[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121339-en":3,"doc-seo-121339-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121339,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine learning for stochastic parametrization - Position Paper","Atmospheric models for weather and climate prediction have traditionally treated subgrid-scale forcing in a deterministic way, which introduces major forecast error because scale separation is limited in the real atmosphere. Stochastic approaches have emerged to represent uncertainty in small-scale processes and are now used across multiple forecasting timescales. Separately, machine learning is increasingly used to replace parametrization schemes and can enhance both speed and fidelity. This position paper connects stochastic methods with ML, surveys early studies, and identifies remaining challenges for data-driven stochastic parametrization.","Environmental Data Science (2024), 3: e38, 1–12  \ndoi:10.1017/eds.2024.45  \nPOSITION PAPER  \nMachine learning for stochastic parametrization  \nHannah M. Christensen 1 , Salah Kouhen 1 , Greta Miller1  and Raghul Parthipan2,3   \n1Department of Physics, University of Oxford, Oxford, UK  \n2Department of Computer Science, University of Cambridge, Cambridge, UK 3British Antarctic Survey, Cambridge, UK  \nCorresponding author: Hannah M. Christensen; [Email: hannah.christensen@physics.ox.ac.uk](Email: hannah.christensen@physics.ox.ac.uk)  \nReceived: 24 June 2024; Accepted: 28 September 2024  \nKeywords: machine learning; stochastic parametrization; uncertainty quantification; model error  \nAbstract  \nAtmospheric models used for weather and climate prediction are traditionally formulated ina deterministic manner. In other words, given a particular state of the resolved scale variables, the most likely forcing from the subgrid scale processes is estimated and used to predict the evolution of the large-scale flow. However, the lack of scale separation in the atmosphere means that this approach is a large source of error in forecasts. Over recent years, an alternative paradigm has developed: the use of stochastic techniques to characterize uncertainty in small-scale processes. These techniques are now widely used across weather, subseasonal, seasonal, and climate timescales. In parallel, recent years have also seen significant progress in replacing parametrization schemes using machine learning(ML) . This has the potential to both speed up and improve our numerical models. However, the focus to date has largely been on deterministic approaches. In this position paper, we bring together these two key developments and discuss the potential for data-driven approaches for stochastic parametrization. We highlight early studies in this area and draw attention to the novel challenges that remain.  \nImpact Statement  \nWeather and climate predictions are relied on by users from industry, charities, governments, and the general public. The largest source of uncertainty in these predictions arises from approximations made when building the computer model used to make them. In particular, the representation of small-scale processes such as clouds and thunderstorms is a large source of uncertainty because of their complexity and their unpredictability. Machine learning (ML) approaches, trained to mimic high-quality datasets, present an unparalleled opportunity to improve the representation of these small-scale processes in models. However, it is important to account for the unpredictability of these processes while doing so. In this article, we demonstrate the untapped potential of such probabilistic ML approaches for improving weather and climate prediction.  \n1. Introduction  \nWeather and climate models exhibit long-standing biases in mean state, modes of variability, and the representation of extremes. These biases hinder progress across the World Climate Research Programme grand challenges. Understanding and reducing these biases is a key focus for the research community.  \nAt the heart of weather and climate models are the physical equations of motion, which describe the atmosphere and ocean systems. To predict the evolution of the climate system, these equations are discretized in space and time. The resolution ranges from order 100 km and 30–60 minutes in a typical  \n©The Author(s), 2024. Published by Cambridge University Press. This is an Open Access article, distributed under the terms ofthe Creative Commons Attribution licence ([http://creativecommons.org/licenses/by/4.0](http://creativecommons.org/licenses/by/4.0)), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.  \n[https://doi.org/10.1017/eds.2024.45](https://doi.org/10.1017/eds.2024.45) Published online by Cambridge University Press  \ne38-2 Hannah M. Christensen et al.  \nclimate model, through 10 km and 5–10 minutes in glob","cbCaiaX5cDmwjjxd","https://ap.wps.com/l/cbCaiaX5cDmwjjxd","pdf",2158680,1,12,"English","en",105,"# Introduction\n## Motivation and biases in weather and climate models\n## Parametrization schemes and sources of uncertainty\n# Machine learning emulation of parametrizations\n## Speed and accuracy improvements","[{\"question\":\"为什么传统确定性气象与气候模型会带来较大误差？\",\"answer\":\"由于大气中缺乏理想的尺度分离，传统做法在已解析尺度状态给定时对次网格过程进行确定性估计，无法充分刻画小尺度的不确定性，从而导致预测误差。\"},{\"question\":\"文中提出的核心研究方向是什么？\",\"answer\":\"将随机（stochastic）刻画不确定性的技术与机器学习替代/改进参数化（parametrization）方案结合，讨论数据驱动的随机参数化潜力，并总结早期研究与仍待解决的挑战。\"},{\"question\":\"机器学习用于参数化有哪些潜在收益？\",\"answer\":\"机器学习可在尽量降低计算成本的同时提高数值模型表示能力：既能对现有参数化进行模拟以获得速度提升，也能通过高保真数据训练增强精度，同时还需要关注对小尺度不可预测性的概率刻画。\"}]","Machine learning for stochastic parametrization - Position Paper | PDF",1785735140,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-for-stochastic-parametrization-position-paper","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-for-stochastic-parametrization-position-paper/121339/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"为什么传统确定性气象与气候模型会带来较大误差？","Question",{"text":75,"@type":76},"由于大气中缺乏理想的尺度分离，传统做法在已解析尺度状态给定时对次网格过程进行确定性估计，无法充分刻画小尺度的不确定性，从而导致预测误差。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"文中提出的核心研究方向是什么？",{"text":80,"@type":76},"将随机（stochastic）刻画不确定性的技术与机器学习替代/改进参数化（parametrization）方案结合，讨论数据驱动的随机参数化潜力，并总结早期研究与仍待解决的挑战。",{"name":82,"@type":73,"acceptedAnswer":83},"机器学习用于参数化有哪些潜在收益？",{"text":84,"@type":76},"机器学习可在尽量降低计算成本的同时提高数值模型表示能力：既能对现有参数化进行模拟以获得速度提升，也能通过高保真数据训练增强精度，同时还需要关注对小尺度不可预测性的概率刻画。","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]