{"status":"ok","message-type":"work-list","message-version":"1.0.0","message":{"facets":{},"total-results":247954,"items":[{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T04:08:33Z","timestamp":1789963713024,"version":"4.0.1"},"reference-count":37,"publisher":"American Meteorological Society","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"published-print":{"date-parts":[[2013,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>A microphysical parameterization for shallow cumulus and boundary layer stratocumulus clouds has been developed. Similar to the Khairoutdinov and Kogan parameterization for stratocumulus clouds, the new parameterization is based on an explicit microphysical large-eddy simulation (LES) model as a data source and benchmark for comparison. The predictions of the bulk model using the new parameterization were tested in simulations of shallow cumulus and boundary layer stratocumulus clouds; in both cases the new parameterization matched the predictions of the explicit microphysics LES quite accurately. These results show the importance of the choice of the dataset in parameterization development and the need for it to be balanced by realistic dynamic conditions. The strong sensitivity to representation of rain evaporation is also demonstrated. Accurate formulation of this process, tuned for the case of cumulus convection, has substantially improved precision of rain production.<\/jats:p>","DOI":"10.1175\/jas-d-12-0183.1","type":"journal-article","created":{"date-parts":[[2012,11,5]],"date-time":"2012-11-05T19:52:46Z","timestamp":1352145166000},"page":"1423-1436","source":"Crossref","is-referenced-by-count":63,"title":["A Cumulus Cloud Microphysics Parameterization for Cloud-Resolving Models"],"prefix":"10.1175","volume":"70","author":[{"given":"Yefim","family":"Kogan","sequence":"first","affiliation":[{"name":"Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"12","published-online":{"date-parts":[[2013,4,23]]},"reference":[{"key":"2020061815315108700_bib1","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1175\/1520-0477(1995)076<0889:TASTE>2.0.CO;2","article-title":"The Atlantic stratocumulus transition experiment\u2014ASTEX","volume":"76","author":"Albrecht","year":"1995","journal-title":"Bull. 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However, bulk cloud microphysics schemes used in current operational weather prediction models cannot explicitly represent wet snow. This study modifies the Milbrandt\u2013Yau (MY) microphysics scheme of the Weather Research and Forecasting Model to explicitly simulate melting snow. The modification introduces an additional prognostic variable for the snow liquid mixing ratio, representing the liquid component of melting snow, along with its source and sink terms. Furthermore, the formulation of fall speed for melting snow and the assumptions regarding meltwater distribution are designed to be consistent with observations. The modified scheme was evaluated during a heavy wet snow accretion event that occurred in Hokkaido, Japan, on 22\u201323 December 2022. Two numerical simulations with a horizontal resolution of 1.667 km, using the modified and original MY schemes, respectively, were compared with observations from the Kushiro test power line in eastern Hokkaido. The results showed slightly colder surface temperatures in the modified scheme due to meltwater evaporation. Disdrometer observations indicated that the precipitation particles included melting snow with low snow liquid fractions, as well as rain and snow, which were reasonably well reproduced by the modified scheme. The analysis of source\u2013sink terms revealed that the partial melting of snowflakes along with refreezing and evaporation of meltwater are dominant processes governing the tendency equation. Sensitivity experiments further highlighted the influence of fall velocity formulation on precipitation characteristics. Overall, the modified scheme effectively represents melting snow, demonstrating its potential for improving the prediction of wet snow events.<\/jats:p>\n                  <jats:sec>\n                    <jats:title>Significance Statement<\/jats:title>\n                    <jats:p>Partially melted snow is one of the greatest threats to infrastructure during winter. This study enhances cloud modeling in numerical weather prediction models to better simulate melting snow, a key factor in forecasting such damage. A case study of heavy wet snow in northern Japan showed that the improved model successfully predicted wet snow occurrence and reproduced the observed relationship between precipitation particle size and fall speed. This advancement has the potential to improve winter weather forecasts and help reduce infrastructure risks.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1175\/mwr-d-25-0210.1","type":"journal-article","created":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T15:41:32Z","timestamp":1771602092000},"page":"505-522","source":"Crossref","is-referenced-by-count":0,"title":["Parameterization of Melting Snow for Bulk Cloud Microphysics Schemes"],"prefix":"10.1175","volume":"154","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7587-6697","authenticated-orcid":true,"given":"Yuki","family":"Kanno","sequence":"first","affiliation":[{"name":"Central Research Institute of Electric Power Industry, Abiko, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soichiro","family":"Sugimoto","sequence":"additional","affiliation":[{"name":"Central Research Institute of Electric Power Industry, Abiko, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juanzhen","family":"Sun","sequence":"additional","affiliation":[{"name":"National Center for Atmospheric Research, Boulder, Colorado"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"12","reference":[{"key":"bib1","article-title":"Wet snow","year":"2026","unstructured":"American Meteorological Society, 2026: \nWet snow. \nGlossary of Meteorology, https:\/\/glossary.ametsoc.org\/wiki\/Wet_snow."},{"key":"bib2","series-title":"Meteor. Appl.","first-page":"e2162","article-title":"Future projections of wet snow accretion and snowfall in Kanto Plain, Japan, using a large ensemble climate simulation","volume":"30","author":"Asano, Y.","year":"2023","unstructured":"Asano, Y., H. Kusaka, and M. Inatsu, 2023: \nFuture projections of wet snow accretion and snowfall in Kanto Plain, Japan, using a large ensemble climate simulation. Meteor. Appl., 30, e2162, https:\/\/doi.org\/10.1002\/met.2162."},{"key":"bib3","series-title":"Asia-Pac. J. Atmos. Sci.","first-page":"233","article-title":"Development of a single-moment cloud microphysics scheme with prognostic hail for the Weather Research and Forecasting (WRF) model","volume":"55","author":"Bae, S. Y.","year":"2019","unstructured":"Bae, S. Y., S.-Y. Hong, and W.-K. Tao, 2019: \nDevelopment of a single-moment cloud microphysics scheme with prognostic hail for the Weather Research and Forecasting (WRF) model. Asia-Pac. J. Atmos. 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Rev.","first-page":"833","article-title":"Improving high-resolution weather forecasts using the Weather Research and Forecasting (WRF) Model with an updated Kain\u2013Fritsch scheme","volume":"144","author":"Zheng, Y.","year":"2016","unstructured":"Zheng, Y., K. Alapaty, J. A. Herwehe, A. D. Del Genio, and D. Niyogi, 2016: \nImproving high-resolution weather forecasts using the Weather Research and Forecasting (WRF) Model with an updated Kain\u2013Fritsch scheme. Mon. Wea. Rev., 144, 833\u2013860, https:\/\/doi.org\/10.1175\/MWR-D-15-0005.1."}],"container-title":["Monthly Weather Review"],"link":[{"URL":"https:\/\/journals.ametsoc.org\/view\/journals\/mwre\/154\/3\/MWR-D-25-0210.1.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.ametsoc.org\/downloadpdf\/journals\/mwre\/154\/3\/MWR-D-25-0210.1.xml","content-type":"text\/html","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/journals.ametsoc.org\/downloadpdf\/journals\/mwre\/154\/3\/MWR-D-25-0210.1.xml","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T11:30:57Z","timestamp":1774006257000},"score":29.607203,"resource":{"primary":{"URL":"https:\/\/journals.ametsoc.org\/view\/journals\/mwre\/154\/3\/MWR-D-25-0210.1.xml"}},"issued":{"date-parts":[[2026,3]]},"references-count":112,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1175\/mwr-d-25-0210.1","ISSN":["0027-0644","1520-0493"],"issn-type":[{"value":"0027-0644","type":"print"},{"value":"1520-0493","type":"electronic"}],"published":{"date-parts":[[2026,3]]}},{"indexed":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T13:20:26Z","timestamp":1648646426997},"reference-count":0,"publisher":"IMPERIAL COLLEGE PRESS","content-domain":{"domain":[],"crossmark-restriction":false},"published-print":{"date-parts":[[2015,10]]},"DOI":"10.1142\/9781783266913_0021","type":"book-chapter","created":{"date-parts":[[2015,8,26]],"date-time":"2015-08-26T10:25:05Z","timestamp":1440584705000},"page":"75-112","source":"Crossref","is-referenced-by-count":0,"title":["Microphysics of convective cloud and its treatment in parameterization"],"prefix":"10.1142","author":[{"given":"V.T.J.","family":"Phillips","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J.-I.","family":"Yano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2015,8,26]]},"container-title":["Series on the Science of Climate Change","Parameterization of Atmospheric Convection"],"deposited":{"date-parts":[[2017,7,19]],"date-time":"2017-07-19T22:05:45Z","timestamp":1500501945000},"score":29.58662,"resource":{"primary":{"URL":"http:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/9781783266913_0021"}},"issued":{"date-parts":[[2015,8,26]]},"references-count":0,"alternative-id":["10.1142\/9781783266913_0021","10.1142\/SSCC","10.1142\/p1005"],"URL":"https:\/\/doi.org\/10.1142\/9781783266913_0021","ISSN":["2045-9726"],"issn-type":[{"value":"2045-9726","type":"print"}],"published":{"date-parts":[[2015,8,26]]}},{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T13:02:21Z","timestamp":1747227741551,"version":"3.40.5"},"posted":{"date-parts":[[2023,5,15]]},"group-title":"oral","reference-count":0,"publisher":"Copernicus GmbH","content-domain":{"domain":[],"crossmark-restriction":false},"abstract":"<jats:p>Machine learning approaches have been widely used for improving the representation of subgrid scale parameterizations in Earth System Models. In our study we target the Cloud Microphysics parameterization, in particular the two-moment bulk scheme of the ICON (Icosahedral Non-hydrostatic) Model.&amp;#160;&amp;#160;Cloud microphysics parameterization schemes suffer from an accuracy\/speed tradeoff. The simplest schemes, often heavy with assumptions (such as the bulk moment schemes) are most common in operational weather prediction models. Conversely, the more complex schemes with fewer assumptions &amp;#8211;e.g. Lagrangian schemes such as the super-droplet method (SDM)&amp;#8211; are computationally expensive and used only within research and development. SDM allows easy representation of complex scenarios with multiple hydrometeors and can also be used for simulating cloud-aerosol interactions. To bridge this gap and to make the use of more complex microphysical schemes feasible within operational models, we use a data-driven approach.&amp;#160;&amp;#160;Here we train a neural network to mimic the behavior of SDM simulations in a warm-rain scenario in a dimensionless control volume. The network behaves like a dynamical system that converts cloud droplets to rain droplets&amp;#8211;represented as bulk moments&amp;#8211;with only the current system state as the input. We use a multi-step training loss to stabilize the network over long integration periods, especially in cases with extremely low cloud water to start with. We find that the network is stable across various initial conditions and in many cases, emulates the SDM simulations better than the traditional bulk moment schemes. Our network also performs better than any previous ML-based attempts to learn from SDM. This opens the possibility of using the trained network as a proxy for imitating the computationally expensive SDM within operational weather prediction models with minimum computational overhead.&amp;#160;<\/jats:p>","DOI":"10.5194\/egusphere-egu23-5149","type":"posted-content","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T17:46:35Z","timestamp":1677087995000},"source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning Parameterization for Super-droplet Cloud Microphysics Scheme"],"prefix":"10.5194","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6973-5660","authenticated-orcid":false,"given":"Shivani","family":"Sharma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Greenberg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"3145","deposited":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T21:37:02Z","timestamp":1684186622000},"score":29.462103,"resource":{"primary":{"URL":"https:\/\/meetingorganizer.copernicus.org\/EGU23\/EGU23-5149.html"}},"issued":{"date-parts":[[2023,5,15]]},"references-count":0,"URL":"https:\/\/doi.org\/10.5194\/egusphere-egu23-5149","published":{"date-parts":[[2023,5,15]]},"subtype":"other"},{"indexed":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T21:07:52Z","timestamp":1764364072612},"reference-count":32,"publisher":"American Meteorological Society","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"published-print":{"date-parts":[[2012,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>This paper describes a microphysics parameterization based on integral moments of the full drop size distributions (DSDs) as opposed to a partial moments approach (sometimes referred to as Kessler-type parameterization) based on the moments integrated separately over the cloud and rain drop portion of the drop spectrum. This approach does not assume a prescribed form of a DSD but employs as model variables full moments that have clear physical meaning: drop concentration and surface area, water content, precipitation flux, and radar reflectivity. These variables can be directly measured and assimilated into the model forecast cycle without intermediate retrievals. The approach avoids division of DSDs into cloud and rain drops. This eliminates the problem of defining the threshold between these two categories and subdivision of the physical coagulation process into artificial processes of autoconversion, accretion, and self-collection. The development and testing of the parameterization was made using the Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) large-eddy simulation (LES) explicit warm rain microphysical model. The conversion and sedimentation rates were parameterized in the form of a product of power functions using nonlinear regression analysis to determine exponents of the approximated expressions. The comparison of bulk and explicit microphysics models demonstrated reasonably good prediction of both thermodynamic and microphysical parameters of the stratocumulus-topped boundary layer (STBL). The weaknesses and problems of the numerical implementation of the full moment approach are also discussed.<\/jats:p>","DOI":"10.1175\/jas-d-11-0268.1","type":"journal-article","created":{"date-parts":[[2012,3,12]],"date-time":"2012-03-12T19:10:15Z","timestamp":1331579415000},"page":"2229-2242","source":"Crossref","is-referenced-by-count":18,"title":["Parameterization of Cloud Microphysics Based on Full Integral Moments"],"prefix":"10.1175","volume":"69","author":[{"given":"Yefim L.","family":"Kogan","sequence":"first","affiliation":[{"name":"Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexei","family":"Belochitski","sequence":"additional","affiliation":[{"name":"Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"12","published-online":{"date-parts":[[2012,7,2]]},"reference":[{"key":"2020061201475966000_bib1","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1175\/1520-0477(1995)076<0889:TASTE>2.0.CO;2","article-title":"The Atlantic Stratocumulus Transition Experiment\u2014ASTEX","volume":"76","author":"Albrecht","year":"1995","journal-title":"Bull. Amer. Meteor. Soc."},{"key":"2020061201475966000_bib2","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/0169-8095(94)90020-5","article-title":"A parameterization of warm cloud microphysical conversion processes","volume":"33","author":"Beheng","year":"1994","journal-title":"Atmos. Res."},{"key":"2020061201475966000_bib3","doi-asserted-by":"crossref","first-page":"1825","DOI":"10.1175\/1520-0469(1974)031<1825:AAOCDG>2.0.CO;2","article-title":"An analysis of cloud drop growth by collection: Part II. Single initial distributions","volume":"31","author":"Berry","year":"1974","journal-title":"J. Atmos. Sci."},{"key":"2020061201475966000_bib5","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1175\/1520-0469(1974)031<0142:ASICPP>2.0.CO;2","article-title":"A study in cloud phase parameterization using the gamma distribution","volume":"31","author":"Clark","year":"1974","journal-title":"J. Atmos. 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Part I: Description","volume":"51","author":"Ferrier","year":"1994","journal-title":"J. Atmos. Sci."},{"key":"2020061201475966000_bib9","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1175\/1520-0469(1995)052<1001:ADMMPF>2.0.CO;2","article-title":"A double-moment multiple-phase four-class bulk ice scheme. Part II: Simulations of convective storms in different large-scale environments and comparisons with other bulk parameterizations","volume":"52","author":"Ferrier","year":"1995","journal-title":"J. Atmos. Sci."},{"key":"2020061201475966000_bib10","first-page":"482","article-title":"The solution of the coagulation equation for cloud droplets in a rising air current","volume":"5","author":"Golovin","year":"1963","journal-title":"Bull. Acad. Sci. SSSR Geophys. Ser."},{"key":"2020061201475966000_bib11","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1016\/0009-2509(64)85047-8","article-title":"Some problems in particle technology: A statistical mechanical formulation","volume":"19","author":"Hulburt","year":"1964","journal-title":"Chem. Eng. Sci."},{"key":"2020061201475966000_bib12","author":"Kessler","year":"1969"},{"key":"2020061201475966000_bib13","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1175\/1520-0469(1999)056<2115:ALESMW>2.0.CO;2","article-title":"A large eddy simulation model with explicit microphysics: Validation against aircraft observations of a stratocumulus-topped boundary layer","volume":"56","author":"Khairoutdinov","year":"1999","journal-title":"J. Atmos. 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Sci."},{"key":"2020061201475966000_bib16","doi-asserted-by":"crossref","first-page":"2923","DOI":"10.1175\/1520-0469(1995)052<2923:MOSCLI>2.0.CO;2","article-title":"Modeling of stratocumulus cloud layers in a large eddy simulation model with explicit microphysics","volume":"52","author":"Kogan","year":"1995","journal-title":"J. Atmos. Sci."},{"key":"2020061201475966000_bib17","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1175\/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2","article-title":"Bulk parameterization of the snow field in a cloud model","volume":"22","author":"Lin","year":"1983","journal-title":"J. Climate Appl. Meteor."},{"key":"2020061201475966000_bib18","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.1175\/1520-0469(2004)061<1539:POTAPI>2.0.CO;2","article-title":"Parameterization of the autoconversion process. Part I: Analytical formulation of the Kessler-type parameterizations","volume":"61","author":"Liu","year":"2004","journal-title":"J. Atmos. Sci."},{"key":"2020061201475966000_bib19","doi-asserted-by":"crossref","first-page":"L06121","DOI":"10.1029\/2003GL019117","article-title":"An analytical expression for predicting the critical radius in the autoconversion parameterization","volume":"31","author":"Liu","year":"2004","journal-title":"Geophys. Res. Lett."},{"key":"2020061201475966000_bib20","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.1175\/JAS3675.1","article-title":"Parameterization of the autoconversion process. Part II: Generalization of Sundqvist-type parameterizations","volume":"63","author":"Liu","year":"2006","journal-title":"J. Atmos. Sci."},{"key":"2020061201475966000_bib21","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1175\/1520-0469(1948)005<0165:TDORWS>2.0.CO;2","article-title":"The distribution of rain drops with size","volume":"5","author":"Marshall","year":"1948","journal-title":"J. Meteor."},{"key":"2020061201475966000_bib22","doi-asserted-by":"crossref","first-page":"3051","DOI":"10.1175\/JAS3534.1","article-title":"A multimoment bulk microphysics parameterization. Part I: Analysis of the role of the spectral shape parameter","volume":"62","author":"Milbrandt","year":"2005","journal-title":"J. Atmos. Sci."},{"key":"2020061201475966000_bib23","doi-asserted-by":"crossref","first-page":"3065","DOI":"10.1175\/JAS3535.1","article-title":"A multimoment bulk microphysics parameterization. Part II: A proposed three-moment closure and scheme description","volume":"62","author":"Milbrandt","year":"2005","journal-title":"J. Atmos. Sci."},{"key":"2020061201475966000_bib24","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1175\/JAS3446.1","article-title":"A new double-moment microphysics parameterization for application in cloud and climate models. Part I: Description","volume":"62","author":"Morrison","year":"2005","journal-title":"J. Atmos. Sci."},{"key":"2020061201475966000_bib25","author":"Rogers","year":"1989"},{"key":"2020061201475966000_bib26","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1175\/1520-0469(1968)025<0054:ASOCDC>2.0.CO;2","article-title":"Analytic studies of cloud droplet coalescence I","volume":"25","author":"Scott","year":"1968","journal-title":"J. Atmos. Sci."},{"key":"2020061201475966000_bib27","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/S0169-8095(01)00126-0","article-title":"A double-moment parameterization for simulating autoconversion, accretion and self-collection","volume":"59-60","author":"Seifert","year":"2001","journal-title":"Atmos. Res."},{"key":"2020061201475966000_bib28","doi-asserted-by":"crossref","first-page":"4206","DOI":"10.1175\/JAS3620.1","article-title":"A microphysical bulk formulation based on scaling normalization of the particle size distribution. Part I: Description","volume":"62","author":"Szyrmer","year":"2005","journal-title":"J. Atmos. Sci."},{"key":"2020061201475966000_bib29","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1175\/1520-0450(1980)019<1037:ANIOSF>2.0.CO;2","article-title":"A numerical investigation of several factors contributing to the observed variable intensity of deep convection of south Florida","volume":"19","author":"Tripoli","year":"1980","journal-title":"J. Appl. Meteor."},{"key":"2020061201475966000_bib30","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/BF01993560","article-title":"The nuclei of natural cloud formation. Part II: The supersaturation in natural clouds and the variation of cloud droplet concentrations","volume":"43","author":"Twomey","year":"1959","journal-title":"Geophys. Pura Appl."},{"key":"2020061201475966000_bib31","doi-asserted-by":"crossref","first-page":"3034","DOI":"10.1175\/JAS3530.1","article-title":"Drizzle in stratiform boundary layer clouds. Part II: Microphysical aspects","volume":"62","author":"Wood","year":"2005","journal-title":"J. Atmos. Sci."},{"key":"2020061201475966000_bib32","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0169-8095(02)00071-6","article-title":"Autoconversion rate bias in stratiform boundary layer cloud parameterizations","volume":"65","author":"Wood","year":"2002","journal-title":"Atmos. Res."}],"container-title":["Journal of the Atmospheric Sciences"],"language":"en","link":[{"URL":"http:\/\/journals.ametsoc.org\/jas\/article-pdf\/69\/7\/2229\/3627915\/jas-d-11-0268_1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/journals.ametsoc.org\/jas\/article-pdf\/69\/7\/2229\/3627915\/jas-d-11-0268_1.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,7]],"date-time":"2020-12-07T20:55:15Z","timestamp":1607374515000},"score":29.337215,"resource":{"primary":{"URL":"https:\/\/journals.ametsoc.org\/doi\/10.1175\/JAS-D-11-0268.1"}},"issued":{"date-parts":[[2012,7,1]]},"references-count":32,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2012,7,1]]}},"URL":"https:\/\/doi.org\/10.1175\/jas-d-11-0268.1","ISSN":["0022-4928","1520-0469"],"issn-type":[{"value":"0022-4928","type":"print"},{"value":"1520-0469","type":"electronic"}],"published":{"date-parts":[[2012,7,1]]}},{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T19:13:49Z","timestamp":1789758829041,"version":"4.0.1"},"reference-count":34,"publisher":"Cambridge University Press (CUP)","license":[{"start":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T00:00:00Z","timestamp":1756252800000},"content-version":"unspecified","delay-in-days":238,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Environ. Data Science"],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    We developed a cloud microphysics parameterization for the icosahedral nonhydrostatic modeling framework (ICON) model based on physics-informed machine learning (ML). By training our ML model on high-resolution simulation data, we enhance the representation of cloud microphysics in Earth system models (ESMs) compared to traditional parameterization schemes, in particular by considering the influence of high-resolution dynamics that are not resolved in coarse ESMs. We run a global, kilometer-scale ICON simulation with a one-moment cloud microphysics scheme, the complex graupel scheme, to generate 12\u00a0days of training data. Our ML approach combines a microphysics trigger classifier and a regression model. The microphysics trigger classifier identifies the grid cells where changes due to the cloud microphysical parameterization are expected. In those, the workflow continues by calling the regression model and additionally includes physical constraints for mass positivity and water mass conservation to ensure physical consistency. The microphysics trigger classifier achieves an F1 score of 0.93 on classifying unseen grid cells. The regression model reaches an\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" mime-subtype=\"png\" xlink:href=\"S2634460225100162_inline90001.png\"\/>\n                        <jats:tex-math>$ {R}^2 $<\/jats:tex-math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    score of 0.72 averaged over all seven microphysical tendencies on simulated days used for validation only. This results in a combined offline performance of 0.78. Using explainability techniques, we explored the correlations between input and output features, finding a strong alignment with the graupel scheme and, hence, physical understanding of cloud microphysical processes. 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The autoconversion and accretion rates, A and C, respectively, are calculated directly by capturing the moment of the conversion of individual Lagrangian droplets from cloud droplets to raindrops, and it results in the reproduction of the formulas of A and C for the first time. Comparison with various parameterizations reveals the closest agreement with Tripoli and Cotton, such as and , where and are the mixing ratio and the number concentration of cloud droplets, is the mixing ratio of raindrops, is the threshold volume radius, and H is the Heaviside function. Furthermore, it is found that increases linearly with the dissipation rate and the standard deviation of radius and that decreases rapidly with while disappearing at &amp;gt; 3.5 \u03bcm. The LCM also reveals that and increase with time during the period of autoconversion, which helps to suppress the early precipitation by reducing A with smaller and larger in the initial stage. Finally, is found to be affected by the accumulated collisional growth, which determines the drop size distribution.<\/jats:p>","DOI":"10.1175\/jas-d-18-0080.1","type":"journal-article","created":{"date-parts":[[2018,10,2]],"date-time":"2018-10-02T20:07:17Z","timestamp":1538510837000},"page":"4031-4047","source":"Crossref","is-referenced-by-count":16,"title":["A Cloud Microphysics Parameterization for Shallow Cumulus Clouds Based on Lagrangian Cloud Model Simulations"],"prefix":"10.1175","volume":"75","author":[{"given":"Yign","family":"Noh","sequence":"first","affiliation":[{"name":"Department of Atmospheric Sciences, Yonsei University, Seoul, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donggun","family":"Oh","sequence":"first","affiliation":[{"name":"Department of Atmospheric Sciences, Yonsei University, Seoul, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabian","family":"Hoffmann","sequence":"additional","affiliation":[{"name":"Institute of Meteorology and Climatology, Leibniz Universit\u00e4t Hannover, Hannover, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siegfried","family":"Raasch","sequence":"additional","affiliation":[{"name":"Institute of Meteorology and Climatology, Leibniz Universit\u00e4t Hannover, Hannover, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"12","published-online":{"date-parts":[[2018,10,31]]},"reference":[{"key":"2020062008294491300_bib1","doi-asserted-by":"crossref","first-page":"D22214","DOI":"10.1029\/2010JD014248","article-title":"Cloud\u2013aerosol interactions for boundary layer stratocumulus in the Lagrangian cloud model","volume":"115","author":"Andrejczuk","year":"2010","journal-title":"J. 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All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"106171"},{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T02:27:45Z","timestamp":1791340065598,"version":"4.3.1"},"reference-count":336,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T00:00:00Z","timestamp":1680566400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T00:00:00Z","timestamp":1680566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Adv. Atmos. Sci."],"published-print":{"date-parts":[[2023,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Cloud microphysical processes occur at the smallest end of scales among cloud-related processes and thus must be parameterized not only in large-scale global circulation models (GCMs) but also in various higher-resolution limited-area models such as cloud-resolving models (CRMs) and large-eddy simulation (LES) models. Instead of giving a comprehensive review of existing microphysical parameterizations that have been developed over the years, this study concentrates purposely on several topics that we believe are understudied but hold great potential for further advancing bulk microphysics parameterizations: multi-moment bulk microphysics parameterizations and the role of the spectral shape of hydrometeor size distributions; discrete vs \u201ccontinuous\u201d representation of hydrometeor types; turbulence-microphysics interactions including turbulent entrainment-mixing processes and stochastic condensation; theoretical foundations for the mathematical expressions used to describe hydrometeor size distributions and hydrometeor morphology; and approaches for developing bulk microphysics parameterizations. Also presented are the spectral bin scheme and particle-based scheme (especially, super-droplet method) for representing explicit microphysics. Their advantages and disadvantages are elucidated for constructing cloud models with detailed microphysics that are essential to developing processes understanding and bulk microphysics parameterizations. Particle-resolved direct numerical simulation (DNS) models are described as an emerging technique to investigate turbulence-microphysics interactions at the most fundamental level by tracking individual particles and resolving the smallest turbulent eddies in turbulent clouds. Outstanding challenges and future research directions are explored as well.<\/jats:p>","DOI":"10.1007\/s00376-022-2077-3","type":"journal-article","created":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T12:23:43Z","timestamp":1680611023000},"page":"747-790","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Parameterization and Explicit Modeling of Cloud Microphysics: Approaches, Challenges, and Future Directions"],"prefix":"10.1007","volume":"40","author":[{"given":"Yangang","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Man-Kong","family":"Yau","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shin-ichiro","family":"Shima","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunsong","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sisi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,4,4]]},"reference":[{"key":"2077_CR1","doi-asserted-by":"publisher","first-page":"3365","DOI":"10.1175\/JAS-D-18-0078.1","volume":"75","author":"G C Abade","year":"2018","unstructured":"Abade, G. 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Meteor"},{"key":"ref65","doi-asserted-by":"crossref","first-page":"1648","DOI":"10.1175\/1520-0469(1984)041<1648:FFTSOD>2.0.CO;2","article-title":"Functional fits to some observed drop size distributions and parameterization of rain","volume":"41","author":"P T Willis","year":"1984","journal-title":"Journal of Atmospheric Sciences"},{"key":"ref66","author":"P T Willis","year":"2021","journal-title":"Rain Drop Size Distributions and Radar Rain Measurements in South Florida"},{"key":"ref67","doi-asserted-by":"crossref","first-page":"1755","DOI":"10.5194\/amt-9-1755-2016","article-title":"Measuring droplet fall speed with a high-speed camera: indoor accuracy and potential outdoor applications","volume":"9","author":"C.-K Yu","year":"2016","journal-title":"Atmospheric Measurement Techniques"}],"container-title":["SSRN Electronic Journal"],"language":"en","deposited":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T10:29:46Z","timestamp":1679048986000},"score":25.64225,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=3980349"}},"issued":{"date-parts":[[2021]]},"references-count":67,"URL":"https:\/\/doi.org\/10.2139\/ssrn.3980349","ISSN":["1556-5068"],"issn-type":[{"value":"1556-5068","type":"electronic"}],"published-other":{"date-parts":[[2021]]},"published":{"date-parts":[[2021]]}},{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T06:03:27Z","timestamp":1780034607218,"version":"3.53.1"},"posted":{"date-parts":[[2025,10,9]]},"group-title":"Clouds and Precipitation\/Atmospheric Modelling and Data Analysis\/Troposphere\/Physics (physical properties and processes)","reference-count":0,"publisher":"Copernicus GmbH","license":[{"start":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:00:00Z","timestamp":1759968000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001862","name":"Svenska Forskningsr\u00e5det Formas","doi-asserted-by":"publisher","award":["2018-01795"],"award-info":[{"award-number":["2018-01795"]}],"id":[{"id":"10.13039\/501100001862","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001862","name":"Svenska Forskningsr\u00e5det Formas","doi-asserted-by":"publisher","award":["2021-01463"],"award-info":[{"award-number":["2021-01463"]}],"id":[{"id":"10.13039\/501100001862","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001858","name":"VINNOVA","doi-asserted-by":"publisher","award":["2020-03406"],"award-info":[{"award-number":["2020-03406"]}],"id":[{"id":"10.13039\/501100001858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"abstract":"<jats:p>Abstract. Large-scale stratiform clouds are widespread and dominate the Earth's radiation budget. Their radiative and microphysical properties are inseparable, depending on ambient aerosol conditions and on properties of any convective outflow. In the Community Atmospheric Model, version 6 (CAM6), large-scale clouds were originally treated two decades ago with a two-moment bulk microphysics approach. Since then, the technological and empirical basis of global models has improved, for example by representing cloud microphysics to encompass extra processes of ice and droplet initiation, including dependencies on aerosol conditions of size, composition, and loading. To advance the microphysical realism of the large-scale cloud scheme of the global model CAM6, most of the known mechanisms of secondary ice production (SIP) and an empirical formulation for heterogeneous ice nucleation have been represented in the stratiform scheme of the Global model CAM6. We included a hybrid bin\/bulk scheme that treats aerosol activation, growth processes of accretion, aggregation, and riming, and three SIP mechanisms in the stratiform cloud scheme. We simulated an observed case of a mesoscale convective system during the Mid-latitude Continental Convective Clouds Experiment (MC3E) in Oklahoma, USA, using the Single-Column Atmosphere Model (SCAM6). The results from the simulations are validated against the aircraft, satellite, and ground measurements. Results show that the modified stratiform scheme can predict the cloud properties of the observed stratiform clouds realistically. Together with our improved convective scheme in CAM6, this paves the way for more realism in the treatment of aerosol cloud interaction in global climate change by conventional climate Models.<\/jats:p>","DOI":"10.5194\/egusphere-2025-4740","type":"posted-content","created":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T07:56:36Z","timestamp":1759996596000},"source":"Crossref","is-referenced-by-count":0,"title":["A modified parameterization of stratiform cloud microphysics for the Community Earth System Model"],"prefix":"10.5194","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6520-5937","authenticated-orcid":false,"given":"Chandra Shekhar","family":"Pant","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8640-648X","authenticated-orcid":false,"given":"Deepak","family":"Waman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sachin","family":"Patade","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7334-446X","authenticated-orcid":false,"given":"Akash","family":"Deshmukh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3768-6718","authenticated-orcid":false,"given":"Vaughan","family":"Phillips","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"3145","deposited":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T05:45:53Z","timestamp":1780033553000},"score":25.64225,"resource":{"primary":{"URL":"https:\/\/egusphere.copernicus.org\/preprints\/2025\/egusphere-2025-4740\/"}},"issued":{"date-parts":[[2025,10,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.5194\/egusphere-2025-4740","relation":{"has-comment":[{"id-type":"doi","id":"10.5194\/egusphere-2025-4740-AC2","asserted-by":"subject"},{"id-type":"doi","id":"10.5194\/egusphere-2025-4740-AC3","asserted-by":"subject"},{"id-type":"doi","id":"10.5194\/egusphere-2025-4740-AC1","asserted-by":"subject"}],"has-review":[{"id-type":"doi","id":"10.5194\/egusphere-2025-4740-RC1","asserted-by":"subject"},{"id-type":"doi","id":"10.5194\/egusphere-2025-4740-RC2","asserted-by":"subject"},{"id-type":"doi","id":"10.5194\/egusphere-2025-4740-AC3","asserted-by":"object"},{"id-type":"doi","id":"10.5194\/egusphere-2025-4740-AC2","asserted-by":"object"},{"id-type":"doi","id":"10.5194\/egusphere-2025-4740-AC1","asserted-by":"object"},{"id-type":"doi","id":"10.5194\/egusphere-2025-4740-RC2","asserted-by":"object"},{"id-type":"doi","id":"10.5194\/egusphere-2025-4740-RC1","asserted-by":"object"}],"is-preprint-of":[{"id-type":"doi","id":"10.5194\/acp-26-7407-2026","asserted-by":"subject"},{"id-type":"doi","id":"10.5194\/acp-26-7407-2026","asserted-by":"object"}]},"published":{"date-parts":[[2025,10,9]]},"subtype":"preprint"},{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T01:18:50Z","timestamp":1780449530850,"version":"3.54.1"},"reference-count":117,"publisher":"American Meteorological Society","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.ametsoc.org\/PUBSReuseLicenses"}],"funder":[{"name":"Svenska Forskningsr\u00e5det Formas","award":["2018-01795"],"award-info":[{"award-number":["2018-01795"]}]},{"DOI":"10.13039\/501100001858","name":"VINNOVA","doi-asserted-by":"crossref","award":["2020-03406"],"award-info":[{"award-number":["2020-03406"]}],"id":[{"id":"10.13039\/501100001858","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"crossref","award":["DE-SC0018932"],"award-info":[{"award-number":["DE-SC0018932"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"crossref","award":["DESC0018967"],"award-info":[{"award-number":["DESC0018967"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n<jats:p>A new microphysical treatment that includes aerosol\u2013cloud interactions and secondary ice production (SIP) mechanisms is implemented in the convection scheme of the Community Atmosphere Model, version 6 (CAM6). The approach is to embed a 1D Lagrangian parcel model in the bulk convective plume of the existing deep convection parameterization. Aerosol activation, growth processes including collision\/coalescence, and three processes of SIP mechanisms, two of which are normally overlooked in atmospheric models, are represented in this embedded parcel model. These microphysical processes are treated with a hybrid bin\/bulk scheme and a high spatial and temporal resolution for the integration of the embedded parcel in 1D, allowing vertical velocity to determine the microphysical evolution following the in-cloud motion during ascent. Simulations of an observed case (Midlatitude Continental Convective Clouds Experiment) of a mesoscale convective system in Oklahoma, United States, with a single-column model (SCAM) version of CAM, are compared with aircraft in situ and ground-based observations of microphysical properties from the convection and precipitation. Results from the validation show the new microphysical scheme has a good representation of the ice initiation in the bulk convective plume, including the known and empirically quantified pathways of primary and secondary initiation, with benefits for the accuracy of properties of its supercooled cloud liquid. The sensitivity simulations and use of tagging tracers for the validated simulation confirm that the newly included SIP mechanisms are of paramount importance for convective microphysics and can be successfully treated in the global model.<\/jats:p>","DOI":"10.1175\/jas-d-23-0175.1","type":"journal-article","created":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T15:54:34Z","timestamp":1734105274000},"page":"197-231","source":"Crossref","is-referenced-by-count":1,"title":["An Improved Convection Parameterization with Detailed Aerosol\u2013Cloud Microphysics for a Global Model"],"prefix":"10.1175","volume":"82","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6384-5957","authenticated-orcid":true,"given":"Arti","family":"Jadav","sequence":"first","affiliation":[{"name":"Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deepak","family":"Waman","sequence":"additional","affiliation":[{"name":"Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden"},{"name":"Department of Troposphere, Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chandra Shekhar","family":"Pant","sequence":"additional","affiliation":[{"name":"Department of Hydro and Renewable Energy, Indian Institute of Technology Roorkee, Roorkee, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sachin","family":"Patade","sequence":"additional","affiliation":[{"name":"Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martanda","family":"Gautam","sequence":"additional","affiliation":[{"name":"Johannes Gutenberg University, Mainz, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vaughan","family":"Phillips","sequence":"additional","affiliation":[{"name":"Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aaron","family":"Bansemer","sequence":"additional","affiliation":[{"name":"Dynamical and Physical Meteorology Section, National Center for Atmospheric Research, Boulder, Colorado"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donifan","family":"Barahona","sequence":"additional","affiliation":[{"name":"Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Trude","family":"Storelmov","sequence":"additional","affiliation":[{"name":"Department of Geosciences, University of Oslo, Oslo, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"12","reference":[{"key":"bib1","series-title":"Mon. 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