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Simulation and detection efficiency analysis for polar mesospheric clouds measurements using a spaceborne wide field of view ultraviolet imager

Atmos. Meas. techniques - Wed, 05/15/2024 - 18:58
Simulation and detection efficiency analysis for polar mesospheric clouds measurements using a spaceborne wide field of view ultraviolet imager
Ke Ren, Haiyang Gao, Shuqi Niu, Shaoyang Sun, Leilei Kou, Yanqing Xie, Liguo Zhang, and Lingbing Bu
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-186,2024
Preprint under review for AMT (discussion: open, 0 comments)
Ultraviolet (UV) imaging technology has significantly advanced the research and development of polar mesospheric clouds (PMCs). In this study, we proposed the wide field-of-view ultraviolet imager (WFUI) and built a forward model to evaluate the detection capability and efficiency. The research results demonstrate that the WFUI performs well in PMCs detection and has high detection efficiency. The relationship between IWC and detection efficiency follows an exponential function distribution.

Analysis of three-dimensional slope stability combined with rainfall and earthquake

Natural Hazards and Earth System Sciences - Wed, 05/15/2024 - 18:11
Analysis of three-dimensional slope stability combined with rainfall and earthquake
Jiao Wang, Zhangxing Wang, Guanhua Sun, and Hongming Luo
Nat. Hazards Earth Syst. Sci., 24, 1741–1756, https://doi.org/10.5194/nhess-24-1741-2024, 2024
With a simplified formula linking rainfall and groundwater level, the rise of the phreatic surface within the slope can be obtained. Then, a global analysis method that considers both seepage and seismic forces is proposed to determine the safety factor of slopes subjected to the combined effect of rainfall and earthquakes. By taking a slope in the Three Gorges Reservoir area as an example, the safety evolution of the slope combined with both rainfall and earthquake is also examined.

Three-dimensional variational assimilation with a multivariate background error covariance for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 2.0.0-beta)

Geoscientific Model Development - Wed, 05/15/2024 - 17:38
Three-dimensional variational assimilation with a multivariate background error covariance for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 2.0.0-beta)
Byoung-Joo Jung, Benjamin Ménétrier, Chris Snyder, Zhiquan Liu, Jonathan J. Guerrette, Junmei Ban, Ivette Hernández Baños, Yonggang G. Yu, and William C. Skamarock
Geosci. Model Dev., 17, 3879–3895, https://doi.org/10.5194/gmd-17-3879-2024, 2024
We describe the multivariate static background error covariance (B) for the JEDI-MPAS 3D-Var data assimilation system. With tuned B parameters, the multivariate B gives physically balanced analysis increment fields in the single-observation test framework. In the month-long cycling experiment with a global 60 km mesh, 3D-Var with static B performs stably. Due to its simple workflow and minimal computational requirements, JEDI-MPAS 3D-Var can be useful for the research community.

Quantifying the impact of SST feedback frequency on Madden–Julian oscillation simulations

Geoscientific Model Development - Wed, 05/15/2024 - 17:38
Quantifying the impact of SST feedback frequency on Madden–Julian oscillation simulations
Yung-Yao Lan, Huang-Hsiung Hsu, and Wan-Ling Tseng
Geosci. Model Dev., 17, 3897–3918, https://doi.org/10.5194/gmd-17-3897-2024, 2024
This study uses the CAM5–SIT coupled model to investigate the effects of SST feedback frequency on the MJO simulations with intervals at 30 min, 1, 3, 6, 12, 18, 24, and 30 d. The simulations become increasingly unrealistic as the frequency of the SST feedback decreases. Our results suggest that more spontaneous air--sea interaction (e.g., ocean response within 3 d in this study) with high vertical resolution in the ocean model is key to the realistic simulation of the MJO.

Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3

Geoscientific Model Development - Wed, 05/15/2024 - 17:38
Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3
Jiwoo Lee, Peter J. Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A. Ullrich, Kenneth R. Sperber, Karl E. Taylor, Yann Y. Planton, Eric Guilyardi, Paul Durack, Celine Bonfils, Mark D. Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F. Wehner, Angeline G. Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T. Wittenberg, and John Krasting
Geosci. Model Dev., 17, 3919–3948, https://doi.org/10.5194/gmd-17-3919-2024, 2024
We introduce an open-source software, the PCMDI Metrics Package (PMP), developed for a comprehensive comparison of Earth system models (ESMs) with real-world observations. Using diverse metrics evaluating climatology, variability, and extremes simulated in thousands of simulations from the Coupled Model Intercomparison Project (CMIP), PMP aids in benchmarking model improvements across generations. PMP also enables efficient tracking of performance evolutions during ESM developments.

A revised model of global silicate weathering considering the influence of vegetation cover on erosion rate

Geoscientific Model Development - Wed, 05/15/2024 - 17:38
A revised model of global silicate weathering considering the influence of vegetation cover on erosion rate
Haoyue Zuo, Yonggang Liu, Gaojun Li, Zhifang Xu, Liang Zhao, Zhengtang Guo, and Yongyun Hu
Geosci. Model Dev., 17, 3949–3974, https://doi.org/10.5194/gmd-17-3949-2024, 2024
Compared to the silicate weathering fluxes measured at large river basins, the current models tend to systematically overestimate the fluxes over the tropical region, which leads to an overestimation of the global total weathering flux. The most possible cause of such bias is found to be the overestimation of tropical surface erosion, which indicates that the tropical vegetation likely slows down physical erosion significantly. We propose a way of taking this effect into account in models.

FUME 2.0 – Flexible Universal processor for Modeling Emissions

Geoscientific Model Development - Wed, 05/15/2024 - 17:38
FUME 2.0 – Flexible Universal processor for Modeling Emissions
Michal Belda, Nina Benešová, Jaroslav Resler, Peter Huszár, Ondřej Vlček, Pavel Krč, Jan Karlický, Pavel Juruš, and Kryštof Eben
Geosci. Model Dev., 17, 3867–3878, https://doi.org/10.5194/gmd-17-3867-2024, 2024
For modeling atmospheric chemistry, it is necessary to provide data on emissions of pollutants. These can come from various sources and in various forms, and preprocessing of the data to be ingestible by chemistry models can be quite challenging. We developed the FUME processor to use a database layer that internally transforms all input data into a rigid structure, facilitating further processing to allow for emission processing from the continental to the street scale.

NAQPMS-PDAF v2.0: A Novel Hybrid Nonlinear Data Assimilation System for Improved Simulation of PM2.5 Chemical Components

Geoscientific Model Development - Wed, 05/15/2024 - 17:38
NAQPMS-PDAF v2.0: A Novel Hybrid Nonlinear Data Assimilation System for Improved Simulation of PM2.5 Chemical Components
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-78,2024
Preprint under review for GMD (discussion: open, 0 comments)
To accurately characterize the spatiotemporal distribution of PM2.5 chemical components, we developed a hybrid nonlinear data assimilation system (NAQPMS-PDAF v2.0), which is optimal for chemical components with non-Gaussian and nonlinear properties. NAQPMS-PDAF v2.0 has superior computing efficiency and excels when used a small ensemble size. The one-month assimilation experiments show that NAQPMS-PDAF v2.0 can significantly improve the simulation performance of chemical components.

Evaluation of global fire simulations in CMIP6 Earth system models

Geoscientific Model Development - Wed, 05/15/2024 - 17:38
Evaluation of global fire simulations in CMIP6 Earth system models
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-85,2024
Preprint under review for GMD (discussion: open, 0 comments)
This study provides the first comprehensive assessment of historical fire simulations from 19 CMIP6 ESMs. Most models reproduce global total, spatial pattern, seasonality, and regional historical changes well, but fail to simulate the recent decline in global burned area and underestimate the fire sensitivity to wet-dry conditions. They addressed three critical issues in CMIP5. We present targeted guidance for fire scheme development and methodologies to generate reliable fire projections.

LB-SCAM: a learning-based method for efficient large-scale sensitivity analysis and tuning of the Single Column Atmosphere Model (SCAM)

Geoscientific Model Development - Wed, 05/15/2024 - 16:52
LB-SCAM: a learning-based method for efficient large-scale sensitivity analysis and tuning of the Single Column Atmosphere Model (SCAM)
Jiaxu Guo, Juepeng Zheng, Yidan Xu, Haohuan Fu, Wei Xue, Lanning Wang, Lin Gan, Ping Gao, Wubing Wan, Xianwei Wu, Zhitao Zhang, Liang Hu, Gaochao Xu, and Xilong Che
Geosci. Model Dev., 17, 3975–3992, https://doi.org/10.5194/gmd-17-3975-2024, 2024
To enhance the efficiency of experiments using SCAM, we train a learning-based surrogate model to facilitate large-scale sensitivity analysis and tuning of combinations of multiple parameters. Employing a hybrid method, we investigate the joint sensitivity of multi-parameter combinations across typical cases, identifying the most sensitive three-parameter combination out of 11. Subsequently, we conduct a tuning process aimed at reducing output errors in these cases.

A radiative–convective model computing precipitation with the maximum entropy production hypothesis

Geoscientific Model Development - Tue, 05/14/2024 - 18:09
A radiative–convective model computing precipitation with the maximum entropy production hypothesis
Quentin Pikeroen, Didier Paillard, and Karine Watrin
Geosci. Model Dev., 17, 3801–3814, https://doi.org/10.5194/gmd-17-3801-2024, 2024
All accurate climate models use equations with poorly defined parameters, where knobs for the parameters are turned to fit the observations. This process is called tuning. In this article, we use another paradigm. We use a thermodynamic hypothesis, the maximum entropy production, to compute temperatures, energy fluxes, and precipitation, where tuning is impossible. For now, the  1D vertical model is used for a tropical atmosphere. The correct order of magnitude of precipitation is computed.

Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data

Geoscientific Model Development - Tue, 05/14/2024 - 18:09
Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data
Tim Radke, Susanne Fuchs, Christian Wilms, Iuliia Polkova, and Marc Rautenhaus
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-60,2024
Preprint under review for GMD (discussion: open, 0 comments)
In our study, we built upon previous work to investigate the patterns artificial intelligence (AI) learns to detect atmospheric features like tropical cyclones (TCs) and atmospheric rivers (ARs). As primary objective, we adopt a method to explain the used AI and investigate the plausibility of learned patterns. We find that plausible patterns are learned for both TCs and ARs. Hence, the chosen method is very useful for gaining confidence in the AI-based detection of atmospheric features.

Selecting CMIP6 GCMs for CORDEX Dynamical Downscaling over Southeast Asia Using a Standardised Benchmarking Framework

Geoscientific Model Development - Tue, 05/14/2024 - 18:09
Selecting CMIP6 GCMs for CORDEX Dynamical Downscaling over Southeast Asia Using a Standardised Benchmarking Framework
Phuong Loan Nguyen, Lisa V. Alexander, Marcus J. Thatcher, Son C. H. Truong, Rachael N. Isphording, and John L. McGregor
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-84,2024
Preprint under review for GMD (discussion: open, 0 comments)
We apply a comprehensive approach to select a subset of CMIP6 that is suitable for dynamical downscaling over Southeast Asia by considering model performance, model independence, data availability, and future climate change spread. The standardised benchmarking framework is applied to identify a subset of models through two stages of assessment: statistical-based and process-based metrics. We finalize a sub-set of two independent models for dynamical downscaling over Southeast Asia.

Design, evaluation and future projections of the NARCliM2.0 CORDEX-CMIP6 Australasia regional climate ensemble

Geoscientific Model Development - Tue, 05/14/2024 - 18:09
Design, evaluation and future projections of the NARCliM2.0 CORDEX-CMIP6 Australasia regional climate ensemble
Giovanni Di Virgilio, Jason Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew Riley, and Jyothi Lingala
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-87,2024
Preprint under review for GMD (discussion: open, 1 comment)
We introduce new climate models that simulate Australia’s future climate at regional scales, including at an unprecedented resolution of 4 km for 1950–2100. We describe the model design process used to create these new climate models. We show how the new models perform relative to previous-generation models, and compare their climate projections. This work is of national and international relevance as it can help guide climate model design and the use and interpretation of climate projections.

DEUCE v1.0: a neural network for probabilistic precipitation nowcasting with aleatoric and epistemic uncertainties

Geoscientific Model Development - Tue, 05/14/2024 - 17:38
DEUCE v1.0: a neural network for probabilistic precipitation nowcasting with aleatoric and epistemic uncertainties
Bent Harnist, Seppo Pulkkinen, and Terhi Mäkinen
Geosci. Model Dev., 17, 3839–3866, https://doi.org/10.5194/gmd-17-3839-2024, 2024
Probabilistic precipitation nowcasting (local forecasting for 0–6 h) is crucial for reducing damage from events like flash floods. For this goal, we propose the DEUCE neural-network-based model which uses data and model uncertainties to generate an ensemble of potential precipitation development scenarios for the next hour. Trained and evaluated with Finnish precipitation composites, DEUCE was found to produce more skillful and reliable nowcasts than established models.

Evaluation of multi-season convection-permitting atmosphere – mixed-layer ocean simulations of the Maritime Continent

Geoscientific Model Development - Tue, 05/14/2024 - 17:38
Evaluation of multi-season convection-permitting atmosphere – mixed-layer ocean simulations of the Maritime Continent
Emma Howard, Steven Woolnough, Nicholas Klingaman, Daniel Shipley, Claudio Sanchez, Simon C. Peatman, Cathryn E. Birch, and Adrian J. Matthews
Geosci. Model Dev., 17, 3815–3837, https://doi.org/10.5194/gmd-17-3815-2024, 2024
This paper describes a coupled atmosphere–mixed-layer ocean simulation setup that will be used to study weather processes in Southeast Asia. The set-up has been used to compare high-resolution simulations, which are able to partially resolve storms, to coarser simulations, which cannot. We compare the model performance at representing variability of rainfall and sea surface temperatures across length scales between the coarse and fine models.

Минпромторг - Результаты отбора организаций для включения в реестр организаций, имеющих право на получение субсид

Founding - Thu, 03/03/2022 - 11:28
Результаты отбора организаций для включения в реестр организаций, имеющих право на получение субсидий на возмещение части затрат на уплату процентов по кредитам, полученным в российских кредитных организациях и в государственной корпорации развития «ВЭБ.РФ» в 2009–2023 годах, а также на уплату лизинговых платежей по договорам лизинга, заключенным в 2009–2023 годах с российскими лизинговыми компаниями на приобретение гражданских судов
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Опубликовано:01.03.22

Минпромторг - Объявление o проведении Министерством промышленности и торговли Российской Федерации отбора на прав

Founding - Thu, 03/03/2022 - 11:28
Объявление o проведении Министерством промышленности и торговли Российской Федерации отбора на право получения субсидий из федерального бюджета российскими организациями на возмещение части затрат на приобретение (строительство) новых гражданских судов взамен судов, сданных на утилизацию в соответствии с постановлением Правительства Российской Федерации от 27 апреля 2017 года № 502
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Опубликовано:28.02.22

Минпромторг - Приказ Минпромторга России № 606 от 28 февраля 2022 г.О проведении Министерством промышленности и т

Founding - Thu, 03/03/2022 - 11:28
Приказ Минпромторга России № 606 от 28 февраля 2022 г. О проведении Министерством промышленности и торговли Российской Федерации отбора на право получения субсидий из федерального бюджета российскими организациями на возмещение части затрат на приобретение (строительство) новых гражданских судов взамен судов, сданных на утилизацию
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Опубликовано:28.02.22

2021 Class of AGU Fellows Announced

EOS - Tue, 09/28/2021 - 15:59

Congratulations to the 2021 class of Fellows! These 59 individuals have made outstanding achievements and contributions by pushing the frontiers of our science forward. They have also embodied AGU’s shared vision of a thriving, sustainable, and equitable future for all powered by discovery, innovation, and action. Equally important is that they conducted themselves with integrity, respect, diversity, and collaboration while creating deep engagement in education and outreach.

Since 1962, AGU has elected fewer than 0.1% of members to join this prestigious group of individuals.

Thanks to their dedication and sacrifice, AGU Fellows serve as global leaders and experts who have propelled our understanding of geosciences. We are confident that they will remain curious and relentlessly focused on answers as they continue to advance their research, which pushes our boundaries of knowledge to create a healthy planet and beyond.

We are grateful for their invaluable contributions. We also recognize that numerous individuals were pivotal to their success, and we thank them too.

AGU will formally recognize this year’s recipients during #AGU21 Fall Meeting, where we will celebrate and honor the exceptional achievements, visionary leadership, talents, and dedication of all 59 new AGU Fellows.

On behalf of AGU, we welcome to our community the 2021 AGU Fellows, who are listed below in alphabetical order.

—Susan Lozier, President, AGU; and LaToya Myles (unionfellows@agu.org), Chair, Honors and Recognition Committee, AGU

 

2021 AGU Fellows

Ariel D. Anbar, Arizona State University

Suzanne Prestrud Anderson, University of Colorado Boulder

Richard C. Aster, Colorado State University

Sushil Atreya, University of Michigan Ann Arbor

Andy Baker, University of New South Wales

Leonard Barrie, McGill University, Stockholm University, and the Cyprus Institute

Kristie A. Boering, University of California, Berkeley

Simon Brassell, Indiana University

Paul D. Brooks, University of Utah

V. Chandrasekar, Colorado State University

Daniele Cherniak, University at Albany

Mian Chin, NASA Goddard Space Flight Center

Paul Judson DeMott, Colorado State University

Andrea Donnellan, Jet Propulsion Laboratory, California Institute of Technology

Christopher Fairall, NOAA Boulder

Harindra Joseph Fernando, University of Notre Dame

Fabio Florindo, National Institute of Geophysics and Volcanology, Rome, Italy

Steve Frolking, University of New Hampshire

Ferran Garcia-Pichel, Arizona State University

Darryl Granger, Purdue University

Kaj Hoernle, GEOMAR Helmholtz Centre for Ocean Research Kiel

David Holland, New York University

Niels Hovius, GFZ German Research Centre for Geosciences

Fumio Inagaki, Japan Agency for Marine-Earth Science and Technology (JAMSTEC)

Vania K. Jordanova, Los Alamos National Laboratory

Kim Kastens, Lamont-Doherty Earth Observatory

Sukyoung Lee, Pennsylvania State University, University Park

Xinlin Li, University of Colorado Boulder

Peter C. Lichtner, OFM Research and the University of New Mexico

Carolina Lithgow-Bertelloni, University of California, Los Angeles

Parker MacCready, University of Washington Seattle

Donald R. MacGorman, NOAA/National Severe Storms Laboratory (retired) and Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma

Michelle Cailin Mack, Northern Arizona University

Wendy Mao, Stanford University

Jerry F. McManus, Lamont-Doherty Earth Observatory, Columbia University

Andrew J. Michael, USGS Earthquake Science Center

Glenn Milne, University of Ottawa

Onno Oncken, Helmholtz Centre Potsdam – GFZ German Research Centre for Geosciences

Victoria Orphan, California Institute of Technology

Bo Qiu, University of Hawaii at Manoa

Andy Ridgwell, University of California, Riverside

Stephen R. Rintoul, CSIRO Oceans & Atmosphere

Allen Robinson, Carnegie Mellon University

Stanley Sander, NASA Jet Propulsion Laboratory, California Institute of Technology

Keith P. Shine, University of Reading

Whendee L. Silver, University of California, Berkeley

Craig T. Simmons, Flinders University

John R. Spencer, Southwest Research Institute Boulder

S. Alan Stern, Southwest Research Institute

Dimitri A. Sverjensky, Johns Hopkins University

Roy Torbert, University of New Hampshire Main Campus and Southwest Research Institute, EOS department, Durham

Philippe Van Cappellen, University of Waterloo

Peter van Keken, Carnegie Institution for Science

Thorsten Wagener, University of Potsdam

David Wald, USGS National Earthquake Information Center

Yigang Xu, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences

Edward D. Young, University of California, Los Angeles

Xubin Zeng, The University of Arizona

Yan Zheng, Southern University of Science and Technology

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