(<\/span>N<span style=\"text-decoration: underline;\">),<\/span><\/span> <span style=\"text-decoration: underline;\">Y. Cohen<\/span><span>\u00a0<span style=\"text-decoration: underline;\">(<\/span>N<span style=\"text-decoration: underline;\">),<\/span><\/span> <span style=\"text-decoration: underline;\">J. Pathak<\/span><span>\u00a0<span style=\"text-decoration: underline;\">(<\/span>N<span style=\"text-decoration: underline;\">),<\/span><\/span> <strong>M. Pritchard<\/strong>, <span style=\"text-decoration: underline;\">P. Garg<\/span> <span style=\"text-decoration: underline;\"><span>(<\/span><\/span>N<span style=\"text-decoration: underline;\"><span>),<\/span><\/span>\u00a0M. Mardani, K. Kashinath, S. Byrne, <span style=\"text-decoration: underline;\">N. Brenowitz<\/span><span style=\"text-decoration: underline;\"> <\/span><span><span style=\"text-decoration: underline;\">(<\/span>N<span style=\"text-decoration: underline;\">),<\/span><\/span> Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales, in revision [<a href=\"https:\/\/arxiv.org\/abs\/2406.16947\">preprint<\/a>].<\/div>\n<div><\/div>\n<div id=\"gsc_oci_title_wrapper\"><span style=\"text-decoration: underline;\"><br \/><\/span><em><sup>+<\/sup><\/em><span style=\"text-decoration: underline;\">Pathak, J<\/span>.<span>\u00a0<span style=\"text-decoration: underline;\">(<\/span>N<span style=\"text-decoration: underline;\">),<\/span><\/span>\u00a0<span style=\"text-decoration: underline;\">Y. Cohen<\/span> (N)<span style=\"text-decoration: underline;\"><span>,<\/span><\/span>\u00a0<span style=\"text-decoration: underline;\">P. Garg<\/span> <span style=\"text-decoration: underline;\"><span>(<\/span><\/span>N<span style=\"text-decoration: underline;\"><span>)<\/span><\/span>, P. Harrington, <span style=\"text-decoration: underline;\">N. Brenowitz<\/span><span>\u00a0<span style=\"text-decoration: underline;\">(<\/span>N<span style=\"text-decoration: underline;\">),<\/span><\/span> <span style=\"text-decoration: underline;\">D. Durran<\/span><span>\u00a0<span style=\"text-decoration: underline;\">(<\/span>N<span style=\"text-decoration: underline;\">),<\/span><\/span> M. Mardani, A. Vahdat, S. Xu, K. Kashinath and <strong>M. Pritchard<\/strong>, Kilometer-scale convection allowing model emulation using generative diffusion modeling, in review [<a href=\"https:\/\/arxiv.org\/abs\/2408.10958\">preprint<\/a>].<\/div>\n<div><\/div>\n<div>\n<div><sup>+<\/sup><span style=\"text-decoration: underline;\">Wang, C.<\/span><span>\u00a0<span style=\"text-decoration: underline;\">(<\/span>N<span style=\"text-decoration: underline;\">),<\/span><\/span> <strong>M. Pritchard<\/strong>, <span style=\"text-decoration: underline;\">N. Brenowitz<\/span> <span> <span style=\"text-decoration: underline;\">(<\/span>N<span style=\"text-decoration: underline;\">)<\/span><\/span>, <span style=\"text-decoration: underline;\">Y. Cohen<\/span> <span> <span style=\"text-decoration: underline;\">(<\/span>N<span style=\"text-decoration: underline;\">)<\/span><\/span>, B. Bonev, T. Kurth, <span style=\"text-decoration: underline;\">D. Durran<\/span> <span> <span style=\"text-decoration: underline;\">(<\/span>N<span style=\"text-decoration: underline;\">)<\/span><\/span>, <span style=\"text-decoration: underline;\">J. Pathak<\/span> <span> <span style=\"text-decoration: underline;\">(<\/span>N<span style=\"text-decoration: underline;\">)<\/span><\/span>, Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model, in review [<a href=\"https:\/\/arxiv.org\/abs\/2406.08632\">preprint<\/a>].<\/div>\n<\/div>\n<div><\/div>\n<div>\n<div><sup>+<\/sup>A. Mahesh, W. Collins, B. Bonev, <span style=\"text-decoration: underline;\">N. Brenowitz <\/span>(N), <span style=\"text-decoration: underline;\">Y. Cohen<\/span><span>\u00a0<span style=\"text-decoration: underline;\">(<\/span>N<span style=\"text-decoration: underline;\">),<\/span><\/span> J. Elms, P. Harrington, K. Kashinath, T. Kurth, J. North, T. O&#8217;Brien, <strong>M. Pritchard<\/strong>, D. Pruitt, M. Risser, S. Subramanian, J. Willard, Huge Ensembles Part II: Properties of a Huge Ensemble of Hindcasts Generated with Spherical Fourier Neural Operators, in revision [<a href=\"https:\/\/arxiv.org\/abs\/2408.01581\">preprint<\/a>].<\/div>\n<div><\/div>\n<div><sup>+<\/sup>A. Mahesh, W. Collins, B. Bonev, <span style=\"text-decoration: underline;\">N. Brenowitz <\/span>(N), <span style=\"text-decoration: underline;\">Y. Cohen<\/span><span>\u00a0<span style=\"text-decoration: underline;\">(<\/span>N<span style=\"text-decoration: underline;\">),<\/span><\/span> J. Elms, P. Harrington, K. Kashinath, T. Kurth, J. North, T. O&#8217;Brien, <strong>M. Pritchard<\/strong>, D. Pruitt, M. Risser, S. Subramanian, J. Willard, Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators, in revision [<a href=\"https:\/\/arxiv.org\/abs\/2408.03100\">preprint<\/a>].<\/p>\n<\/div>\n<div>*S. Yu (P), <span style=\"text-decoration: underline;\">Z. Hu<\/span> (N), <span style=\"text-decoration: underline;\">A. Subramaniam (N)<\/span>, W. Hannah, <span style=\"text-decoration: underline;\">L. Peng (P)<\/span>, <span style=\"text-decoration: underline;\">J. Lin (G)<\/span>, M. A. Bhouri. R. Gupta, B. L\u00fctjens, J. C. Wills, G. Behrens, J. J. M. Busecke, N. Loose, C. I. Stern, T. Beucler, B. Harrop, H. Heuer, B. R. Hillman, <span style=\"text-decoration: underline;\">A. Jenney (P)<\/span>, <span style=\"text-decoration: underline;\">N. Liu (P)<\/span>, A. White, T. Zheng, Z. Kuang, F. Ahmed, E. Barnes, N. D. Brenowitz, C. Bretherton, V. Eyring, S. Ferretti, N. Lutsko, P. Gentine, S. Mandt, J. D. Neelin, R. Yu, L. Zanna, N. Urban, J. Yuval, R. Abernathey, P. Baldi, W. Chuang, Y. Huang, F. Iglesias-Suarez, S. Jantre, P.-L. Ma, S. Shamekh, G. Zhang. M. Pritchard, ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation, in revision [<a href=\"https:\/\/arxiv.org\/abs\/2306.08754\">preprint<\/a>].<\/p>\n<\/div>\n<div><sup>+<\/sup><span style=\"text-decoration: underline;\">Z. Hu<\/span> <span style=\"text-decoration: underline;\">(<\/span>N<span style=\"text-decoration: underline;\">),<\/span>\u00a0A. Subramaniam, Z. Kuang, <span style=\"text-decoration: underline;\">J. Lin<\/span> (G), S. Yu, W. M. Hannah, <span style=\"text-decoration: underline;\">N. Brenowitz<\/span><span style=\"text-decoration: underline;\"> <\/span><span style=\"text-decoration: underline;\">(<\/span>N<span style=\"text-decoration: underline;\">),<\/span> J. Romero, <strong>M. Pritchard<\/strong>, Stable machine-learning parameterization of subgrid processes with real geography and full-physics emulation, in revision [<a href=\"https:\/\/arxiv.org\/abs\/2407.00124\">preprint<\/a>].<\/div>\n<\/div>\n<p>* <u>Lin, J.<\/u> (G), S. Yu, T. Beucler, P. Gentine, and <strong>M. Pritchard<\/strong>: Systematic Sampling and Validation of Machine Learning-Parameterizations in Climate Models, in revision [<a href=\"https:\/\/arxiv.org\/abs\/2309.16177\">preprint<\/a>].<\/p>\n<p>Behrens, G., T. Beucler, F. Iglesias-Suarez, <span style=\"text-decoration: underline;\">S. Yu<\/span> (P), P. Gentine, <strong>M. Pritchard<\/strong>, M. Schwabe, V. Eyring, Improving Atmospheric Processes in Earth System Models with Deep Learning Ensembles and Stochastic Parameterizations, in revision [<a href=\"https:\/\/arxiv.org\/abs\/2402.03079\">preprint<\/a>].<\/p>\n<p>Bhouri, M., <u>L. Peng (P)<\/u>, <strong>M. Pritchard<\/strong>, and P. Gentine, Multi-fidelity climate model parameterization for better generalization and extrapolation, in review [<a href=\"https:\/\/arxiv.org\/abs\/2309.10231\">preprint<\/a>].<\/p>\n<\/div>\n<\/div>\n<div>\n<h3>2024<\/h3>\n<div>\n<p><sup>+<\/sup>73. Mardani, M., <u>N. Brenowitz (N), Y. Cohen (N), J. Pathak (N)<\/u>, C.-Y. Chen, C.-C. Liu, A. Vahdat, K. Kashinath, J. Kautz and <strong>M. Pritchard<\/strong>. Generative residual diffusion modeling for km-scale atmospheric downscaling, <em>Nature Communications Earth &amp; Environment<\/em>, in press [<a href=\"https:\/\/arxiv.org\/abs\/2309.15214\">preprint<\/a>].<\/p>\n<p><span class=\"author\">72. <span>Gopalakrishnan Meena, M., M. Norman, D. Hall, and <strong>M. Pritchard<\/strong>,\u00a0Spatially Local Surrogate Modeling of Subgrid-Scale Effects in Idealized Atmospheric Flows: A Deep Learned Approach Using High-Resolution Simulation Data. <\/span><i>Artificial Intelligence for Earth Systems<\/i><span>,\u00a0<\/span><b>3<\/b><span>, e230043, 2024 [<a href=\"https:\/\/doi.org\/10.1175\/AIES-D-23-0043.1\">link<\/a>].\u00a0<\/span><\/span><\/p>\n<p><span class=\"author\">71. Duncan,<span>\u00a0<\/span><text><\/text>J. P. C.<\/span><span>, E. <\/span><span class=\"author\">Wu<\/span><span>, J.-C. <\/span><span class=\"author\">Golaz, P. <\/span><span class=\"author\">Caldwell, O. <\/span><span class=\"author\">Watt-Meyer,<span> S. <\/span><\/span><span class=\"author\">Clark, <span class=\"comma__item\"><span>J. McGibbon<\/span><span class=\"comma-separator\">,\u00a0<\/span><\/span><span class=\"comma__item\"><span> G. Dresdner<\/span><span class=\"comma-separator\">,\u00a0<\/span><\/span><span class=\"comma__item\"><span> K. Kashinath<\/span><span class=\"comma-separator\">,\u00a0<\/span><\/span><span class=\"comma__item\"><span> B. Bonev<\/span><span class=\"comma-separator\">,\u00a0<\/span><\/span><span class=\"comma__item\"><strong> M. Pritchard <\/strong>and <\/span><span class=\"comma__item\"><span>C. S. Bretherton,\u00a0<\/span><\/span><\/span><span><\/span><span class=\"articleTitle\">Application of the AI2 climate emulator to E3SMv2&#8217;s global atmosphere model, with a focus on precipitation fidelity<\/span><span>.\u00a0<\/span><i>Journal of Geophysical Research: Machine Learning and Computation<\/i><span>,\u00a0<\/span><span class=\"vol\">1<\/span><span>, e2024JH000136 [<a href=\"https:\/\/doi.org\/10.1029\/2024JH000136\">link<\/a>].\u00a0<\/span><\/p>\n<p>70. V. Eyring, W. D Collins, P. Gentine, E. A Barnes, M. Barreiro, T. Beucler, M. Bocquet, C. S Bretherton, H. M Christensen, K. Dagon, D. John Gagne, D. Hall, D. Hammerling, S. Hoyer, F. Iglesias-Suarez, I. Lopez-Gomez, M. C McGraw, G. A Meehl, M. J Molina, C. Monteleoni, J. Mueller, <strong>M. Pritchard<\/strong>, D. Rolnick, J. Runge, P. Stier, O. Watt-Meyer, K. Weigel, R. Yu, L. Zanna, Pushing the frontiers in climate modelling and analysis with machine learning, <i>Nature Climate Change,<\/i><span>\u00a0<\/span><b>14<\/b><span>, 916\u2013928, 2024 [<a href=\"https:\/\/doi.org\/10.1038\/s41558-024-02095-y\">link<\/a>].\u00a0<\/span><\/p>\n<p>* 69. <u>Peng, L.<\/u>(P), \u00a0P. Blossey, W. Hannah, C. Bretherton, C. Terai,\u00a0 <u>A. Jenney<\/u> (P) &amp;\u00a0 <strong>M. Pritchard<\/strong>.\u00a0Improving stratocumulus cloud amounts in a 200-m resolution multi-scale modeling framework through tuning of its interior physics, <em>Journal of Advances in Modeling Earth Systems<\/em>,\u00a016, e2023MS003632, 2024 [<a href=\"https:\/\/doi.org\/10.1029\/2023MS003632\">link<\/a>].<\/p>\n<p>* 68. <u>Mooers, G.\u00a0(G), T. Beucler (P)<\/u>, <strong>M. Pritchard<\/strong>, &amp; S. Mandt. An Unsupervised Learning Perspective on the Dynamic Contribution to Extreme Precipitation Changes, <em>Environmental Data Science<\/em> ,2024 [<a href=\"https:\/\/www.cambridge.org\/core\/journals\/environmental-data-science\/article\/understanding-precipitation-changes-through-unsupervised-machine-learning\/C9A0CA3A98D2AC06E7334DCF143796CD\">link<\/a>].<\/p>\n<p>* 67. <u>Beucler, T.<\/u> (P), J. Yuval, A. Gupta, <u>L. Peng<\/u> (P), S. Rasp, F. Ahmed, P. O&#8217;Gorman, J. Neelin, N. Lutsko, P. Gentine &amp; <strong>M. Pritchard<\/strong>. Climate-invariant machine learning, <em>Science Advances, <\/em><strong>10<\/strong>, eadj7250(2024), 2024 [<a href=\"https:\/\/www.science.org\/doi\/10.1126\/sciadv.adj7250\">link<\/a>].<\/p>\n<p>66. Iglesias-Suarez, F., P. Gentine, B. Solino-Fernandez, T. Beucler, <strong>M. Pritchard<\/strong>, J. Runge and V. Eyring, Causally-informed deep learning to improve climate models and projections, <em>Journal of Geophysical Research \u2013 Atmospheres, <\/em>129, 2024 [<a href=\"https:\/\/doi.org\/10.1029\/2023JD039202\">link<\/a>]. <em><\/em><\/p>\n<p>* 65. <u>Yu, S.<\/u>(P),\u00a0P.-L. Ma, B. Singh, S. Silva and <strong>M. Pritchard<\/strong>, Two-step hyperparameter optimization method: Accelerating hyperparameter search by using a fraction of a training dataset, <em>Artificial Intelligence for the Earth Systems,\u00a0<\/em>3<em>,<\/em>2024 [<a href=\"https:\/\/doi.org\/10.1175\/AIES-D-23-0013.1\">link<\/a>]<em>.<\/em><\/p>\n<p>* 64. <u>Yu, S.<\/u>(P), co-authors and <strong>M. Pritchard<\/strong>: ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation, <em>Advances in Neural Processing Systems <\/em>2024 [<a href=\"https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2023\/file\/45fbcc01349292f5e059a0b8b02c8c3f-Paper-Datasets_and_Benchmarks.pdf\">link<\/a>]. <strong>Outstanding Paper Award \u2013 Benchmarks and Datasets track. <\/strong><\/p>\n<\/div>\n<\/div>\n<ul>\n<li>\n<h3>2023<\/h3>\n<div>\n<p>* 63. <u>Mooers, G.<\/u> (G),\u00a0<strong>M. Pritchard<\/strong>, <u>T. Beucler<\/u> (P), P. Srivastava, H. Mangipudi,\u00a0<u>L. Peng<\/u> (P), P. Gentine &amp; S. Mandt. Comparing storm resolving models and climates via unsupervised machine learning, <em>Scientific Reports<\/em>13, 22365, 2023 [<a href=\"https:\/\/doi.org\/10.1038\/s41598-023-49455-w\">link<\/a>].<\/p>\n<p>* 62. <u>Liu, N<\/u>.,\u00a0<strong>M. Pritchard<\/strong>, <u>A. Jenney<\/u>, W. Hannah.\u00a0 Understanding Precipitation Bias Sensitivities in E3SM-Multi-scale Modeling Framework from a Dilution Framework.\u00a0<em>Journal of Advances in Modeling Earth Systems<\/em>,\u00a015, 2023 [<a href=\"https:\/\/doi.org\/10.1029\/2022MS003460\">link<\/a>].\u00a0<\/p>\n<p>61. Connolley C., E. A. Barnes, P. Hassanzadeh and <strong>M. Pritchard<\/strong>,\u00a0Using Neural Networks to Learn the Jet Stream Forced Response from Natural Variability,\u00a0<em>Artificial Intelligence for the Earth Systems<\/em>, 2, 2023 [<a href=\"https:\/\/journals.ametsoc.org\/view\/journals\/aies\/2\/2\/AIES-D-22-0094.1.xml\">link<\/a>].<\/p>\n<p>* 60. <u>Jenney, A. M.<\/u>(P),\u00a0<u>S. L. Ferretti<\/u>(G),\u00a0<strong>M. Pritchard<\/strong>. Vertical resolution impacts explicit simulation of deep convection,\u00a0<em>Journal of Advances in Modeling Earth Systems<\/em>, 15, 2023 [<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/doi\/full\/10.1029\/2022MS003444\">link<\/a>].<\/p>\n<\/div>\n<\/li>\n<li>\n<h3>2022<\/h3>\n<div>\n<p><span>59. Graubner, A., K. Azizzadenesheli, J. Pathak, M. Mardani, <strong>M. Pritchard<\/strong>, K. Kashinath, &amp; A. Anandkumar, Calibration of Large Neural Weather Models, <em>NeurIPS workshop, <\/em>2022. [<a href=\"https:\/\/s3.us-east-1.amazonaws.com\/climate-change-ai\/papers\/neurips2022\/87\/paper.pdf\">link<\/a>].<\/span><\/p>\n<p>58. Behrens, G., T. Beucler, P. Gentine, F. Iglesias-Suarez, <strong>M. Pritchard<\/strong> &amp; V. Eyring. Non-linear dimensionality reduction with a variational encoder decoder to understand convective processes in climate models, <em>Journal of Advances in Modeling Earth Systems<\/em>, 14, 2022 [<a href=\"https:\/\/doi.org\/10.1029\/2022MS003130\">link<\/a>].<\/p>\n<p>*<span> 57. <\/span><span style=\"text-decoration: underline;\">Peng, L.\u00a0(P)<\/span>,\u00a0<strong>M. Pritchard<\/strong>, W. Hannah, P. Blossey, P. Worley and C. Bretherton: Load balancing intense physics calculations to embed regionalized high-resolution cloud resolving models in the E3SM and CESM climate models,\u00a0<em>Journal of Advances in Modeling Earth Systems<\/em>, 14, 2022\u00a0[<a href=\"https:\/\/doi.org\/10.1029\/2021MS002841\">link<\/a>].<\/p>\n<p>56. Ma, P., and co-authors: Better calibration of cloud parameterizations and subgrid effects increases the fidelity of E3SM Atmosphere Model version 1, <em>Geoscientific Model Development<\/em>, 15, 2881-2916, 2022 [<a href=\"https:\/\/doi.org\/10.5194\/gmd-15-2881-2022\">link<\/a>].<\/p>\n<p><span>55. Harrop, B., <strong>M. Pritchard<\/strong>.,\u00a0<span style=\"text-decoration: underline;\">H. Parishani\u00a0(P)<\/span>, A. Gettelman, S. Hagos, P. Lauritzen, R. Leung, J. Lu, K. Pressel, K. Sakaguchi: Conservation of dry air, water, and energy in CAM and its potential impact on tropical rainfall, <em>Journal of Climate<\/em>,\u00a0<em>35<\/em>(9), 2895-2917, 2022 [<a href=\"https:\/\/doi.org\/10.1175\/JCLI-D-21-0512.1\">link<\/a>].<\/span><\/p>\n<\/div>\n<\/li>\n<li>\n<h3>2021<\/h3>\n<div>\n<p>*<span> H. Mangipudi, G. Mooers, <strong>M. Pritchard<\/strong>, T. Beucler, &amp; S. Mandt, Analyzing high-resolution clouds and convection using multi-channel VAEs (2021)<\/span>,<span>\u00a0<\/span><i>Proceedings of the 35th Conference\u00a0 on Neural Information Processing (NeurIPS)<\/i><span>, 2021 [<a href=\"https:\/\/arxiv.org\/abs\/2112.01221\">link<\/a>].<\/span><\/p>\n<p>* 54.<span>\u00a0<\/span><span>Mooers, G.\u00a0<\/span>(G),\u00a0<span>\u00a0<\/span><strong>M. Pritchard<\/strong>,\u00a0<span>T. Beucler (P)<\/span>,\u00a0<span>J. Ott (G)<\/span>,\u00a0<span>G. Yacalis (G)<\/span>, P. Baldi and P. Gentine: Assessing the potential of deep learning for emulating cloud superparameterization in climate models with real-geography boundary conditions,\u00a0<em>Journal of Advances in Modeling Earth Systems<\/em>, 2021 [<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/doi\/full\/10.1029\/2020MS002385\" class=\"customize-unpreviewable\">link<\/a>].<\/p>\n<p>* 53.<span>\u00a0<\/span><span>Hendrickson,\u00a0 J<\/span>. (G), C. Terai,\u00a0<strong>M. Pritchard<\/strong><span>\u00a0<\/span>and P. Caldwell: Lower Tropospheric Processes: A Control on the Global Mean Precipitation Rate,<span>\u00a0<\/span><em>Geophysical Research Letters<\/em>, 48, 2021 [<a href=\"https:\/\/doi.org\/10.1029\/2020GL091169\" class=\"customize-unpreviewable\">link<\/a>].<\/p>\n<p>* 52.<span>\u00a0<\/span><u>Beucler, T.<\/u><span>\u00a0<\/span>(P),<span>\u00a0<\/span><strong>M. Pritchard<\/strong>, S. Rasp, P. Gentine, J. Ott and P. Baldi: Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems,<span>\u00a0<\/span><em>Physical Review Letters<\/em>, 126, 2021\u00a0[<a href=\"https:\/\/link.aps.org\/doi\/10.1103\/PhysRevLett.126.098302\" class=\"customize-unpreviewable\">link<\/a>]<\/p>\n<\/div>\n<\/li>\n<li>\n<h3>2020<\/h3>\n<div>\n<p>*<span> <\/span><span>\u00a051. <\/span><span>Mooers, G.\u00a0<\/span>(G), J. Tuyls, S. Mandt,\u00a0<strong>M. Pritchard<\/strong><span>\u00a0<\/span>and<span>\u00a0<\/span><span>T. Beucler<\/span><span>\u00a0<\/span>(P): Generative Modeling for Atmospheric Convection,<span>\u00a0<\/span><i>Proceedings of the 10th International Conference on Climate Informatics<\/i><span>\u00a0(<\/span><i>CI2020<\/i><span>), 2020 [<a href=\"https:\/\/doi.org\/10.1145\/3429309.3429324\" class=\"customize-unpreviewable\">link<\/a>].<\/span><\/p>\n<p>50. Mamalakis, A., J. T. Randerson, J.-Y. Yu,\u00a0<strong>M. Pritchard<\/strong>, G. Magnusdottir, P. Smyth, P. Levine, S. Yu and E. Foufoula-Georgiou: Zonally opposing shifts of the intertropical convergence zone in response to climate change,<span>\u00a0<\/span><em>Nature Climate Change<\/em>, 11, 2020 [<a href=\"https:\/\/doi.org\/10.1038\/s41558-020-00963-x\" class=\"customize-unpreviewable\">link<\/a>].\u00a0<\/p>\n<p>* 49.<span>\u00a0<\/span><span>Terai, C.<\/span><span>\u00a0<\/span>(P),<span>\u00a0<\/span><strong>M. Pritchard<\/strong>, P. Blossey and C. Bretherton: The impact of resolving subkilometer processes on aerosol-cloud interactions in global model simulations,<span>\u00a0<\/span><em>J. Adv. Model. Earth Sys.<\/em>, 12, 2020. [<a href=\"https:\/\/doi.org\/10.1029\/2020MS002274\" class=\"customize-unpreviewable\">link<\/a>]<\/p>\n<p>48. Brenowitz, N.,\u00a0<span>T. Beucler<\/span><span>\u00a0<\/span>(P),\u00a0<strong>M. Pritchard<\/strong>, and C. S. Bretherton: Interpreting and stabilizing machine-learning parameterizations of convection,<span>\u00a0<\/span><em>J. Atmos. Sci.,<span>\u00a0<\/span><\/em>2020. [<a href=\"https:\/\/doi.org\/10.1175\/JAS-D-20-0082.1\" class=\"customize-unpreviewable\">link<\/a>]<\/p>\n<p>47.<span>\u00a0<\/span><u>Ott, J.<\/u>,<span>\u00a0<\/span><b>M. Pritchard<\/b>, N. Best, E. Linstead, M. Curcic, and P. Baldi: A Fortran-Keras Deep Learning Bridge for Scientific Computing.<span>\u00a0<\/span><i>Scientific Programming<\/i>, 2020. [<a href=\"https:\/\/doi.org\/10.1155\/2020\/8888811\" class=\"customize-unpreviewable\">link<\/a>]<\/p>\n<p>*46.\u00a0Fowler, M. (G) and\u00a0<strong>M. Pritchard<\/strong>, Regional MJO modulation of West Pacific tropical cyclones driven by multiple transient controls,<span>\u00a0<\/span><em>Geophysical Research Letters,<\/em>\u00a047 (11), 2020.\u00a0[<a href=\"https:\/\/doi.org\/10.1029\/2020GL087148\" class=\"customize-unpreviewable\">link<\/a>]<\/p>\n<p>45.\u00a0Gutowski et al.,\u00a0<span class=\"s1\">The ongoing need for high-resolution regional climate models: Process understanding and stakeholder information,<span>\u00a0<\/span><em>Bulletin of the American Meteorological Society<\/em>, 2020<em>.\u00a0<\/em>[<a href=\"https:\/\/doi.org\/10.1175\/BAMS-D-19-0113.1\" class=\"customize-unpreviewable\">link<\/a>]<\/span><\/p>\n<p>44. Hannah, W., C. Jones, B. Hillman, M. Norman, D. Bader, M. Taylor, R. Leung,<span>\u00a0<\/span><strong>M. Pritchard<\/strong>, M. Branson, G. Lin, K. Pressel, J. Lee. Initial Results from the Super-Parameterized E3SM,<span>\u00a0<\/span><em>J. Adv. Model. Earth Sys.<\/em>, 12, 2020 [<a href=\"https:\/\/doi.org\/10.1029\/2019MS001863\" class=\"customize-unpreviewable\">link<\/a>].<\/p>\n<\/div>\n<\/li>\n<li>\n<h3>2019<\/h3>\n<div>\n<p>43. * Fowler, M. (G), G. Kooperman, J. T. Randerson and\u00a0<strong>M. Pritchard<\/strong>. The effect of plant-physiological responses to rising CO<sub>2<\/sub>\u00a0on global streamflow, <em>Nature Climate Change<\/em>, 9, 2019. [<a href=\"https:\/\/doi.org\/10.1038\/s41558-019-0602-x\">link<\/a>]<\/p>\n<p>42. Beucler, T. (P), T. Abbott, T. Cronin and <strong>M. Pritchard<\/strong>. Linking Convective Self-Aggregation in Idealized Models to Observed Moist Static Energy Variability near the Equator, <em>Geophysical Research Letters<\/em>, 46, 2019. [<a href=\"https:\/\/doi.org\/10.1029\/2019GL084130\">link<\/a>]<\/p>\n<p>41. *\u00a0Beucler, T. (P),\u00a0\u00a0S. Rasp, <strong>M. Pritchard<\/strong> and P. Gentine (2019). Achieving Conservation of Energy in Neural Network Emulators for\u00a0Climate Modeling, C<em>limate Change and Artificial Intelligence workshop of the 2019 International Conference on Machine\u00a0Learning, <i>arXiv preprint <\/i><\/em>arXiv:1906.06622<em>, <\/em>2019<em>. <\/em>[<a href=\"https:\/\/arxiv.org\/pdf\/1906.06622.pdf\">link<\/a>]<\/p>\n<p>40. * Langenbrunner, B. (P), <strong>M. Pritchard<\/strong>, G. Kooperman and J. Randerson.\u00a0Why does Amazon precipitation decrease when tropical forests respond to increasing CO2?, <em>Earth\u2019s<\/em> Future, , 7, 450\u2013 468, 2019. [<a href=\"https:\/\/doi.org\/10.1029\/2018EF001026\">link<\/a>]<\/p>\n<p>39. * Yu, S. (G) and\u00a0<strong>M. S. Pritchard<\/strong>. A strong role for the AMOC in partitioning global energy transport and shifting ITCZ position in response to latitudinally discrete solar forcing in the CESM1.2, <em>J.<\/em><em>\u00a0Climate<\/em>, 32, 2019. [<a href=\"https:\/\/doi.org\/10.1175\/JCLI-D-18-0360.1\">link<\/a>]<\/p>\n<\/div>\n<\/li>\n<li>\n<h3>2018<\/h3>\n<div>\n<p>38.\u00a0Levine. P., M. Xu, F. M. Hoffman, Y. Chen,<strong> M. S. Pritchard<\/strong> and J. T. Randerson.\u00a0Soil moisture variability intensifies and prolongs Amazon temperature and carbon cycle response to El Ni\u00f1o-Southern Oscillation,\u00a0<em>J. Climate<\/em>, 32, 2018. [<a href=\"https:\/\/doi.org\/10.1175\/JCLI-D-18-0150.1\">link<\/a>]<\/p>\n<p>37. * Parishani, H. (P), <strong>M. S. Pritchard<\/strong>, C. S. Bretherton, C. R. Terai (P), M. C. Wyant, M. Khairoutdinov and B. Singh. Insensitivity of the cloud response to surface warming under radical changes to boundary layer turbulence and cloud microphysics: Results from the UltraParameterized CAM, <i>J. Adv. Model. Earth Sys.<\/i>, 2018 [<a href=\"https:\/\/doi.org\/10.1029\/2018MS001409\">link<\/a>].<\/p>\n<p>36. Kooperman, G., M. Fowler (G), F. Hoffman, C. Koven, K. Lindsay, <strong>M. Pritchard,<\/strong> A. Swann and J. Randerson. Plant-physiological responses to rising CO2 modify daily runoff intensity with implications for global-scale flood risk assessment,\u00a0<em>Geophys. Res. Lett.<\/em>, 2018 [<a href=\"https:\/\/doi.org\/10.1029\/2018GL079901\">link<\/a>]<\/p>\n<p>35. * Sun, J. (P) and\u00a0<strong>M. S. Pritchard<\/strong>. Effects of explicit convection on land surface air temperature and land-atmosphere coupling in the thermal feedback pathway, \u00a0<em>J. Adv. Model. Earth Sys.<\/em>, 2018 [<a href=\"https:\/\/doi.org\/10.1029\/2018MS001301\">link<\/a>]<\/p>\n<p>34. Zeng, X., D. Klocke, B. J. Shipway, M. S. Singh, I. Sandu, W. Hannah, P. Bogenschutz, Y. Zhang, H. Morrison, <strong>M. S. Pritchard<\/strong>, and C. Rio, 2018. Community Efforts in Understanding and Modeling Atmospheric Processes. Community efforts in understanding and modeling atmospheric processes, <em>Bull. Amer. Met. Soc.<\/em>, 2018. [<a href=\"https:\/\/doi.org\/10.1175\/BAMS-D-18-0139.1\">link<\/a>]<\/p>\n<p>33.* <u>Rasp, S. (Visiting G)<\/u>, <strong>M. S. Pritchard<\/strong>, and P. Gentine. Deep learning to represent sub-grid processes in climate models, <em>PNAS<\/em>, 2018. [<a href=\"https:\/\/doi.org\/10.1073\/pnas.1810286115\">link<\/a>]<\/p>\n<p>32. Gentine, P., <strong>M. S. Pritchard<\/strong>, <u>S. Rasp<\/u>(Visiting G), G. Reinaudi and G. Yacalis (G). Could machine learning break the convection parameterization deadlock?,\u00a0<em>Geophys. Res. Lett.<\/em>,\u00a045, 2018. [<a href=\"https:\/\/doi.org\/10.1029\/2018GL078202\">link<\/a>]<\/p>\n<p>31. Kooperman, G. K, Y. Chen, F. M. Hoffman, C. D. Koven, K. Lindsay, <strong>M. S. Pritchard<\/strong>, A. L. S. Swann, and J. T. Randerson, 2018.\u00a0Forest response to rising CO2 drives zonally asymmetric rainfall change over tropical land, <em>Nature Climate Change<\/em>,\u00a08, \u00a0434\u2013440, 2018. [<a href=\"https:\/\/doi.org\/10.1038\/s41558-018-0144-7\">link<\/a>]<\/p>\n<p>30.* <u>Kooperman, G. J.<\/u>(P), <strong>M. S. Pritchard<\/strong>, M. S., T. A. O\u2019Brien and B. W. Timmermans. Rainfall from resolved rather than parameterized processes better represents the present\u2010day and climate change response of moderate rates in the community atmosphere model. <em>J. Adv. Model. Earth Syst.<\/em>, 10, 2018. [<a href=\"https:\/\/doi.org\/10.1002\/2017MS001188\">link<\/a>]<\/p>\n<p>29.* <u>Qin, H. (G)<\/u>,\u00a0<strong>M. S. Pritchard<\/strong>,<u>G. J. Kooperman (P)<\/u>and <u>H. Parishani <\/u>(P), 2018.\u00a0Global Effects of SuperParameterization on Hydro-Thermal Land\u2013Atmosphere Coupling on Multiple Timescales,\u00a0<em>J. Adv. Model. Earth Syst.<\/em>, 10, 2018. [<a href=\"https:\/\/doi.org\/10.1002\/2017MS001185\">link<\/a>]<\/p>\n<p>28.* <u>Fowler, M. (G)<\/u>,\u00a0<strong>M. S. Pritchard<\/strong>and <u>G. J. Kooperman (P)<\/u>.\u00a0Assessing the impact of Californian and Indian irrigation on precipitation in the irrigation-enabled Community Earth System Model, <em>J. Hydromet.<\/em>,19(2), 427-443, 2018. [<a href=\"https:\/\/doi.org\/10.1175\/JHM-D-17-0038.1\">link<\/a>]<\/p>\n<p>27. Woelfle, M., <u>S. Yu, (G)<\/u>., C. S. Bretherton and <strong>M. S. Pritchard<\/strong>.\u00a0Sensitivity of coupled tropical Pacific model biases to convective parameterization in CESM1, <em>J. Adv. Model. Earth Syst.<\/em>, 10, 2018. [<a href=\"https:\/\/doi.org\/10.1002\/2017MS001176\">link<\/a>]<\/p>\n<\/div>\n<\/li>\n<li>\n<h3>2017<\/h3>\n<div>\n<p>26.* <u>Parishani, H. (P).,<\/u><strong>M. S. Pritchard<\/strong>, C. S. Bretherton, M. C. Wyant and M. Khairoutdinov. Towards low cloud-permitting cloud superparameterization with explicit boundary layer turbulence, <em>J. Adv. Model Earth Syst.<\/em>, 9, 1542\u20131571, 2017. [<a href=\"https:\/\/doi.org\/10.1002\/2017MS000968\">link<\/a>]<\/p>\n<\/div>\n<\/li>\n<li>\n<h3>2016<\/h3>\n<div>\n<p>25.* <u>Kooperman, G. J (P).,<\/u><strong>M. S. Pritchard<\/strong>, M. A. Burt, M. D. Branson, and D. A. Randall. Impacts of cloud superparameterization on projected daily rainfall intensity climate changes in multiple versions of the Community Earth System Model, <em>J. Adv. Model. Earth Syst.<\/em>, 8, 2016. [<a href=\"https:\/\/doi.org\/10.1002\/2016MS000715\">link<\/a>]<\/p>\n<p>24.* <u>Sun, S. (P)<\/u>and <strong>M. S. Pritchard<\/strong>. Effects of explicit convection on global land-atmosphere coupling in the superparameterized CAM, <em>J. Adv. Model. Earth Syst.<\/em>, 8, 1248\u20131269, 2016. [<a href=\"https:\/\/doi.org\/10.1002\/2016MS000689\">link<\/a>]<\/p>\n<p>23.* <u>Elliott, E. J. (U), S. Yu (G), G. Kooperman (P)<\/u>, H. Morrison , M. Wang and <strong>M. S. Pritchard<\/strong>. Sensitivity of summer ensembles of superparameterized US mesoscale convective systems to cloud resolving model microphysics and grid configuration, <em>J. Adv. Model. Earth Syst.<\/em>,2016.[<a href=\"http:\/\/dx.doi.org\/10.1002\/2015MS000567\">link<\/a>]<\/p>\n<p>22.* <strong>Pritchard, M. S<\/strong>. and D. Yang. Response of the superparameterized Madden-Julian Oscillation to extreme climate and basic state variation challenges a moisture mode view. <em>J. Climate<\/em>, 29, 4995-5008, 2016. [<a href=\"http:\/\/dx.doi.org\/10.1175\/JCLI-D-15-0790.1\">link<\/a>]<\/p>\n<p>21.* <u>Kooperman, G. J. (P)<\/u>, <strong>M. S. Pritchard<\/strong>, M. A. Burt, M. D. Branson, and D. A. Randall. Robust effects of cloud super-parameterization on simulated daily rainfall intensity statistics across multiple versions of CESM. <em>J. Adv. Model. Earth Syst.<\/em>, 8, 2016. [<a href=\"https:\/\/doi.org\/10.1002\/2015MS000574\">link<\/a>]<\/p>\n<\/div>\n<\/li>\n<li>\n<h3>2015<\/h3>\n<div>\n<p>20. Benedict, J. J., <strong>M. S. Pritchard<\/strong>, and W. D. Collins. Sensitivity of MJO propagation to a robust positive Indian Ocean dipole event in the superparameterized CAM, <em>J. Adv. Model. Earth Syst.<\/em>, 7, 1901\u20131917, 2015. [<a href=\"https:\/\/doi.org\/10.1002\/2015MS000530\">link<\/a>]<\/p>\n<p>19. * <u>Yu, S. (G) <\/u>and <strong>M. S. Pritchard<\/strong>. The effect of large-scale model time step and multiscale coupling frequency on cloud climatology, vertical structure, and rainfall extremes in a superparameterized GCM, <em>J. Adv. Model. Earth Syst.<\/em>, 7, 1977\u20131996, 2015. [<a href=\"http:\/\/doi.org\/10.1002\/2015MS000493\">link<\/a>]<\/p>\n<p>18. Jones, C., C. S. Bretherton and <strong>M. S. Pritchard<\/strong>. Mean-state acceleration of cloud-resolving model simulations. <em>J. Adv. Model. Earth Syst.<\/em>,07, 2015. [<a href=\"http:\/\/doi.org\/10.1002\/2015MS000488\">link<\/a>]<\/p>\n<p>17. Klingaman, N. P., S. J. Woolnough, X. Jiang, D. Waliser, P. K. Xavier, J. Petch, M. Caian, C. Hannay, D. Kim, H.-Y. Ma, W. J. Merryfield, T. Miyakawa, <strong>M. S. Pritchard<\/strong>, J. A. Ridout, R. Roehrig, E. Shindo, F. Vitart, H. Wang, N. Cavanaugh, B. E. Mapes, A. Shelly, G. J. Zhang. Vertical structure and physical processes of the Madden-Julian Oscillation: Linking hindcast fidelity to simulated diabatic heating and moistening, <em>J. Geophys. Res. Atm.<\/em>120, 10, 4690-4717, 2015. [<a href=\"http:\/\/doi.org\/%2010.1002\/2014JD022374\">link<\/a>]<\/p>\n<p>16. Xavier, P.K., J. C. Petch, N. P. Klingaman, S. J. Woolnough, X. Jiang, D. E. Waliser, M. Caian, J. Cole, S. Hagos, C. Hannay, D. Kim, T. Miyakawa, <strong>M. S. Pritchard<\/strong>, R. Roehrig, E. Shindo, F. Vitart, H. Wang. Vertical structure and diabatic processes of the Madden-Julian Oscillation: Biases and uncertainties at short range, <em>J. Geophys. Res. Atm.<\/em>120, 10, 4749 -4763, 2015. [<a href=\"https:\/\/doi.org\/10.1002\/2014JD022718\">link<\/a>]<\/p>\n<p>15. Hannah, W, <strong>M. S. Pritchard<\/strong>and E. Maloney. Consequences of systematic model drift in DYNAMO MJO hindcasts with SP-CAM and CAM5, <em>J. Adv. Model. Earth Syst.<\/em>, 07, 2015. [<a href=\"https:\/\/doi.org\/10.1002\/2014MS000423\">link<\/a>]<\/p>\n<\/div>\n<\/li>\n<li>\n<h3>2014<\/h3>\n<div>\n<p>14.\u00a0<strong>Pritchard M. S.<\/strong>, C. DeMott and C. S. Bretherton. Restricting 32\u2013128 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, <em>J. Adv. Model. Earth Syst.<\/em>, 06, 2014. [<a href=\"http:\/\/doi.org\/10.1002\/2014MS000340\">link<\/a>]<\/p>\n<p>13. Kooperman, G., <strong>M. S. Pritchard<\/strong>and R. C. J. Somerville.The response of US summer rainfall to quadrupled CO2 climate change in conventional and superparameterized versions of the NCAR Community Atmosphere Model<em>, <\/em><em>J. Adv. Model. Earth Syst.<\/em>, 06, 2014. [<a href=\"http:\/\/doi.org\/%2010.1002\/2014MS000306\">link<\/a>]<\/p>\n<p>12. Zhao, Z., G. Kooperman, <strong>M. S. Pritchard<\/strong>, L. M. Russell and R. C. J. Somerville. Investigating impacts of forest fires in Alaska and western Canada on regional weather over the northeastern United States using CAM5 global simulations to constrain transport to a WRF-Chem regional domain, <em>J. Geophys. Res. Atm.<\/em>, 199(12): 7515-7536, 2014. [<a href=\"http:\/\/doi.org\/10.1002\/2013JD020973\">link<\/a>]<\/p>\n<p>11. <strong>Pritchard, M. S.<\/strong>and C. S. Bretherton. Causal evidence that rotational moisture advection is critical to the superparameterized Madden-Julian Oscillation, <em>J. Atmos. Sci.<\/em>, 71(2) 800-815, 2014. [<a href=\"https:\/\/doi.org\/10.1175\/JAS-D-13-0119.1\">link<\/a>]<\/p>\n<\/div>\n<\/li>\n<li>\n<h3>Pre-2014<\/h3>\n<div>\n<p>10. Kooperman, G., <strong>M. S. Pritchard <\/strong>and R. C. J. Somerville. Robustness and sensitivities of central U.S. summer convection in the superparameterized CAM: Multi-model intercomparison with a new regional EOF index, 2013. <em>Geophys. Res. Lett., <\/em>40 (12), 3287-3291, 2013. [<a href=\"https:\/\/doi.org\/10.1002\/grl.50597\">link<\/a>]<\/p>\n<p>9. Kooperman, G., <strong>M. S. Pritchard<\/strong>, S. Ghan, R. C. J. Somerville and L. M. Russell. Constraining the influence of natural variability to improve estimates of global aerosol indirect effects in a nudged version of the Community Atmosphere Model 5. <em>J. Geophys. Res. Atm..<\/em>, 117, D23204, 2012. [<a href=\"http:\/\/doi.org\/10.1029\/2012JD018588\">link<\/a>]<\/p>\n<p>8. Schmidt, J. M., P. J. Flatau. P. R. Harasti, R. D. Yates, R. Littleton, <strong>M. S. Pritchard<\/strong>, and co-authors. Radar observations of individual rain drops in the free atmosphere, <em>Proceedings of the National Academy of Sciences<\/em>, 2012. [<a href=\"http:\/\/doi.org\/10.1073\/pnas.1117776109\">link<\/a>]<\/p>\n<p>7. Zhao, Z, <strong>M. S. Pritchard<\/strong>, and L. M. Russell. Effects on precipitation, clouds, and temperature from long-range transport of idealized aerosol plumes in WRF-Chem simulations, <em>J. Geophys. Res. Atm.<\/em>, 117 (D5), 2012. [<a href=\"https:\/\/doi.org\/10.1029\/2011JD016744\">link<\/a>]<\/p>\n<p>6.\u00a0<strong>Pritchard, M. S.<\/strong>, M. W. Moncrieff and R. C. J. Somerville. Orogenic propagating precipitation systems over the US in a global climate model with embedded explicit convection,\u00a0 <em>J. Atmos. Sci. <\/em>\u00a068 (8), 1821-1840, 2011. [<a href=\"http:\/\/doi.org\/10.1175\/2011JAS3699.1\">link<\/a>]<\/p>\n<p>5.\u00a0<strong>Pritchard, M. S.<\/strong>and R. C. J. Somerville. Assessing the Diurnal Cycle of Precipitation in a Multi-Scale Climate Model, <em>J. Adv. Model. Earth Syst.<\/em>, 1, 2009. [<a href=\"https:\/\/doi.org\/10.3894\/JAMES.2009.1.12\">link<\/a>]<\/p>\n<p>4.\u00a0<strong>Pritchard, M. S.<\/strong>and R. C. J. Somerville. Empirical orthogonal function analysis of the diurnal cycle of precipitation in a multi-scale climate model, <em>Geophys. Res. Lett<\/em>36 (5), 2009. [<a href=\"http:\/\/doi.org\/10.1029\/2008GL036964\">link<\/a>]<\/p>\n<p>3.\u00a0<strong>Pritchard, M. S.<\/strong>, A. B. G. Bush and S. J. Marshall. Interannual atmospheric variability affects continental ice sheet simulations on millennial time scales. <em>J. Climate<\/em>21 (22), 5976-5992, 2008. [<a href=\"https:\/\/doi.org\/10.1175\/2008JCLI2327.1\">link<\/a>]<\/p>\n<p>2. Pendlebury, D., T. G. Shepherd, <strong>M. S. Pritchard<\/strong>, and C. McLandress. Normal mode Rossby waves and their effects on chemical composition in the late summer stratosphere. <em>Atmos. Chem. Phys.<\/em>, 8 (7), 1925-1935, 2008. [<a href=\"https:\/\/doi.org\/10.5194\/acp-8-1925-2008\">link<\/a>]<\/p>\n<p>1. <strong>Pritchard, M. S.<\/strong>, A. B. G. Bush and S. J. Marshall. Neglecting ice-atmosphere interactions underestimates ice sheet melt in millennial-scale deglaciation simulations. <em>Geophys. Res. Lett. <\/em>35 (1), L01503, 2008. [<a href=\"http:\/\/doi.org\/10.1029\/2007GL031738\">link<\/a>]<\/p>\n<\/div>\n<\/li>\n<\/ul>\n<h1>\n<p>Group members<\/p>\n<\/h1>\n<hr>\n<div>\n<p style=\"text-align: center;\">\u00a0<\/p>\n<p>\u00a0<\/p>\n<\/div>\n<ul>\n<li>\n<p>        <img decoding=\"async\" src=\"\/pritchard\/wp-content\/uploads\/sites\/23\/2025\/04\/cropped.png\" alt=\"\"><\/p>\n<h3>Mike Pritchard<\/h3>\n<p>Associate Professor at UCI &#038; Director of Climate Simulation Research at NVIDIA<\/p>\n<div>\n<p>Principal Investigator<\/p>\n<p><button class=\"uk-button uk-button-secondary uk-margin-small-right\" type=\"button\" uk-toggle=\"target: #modal-pritchard\">Learn More<\/button><\/p>\n<p><!-- This is the modal --><\/p>\n<div id=\"modal-pritchard\" uk-modal=\"\">\n<div class=\"uk-modal-dialog uk-modal-body\">\n<h2 class=\"uk-modal-title\">Mike Pritchard<\/h2>\n<p>My research is on advancing understanding of how the planetary water cycle works, and how it may change in the future, focusing especially on cloud physics and moist convection processes. My tools are a blend of next-generation global atmospheric simulation algorithms, theoretical climate dynamics, and high-performance computing. Projects are guided by the problems of interest, and are intentionally explorative of new potentially breakthrough physical algorithms that attempt to avoid traditional approximations of cloud physics in global climate simulations. Lately this has meant significant exploration of emerging tools in the data sciences such as deep machine learning for physical process emulation and neural-network assisted dynamical inquiry. I now hold a partial industry appointment: In July 2022 I began a 80% leave of absence from UCI to lead a new research group at NVIDIA as their Director of Climate Simulation Research, helping lead their Earth-2 climate simulation initiative.\u00a0<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/li>\n<li>\n<p>        <img decoding=\"async\" src=\"\/pritchard\/wp-content\/uploads\/sites\/23\/2020\/12\/Liran-scaled.jpg\" alt=\"\"><\/p>\n<h3>Liran Peng<\/h3>\n<div>\n<p><strong>Postdoctoral Scholar<\/strong><\/p>\n<p>March 2020 &#8211; Present<\/p>\n<\/div>\n<\/li>\n<li>\n<p>        <img decoding=\"async\" src=\"\/pritchard\/wp-content\/uploads\/sites\/23\/2020\/12\/jerrylin.png\" alt=\"\"><\/p>\n<h3>Jerry Lin<\/h3>\n<div>\n<p><strong>PhD Candidate<\/strong><\/p>\n<p>September 2020 &#8211; Present<\/p>\n<\/div>\n<\/li>\n<li>\n<p>        <img decoding=\"async\" src=\"\/pritchard\/wp-content\/uploads\/sites\/23\/2024\/06\/IMG_2877.jpg\" alt=\"\"><\/p>\n<h3>Savannah Ferretti<\/h3>\n<div>\n<p><strong>PhD Candidate<\/strong><\/p>\n<p>September 2021 &#8211; Present<\/p>\n<\/div>\n<\/li>\n<li>\n<p>        <img decoding=\"async\" src=\"\/pritchard\/wp-content\/uploads\/sites\/23\/2022\/09\/Yan.jpg\" alt=\"\"><\/p>\n<h3>Yan Xia<\/h3>\n<p>PhD student <\/p>\n<div>\n<p>September 2022 &#8211; Present<\/p>\n<\/div>\n<\/li>\n<\/ul>\n<h1>\n<p>Find us on Twitter<\/p>\n<\/h1>\n<hr>\n<div>\n<p>    <a class=\"twitter-timeline\" data-height=\"600\" data-theme=\"light\" href=\"https:\/\/twitter.com\/SciPritchard?ref_src=twsrc%5Etfw\">Tweets by Pritchard_UCI<\/a> <script async src=\"https:\/\/platform.twitter.com\/widgets.js\" charset=\"utf-8\"><\/script>\n<\/div>\n<p><!--more--><br \/>\n<!-- {\"name\":\"Home\",\"type\":\"layout\",\"children\":[{\"name\":\"Hero\",\"type\":\"section\",\"props\":{\"style\":\"default\",\"width\":\"\",\"vertical_align\":\"middle\",\"title_position\":\"top-left\",\"title_rotation\":\"left\",\"title_breakpoint\":\"xl\",\"image_position\":\"center-center\",\"padding\":\"xlarge\",\"image\":\"wp-content\\\/uploads\\\/sites\\\/23\\\/2021\\\/10\\\/Cumulonimbus_Clouds-scaled.jpg\",\"image_size\":\"cover\",\"image_effect\":\"parallax\",\"height\":\"full\",\"image_parallax_easing\":\"0\",\"image_parallax_bgy_start\":\"-100\",\"image_parallax_bgy_end\":\"100\",\"padding_remove_top\":true},\"children\":[{\"type\":\"row\",\"children\":[{\"type\":\"column\",\"props\":{\"image_position\":\"center-center\",\"media_overlay_gradient\":\"\",\"width_medium\":\"1-1\",\"position_sticky_breakpoint\":\"m\"},\"children\":[{\"type\":\"image\",\"props\":{\"margin\":\"default\",\"image_svg_color\":\"emphasis\",\"text_align\":\"center\",\"image\":\"wp-content\\\/uploads\\\/sites\\\/23\\\/2020\\\/12\\\/ccc-logo.png\",\"css\":\"img {\\n  opacity: 0.65;\\n}\"}}]}]}]},{\"type\":\"section\",\"props\":{\"style\":\"default\",\"width\":\"default\",\"vertical_align\":\"middle\",\"title_position\":\"top-left\",\"title_rotation\":\"left\",\"title_breakpoint\":\"xl\",\"image_position\":\"center-center\"},\"children\":[{\"type\":\"row\",\"children\":[{\"type\":\"column\",\"props\":{\"image_position\":\"center-center\",\"media_overlay_gradient\":\"\",\"width_medium\":\"1-1\",\"position_sticky_breakpoint\":\"m\"},\"children\":[{\"type\":\"headline\",\"props\":{\"title_element\":\"h1\",\"content\":\"\n\n<p>About<\\\/p>\",\"text_align\":\"center\"}},{\"type\":\"divider\",\"props\":{\"divider_element\":\"hr\",\"margin_remove_top\":true,\"margin_remove_bottom\":true,\"maxwidth\":\"small\",\"block_align\":\"center\"}},{\"type\":\"text\",\"props\":{\"margin\":\"default\",\"column_breakpoint\":\"m\",\"content\":\"\n\n<p>We study how the planetary water cycle and climate work, and how it may change in the future, focusing on cloud physics and moist convection processes. Our tools are next-gen global atmospheric simulations, ocean-coupled climate dynamics, high-performance computing, and machine learning for turbulent process emulation and neural-network assisted inquiry. <\\\/p>\",\"text_style\":\"lead\",\"text_align\":\"justify\"}}]}],\"props\":{\"width\":\"xlarge\"}}],\"name\":\"Mission \\\/ About\"},{\"type\":\"section\",\"props\":{\"style\":\"default\",\"width\":\"default\",\"vertical_align\":\"middle\",\"title_position\":\"top-left\",\"title_rotation\":\"left\",\"title_breakpoint\":\"xl\",\"image_position\":\"center-center\",\"title\":\"POSTDOC POSITION\"},\"children\":[{\"type\":\"row\",\"children\":[{\"type\":\"column\",\"props\":{\"image_position\":\"center-center\",\"media_overlay_gradient\":\"\",\"position_sticky_breakpoint\":\"m\"},\"children\":[]}]}]},{\"type\":\"section\",\"props\":{\"style\":\"primary\",\"width\":\"default\",\"vertical_align\":\"middle\",\"title_position\":\"top-left\",\"title_rotation\":\"left\",\"title_breakpoint\":\"xl\",\"image_position\":\"center-center\"},\"children\":[{\"type\":\"row\",\"props\":{\"width\":\"xlarge\",\"column_gap\":\"collapse\",\"match\":true},\"children\":[{\"type\":\"column\",\"props\":{\"image_position\":\"center-center\",\"media_overlay_gradient\":\"\",\"width_medium\":\"1-1\",\"image\":\"\",\"image_height\":\"300px\",\"vertical_align\":\"middle\",\"position_sticky_breakpoint\":\"m\"},\"children\":[{\"type\":\"headline\",\"props\":{\"title_element\":\"h1\",\"content\":\"\n\n<p>Research Themes<\\\/p>\",\"text_align\":\"center\"}},{\"type\":\"divider\",\"props\":{\"divider_element\":\"hr\",\"divider_style\":\"\",\"css\":\".el-element {\\n    border-bottom: 2px solid #FFD200;\\n}\",\"maxwidth\":\"small\",\"block_align\":\"center\"}},{\"type\":\"overlay-slider\",\"props\":{\"show_title\":true,\"show_meta\":true,\"show_content\":true,\"show_link\":true,\"slider_width\":\"fixed\",\"slider_width_default\":\"1-1\",\"slider_width_medium\":\"1-3\",\"slider_gap\":\"default\",\"slider_autoplay_pause\":true,\"nav\":\"\",\"nav_align\":\"center\",\"nav_breakpoint\":\"\",\"slidenav\":\"default\",\"slidenav_margin\":\"medium\",\"slidenav_breakpoint\":\"\",\"slidenav_outside_breakpoint\":\"xl\",\"overlay_mode\":\"cover\",\"overlay_position\":\"center\",\"overlay_transition\":\"fade\",\"title_hover_style\":\"reset\",\"title_element\":\"h3\",\"meta_style\":\"meta\",\"meta_align\":\"below-title\",\"meta_element\":\"div\",\"link_text\":\"Read more\",\"link_style\":\"\",\"text_align\":\"center\",\"margin\":\"default\",\"slider_sets\":false,\"slidenav_hover\":false,\"overlay_hover\":false,\"overlay_style\":\"overlay-default\",\"image_height\":\"300\",\"overlay_link\":true,\"class\":\"\",\"slider_height\":\"\",\"slider_width_small\":\"\",\"slider_width_large\":\"\",\"slider_width_xlarge\":\"\",\"nav_margin\":\"\",\"nav_color\":\"\",\"slidenav_color\":\"\",\"slidenav_outside_color\":\"\",\"visibility\":\"hidden-m\",\"show_hover_image\":true},\"children\":[{\"type\":\"overlay-slider_item\",\"props\":{\"image\":\"wp-content\\\/uploads\\\/sites\\\/23\\\/2020\\\/10\\\/climate-dynamics-icon-300.jpg\",\"title\":\"Climate Dynamics \",\"link\":\"climate-dynamics\\\/\"}},{\"type\":\"overlay-slider_item\",\"props\":{\"image\":\"wp-content\\\/uploads\\\/sites\\\/23\\\/2020\\\/10\\\/land-atmosphere-icon.jpg\",\"title\":\"Land-Atmosphere Interactions \",\"link\":\"land-atmosphere-interactions\\\/\"}},{\"type\":\"overlay-slider_item\",\"props\":{\"image\":\"wp-content\\\/uploads\\\/sites\\\/23\\\/2020\\\/10\\\/low-cloud-icon.jpg\",\"title\":\"Shallow Clouds \",\"link\":\"shallow-clouds\\\/\"}},{\"type\":\"overlay-slider_item\",\"props\":{\"image\":\"wp-content\\\/uploads\\\/sites\\\/23\\\/2020\\\/10\\\/machine-learning-icon.jpg\",\"title\":\"Machine Learning\",\"link\":\"machine-learning\\\/\"}},{\"type\":\"overlay-slider_item\",\"props\":{\"image\":\"wp-content\\\/uploads\\\/sites\\\/23\\\/2020\\\/10\\\/multiscale-modeling-icon-300.jpg\",\"title\":\"Multi-Scale Modeling \",\"link\":\"multi-scale-modeling\\\/\"}}],\"name\":\"slider - 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desktop\"}]}]}],\"name\":\"Research Themes\"},{\"type\":\"section\",\"props\":{\"style\":\"default\",\"width\":\"default\",\"vertical_align\":\"middle\",\"title_position\":\"top-left\",\"title_rotation\":\"left\",\"title_breakpoint\":\"xl\",\"image_position\":\"center-center\",\"title\":\"RECRUITING NEW PHD STUDENTS\"},\"children\":[{\"type\":\"row\",\"children\":[{\"type\":\"column\",\"props\":{\"image_position\":\"center-center\",\"media_overlay_gradient\":\"\",\"width_medium\":\"1-1\",\"position_sticky_breakpoint\":\"m\"},\"children\":[]}]},{\"type\":\"row\",\"children\":[{\"type\":\"column\",\"props\":{\"image_position\":\"center-center\",\"media_overlay_gradient\":\"\",\"width_medium\":\"1-1\",\"position_sticky_breakpoint\":\"m\"},\"children\":[]}]}]},{\"type\":\"section\",\"props\":{\"style\":\"muted\",\"width\":\"default\",\"vertical_align\":\"middle\",\"title_position\":\"top-left\",\"title_rotation\":\"left\",\"title_breakpoint\":\"xl\",\"image_position\":\"center-center\",\"status\":\"\"},\"children\":[{\"type\":\"row\",\"children\":[{\"type\":\"column\",\"props\":{\"image_position\":\"center-center\",\"media_overlay_gradient\":\"\",\"width_medium\":\"1-1\",\"position_sticky_breakpoint\":\"m\"},\"children\":[{\"type\":\"headline\",\"props\":{\"title_element\":\"h1\",\"content\":\"\n\n<p>Publications<\\\/p>\",\"text_align\":\"center\"}},{\"type\":\"divider\",\"props\":{\"divider_element\":\"hr\",\"margin_remove_top\":true,\"margin_remove_bottom\":true,\"maxwidth\":\"small\",\"block_align\":\"center\"}},{\"type\":\"text\",\"props\":{\"margin\":\"default\",\"column_breakpoint\":\"m\",\"content\":\"\n\n<p>*<sup>,+<\\\/sup> denotes led by Pritchard\\u2019s UCI* (NVIDIA<sup>+<\\\/sup>) group; <u>underlined<\\\/u>\\u00a0are: PI-advised (P)<u>ostdoctoral scholar or project scientist<\\\/u>, (G)<u>raduate\\u00a0student<\\\/u>, (U)<u>ndergraduate<\\\/u> student and\\\/or (N)<span style=\\\"text-decoration: underline;\\\">VIDIA scientis<\\\/span>t. Prior to 2023, PI tended to occupy second author slot for closely advised work, following atmospheric science conventions.<\\\/p>\",\"text_style\":\"lead\",\"text_align\":\"justify\"}}]}],\"props\":{\"width\":\"xlarge\"}},{\"type\":\"row\",\"props\":{\"layout\":\"1-2,1-2\"},\"children\":[{\"type\":\"column\",\"props\":{\"image_position\":\"center-center\",\"media_overlay_gradient\":\"\",\"width_medium\":\"1-2\",\"position_sticky_breakpoint\":\"m\"},\"children\":[{\"type\":\"panel\",\"props\":{\"link_text\":\"Read more\",\"title_hover_style\":\"reset\",\"title_element\":\"h3\",\"title_align\":\"top\",\"title_grid_width\":\"1-2\",\"title_grid_breakpoint\":\"m\",\"meta_style\":\"meta\",\"meta_align\":\"below-title\",\"meta_element\":\"div\",\"content_column_breakpoint\":\"m\",\"icon_width\":80,\"image_align\":\"top\",\"image_grid_width\":\"1-2\",\"image_grid_breakpoint\":\"m\",\"image_svg_color\":\"emphasis\",\"link_style\":\"default\",\"margin\":\"default\",\"panel_style\":\"card-primary\",\"title\":\"2025 & In Review\",\"content\":\"\n\n<div>\\n\n\n<p>* <span style=\\\"text-decoration: underline;\\\">Ferretti, S. L.<\\\/span> (G), <strong>M. S. Pritchard<\\\/strong>, F. Ahmed, <span style=\\\"text-decoration: underline;\\\">L. Peng<\\\/span> (P) and J. W. Baldwin, Stress-Testing a Process-Oriented Diagnostic Relating Tropical Rainfall to Plume Buoyancy [<a href=\\\"https:\\\/\\\/essopenarchive.org\\\/doi\\\/pdf\\\/10.22541\\\/essoar.174078542.28042108\\\/v1\\\">preprint<\\\/a>].<\\\/p>\\n\n\n<p><span class=\\\"preview-author-name\\\" data-collab-id=\\\"1884371\\\">* Peng,<span style=\\\"text-decoration: underline;\\\"> L.<\\\/span> (P), P. N. Blossey, W. M. Hannah, C. S. Bretherton, C. Terai, A. M. Jenney, <span style=\\\"text-decoration: underline;\\\">S. L. Ferretti<\\\/span> (G) and <strong>M. S. Pritchard<\\\/strong>, Resolving Low Cloud Feedbacks Globally with HR-MMF: Agreement with LES but Stronger Shortwave Effects [<a href=\\\"https:\\\/\\\/essopenarchive.org\\\/doi\\\/pdf\\\/10.22541\\\/essoar.173939558.83159342\\\/v1\\\">preprint<\\\/a>].<\\\/span><\\\/p>\\n<\\\/div>\\n\n\n<div><sup>+<\\\/sup>Pandey, K. (N), <span style=\\\"text-decoration: underline;\\\">Pathak, J.<\\\/span> (N<span style=\\\"text-decoration: underline;\\\">),<\\\/span>\\u00a0Y. Xu, S. Mandt, <strong>M. Pritchard<\\\/strong>, A. Vahdat and M. Mardani, Heavy-tailed diffusion models, in review [<a href=\\\"https:\\\/\\\/arxiv.org\\\/abs\\\/2410.14171\\\">preprint<\\\/a>].<\\\/div>\\n\n\n<div><\\\/div>\\n\n\n<div><span><span><br \\\/><sup>+<\\\/sup>Fotiadis, S., <span style=\\\"text-decoration: underline;\\\">N. Brenowitz<\\\/span> <span style=\\\"text-decoration: underline;\\\">(<\\\/span>N<span style=\\\"text-decoration: underline;\\\">)<\\\/span>, T. Geffner, <span style=\\\"text-decoration: underline;\\\">Y. Cohen<\\\/span>\\u00a0<span style=\\\"text-decoration: underline;\\\">(<\\\/span>N<span style=\\\"text-decoration: underline;\\\">),<\\\/span> <strong>M. Pritchard<\\\/strong>, A. Vahdat and M. Mardani, Stochastic Flow Matching for Resolving Small-Scale Physics, in review [<a href=\\\"https:\\\/\\\/arxiv.org\\\/abs\\\/2410.19814\\\">preprint<\\\/a>]<\\\/span><\\\/span>.<br \\\/><br \\\/><\\\/div>\\n\n\n<div><sup>+<\\\/sup><span style=\\\"text-decoration: underline;\\\">P. Manshausen<\\\/span><span>\\u00a0<span style=\\\"text-decoration: underline;\\\">(<\\\/span>N<span style=\\\"text-decoration: underline;\\\">),<\\\/span><\\\/span> <span style=\\\"text-decoration: underline;\\\">Y. Cohen<\\\/span><span>\\u00a0<span style=\\\"text-decoration: underline;\\\">(<\\\/span>N<span style=\\\"text-decoration: underline;\\\">),<\\\/span><\\\/span> <span style=\\\"text-decoration: underline;\\\">J. Pathak<\\\/span><span>\\u00a0<span style=\\\"text-decoration: underline;\\\">(<\\\/span>N<span style=\\\"text-decoration: underline;\\\">),<\\\/span><\\\/span> <strong>M. Pritchard<\\\/strong>, <span style=\\\"text-decoration: underline;\\\">P. Garg<\\\/span> <span style=\\\"text-decoration: underline;\\\"><span>(<\\\/span><\\\/span>N<span style=\\\"text-decoration: underline;\\\"><span>),<\\\/span><\\\/span>\\u00a0M. Mardani, K. Kashinath, S. Byrne, <span style=\\\"text-decoration: underline;\\\">N. Brenowitz<\\\/span><span style=\\\"text-decoration: underline;\\\"> <\\\/span><span><span style=\\\"text-decoration: underline;\\\">(<\\\/span>N<span style=\\\"text-decoration: underline;\\\">),<\\\/span><\\\/span> Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales, in revision [<a href=\\\"https:\\\/\\\/arxiv.org\\\/abs\\\/2406.16947\\\">preprint<\\\/a>].<\\\/div>\\n\n\n<div><\\\/div>\\n\n\n<div id=\\\"gsc_oci_title_wrapper\\\"><span style=\\\"text-decoration: underline;\\\"><br \\\/><\\\/span><em><sup>+<\\\/sup><\\\/em><span style=\\\"text-decoration: underline;\\\">Pathak, J<\\\/span>.<span>\\u00a0<span style=\\\"text-decoration: underline;\\\">(<\\\/span>N<span style=\\\"text-decoration: underline;\\\">),<\\\/span><\\\/span>\\u00a0<span style=\\\"text-decoration: underline;\\\">Y. Cohen<\\\/span> (N)<span style=\\\"text-decoration: underline;\\\"><span>,<\\\/span><\\\/span>\\u00a0<span style=\\\"text-decoration: underline;\\\">P. Garg<\\\/span> <span style=\\\"text-decoration: underline;\\\"><span>(<\\\/span><\\\/span>N<span style=\\\"text-decoration: underline;\\\"><span>)<\\\/span><\\\/span>, P. Harrington, <span style=\\\"text-decoration: underline;\\\">N. Brenowitz<\\\/span><span>\\u00a0<span style=\\\"text-decoration: underline;\\\">(<\\\/span>N<span style=\\\"text-decoration: underline;\\\">),<\\\/span><\\\/span> <span style=\\\"text-decoration: underline;\\\">D. Durran<\\\/span><span>\\u00a0<span style=\\\"text-decoration: underline;\\\">(<\\\/span>N<span style=\\\"text-decoration: underline;\\\">),<\\\/span><\\\/span> M. Mardani, A. Vahdat, S. Xu, K. Kashinath and <strong>M. Pritchard<\\\/strong>, Kilometer-scale convection allowing model emulation using generative diffusion modeling, in review [<a href=\\\"https:\\\/\\\/arxiv.org\\\/abs\\\/2408.10958\\\">preprint<\\\/a>].<\\\/div>\\n\n\n<div><\\\/div>\\n\n\n<div>\\n\n\n<div><br \\\/><sup>+<\\\/sup><span style=\\\"text-decoration: underline;\\\">Wang, C.<\\\/span><span>\\u00a0<span style=\\\"text-decoration: underline;\\\">(<\\\/span>N<span style=\\\"text-decoration: underline;\\\">),<\\\/span><\\\/span> <strong>M. Pritchard<\\\/strong>, <span style=\\\"text-decoration: underline;\\\">N. Brenowitz<\\\/span> <span> <span style=\\\"text-decoration: underline;\\\">(<\\\/span>N<span style=\\\"text-decoration: underline;\\\">)<\\\/span><\\\/span>, <span style=\\\"text-decoration: underline;\\\">Y. Cohen<\\\/span> <span> <span style=\\\"text-decoration: underline;\\\">(<\\\/span>N<span style=\\\"text-decoration: underline;\\\">)<\\\/span><\\\/span>, B. Bonev, T. Kurth, <span style=\\\"text-decoration: underline;\\\">D. Durran<\\\/span> <span> <span style=\\\"text-decoration: underline;\\\">(<\\\/span>N<span style=\\\"text-decoration: underline;\\\">)<\\\/span><\\\/span>, <span style=\\\"text-decoration: underline;\\\">J. Pathak<\\\/span> <span> <span style=\\\"text-decoration: underline;\\\">(<\\\/span>N<span style=\\\"text-decoration: underline;\\\">)<\\\/span><\\\/span>, Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model, in review [<a href=\\\"https:\\\/\\\/arxiv.org\\\/abs\\\/2406.08632\\\">preprint<\\\/a>].<\\\/div>\\n<\\\/div>\\n\n\n<div><\\\/div>\\n\n\n<div>\\n\n\n<div><br \\\/><sup>+<\\\/sup>A. Mahesh, W. Collins, B. Bonev, <span style=\\\"text-decoration: underline;\\\">N. Brenowitz <\\\/span>(N), <span style=\\\"text-decoration: underline;\\\">Y. Cohen<\\\/span><span>\\u00a0<span style=\\\"text-decoration: underline;\\\">(<\\\/span>N<span style=\\\"text-decoration: underline;\\\">),<\\\/span><\\\/span> J. Elms, P. Harrington, K. Kashinath, T. Kurth, J. North, T. O'Brien, <strong>M. Pritchard<\\\/strong>, D. Pruitt, M. Risser, S. Subramanian, J. Willard, Huge Ensembles Part II: Properties of a Huge Ensemble of Hindcasts Generated with Spherical Fourier Neural Operators, in revision [<a href=\\\"https:\\\/\\\/arxiv.org\\\/abs\\\/2408.01581\\\">preprint<\\\/a>].<\\\/div>\\n\n\n<div><\\\/div>\\n\n\n<div><br \\\/><sup>+<\\\/sup>A. Mahesh, W. Collins, B. Bonev, <span style=\\\"text-decoration: underline;\\\">N. Brenowitz <\\\/span>(N), <span style=\\\"text-decoration: underline;\\\">Y. Cohen<\\\/span><span>\\u00a0<span style=\\\"text-decoration: underline;\\\">(<\\\/span>N<span style=\\\"text-decoration: underline;\\\">),<\\\/span><\\\/span> J. Elms, P. Harrington, K. Kashinath, T. Kurth, J. North, T. O'Brien, <strong>M. Pritchard<\\\/strong>, D. Pruitt, M. Risser, S. Subramanian, J. Willard, Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators, in revision [<a href=\\\"https:\\\/\\\/arxiv.org\\\/abs\\\/2408.03100\\\">preprint<\\\/a>].<br \\\/><br \\\/><\\\/div>\\n\n\n<div>*S. Yu (P), <span style=\\\"text-decoration: underline;\\\">Z. Hu<\\\/span> (N), <span style=\\\"text-decoration: underline;\\\">A. Subramaniam (N)<\\\/span>, W. Hannah, <span style=\\\"text-decoration: underline;\\\">L. Peng (P)<\\\/span>, <span style=\\\"text-decoration: underline;\\\">J. Lin (G)<\\\/span>, M. A. Bhouri. R. Gupta, B. L\\u00fctjens, J. C. Wills, G. Behrens, J. J. M. Busecke, N. Loose, C. I. Stern, T. Beucler, B. Harrop, H. Heuer, B. R. Hillman, <span style=\\\"text-decoration: underline;\\\">A. Jenney (P)<\\\/span>, <span style=\\\"text-decoration: underline;\\\">N. Liu (P)<\\\/span>, A. White, T. Zheng, Z. Kuang, F. Ahmed, E. Barnes, N. D. Brenowitz, C. Bretherton, V. Eyring, S. Ferretti, N. Lutsko, P. Gentine, S. Mandt, J. D. Neelin, R. Yu, L. Zanna, N. Urban, J. Yuval, R. Abernathey, P. Baldi, W. Chuang, Y. Huang, F. Iglesias-Suarez, S. Jantre, P.-L. Ma, S. Shamekh, G. Zhang. M. Pritchard, ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation, in revision [<a href=\\\"https:\\\/\\\/arxiv.org\\\/abs\\\/2306.08754\\\">preprint<\\\/a>].<br \\\/><br \\\/><\\\/div>\\n\n\n<div><sup>+<\\\/sup><span style=\\\"text-decoration: underline;\\\">Z. Hu<\\\/span> <span style=\\\"text-decoration: underline;\\\">(<\\\/span>N<span style=\\\"text-decoration: underline;\\\">),<\\\/span>\\u00a0A. Subramaniam, Z. Kuang, <span style=\\\"text-decoration: underline;\\\">J. Lin<\\\/span> (G), S. Yu, W. M. Hannah, <span style=\\\"text-decoration: underline;\\\">N. Brenowitz<\\\/span><span style=\\\"text-decoration: underline;\\\"> <\\\/span><span style=\\\"text-decoration: underline;\\\">(<\\\/span>N<span style=\\\"text-decoration: underline;\\\">),<\\\/span> J. Romero, <strong>M. Pritchard<\\\/strong>, Stable machine-learning parameterization of subgrid processes with real geography and full-physics emulation, in revision [<a href=\\\"https:\\\/\\\/arxiv.org\\\/abs\\\/2407.00124\\\">preprint<\\\/a>].<\\\/div>\\n<\\\/div>\\n\n\n<p>* <u>Lin, J.<\\\/u> (G), S. Yu, T. Beucler, P. Gentine, and <strong>M. Pritchard<\\\/strong>: Systematic Sampling and Validation of Machine Learning-Parameterizations in Climate Models, in revision [<a href=\\\"https:\\\/\\\/arxiv.org\\\/abs\\\/2309.16177\\\">preprint<\\\/a>].<br \\\/><br \\\/>Behrens, G., T. Beucler, F. Iglesias-Suarez, <span style=\\\"text-decoration: underline;\\\">S. Yu<\\\/span> (P), P. Gentine, <strong>M. Pritchard<\\\/strong>, M. Schwabe, V. Eyring, Improving Atmospheric Processes in Earth System Models with Deep Learning Ensembles and Stochastic Parameterizations, in revision [<a href=\\\"https:\\\/\\\/arxiv.org\\\/abs\\\/2402.03079\\\">preprint<\\\/a>].<br \\\/><br \\\/>Bhouri, M., <u>L. Peng (P)<\\\/u>, <strong>M. Pritchard<\\\/strong>, and P. Gentine, Multi-fidelity climate model parameterization for better generalization and extrapolation, in review [<a href=\\\"https:\\\/\\\/arxiv.org\\\/abs\\\/2309.10231\\\">preprint<\\\/a>].<\\\/p>\",\"panel_padding\":\"default\"}}]},{\"type\":\"column\",\"props\":{\"image_position\":\"center-center\",\"media_overlay_gradient\":\"\",\"width_medium\":\"1-2\",\"position_sticky_breakpoint\":\"m\"},\"children\":[{\"type\":\"panel\",\"props\":{\"link_text\":\"Read more\",\"title_hover_style\":\"reset\",\"title_element\":\"h3\",\"title_align\":\"top\",\"title_grid_width\":\"1-2\",\"title_grid_breakpoint\":\"m\",\"meta_style\":\"meta\",\"meta_align\":\"below-title\",\"meta_element\":\"div\",\"content_column_breakpoint\":\"m\",\"icon_width\":80,\"image_align\":\"top\",\"image_grid_width\":\"1-2\",\"image_grid_breakpoint\":\"m\",\"image_svg_color\":\"emphasis\",\"link_style\":\"default\",\"margin\":\"default\",\"panel_style\":\"card-primary\",\"title\":\"2024\",\"content\":\"\n\n<p><sup>+<\\\/sup>73. Mardani, M., <u>N. Brenowitz (N), Y. Cohen (N), J. Pathak (N)<\\\/u>, C.-Y. Chen, C.-C. Liu, A. Vahdat, K. Kashinath, J. Kautz and <strong>M. Pritchard<\\\/strong>. Generative residual diffusion modeling for km-scale atmospheric downscaling, <em>Nature Communications Earth &amp; Environment<\\\/em>, in press [<a href=\\\"https:\\\/\\\/arxiv.org\\\/abs\\\/2309.15214\\\">preprint<\\\/a>].<\\\/p>\\n\n\n<p><span class=\\\"author\\\">72. <span>Gopalakrishnan Meena, M., M. Norman, D. Hall, and <strong>M. Pritchard<\\\/strong>,\\u00a0Spatially Local Surrogate Modeling of Subgrid-Scale Effects in Idealized Atmospheric Flows: A Deep Learned Approach Using High-Resolution Simulation Data. <\\\/span><i>Artificial Intelligence for Earth Systems<\\\/i><span>,\\u00a0<\\\/span><b>3<\\\/b><span>, e230043, 2024 [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1175\\\/AIES-D-23-0043.1\\\">link<\\\/a>].\\u00a0<\\\/span><\\\/span><\\\/p>\\n\n\n<p><span class=\\\"author\\\">71. Duncan,<span>\\u00a0<\\\/span><text><\\\/text>J. P. C.<\\\/span><span>, E. <\\\/span><span class=\\\"author\\\">Wu<\\\/span><span>, J.-C. <\\\/span><span class=\\\"author\\\">Golaz, P. <\\\/span><span class=\\\"author\\\">Caldwell, O. <\\\/span><span class=\\\"author\\\">Watt-Meyer,<span> S. <\\\/span><\\\/span><span class=\\\"author\\\">Clark, <span class=\\\"comma__item\\\"><span>J. McGibbon<\\\/span><span class=\\\"comma-separator\\\">,\\u00a0<\\\/span><\\\/span><span class=\\\"comma__item\\\"><span> G. Dresdner<\\\/span><span class=\\\"comma-separator\\\">,\\u00a0<\\\/span><\\\/span><span class=\\\"comma__item\\\"><span> K. Kashinath<\\\/span><span class=\\\"comma-separator\\\">,\\u00a0<\\\/span><\\\/span><span class=\\\"comma__item\\\"><span> B. Bonev<\\\/span><span class=\\\"comma-separator\\\">,\\u00a0<\\\/span><\\\/span><span class=\\\"comma__item\\\"><strong> M. Pritchard <\\\/strong>and <\\\/span><span class=\\\"comma__item\\\"><span>C. S. Bretherton,\\u00a0<\\\/span><\\\/span><\\\/span><span><\\\/span><span class=\\\"articleTitle\\\">Application of the AI2 climate emulator to E3SMv2's global atmosphere model, with a focus on precipitation fidelity<\\\/span><span>.\\u00a0<\\\/span><i>Journal of Geophysical Research: Machine Learning and Computation<\\\/i><span>,\\u00a0<\\\/span><span class=\\\"vol\\\">1<\\\/span><span>, e2024JH000136 [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1029\\\/2024JH000136\\\">link<\\\/a>].\\u00a0<\\\/span><\\\/p>\\n\n\n<p>70. V. Eyring, W. D Collins, P. Gentine, E. A Barnes, M. Barreiro, T. Beucler, M. Bocquet, C. S Bretherton, H. M Christensen, K. Dagon, D. John Gagne, D. Hall, D. Hammerling, S. Hoyer, F. Iglesias-Suarez, I. Lopez-Gomez, M. C McGraw, G. A Meehl, M. J Molina, C. Monteleoni, J. Mueller, <strong>M. Pritchard<\\\/strong>, D. Rolnick, J. Runge, P. Stier, O. Watt-Meyer, K. Weigel, R. Yu, L. Zanna, Pushing the frontiers in climate modelling and analysis with machine learning, <i>Nature Climate Change,<\\\/i><span>\\u00a0<\\\/span><b>14<\\\/b><span>, 916\\u2013928, 2024 [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1038\\\/s41558-024-02095-y\\\">link<\\\/a>].\\u00a0<\\\/span><\\\/p>\\n\n\n<p>* 69. <u>Peng, L.<\\\/u>(P), \\u00a0P. Blossey, W. Hannah, C. Bretherton, C. Terai,\\u00a0 <u>A. Jenney<\\\/u> (P) &amp;\\u00a0 <strong>M. Pritchard<\\\/strong>.\\u00a0Improving stratocumulus cloud amounts in a 200-m resolution multi-scale modeling framework through tuning of its interior physics, <em>Journal of Advances in Modeling Earth Systems<\\\/em>,\\u00a016, e2023MS003632, 2024 [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1029\\\/2023MS003632\\\">link<\\\/a>].<\\\/p>\\n\n\n<p>* 68. <u>Mooers, G.\\u00a0(G), T. Beucler (P)<\\\/u>, <strong>M. Pritchard<\\\/strong>, &amp; S. Mandt. An Unsupervised Learning Perspective on the Dynamic Contribution to Extreme Precipitation Changes, <em>Environmental Data Science<\\\/em> ,2024 [<a href=\\\"https:\\\/\\\/www.cambridge.org\\\/core\\\/journals\\\/environmental-data-science\\\/article\\\/understanding-precipitation-changes-through-unsupervised-machine-learning\\\/C9A0CA3A98D2AC06E7334DCF143796CD\\\">link<\\\/a>].<\\\/p>\\n\n\n<p>* 67. <u>Beucler, T.<\\\/u> (P), J. Yuval, A. Gupta, <u>L. Peng<\\\/u> (P), S. Rasp, F. Ahmed, P. O'Gorman, J. Neelin, N. Lutsko, P. Gentine &amp; <strong>M. Pritchard<\\\/strong>. Climate-invariant machine learning, <em>Science Advances, <\\\/em><strong>10<\\\/strong>, eadj7250(2024), 2024 [<a href=\\\"https:\\\/\\\/www.science.org\\\/doi\\\/10.1126\\\/sciadv.adj7250\\\">link<\\\/a>].<\\\/p>\\n\n\n<p>66. Iglesias-Suarez, F., P. Gentine, B. Solino-Fernandez, T. Beucler, <strong>M. Pritchard<\\\/strong>, J. Runge and V. Eyring, Causally-informed deep learning to improve climate models and projections, <em>Journal of Geophysical Research \\u2013 Atmospheres, <\\\/em>129, 2024 [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1029\\\/2023JD039202\\\">link<\\\/a>]. <em><\\\/em><\\\/p>\\n\n\n<p>* 65. <u>Yu, S.<\\\/u>(P),\\u00a0P.-L. Ma, B. Singh, S. Silva and <strong>M. Pritchard<\\\/strong>, Two-step hyperparameter optimization method: Accelerating hyperparameter search by using a fraction of a training dataset, <em>Artificial Intelligence for the Earth Systems,\\u00a0<\\\/em>3<em>,<\\\/em>2024 [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1175\\\/AIES-D-23-0013.1\\\">link<\\\/a>]<em>.<\\\/em><\\\/p>\\n\n\n<p>* 64. <u>Yu, S.<\\\/u>(P), co-authors and <strong>M. Pritchard<\\\/strong>: ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation, <em>Advances in Neural Processing Systems <\\\/em>2024 [<a href=\\\"https:\\\/\\\/proceedings.neurips.cc\\\/paper_files\\\/paper\\\/2023\\\/file\\\/45fbcc01349292f5e059a0b8b02c8c3f-Paper-Datasets_and_Benchmarks.pdf\\\">link<\\\/a>]. <strong>Outstanding Paper Award \\u2013 Benchmarks and Datasets track. <\\\/strong><\\\/p>\",\"panel_padding\":\"default\"}}]}]},{\"type\":\"row\",\"props\":{\"width\":\"small\"},\"children\":[{\"type\":\"column\",\"props\":{\"image_position\":\"center-center\",\"media_overlay_gradient\":\"\",\"width_medium\":\"1-1\",\"position_sticky_breakpoint\":\"m\"},\"children\":[{\"type\":\"accordion\",\"props\":{\"show_image\":true,\"show_link\":true,\"collapsible\":true,\"content_column_breakpoint\":\"m\",\"image_svg_color\":\"emphasis\",\"image_align\":\"top\",\"image_grid_width\":\"1-2\",\"image_grid_breakpoint\":\"m\",\"link_text\":\"Read more\",\"link_style\":\"default\",\"content_column\":\"\",\"multiple\":true},\"children\":[{\"type\":\"accordion_item\",\"props\":{\"title\":\"2023\",\"content\":\"\n\n<p>* 63. <u>Mooers, G.<\\\/u> (G),\\u00a0<strong>M. Pritchard<\\\/strong>, <u>T. Beucler<\\\/u> (P), P. Srivastava, H. Mangipudi,\\u00a0<u>L. Peng<\\\/u> (P), P. Gentine &amp; S. Mandt. Comparing storm resolving models and climates via unsupervised machine learning, <em>Scientific Reports<\\\/em>13, 22365, 2023 [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1038\\\/s41598-023-49455-w\\\">link<\\\/a>].<\\\/p>\\n\n\n<p>* 62. <u>Liu, N<\\\/u>.,\\u00a0<strong>M. Pritchard<\\\/strong>, <u>A. Jenney<\\\/u>, W. Hannah.\\u00a0 Understanding Precipitation Bias Sensitivities in E3SM-Multi-scale Modeling Framework from a Dilution Framework.\\u00a0<em>Journal of Advances in Modeling Earth Systems<\\\/em>,\\u00a015, 2023 [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1029\\\/2022MS003460\\\">link<\\\/a>].\\u00a0<\\\/p>\\n\n\n<p>61. Connolley C., E. A. Barnes, P. Hassanzadeh and <strong>M. Pritchard<\\\/strong>,\\u00a0Using Neural Networks to Learn the Jet Stream Forced Response from Natural Variability,\\u00a0<em>Artificial Intelligence for the Earth Systems<\\\/em>, 2, 2023 [<a href=\\\"https:\\\/\\\/journals.ametsoc.org\\\/view\\\/journals\\\/aies\\\/2\\\/2\\\/AIES-D-22-0094.1.xml\\\">link<\\\/a>].<\\\/p>\\n\n\n<p>* 60. <u>Jenney, A. M.<\\\/u>(P),\\u00a0<u>S. L. Ferretti<\\\/u>(G),\\u00a0<strong>M. Pritchard<\\\/strong>. Vertical resolution impacts explicit simulation of deep convection,\\u00a0<em>Journal of Advances in Modeling Earth Systems<\\\/em>, 15, 2023 [<a href=\\\"https:\\\/\\\/agupubs.onlinelibrary.wiley.com\\\/doi\\\/full\\\/10.1029\\\/2022MS003444\\\">link<\\\/a>].<\\\/p>\"}},{\"type\":\"accordion_item\",\"props\":{\"title\":\"2022\",\"content\":\"\n\n<p><span>59. Graubner, A., K. Azizzadenesheli, J. Pathak, M. Mardani, <strong>M. Pritchard<\\\/strong>, K. Kashinath, &amp; A. Anandkumar, Calibration of Large Neural Weather Models, <em>NeurIPS workshop, <\\\/em>2022. [<a href=\\\"https:\\\/\\\/s3.us-east-1.amazonaws.com\\\/climate-change-ai\\\/papers\\\/neurips2022\\\/87\\\/paper.pdf\\\">link<\\\/a>].<\\\/span><\\\/p>\\n\n\n<p>58. Behrens, G., T. Beucler, P. Gentine, F. Iglesias-Suarez, <strong>M. Pritchard<\\\/strong> &amp; V. Eyring. Non-linear dimensionality reduction with a variational encoder decoder to understand convective processes in climate models, <em>Journal of Advances in Modeling Earth Systems<\\\/em>, 14, 2022 [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1029\\\/2022MS003130\\\">link<\\\/a>].<br \\\/><br \\\/>*<span> 57. <\\\/span><span style=\\\"text-decoration: underline;\\\">Peng, L.\\u00a0(P)<\\\/span>,\\u00a0<strong>M. Pritchard<\\\/strong>, W. Hannah, P. Blossey, P. Worley and C. Bretherton: Load balancing intense physics calculations to embed regionalized high-resolution cloud resolving models in the E3SM and CESM climate models,\\u00a0<em>Journal of Advances in Modeling Earth Systems<\\\/em>, 14, 2022\\u00a0[<a href=\\\"https:\\\/\\\/doi.org\\\/10.1029\\\/2021MS002841\\\">link<\\\/a>].<\\\/p>\\n\n\n<p>56. Ma, P., and co-authors: Better calibration of cloud parameterizations and subgrid effects increases the fidelity of E3SM Atmosphere Model version 1, <em>Geoscientific Model Development<\\\/em>, 15, 2881-2916, 2022 [<a href=\\\"https:\\\/\\\/doi.org\\\/10.5194\\\/gmd-15-2881-2022\\\">link<\\\/a>].<\\\/p>\\n\n\n<p><span>55. Harrop, B., <strong>M. Pritchard<\\\/strong>.,\\u00a0<span style=\\\"text-decoration: underline;\\\">H. Parishani\\u00a0(P)<\\\/span>, A. Gettelman, S. Hagos, P. Lauritzen, R. Leung, J. Lu, K. Pressel, K. Sakaguchi: Conservation of dry air, water, and energy in CAM and its potential impact on tropical rainfall, <em>Journal of Climate<\\\/em>,\\u00a0<em>35<\\\/em>(9), 2895-2917, 2022 [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1175\\\/JCLI-D-21-0512.1\\\">link<\\\/a>].<\\\/span><\\\/p>\"}},{\"type\":\"accordion_item\",\"props\":{\"title\":\"2021\",\"content\":\"\n\n<p>*<span> H. Mangipudi, G. Mooers, <strong>M. Pritchard<\\\/strong>, T. Beucler, &amp; S. Mandt, Analyzing high-resolution clouds and convection using multi-channel VAEs (2021)<\\\/span>,<span>\\u00a0<\\\/span><i>Proceedings of the 35th Conference\\u00a0 on Neural Information Processing (NeurIPS)<\\\/i><span>, 2021 [<a href=\\\"https:\\\/\\\/arxiv.org\\\/abs\\\/2112.01221\\\">link<\\\/a>].<\\\/span><\\\/p>\\n\n\n<p>* 54.<span>\\u00a0<\\\/span><span>Mooers, G.\\u00a0<\\\/span>(G),\\u00a0<span>\\u00a0<\\\/span><strong>M. Pritchard<\\\/strong>,\\u00a0<span>T. Beucler (P)<\\\/span>,\\u00a0<span>J. Ott (G)<\\\/span>,\\u00a0<span>G. Yacalis (G)<\\\/span>, P. Baldi and P. Gentine: Assessing the potential of deep learning for emulating cloud superparameterization in climate models with real-geography boundary conditions,\\u00a0<em>Journal of Advances in Modeling Earth Systems<\\\/em>, 2021 [<a href=\\\"https:\\\/\\\/agupubs.onlinelibrary.wiley.com\\\/doi\\\/full\\\/10.1029\\\/2020MS002385\\\" class=\\\"customize-unpreviewable\\\">link<\\\/a>].<\\\/p>\\n\n\n<p>* 53.<span>\\u00a0<\\\/span><span>Hendrickson,\\u00a0 J<\\\/span>. (G), C. Terai,\\u00a0<strong>M. Pritchard<\\\/strong><span>\\u00a0<\\\/span>and P. Caldwell: Lower Tropospheric Processes: A Control on the Global Mean Precipitation Rate,<span>\\u00a0<\\\/span><em>Geophysical Research Letters<\\\/em>, 48, 2021 [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1029\\\/2020GL091169\\\" class=\\\"customize-unpreviewable\\\">link<\\\/a>].<\\\/p>\\n\n\n<p>* 52.<span>\\u00a0<\\\/span><u>Beucler, T.<\\\/u><span>\\u00a0<\\\/span>(P),<span>\\u00a0<\\\/span><strong>M. Pritchard<\\\/strong>, S. Rasp, P. Gentine, J. Ott and P. Baldi: Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems,<span>\\u00a0<\\\/span><em>Physical Review Letters<\\\/em>, 126, 2021\\u00a0[<a href=\\\"https:\\\/\\\/link.aps.org\\\/doi\\\/10.1103\\\/PhysRevLett.126.098302\\\" class=\\\"customize-unpreviewable\\\">link<\\\/a>]<\\\/p>\"}},{\"type\":\"accordion_item\",\"props\":{\"title\":\"2020\",\"content\":\"\n\n<p>*<span> <\\\/span><span>\\u00a051. <\\\/span><span>Mooers, G.\\u00a0<\\\/span>(G), J. Tuyls, S. Mandt,\\u00a0<strong>M. Pritchard<\\\/strong><span>\\u00a0<\\\/span>and<span>\\u00a0<\\\/span><span>T. Beucler<\\\/span><span>\\u00a0<\\\/span>(P): Generative Modeling for Atmospheric Convection,<span>\\u00a0<\\\/span><i>Proceedings of the 10th International Conference on Climate Informatics<\\\/i><span>\\u00a0(<\\\/span><i>CI2020<\\\/i><span>), 2020 [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1145\\\/3429309.3429324\\\" class=\\\"customize-unpreviewable\\\">link<\\\/a>].<\\\/span><\\\/p>\\n\n\n<p>50. Mamalakis, A., J. T. Randerson, J.-Y. Yu,\\u00a0<strong>M. Pritchard<\\\/strong>, G. Magnusdottir, P. Smyth, P. Levine, S. Yu and E. Foufoula-Georgiou: Zonally opposing shifts of the intertropical convergence zone in response to climate change,<span>\\u00a0<\\\/span><em>Nature Climate Change<\\\/em>, 11, 2020 [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1038\\\/s41558-020-00963-x\\\" class=\\\"customize-unpreviewable\\\">link<\\\/a>].\\u00a0<\\\/p>\\n\n\n<p>* 49.<span>\\u00a0<\\\/span><span>Terai, C.<\\\/span><span>\\u00a0<\\\/span>(P),<span>\\u00a0<\\\/span><strong>M. Pritchard<\\\/strong>, P. Blossey and C. Bretherton: The impact of resolving subkilometer processes on aerosol-cloud interactions in global model simulations,<span>\\u00a0<\\\/span><em>J. Adv. Model. Earth Sys.<\\\/em>, 12, 2020. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1029\\\/2020MS002274\\\" class=\\\"customize-unpreviewable\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>48. Brenowitz, N.,\\u00a0<span>T. Beucler<\\\/span><span>\\u00a0<\\\/span>(P),\\u00a0<strong>M. Pritchard<\\\/strong>, and C. S. Bretherton: Interpreting and stabilizing machine-learning parameterizations of convection,<span>\\u00a0<\\\/span><em>J. Atmos. Sci.,<span>\\u00a0<\\\/span><\\\/em>2020. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1175\\\/JAS-D-20-0082.1\\\" class=\\\"customize-unpreviewable\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>47.<span>\\u00a0<\\\/span><u>Ott, J.<\\\/u>,<span>\\u00a0<\\\/span><b>M. Pritchard<\\\/b>, N. Best, E. Linstead, M. Curcic, and P. Baldi: A Fortran-Keras Deep Learning Bridge for Scientific Computing.<span>\\u00a0<\\\/span><i>Scientific Programming<\\\/i>, 2020. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1155\\\/2020\\\/8888811\\\" class=\\\"customize-unpreviewable\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>*46.\\u00a0Fowler, M. (G) and\\u00a0<strong>M. Pritchard<\\\/strong>, Regional MJO modulation of West Pacific tropical cyclones driven by multiple transient controls,<span>\\u00a0<\\\/span><em>Geophysical Research Letters,<\\\/em>\\u00a047 (11), 2020.\\u00a0[<a href=\\\"https:\\\/\\\/doi.org\\\/10.1029\\\/2020GL087148\\\" class=\\\"customize-unpreviewable\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>45.\\u00a0Gutowski et al.,\\u00a0<span class=\\\"s1\\\">The ongoing need for high-resolution regional climate models: Process understanding and stakeholder information,<span>\\u00a0<\\\/span><em>Bulletin of the American Meteorological Society<\\\/em>, 2020<em>.\\u00a0<\\\/em>[<a href=\\\"https:\\\/\\\/doi.org\\\/10.1175\\\/BAMS-D-19-0113.1\\\" class=\\\"customize-unpreviewable\\\">link<\\\/a>]<\\\/span><\\\/p>\\n\n\n<p>44. Hannah, W., C. Jones, B. Hillman, M. Norman, D. Bader, M. Taylor, R. Leung,<span>\\u00a0<\\\/span><strong>M. Pritchard<\\\/strong>, M. Branson, G. Lin, K. Pressel, J. Lee. Initial Results from the Super-Parameterized E3SM,<span>\\u00a0<\\\/span><em>J. Adv. Model. Earth Sys.<\\\/em>, 12, 2020 [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1029\\\/2019MS001863\\\" class=\\\"customize-unpreviewable\\\">link<\\\/a>].<\\\/p>\"}},{\"type\":\"accordion_item\",\"props\":{\"title\":\"2019\",\"content\":\"\n\n<p>43. * Fowler, M. (G), G. Kooperman, J. T. Randerson and\\u00a0<strong>M. Pritchard<\\\/strong>. The effect of plant-physiological responses to rising CO<sub>2<\\\/sub>\\u00a0on global streamflow, <em>Nature Climate Change<\\\/em>, 9, 2019. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1038\\\/s41558-019-0602-x\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>42. Beucler, T. (P), T. Abbott, T. Cronin and <strong>M. Pritchard<\\\/strong>. Linking Convective Self-Aggregation in Idealized Models to Observed Moist Static Energy Variability near the Equator, <em>Geophysical Research Letters<\\\/em>, 46, 2019. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1029\\\/2019GL084130\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>41. *\\u00a0Beucler, T. (P),\\u00a0\\u00a0S. Rasp, <strong>M. Pritchard<\\\/strong> and P. Gentine (2019). Achieving Conservation of Energy in Neural Network Emulators for\\u00a0Climate Modeling, C<em>limate Change and Artificial Intelligence workshop of the 2019 International Conference on Machine\\u00a0Learning, <i>arXiv preprint <\\\/i><\\\/em>arXiv:1906.06622<em>, <\\\/em>2019<em>. <\\\/em>[<a href=\\\"https:\\\/\\\/arxiv.org\\\/pdf\\\/1906.06622.pdf\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>40. * Langenbrunner, B. (P), <strong>M. Pritchard<\\\/strong>, G. Kooperman and J. Randerson.\\u00a0Why does Amazon precipitation decrease when tropical forests respond to increasing CO2?, <em>Earth\\u2019s<\\\/em> Future, , 7, 450\\u2013 468, 2019. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1029\\\/2018EF001026\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>39. * Yu, S. (G) and\\u00a0<strong>M. S. Pritchard<\\\/strong>. A strong role for the AMOC in partitioning global energy transport and shifting ITCZ position in response to latitudinally discrete solar forcing in the CESM1.2, <em>J.<\\\/em><em>\\u00a0Climate<\\\/em>, 32, 2019. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1175\\\/JCLI-D-18-0360.1\\\">link<\\\/a>]<\\\/p>\"}},{\"type\":\"accordion_item\",\"props\":{\"title\":\"2018\",\"content\":\"\n\n<p>38.\\u00a0Levine. P., M. Xu, F. M. Hoffman, Y. Chen,<strong> M. S. Pritchard<\\\/strong> and J. T. Randerson.\\u00a0Soil moisture variability intensifies and prolongs Amazon temperature and carbon cycle response to El Ni\\u00f1o-Southern Oscillation,\\u00a0<em>J. Climate<\\\/em>, 32, 2018. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1175\\\/JCLI-D-18-0150.1\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>37. * Parishani, H. (P), <strong>M. S. Pritchard<\\\/strong>, C. S. Bretherton, C. R. Terai (P), M. C. Wyant, M. Khairoutdinov and B. Singh. Insensitivity of the cloud response to surface warming under radical changes to boundary layer turbulence and cloud microphysics: Results from the UltraParameterized CAM, <i>J. Adv. Model. Earth Sys.<\\\/i>, 2018 [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1029\\\/2018MS001409\\\">link<\\\/a>].<\\\/p>\\n\n\n<p>36. Kooperman, G., M. Fowler (G), F. Hoffman, C. Koven, K. Lindsay, <strong>M. Pritchard,<\\\/strong> A. Swann and J. Randerson. Plant-physiological responses to rising CO2 modify daily runoff intensity with implications for global-scale flood risk assessment,\\u00a0<em>Geophys. Res. Lett.<\\\/em>, 2018 [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1029\\\/2018GL079901\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>35. * Sun, J. (P) and\\u00a0<strong>M. S. Pritchard<\\\/strong>. Effects of explicit convection on land surface air temperature and land-atmosphere coupling in the thermal feedback pathway, \\u00a0<em>J. Adv. Model. Earth Sys.<\\\/em>, 2018 [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1029\\\/2018MS001301\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>34. Zeng, X., D. Klocke, B. J. Shipway, M. S. Singh, I. Sandu, W. Hannah, P. Bogenschutz, Y. Zhang, H. Morrison, <strong>M. S. Pritchard<\\\/strong>, and C. Rio, 2018. Community Efforts in Understanding and Modeling Atmospheric Processes. Community efforts in understanding and modeling atmospheric processes, <em>Bull. Amer. Met. Soc.<\\\/em>, 2018. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1175\\\/BAMS-D-18-0139.1\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>33.* <u>Rasp, S. (Visiting G)<\\\/u>, <strong>M. S. Pritchard<\\\/strong>, and P. Gentine. Deep learning to represent sub-grid processes in climate models, <em>PNAS<\\\/em>, 2018. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1073\\\/pnas.1810286115\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>32. Gentine, P., <strong>M. S. Pritchard<\\\/strong>, <u>S. Rasp<\\\/u>(Visiting G), G. Reinaudi and G. Yacalis (G). Could machine learning break the convection parameterization deadlock?,\\u00a0<em>Geophys. Res. Lett.<\\\/em>,\\u00a045, 2018. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1029\\\/2018GL078202\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>31. Kooperman, G. K, Y. Chen, F. M. Hoffman, C. D. Koven, K. Lindsay, <strong>M. S. Pritchard<\\\/strong>, A. L. S. Swann, and J. T. Randerson, 2018.\\u00a0Forest response to rising CO2 drives zonally asymmetric rainfall change over tropical land, <em>Nature Climate Change<\\\/em>,\\u00a08, \\u00a0434\\u2013440, 2018. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1038\\\/s41558-018-0144-7\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>30.* <u>Kooperman, G. J.<\\\/u>(P), <strong>M. S. Pritchard<\\\/strong>, M. S., T. A. O\\u2019Brien and B. W. Timmermans. Rainfall from resolved rather than parameterized processes better represents the present\\u2010day and climate change response of moderate rates in the community atmosphere model. <em>J. Adv. Model. Earth Syst.<\\\/em>, 10, 2018. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1002\\\/2017MS001188\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>29.* <u>Qin, H. (G)<\\\/u>,\\u00a0<strong>M. S. Pritchard<\\\/strong>,<u>G. J. Kooperman (P)<\\\/u>and <u>H. Parishani <\\\/u>(P), 2018.\\u00a0Global Effects of SuperParameterization on Hydro-Thermal Land\\u2013Atmosphere Coupling on Multiple Timescales,\\u00a0<em>J. Adv. Model. Earth Syst.<\\\/em>, 10, 2018. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1002\\\/2017MS001185\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>28.* <u>Fowler, M. (G)<\\\/u>,\\u00a0<strong>M. S. Pritchard<\\\/strong>and <u>G. J. Kooperman (P)<\\\/u>.\\u00a0Assessing the impact of Californian and Indian irrigation on precipitation in the irrigation-enabled Community Earth System Model, <em>J. Hydromet.<\\\/em>,19(2), 427-443, 2018. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1175\\\/JHM-D-17-0038.1\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>27. Woelfle, M., <u>S. Yu, (G)<\\\/u>., C. S. Bretherton and <strong>M. S. Pritchard<\\\/strong>.\\u00a0Sensitivity of coupled tropical Pacific model biases to convective parameterization in CESM1, <em>J. Adv. Model. Earth Syst.<\\\/em>, 10, 2018. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1002\\\/2017MS001176\\\">link<\\\/a>]<\\\/p>\"}},{\"type\":\"accordion_item\",\"props\":{\"title\":\"2017\",\"content\":\"\n\n<p>26.* <u>Parishani, H. (P).,<\\\/u><strong>M. S. Pritchard<\\\/strong>, C. S. Bretherton, M. C. Wyant and M. Khairoutdinov. Towards low cloud-permitting cloud superparameterization with explicit boundary layer turbulence, <em>J. Adv. Model Earth Syst.<\\\/em>, 9, 1542\\u20131571, 2017. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1002\\\/2017MS000968\\\">link<\\\/a>]<\\\/p>\"}},{\"type\":\"accordion_item\",\"props\":{\"title\":\"2016\",\"content\":\"\n\n<p>25.* <u>Kooperman, G. J (P).,<\\\/u><strong>M. S. Pritchard<\\\/strong>, M. A. Burt, M. D. Branson, and D. A. Randall. Impacts of cloud superparameterization on projected daily rainfall intensity climate changes in multiple versions of the Community Earth System Model, <em>J. Adv. Model. Earth Syst.<\\\/em>, 8, 2016. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1002\\\/2016MS000715\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>24.* <u>Sun, S. (P)<\\\/u>and <strong>M. S. Pritchard<\\\/strong>. Effects of explicit convection on global land-atmosphere coupling in the superparameterized CAM, <em>J. Adv. Model. Earth Syst.<\\\/em>, 8, 1248\\u20131269, 2016. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1002\\\/2016MS000689\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>23.* <u>Elliott, E. J. (U), S. Yu (G), G. Kooperman (P)<\\\/u>, H. Morrison , M. Wang and <strong>M. S. Pritchard<\\\/strong>. Sensitivity of summer ensembles of superparameterized US mesoscale convective systems to cloud resolving model microphysics and grid configuration, <em>J. Adv. Model. Earth Syst.<\\\/em>,2016.[<a href=\\\"http:\\\/\\\/dx.doi.org\\\/10.1002\\\/2015MS000567\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>22.* <strong>Pritchard, M. S<\\\/strong>. and D. Yang. Response of the superparameterized Madden-Julian Oscillation to extreme climate and basic state variation challenges a moisture mode view. <em>J. Climate<\\\/em>, 29, 4995-5008, 2016. [<a href=\\\"http:\\\/\\\/dx.doi.org\\\/10.1175\\\/JCLI-D-15-0790.1\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>21.* <u>Kooperman, G. J. (P)<\\\/u>, <strong>M. S. Pritchard<\\\/strong>, M. A. Burt, M. D. Branson, and D. A. Randall. Robust effects of cloud super-parameterization on simulated daily rainfall intensity statistics across multiple versions of CESM. <em>J. Adv. Model. Earth Syst.<\\\/em>, 8, 2016. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1002\\\/2015MS000574\\\">link<\\\/a>]<\\\/p>\"}},{\"type\":\"accordion_item\",\"props\":{\"title\":\"2015\",\"content\":\"\n\n<p>20. Benedict, J. J., <strong>M. S. Pritchard<\\\/strong>, and W. D. Collins. Sensitivity of MJO propagation to a robust positive Indian Ocean dipole event in the superparameterized CAM, <em>J. Adv. Model. Earth Syst.<\\\/em>, 7, 1901\\u20131917, 2015. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1002\\\/2015MS000530\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>19. * <u>Yu, S. (G) <\\\/u>and <strong>M. S. Pritchard<\\\/strong>. The effect of large-scale model time step and multiscale coupling frequency on cloud climatology, vertical structure, and rainfall extremes in a superparameterized GCM, <em>J. Adv. Model. Earth Syst.<\\\/em>, 7, 1977\\u20131996, 2015. [<a href=\\\"http:\\\/\\\/doi.org\\\/10.1002\\\/2015MS000493\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>18. Jones, C., C. S. Bretherton and <strong>M. S. Pritchard<\\\/strong>. Mean-state acceleration of cloud-resolving model simulations. <em>J. Adv. Model. Earth Syst.<\\\/em>,07, 2015. [<a href=\\\"http:\\\/\\\/doi.org\\\/10.1002\\\/2015MS000488\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>17. Klingaman, N. P., S. J. Woolnough, X. Jiang, D. Waliser, P. K. Xavier, J. Petch, M. Caian, C. Hannay, D. Kim, H.-Y. Ma, W. J. Merryfield, T. Miyakawa, <strong>M. S. Pritchard<\\\/strong>, J. A. Ridout, R. Roehrig, E. Shindo, F. Vitart, H. Wang, N. Cavanaugh, B. E. Mapes, A. Shelly, G. J. Zhang. Vertical structure and physical processes of the Madden-Julian Oscillation: Linking hindcast fidelity to simulated diabatic heating and moistening, <em>J. Geophys. Res. Atm.<\\\/em>120, 10, 4690-4717, 2015. [<a href=\\\"http:\\\/\\\/doi.org\\\/%2010.1002\\\/2014JD022374\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>16. Xavier, P.K., J. C. Petch, N. P. Klingaman, S. J. Woolnough, X. Jiang, D. E. Waliser, M. Caian, J. Cole, S. Hagos, C. Hannay, D. Kim, T. Miyakawa, <strong>M. S. Pritchard<\\\/strong>, R. Roehrig, E. Shindo, F. Vitart, H. Wang. Vertical structure and diabatic processes of the Madden-Julian Oscillation: Biases and uncertainties at short range, <em>J. Geophys. Res. Atm.<\\\/em>120, 10, 4749 -4763, 2015. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1002\\\/2014JD022718\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>15. Hannah, W, <strong>M. S. Pritchard<\\\/strong>and E. Maloney. Consequences of systematic model drift in DYNAMO MJO hindcasts with SP-CAM and CAM5, <em>J. Adv. Model. Earth Syst.<\\\/em>, 07, 2015. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1002\\\/2014MS000423\\\">link<\\\/a>]<\\\/p>\"}},{\"type\":\"accordion_item\",\"props\":{\"title\":\"2014\",\"content\":\"\n\n<p>14.\\u00a0<strong>Pritchard M. S.<\\\/strong>, C. DeMott and C. S. Bretherton. Restricting 32\\u2013128 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, <em>J. Adv. Model. Earth Syst.<\\\/em>, 06, 2014. [<a href=\\\"http:\\\/\\\/doi.org\\\/10.1002\\\/2014MS000340\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>13. Kooperman, G., <strong>M. S. Pritchard<\\\/strong>and R. C. J. Somerville.The response of US summer rainfall to quadrupled CO2 climate change in conventional and superparameterized versions of the NCAR Community Atmosphere Model<em>, <\\\/em><em>J. Adv. Model. Earth Syst.<\\\/em>, 06, 2014. [<a href=\\\"http:\\\/\\\/doi.org\\\/%2010.1002\\\/2014MS000306\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>12. Zhao, Z., G. Kooperman, <strong>M. S. Pritchard<\\\/strong>, L. M. Russell and R. C. J. Somerville. Investigating impacts of forest fires in Alaska and western Canada on regional weather over the northeastern United States using CAM5 global simulations to constrain transport to a WRF-Chem regional domain, <em>J. Geophys. Res. Atm.<\\\/em>, 199(12): 7515-7536, 2014. [<a href=\\\"http:\\\/\\\/doi.org\\\/10.1002\\\/2013JD020973\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>11. <strong>Pritchard, M. S.<\\\/strong>and C. S. Bretherton. Causal evidence that rotational moisture advection is critical to the superparameterized Madden-Julian Oscillation, <em>J. Atmos. Sci.<\\\/em>, 71(2) 800-815, 2014. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1175\\\/JAS-D-13-0119.1\\\">link<\\\/a>]<\\\/p>\"}},{\"type\":\"accordion_item\",\"props\":{\"title\":\"Pre-2014\",\"content\":\"\n\n<p>10. Kooperman, G., <strong>M. S. Pritchard <\\\/strong>and R. C. J. Somerville. Robustness and sensitivities of central U.S. summer convection in the superparameterized CAM: Multi-model intercomparison with a new regional EOF index, 2013. <em>Geophys. Res. Lett., <\\\/em>40 (12), 3287-3291, 2013. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1002\\\/grl.50597\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>9. Kooperman, G., <strong>M. S. Pritchard<\\\/strong>, S. Ghan, R. C. J. Somerville and L. M. Russell. Constraining the influence of natural variability to improve estimates of global aerosol indirect effects in a nudged version of the Community Atmosphere Model 5. <em>J. Geophys. Res. Atm..<\\\/em>, 117, D23204, 2012. [<a href=\\\"http:\\\/\\\/doi.org\\\/10.1029\\\/2012JD018588\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>8. Schmidt, J. M., P. J. Flatau. P. R. Harasti, R. D. Yates, R. Littleton, <strong>M. S. Pritchard<\\\/strong>, and co-authors. Radar observations of individual rain drops in the free atmosphere, <em>Proceedings of the National Academy of Sciences<\\\/em>, 2012. [<a href=\\\"http:\\\/\\\/doi.org\\\/10.1073\\\/pnas.1117776109\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>7. Zhao, Z, <strong>M. S. Pritchard<\\\/strong>, and L. M. Russell. Effects on precipitation, clouds, and temperature from long-range transport of idealized aerosol plumes in WRF-Chem simulations, <em>J. Geophys. Res. Atm.<\\\/em>, 117 (D5), 2012. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1029\\\/2011JD016744\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>6.\\u00a0<strong>Pritchard, M. S.<\\\/strong>, M. W. Moncrieff and R. C. J. Somerville. Orogenic propagating precipitation systems over the US in a global climate model with embedded explicit convection,\\u00a0 <em>J. Atmos. Sci. <\\\/em>\\u00a068 (8), 1821-1840, 2011. [<a href=\\\"http:\\\/\\\/doi.org\\\/10.1175\\\/2011JAS3699.1\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>5.\\u00a0<strong>Pritchard, M. S.<\\\/strong>and R. C. J. Somerville. Assessing the Diurnal Cycle of Precipitation in a Multi-Scale Climate Model, <em>J. Adv. Model. Earth Syst.<\\\/em>, 1, 2009. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.3894\\\/JAMES.2009.1.12\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>4.\\u00a0<strong>Pritchard, M. S.<\\\/strong>and R. C. J. Somerville. Empirical orthogonal function analysis of the diurnal cycle of precipitation in a multi-scale climate model, <em>Geophys. Res. Lett<\\\/em>36 (5), 2009. [<a href=\\\"http:\\\/\\\/doi.org\\\/10.1029\\\/2008GL036964\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>3.\\u00a0<strong>Pritchard, M. S.<\\\/strong>, A. B. G. Bush and S. J. Marshall. Interannual atmospheric variability affects continental ice sheet simulations on millennial time scales. <em>J. Climate<\\\/em>21 (22), 5976-5992, 2008. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.1175\\\/2008JCLI2327.1\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>2. Pendlebury, D., T. G. Shepherd, <strong>M. S. Pritchard<\\\/strong>, and C. McLandress. Normal mode Rossby waves and their effects on chemical composition in the late summer stratosphere. <em>Atmos. Chem. Phys.<\\\/em>, 8 (7), 1925-1935, 2008. [<a href=\\\"https:\\\/\\\/doi.org\\\/10.5194\\\/acp-8-1925-2008\\\">link<\\\/a>]<\\\/p>\\n\n\n<p>1. <strong>Pritchard, M. S.<\\\/strong>, A. B. G. Bush and S. J. Marshall. Neglecting ice-atmosphere interactions underestimates ice sheet melt in millennial-scale deglaciation simulations. <em>Geophys. Res. Lett. <\\\/em>35 (1), L01503, 2008. 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My tools are a blend of next-generation global atmospheric simulation algorithms, theoretical climate dynamics, and high-performance computing. Projects are guided by the problems of interest, and are intentionally explorative of new potentially breakthrough physical algorithms that attempt to avoid traditional approximations of cloud physics in global climate simulations. Lately this has meant significant exploration of emerging tools in the data sciences such as deep machine learning for physical process emulation and neural-network assisted dynamical inquiry. I now hold a partial industry appointment: In July 2022 I began a 80% leave of absence from UCI to lead a new research group at NVIDIA as their Director of Climate Simulation Research, helping lead their Earth-2 climate simulation initiative.\\u00a0<\\\/p>\\n<\\\/div>\\n<\\\/div>&#8220;,&#8221;panel_style&#8221;:&#8221;card-default&#8221;}},{&#8220;type&#8221;:&#8221;grid_item&#8221;,&#8221;props&#8221;:{&#8220;title&#8221;:&#8221;Liran Peng&#8221;,&#8221;content&#8221;:&#8221;<\/p>\n<p><strong>Postdoctoral Scholar<\\\/strong><\\\/p>\\n<\/p>\n<p>March 2020 &#8211; Present<\\\/p>&#8220;,&#8221;image&#8221;:&#8221;wp-content\\\/uploads\\\/sites\\\/23\\\/2020\\\/12\\\/Liran-scaled.jpg&#8221;}},{&#8220;type&#8221;:&#8221;grid_item&#8221;,&#8221;props&#8221;:{&#8220;title&#8221;:&#8221;Jerry Lin&#8221;,&#8221;content&#8221;:&#8221;<\/p>\n<p><strong>PhD Candidate<\\\/strong><\\\/p>\\n<\/p>\n<p>September 2020 &#8211; Present<\\\/p>&#8220;,&#8221;image&#8221;:&#8221;wp-content\\\/uploads\\\/sites\\\/23\\\/2020\\\/12\\\/jerrylin.png&#8221;}},{&#8220;type&#8221;:&#8221;grid_item&#8221;,&#8221;props&#8221;:{&#8220;title&#8221;:&#8221;Savannah Ferretti&#8221;,&#8221;content&#8221;:&#8221;<\/p>\n<p><strong>PhD Candidate<\\\/strong><\\\/p>\\n<\/p>\n<p>September 2021 &#8211; Present<\\\/p>&#8220;,&#8221;image&#8221;:&#8221;wp-content\\\/uploads\\\/sites\\\/23\\\/2024\\\/06\\\/IMG_2877.jpg&#8221;,&#8221;link&#8221;:&#8221;&#8221;}},{&#8220;type&#8221;:&#8221;grid_item&#8221;,&#8221;props&#8221;:{&#8220;title&#8221;:&#8221;Yan Xia&#8221;,&#8221;meta&#8221;:&#8221;PhD student &#8220;,&#8221;content&#8221;:&#8221;<\/p>\n<p>September 2022 &#8211; Present<\\\/p>&#8220;,&#8221;image&#8221;:&#8221;wp-content\\\/uploads\\\/sites\\\/23\\\/2022\\\/09\\\/Yan.jpg&#8221;,&#8221;link&#8221;:&#8221;&#8221;}}]}]}]}],&#8221;name&#8221;:&#8221;Group members&#8221;},{&#8220;type&#8221;:&#8221;section&#8221;,&#8221;props&#8221;:{&#8220;style&#8221;:&#8221;default&#8221;,&#8221;width&#8221;:&#8221;default&#8221;,&#8221;vertical_align&#8221;:&#8221;middle&#8221;,&#8221;title_position&#8221;:&#8221;top-left&#8221;,&#8221;title_rotation&#8221;:&#8221;left&#8221;,&#8221;title_breakpoint&#8221;:&#8221;xl&#8221;,&#8221;image_position&#8221;:&#8221;center-center&#8221;,&#8221;status&#8221;:&#8221;&#8221;},&#8221;children&#8221;:[{&#8220;type&#8221;:&#8221;row&#8221;,&#8221;children&#8221;:[{&#8220;type&#8221;:&#8221;column&#8221;,&#8221;props&#8221;:{&#8220;image_position&#8221;:&#8221;center-center&#8221;,&#8221;media_overlay_gradient&#8221;:&#8221;&#8221;,&#8221;width_medium&#8221;:&#8221;1-1&#8243;,&#8221;position_sticky_breakpoint&#8221;:&#8221;m&#8221;},&#8221;children&#8221;:[{&#8220;type&#8221;:&#8221;headline&#8221;,&#8221;props&#8221;:{&#8220;title_element&#8221;:&#8221;h1&#8243;,&#8221;content&#8221;:&#8221;<\/p>\n<p>Find us on Twitter<\\\/p>&#8220;,&#8221;text_align&#8221;:&#8221;center&#8221;}},{&#8220;type&#8221;:&#8221;divider&#8221;,&#8221;props&#8221;:{&#8220;divider_element&#8221;:&#8221;hr&#8221;,&#8221;margin_remove_top&#8221;:true,&#8221;margin_remove_bottom&#8221;:true,&#8221;maxwidth&#8221;:&#8221;small&#8221;,&#8221;block_align&#8221;:&#8221;center&#8221;}},{&#8220;type&#8221;:&#8221;html&#8221;,&#8221;props&#8221;:{&#8220;content&#8221;:&#8221;<a class=\\\"twitter-timeline\\\" data-height=\\\"600\\\" data-theme=\\\"light\\\" href=\\\"https:\\\/\\\/twitter.com\\\/SciPritchard?ref_src=twsrc%5Etfw\\\">Tweets by Pritchard_UCI<\\\/a> <script async src=\\\"https:\\\/\\\/platform.twitter.com\\\/widgets.js\\\" charset=\\\"utf-8\\\"><\\\/script> \"}}]}]}],\"name\":\"Twitter feed\"},{\"type\":\"section\",\"props\":{\"style\":\"default\",\"width\":\"default\",\"vertical_align\":\"middle\",\"title_position\":\"top-left\",\"title_rotation\":\"left\",\"title_breakpoint\":\"xl\",\"image_position\":\"center-center\"},\"children\":[{\"type\":\"row\",\"children\":[{\"type\":\"column\",\"props\":{\"image_position\":\"center-center\",\"media_overlay_gradient\":\"\",\"width_medium\":\"1-1\",\"position_sticky_breakpoint\":\"m\"},\"children\":[]}]}],\"name\":\"Callout - 2 articles \"}],\"version\":\"2.7.22\"} --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>About We study how the planetary water cycle and climate work, and how it may change in the future, focusing on cloud physics and moist convection processes. Our tools are next-gen global atmospheric simulations, ocean-coupled climate dynamics, high-performance computing, and machine learning for turbulent process emulation and neural-network assisted inquiry. Research Themes Climate Dynamics Read [&hellip;]<\/p>\n","protected":false},"author":20,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-16","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/sites.ps.uci.edu\/pritchard\/wp-json\/wp\/v2\/pages\/16","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.ps.uci.edu\/pritchard\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.ps.uci.edu\/pritchard\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.ps.uci.edu\/pritchard\/wp-json\/wp\/v2\/users\/20"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.ps.uci.edu\/pritchard\/wp-json\/wp\/v2\/comments?post=16"}],"version-history":[{"count":420,"href":"https:\/\/sites.ps.uci.edu\/pritchard\/wp-json\/wp\/v2\/pages\/16\/revisions"}],"predecessor-version":[{"id":776,"href":"https:\/\/sites.ps.uci.edu\/pritchard\/wp-json\/wp\/v2\/pages\/16\/revisions\/776"}],"wp:attachment":[{"href":"https:\/\/sites.ps.uci.edu\/pritchard\/wp-json\/wp\/v2\/media?parent=16"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}