innovative MachIne leaRning to constrain Aerosol-cloud CLimate Impacts (iMIRACLI)

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

iMIRACLI special session at EGU 2021
Presentations at workshops, conferences, interdisci-plinary networking events
General public outreach and dissemination
Appointment of management team
International iMIRACLI workshop on machine learn-ing for climate science in Oxford
Establishment of boards and committees, inc. the Supervisory Board of the Network
iMIRACLI summer school 2 in Valencia
iMIRACLI kick off and summer school 1 at Oxford
Coordinated pan-European iMIRACLI outreach events coinciding with MSCA European Researchers' Nights

Coordinated pan-European iMIRACLI outreach events coinciding with MSCA European Researchers’ Nights

iMIRACLI summer school 3 in Stockholm
Bi-annual meetings of boards & circulation of minutes

Synopsis of causal attribution for cloud changes

A publication or report jointly written by all contributors to WP2, including the machine-learning-centred ESRs, that discusses and quantifies which aspects of cloud/precipitation changes are attributable to aerosol perturbations, and how these can be identified (lead: SU, contribu-tors: UOXF, ULEI, UCL, DLR).

Causal discovery in the presence of multiple time scales

A publication or PhD thesis chapter introducing a novel causal inference technique for time series with interdependencies across multiple time scales (lead: DLR, contributors: UVEG, ETHZ).

Synopsis of aerosol effects on climate

A publication or report jointly written by all contributors to WP3, that summarizes detectable aspects of 20th century climate change that are attributable to aerosol emissions, including progress from machine learning techniques (lead: UEDIN, contributors: SU, ETHZ, DLR, UVEG, EPFL).

Impact of sampling bias on detection/attribution

A publication or PhD thesis chapter explaining the impact of the aerosol-precipitation sampling bias on observed aerosol-precipitation rela-tions (lead: ETHZ, task 3.3)

Synopsis of aerosol-cloud effect detection

A publication or report jointly written by all contributors to WP1, that summarizes the possibility to detect an aerosol-cloud interaction signal in observations (lead: ULEI, contributors: UCL, ETHZ, SU).

Volcanic signal in cirrus

A publication or PhD thesis chapter on the possibility to detect a significant perturbation of cirrus after a volcanic eruption (lead: ETHZ, task 1.2)

Machine learning challenges for noisy and heterogeneous climate datasets

A publication or report written by the contributors to WP4 and WP1 targeting a machine learning audience to raise awareness about the par-ticular (deep) machine learning challenges of climate datasets. (lead: UCL, contributors: ULEI, SU).

Aerosol effects on cloud fraction

A publication or PhD thesis chapter explaining causal effects of aerosol perturbations on cloud changes (lead: UOXF, task 2.1)

Quarterly iMIRACLI newsletters
Complexity reduction using δ-MAPS

A publication or PhD thesis chapter explaining the reduction of highly complex and multidimentional global climate data into vastly simpli-fied dynamic network representation (lead: EPFL, task 3.2)

Deep learning for inference and prediction in multimodal climate data

A publication or PhD thesis chapter reporting about novel deep learning techniques for inference and prediction for noisy multimodal climate datasets (lead: UCL, contributors: ULEI)

Physics-aware and explainable machine learning for satellite retrievals

A publication and PhD thesis chapter introducing a novel ML approach for parameter retrievals that respect physics laws and attains ex-plainable models (lead: UVEG, contributors: ETHZ)

Aerosol-cloud fingerprints in radiances

A publication or PhD thesis chapter reporting about the detectability of aerosol-cloud interaction processes in satellite-observations space (lead: ULEI, task 1.1)

Publications in scientific journals, including iMIRACLI special issue

Publications in scientific journals, including iMIRA-CLI special issue

Aerosol effects on 20th century climate

A publication or PhD thesis chapter reporting about the signals in 20th and early 21rst century temperature and precipitation evolution at-tributable to anthropogenic aerosol (lead: UEDIN, task 3.1)

Cloud effects on aerosol

A publication or PhD thesis chapter summarizing the net effect of cloud sources/sinks on aerosol concentrations (lead: SU, task 1.3)

Locally adaptive predictive modelling for spatio-temporal climate datasets

A publication or PhD thesis chapter introducing a novel approach for spatio-temporal modelling (lead: UOXF, contributors: MetOffice, Am-azon).

Aerosol influence on clouds in the Arctic

A publication or PhD thesis chapter summarizing the influence of aerosols onArctic clouds and how to detect any influence using observa-tions (lead: SU, task 3.2)

Key drivers of aerosol-cloud dynamics

A publication or PhD thesis chapter listing the key drivers for aerosol-cloud dynamics (lead: SU, task 2.3)

Causal inference in climate science

A perspective paper written by the contributors to WP6 as well as the climate WPs on the challenges of causal inference for climatological datasets (lead: DLR, contributors UVEG, ETHZ, UEDIN, UOXF)

Separability of aerosol-cloud effects by regimes

A publication or PhD thesis chapter explaining how cloud regimes should optimally be defined to assess causal aerosol-cloud interactions (lead: ULEI, task 2.4, contributor: ETHZ)

Latent variable causal discovery for climate time series

A publication or PhD thesis chapter introducing a novel causal inference technique for time series accounting for latent variables (lead: DLR, contributors: UOXF).

Predictive modelling for spatio-temporal data

A publication or report written by the contributors to WP5 as well as the climate WPs discussing the challenges of predictive models for climatological datasets (lead: UVEG, contributors, UOXF, ULEI, MetOffice Amazon, UEDIN, ETHZ, DLR)

Isolating aerosol effects through observable analogues

A publication or PhD thesis chapter on the attribution of cloud/precipitation changes to aerosol perturbations (lead: UOXF, task 2.2)

Social media campaigns and press releases accompa-nying each iMIRACLI summer school
iMIRACLI webpage online
Quarterly consortium videoconferences
Social-media / code aggregation on webpage

Develop a data management plan for ODRP

Pubblicazioni

Large uncertainty in future warming due to aerosol forcing

Autori: Duncan Watson-Parris; Christopher J. Smith
Pubblicato in: Nature Climate Change, 2022, ISSN 1758-678X
Editore: Nature Publishing Group
DOI: 10.1038/s41558-022-01516-0

ClimateBench: A benchmark dataset for data-driven climate projections

Autori: Duncan Watson-Parris, Yuhan Rao, Dirk Olivié, Øyvind Seland, Peer J Nowack, Gustau Camps-Valls, Philip Stier, Shahine Bouabid, Maura Dewey, Emilie Fons, Jessenia Margarita Marina Gonzalez, Paula Harder, Kai Jeggle, Julien Lenhardt, Peter Manshausen, Maria Novitasari, Lucile Ricard, Carla Roesch
Pubblicato in: Journal of Advances in Modeling Earth Systems, 2022, ISSN 1942-2466
Editore: American Geophysical Union
DOI: 10.1002/essoar.10509765.2

Shipping regulations lead to large reduction in cloud perturbations

Autori: Duncan Watson-Parris, Matthew W. Christensen, Angus Laurenson, Daniel Clewley, Edward Gryspeerdt, Philip Stier
Pubblicato in: Proceedings of the National Academy of Sciences, Numero 119, 2023, ISSN 0027-8424
Editore: National Academy of Sciences
DOI: 10.1073/pnas.2206885119

Assessing California Wintertime Precipitation Responses to Various Climate Drivers

Autori: Robert J. Allen, Jean‐Francois Lamarque, Duncan Watson‐Parris, Dirk Olivié
Pubblicato in: Journal of Geophysical Research: Atmospheres, Numero 125, 2023, ISSN 2169-897X
Editore: AGU
DOI: 10.1029/2019jd031736

Invisible ship tracks show large cloud sensitivity to aerosol

Autori: Peter Manshausen, Duncan Watson-Parris, Matthew W. Christensen, Jukka-Pekka Jalkanen, Philip Stier
Pubblicato in: Nature, 2022, ISSN 1476-4687
Editore: Nature Publishing Group
DOI: 10.1038/s41586-022-05122-0

Aerosol Forcing Masks and Delays the Formation of the North Atlantic Warming Hole by Three Decades

Autori: Guy Dagan, Philip Stier, Duncan Watson‐Parris
Pubblicato in: Geophysical Research Letters, Numero 47, 2023, ISSN 0094-8276
Editore: American Geophysical Union
DOI: 10.1029/2020gl090778

Climate Impacts of COVID‐19 Induced Emission Changes

Autori: A. Gettelman, R. Lamboll, C. G. Bardeen, P. M. Forster, D. Watson‐Parris
Pubblicato in: Geophysical Research Letters, Numero 48, 2023, ISSN 0094-8276
Editore: American Geophysical Union
DOI: 10.1029/2020gl091805

Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study

Autori: Cynthia H. Whaley, Rashed Mahmood, Knut von Salzen, Barbara Winter, Sabine Eckhardt, Stephen Arnold, Stephen Beagley, Silvia Becagli, Rong-You Chien, Jesper Christensen, Sujay M. Damani, Kostas Eleftheriadis, Nikolaos Evangeliou, Gregory S. Faluvegi, Mark Flanner, Joshua S. Fu, Michael Gauss, Fabio Giardi, Wanmin Gong, Jens Liengaard Hjorth, Lin Huang, Ulas Im, Yugo Kanaya, Srinath Krishnan, Zbign
Pubblicato in: Atmospheric Chemistry and Physics, 2022, ISSN 1680-7324
Editore: EGU
DOI: 10.5194/acp-2021-975

Strong control of effective radiative forcing by the spatial pattern of absorbing aerosol

Autori: Andrew Williams, Philip Stier, Guy Dagan, Duncan Watson-Parris
Pubblicato in: Nature Climate Change, Numero 12, 2022, Pagina/e 735-742, ISSN 1758-6798
Editore: Nature
DOI: 10.21203/rs.3.rs-1015938/v1

The Global Atmosphere‐aerosol Model ICON‐A‐HAM2.3–Initial Model Evaluation and Effects of Radiation Balance Tuning on Aerosol Optical Thickness

Autori: M. Salzmann, S. Ferrachat, C. Tully, S. Münch, D. Watson‐Parris, D. Neubauer, C. Siegenthaler‐Le Drian, S. Rast, B. Heinold, T. Crueger, R. Brokopf, J. Mülmenstädt, J. Quaas, H. Wan, K. Zhang, U. Lohmann, P. Stier, I. Tegen
Pubblicato in: Journal of Advances in Modeling Earth Systems, Numero 14, 2023, ISSN 1942-2466
Editore: American Geophysical Union
DOI: 10.1029/2021ms002699

Aerosol optical depth disaggregation: toward global aerosol vertical profiles

Autori: Shahine Bouabid, Duncan Watson-Parris, Sofija Stefanović, Athanasios Nenes, Dino Sejdinovic
Pubblicato in: Environmental Data Science, Numero 3, 2024, ISSN 2634-4602
Editore: Cambridge University Press
DOI: 10.1017/eds.2024.15

Understanding cirrus clouds using explainable machine learning

Autori: Kai Jeggle, David Neubauer, Gustau Camps-Valls, Ulrike Lohmann
Pubblicato in: Environmental Data Science, Numero 2, 2023, ISSN 2634-4602
Editore: Cambridge University Press
DOI: 10.1017/eds.2023.14

Sink, Source or Something In‐Between? Net Effects of Precipitation on Aerosol Particle Populations

Autori: Théodore Khadir, Ilona Riipinen, Sini Talvinen, Dominic Heslin‐Rees, Christopher Pöhlker, Luciana Rizzo, Luiz A. T. Machado, Marco A. Franco, Leslie A. Kremper, Paulo Artaxo, Tuukka Petäjä, Markku Kulmala, Peter Tunved, Annica M. L. Ekman, Radovan Krejci, Annele Virtanen
Pubblicato in: Geophysical Research Letters, Numero 50, 2024, ISSN 0094-8276
Editore: American Geophysical Union
DOI: 10.1029/2023gl104325

Stratocumulus adjustments to aerosol perturbations disentangled with a causal approach

Autori: Emilie Fons, Jakob Runge, David Neubauer, Ulrike Lohmann
Pubblicato in: npj Climate and Atmospheric Science, Numero 6, 2023, ISSN 2397-3722
Editore: Nature
DOI: 10.1038/s41612-023-00452-w

Rapid saturation of cloud water adjustments to shipping emissions

Autori: Peter Manshausen, Duncan Watson-Parris, Matthew W. Christensen, Jukka-Pekka Jalkanen, Philip Stier
Pubblicato in: Atmospheric Chemistry and Physics, Numero 23, 2023, Pagina/e 12545-12555, ISSN 1680-7324
Editore: EGU
DOI: 10.5194/acp-23-12545-2023

Aerosol absorption in global models from AeroCom Phase III

Autori: Maria Sand, Bjørn H. Samset, Gunnar Myhre, Jonas Gliß, Susanne E. Bauer, Huisheng Bian, Mian Chin, Ramiro Checa-Garcia, Paul Ginoux, Zak Kipling, Alf Kirkevåg, Harri Kokkola, Philippe Le Sager, Marianne T. Lund, Hitoshi Matsui, Twan van Noije, Samuel Remy, Michael Schulz, Philip Stier, Camilla W. Stjern, Toshihiko Takemura, Kostas Tsigaridis, Svetlana G. Tsyro, Duncan Watson-Parris
Pubblicato in: Atmospheric Chemistry and Physics, Numero 21, 2021, ISSN 1680-7324
Editore: EGU
DOI: 10.5194/acp-2021-51

Machine learning for weather and climate are worlds apart

Autori: D. Watson-Parris
Pubblicato in: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Numero 379, 2024, Pagina/e 20200098, ISSN 1364-503X
Editore: Royal Society of London
DOI: 10.1098/rsta.2020.0098

Physics-informed learning of aerosol microphysics

Autori: Paula Harder, Duncan Watson-Parris, Philip Stier, Dominik Strassel, Nicolas R. Gauger, Janis Keuper
Pubblicato in: Environmental Data Science, Numero 1, 2022, ISSN 2634-4602
Editore: Cambridge University Press
DOI: 10.1017/eds.2022.22

On the Contribution of Fast and Slow Responses to Precipitation Changes Caused by Aerosol Perturbations

Autori: Shipeng Zhang, Philip Stier, Duncan Watson-Parris
Pubblicato in: Atmospheric Chemistry and Physics, Numero 21, 2021, ISSN 1680-7324
Editore: EGU
DOI: 10.5194/acp-2020-1317

FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation

Autori: Shahine Bouabid, Dino Sejdinovic, Duncan Watson‐Parris
Pubblicato in: Journal of Advances in Modeling Earth Systems, Numero 16, 2024, ISSN 1942-2466
Editore: American Geophysical Union
DOI: 10.1029/2023ms003926

Marine cloud base height retrieval from MODIS cloud properties using machine learning

Autori: Julien Lenhardt, Johannes Quaas, Dino Sejdinovic
Pubblicato in: Atmospheric Measurement Techniques, Numero 17, 2024, Pagina/e 5655-5677, ISSN 1867-8548
Editore: EGU
DOI: 10.5194/amt-17-5655-2024

Identifying climate model structural inconsistencies allows for tight constraint of aerosol radiative forcing

Autori: Leighton A. Regayre, Lucia Deaconu, Daniel P. Grosvenor, David M. H. Sexton, Christopher Symonds, Tom Langton, Duncan Watson-Paris, Jane P. Mulcahy, Kirsty J. Pringle, Mark Richardson, Jill S. Johnson, John W. Rostron, Hamish Gordon, Grenville Lister, Philip Stier, Ken S. Carslaw
Pubblicato in: Atmospheric Chemistry and Physics, Numero 23, 2023, Pagina/e 8749-8768, ISSN 1680-7324
Editore: EGU
DOI: 10.5194/acp-23-8749-2023

Dependence of Fast Changes in Global and Local Precipitation on the Geographical Location of Absorbing Aerosol

Autori: Andrew I. L. Williams, Duncan Watson-Parris, Guy Dagan, Philip Stier
Pubblicato in: Journal of Climate, Numero 36, 2023, Pagina/e 6163-6176, ISSN 0894-8755
Editore: American Meteorological Society
DOI: 10.1175/jcli-d-23-0022.1

Causal inference for time series

Autori: Jakob Runge, Andreas Gerhardus, Gherardo Varando, Veronika Eyring, Gustau Camps-Valls
Pubblicato in: Nature Reviews Earth & Environment, Numero 4, 2023, Pagina/e 487-505, ISSN 2662-138X
Editore: Nature
DOI: 10.1038/s43017-023-00431-y

Investigating the sign of stratocumulus adjustments to aerosols in the ICON global storm-resolving model

Autori: Emilie Fons, Ann Kristin Naumann, David Neubauer, Theresa Lang, Ulrike Lohmann
Pubblicato in: Atmospheric Chemistry and Physics, Numero 24, 2024, Pagina/e 8653-8675, ISSN 1680-7324
Editore: EGU
DOI: 10.5194/acp-24-8653-2024

Combining Temperature and Precipitation to Constrain the Aerosol Contribution to Observed Climate Change

Autori: Carla M. Roesch, Andrew P. Ballinger, Andrew P. Schurer, Gabriele C. Hegerl
Pubblicato in: Journal of Climate, Numero 37, 2024, Pagina/e 5211-5229, ISSN 0894-8755
Editore: American Meteorological Society
DOI: 10.1175/jcli-d-23-0347.1

Pollution tracker: Finding industrial sources of aerosol emission in satellite imagery

Autori: Peter Manshausen, Duncan Watson-Parris, Lena Wagner, Pirmin Maier, Sybrand J. Muller, Gernot Ramminger and Philip Stier
Pubblicato in: Environmental Data Science, 2023, ISSN 2634-4602
Editore: Cambridge University Press
DOI: 10.1017/eds.2023.20

Exploring Randomly Wired Neural Networks for Climate Model Emulation

Autori: William Yik, Sam J. Silva, Andrew Geiss, Duncan Watson-Parris
Pubblicato in: Artificial Intelligence for the Earth Systems, Numero 2, 2024, ISSN 2769-7525
Editore: American Meteorological Society
DOI: 10.1175/aies-d-22-0088.1

Model calibration using ESEm v1.0.0 – an open, scalable Earth System Emulator

Autori: Duncan Watson-Parris, Andrew Williams, Lucia Deaconu, Philip Stier
Pubblicato in: Geoscientific Model Development, Numero 14, 2022, ISSN 1991-9603
Editore: EGU
DOI: 10.5194/gmd-2021-267

network-based constraint to evaluate climate sensitivity

Autori: Lucile Ricard, Fabrizio Falasca, Jakob Runge, Athanasios Nenes
Pubblicato in: Nature Communications, Numero 15, 2024, ISSN 2041-1723
Editore: Nature Publishing Group
DOI: 10.1038/s41467-024-50813-z

Causal Inference on Process Graphs, Part I: The Structural Equation Process Representation

Autori: Nicolas-Domenic Reiter, Andreas Gerhardus, Jonas Wahl, Jakob Runge
Pubblicato in: arXiv, 2024
Editore: arXiv
DOI: 10.48550/arxiv.2305.11561

Causal Inference on Process Graphs, Part II: Causal Structure and Effect Identification

Autori: Nicolas-Domenic Reiter, Jonas Wahl, Andreas Gerhardus, Jakob Runge
Pubblicato in: arXiv, 2024
Editore: arXiv
DOI: 10.48550/arxiv.2406.17422

Causal inference for temporal patterns

Autori: Reiter, Nicolas-Domenic; Gerhardus, Andreas; Runge, Jakob
Pubblicato in: arXiv publication, 2022
Editore: arXiv Cornell University
DOI: 10.48550/arxiv.2205.15149

Asymptotic Uncertainty in the Estimation of Frequency Domain Causal Effects for Linear Processes

Autori: Nicolas-Domenic Reiter, Jonas Wahl, Gabriele C. Hegerl, Jakob Runge
Pubblicato in: arXiv, 2024
Editore: arXiv
DOI: 10.48550/arxiv.2406.18191

IceCloudNet: Cirrus and mixed-phase cloud prediction from SEVIRI input learned from sparse supervision

Autori: Jeggle, Kai; Czerkawski, Mikolaj; Serva, Federico; Saux, Bertrand Le; Neubauer, David; Lohmann, Ulrike
Pubblicato in: Tackling Climate Change with Machine Learning: workshop at NeurIPS 2023, Numero 1, 2023
Editore: NeurIPS
DOI: 10.48550/arxiv.2310.03499

Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific

Autori: Jesson, Andrew; Manshausen, Peter; Douglas, Alyson; Watson-Parris, Duncan; Gal, Yarin; Stier, Philip
Pubblicato in: Tackling Climate Change with Machine Learning: workshop at NeurIPS 2021, 2021
Editore: NeurIPS
DOI: 10.48550/arxiv.2110.15084

Unleashing the Autoconversion Rates Forecasting: Evidential Regression from Satellite Data

Autori: Novitasari, Maria C and Quaas, Johannes and Rodrigues, Miguel
Pubblicato in: NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning, 2023
Editore: NeurIPS

Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions

Autori: Jesson, Andrew; Douglas, Alyson; Manshausen, Peter; Solal, Maëlys; Meinshausen, Nicolai; Stier, Philip; Gal, Yarin; Shalit, Uri
Pubblicato in: 36th Conference on Neural Information Processing Systems (NeurIPS 2022), 2022
Editore: NeurIPS
DOI: 10.48550/arxiv.2204.10022

Deconditional Downscaling with Gaussian Processes

Autori: Siu Lun Chau, Shahine Bouabid, Dino Sejdinovic
Pubblicato in: 35th Conference on Neural Information Processing Systems (NeurIPS 2021), 2021
Editore: NeurIPS
DOI: 10.48550/arxiv.2105.12909

ALAS: Active Learning for Autoconversion Rates Prediction from Satellite Data

Autori: Maria C. Novitasari, Johannes Quaas, Miguel Rodrigues
Pubblicato in: Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, 2024
Editore: PMLR

Emulating Aerosol Microphysics with Machine Learning

Autori: Harder, Paula; Watson-Parris, Duncan; Strassel, Dominik; Gauger, Nicolas; Stier, Philip; Keuper, Janis
Pubblicato in: ICML 2021 Workshop, Tackling Climate Change with Machine Learning, Numero 1, 2021
Editore: ICML
DOI: 10.48550/arxiv.2109.10593

Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge

Autori: Shahine Bouabid, Jake Fawkes, Dino Sejdinovic
Pubblicato in: Proceedings of the 40th International Conference on Machine Learning, 2023
Editore: ICML

Reconstructing Aerosols Vertical Profiles with Aggregate Output Learning

Autori: Sofija Stefanovic, Shahine Bouabid, Philip Stier, Athanasios Nenes, Dino Sejdinovic
Pubblicato in: Tackling Climate Changewith Machine Learning Workshop at ICML 2021, Numero 2021, 2021
Editore: ICML
DOI: 10.31223/x5qw5s

Leveraging Machine Learning to Predict the Autoconversion Rates from Satellite Data

Autori: Maria C Novitasari, Johannes Quaas, Miguel Rodrigues
Pubblicato in: NeurIPS 2021 Workshop on Tackling Climate Change with Machine Learning, 2021
Editore: ClimateChangeAI

ALAS: Active Learning for Autoconversion Rates Prediction from Satellite Data

Autori: Maria C Novitasari, Johanness Quaas, Miguel Rodrigues
Pubblicato in: NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning, 2023
Editore: NeurIPS

NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations

Autori: Harder, Paula; Jones, William; Lguensat, Redouane; Bouabid, Shahine; Fulton, James; Quesada-Chacón, Dánell; Marcolongo, Aris; Stefanović, Sofija; Rao, Yuhan; Manshausen, Peter; Watson-Parris, Duncan
Pubblicato in: Tackling Climate Change with Machine Learning workshop at NeurIPS 2020., Numero 1, 2020
Editore: NeurIPS
DOI: 10.48550/arxiv.2011.07017

Cirrus formation regimes – Data driven identification and quantification of mineral dust effect

Autori: Kai Jeggle, David Neubauer, Hanin Binder, Ulrike Lohmann
Pubblicato in: Atmospheric Chemistry and Physics Discussions, 2024, ISSN 1680-7375
Editore: Copernicus GmbH
DOI: 10.5194/egusphere-2024-2559

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