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arXiv 2022年

作者： Wu, Minghui Gao, Zhen Huang, Yang Xiao, Zhenyu Kwan Ng, Derrick Wing Zhang, Zhaoyang Information and Electronics Beijing Institute of Technology Beijing100081 China Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Electronic and Information Engineering Beihang University Beijing100191 China School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2025 Australia College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China International Joint Innovation Center Zhejiang University Haining314400 China Hangzhou310007 China

Reconfigurable intelligent surface (RIS) can significantly enhance the service coverage of Tera-Hertz massive multiple-input multiple-output (MIMO) communication systems. However, obtaining accurate high-dimensional channel state information (CSI) with limited pilot and feedback signaling overhead is challenging, severely degrading the performance of conventional spatial division multiple access. To improve the robustness against CSI imperfection, this paper proposes a deep learning (DL)-based rate-splitting multiple access (RSMA) scheme for RIS-aided Tera-Hertz multi-user MIMO systems. Specifically, we first propose a hybrid data-model driven DL-based RSMA precoding scheme, including the passive precoding at the RIS as well as the analog active precoding and the RSMA digital active precoding at the base station (BS). To realize the passive precoding at the RIS, we propose a Transformer-based data-driven RIS reflecting network (RRN). As for the analog active precoding at the BS, we propose a match-filter based analog precoding scheme considering that the BS and RIS adopt the LoS-MIMO antenna array architecture. As for the RSMA digital active precoding at the BS, we propose a low-complexity approximate weighted minimum mean square error (AWMMSE) digital precoding scheme, and further design a model-driven deep unfolding active precoding network (DFAPN) by combining the proposed AWMMSE scheme with DL. Then, to acquire accurate CSI at the BS for the investigated RSMA precoding scheme to achieve higher spectral efficiency, we propose a CSI acquisition network (CAN) with low pilot and feedback signaling overhead. The proposed DL-based RSMA scheme for RIS-aided Tera-Hertz multi-user MIMO systems can exploit the advantages of RSMA and DL to improve the robustness against CSI imperfection, thus achieving higher spectral efficiency with lower signaling overhead. Copyright © 2022, The Authors. All rights reserved.

关键词： Channel state information

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The current state and future directions of modeling thermosphere density enhancements during extreme magnetic storms

arXiv

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arXiv 2021年

作者： Oliveira, Denny M. Zesta, Eftyhia Mehta, Piyush M. Licata, Richard J. Pilinski, Marcin D. Tobiska, W. Kent Hayakawa, Hisashi Goddard Planetary Heliophysics Institute University of Maryland Baltimore County BaltimoreMD United States Geospace Physics Laboratory Nasa Goddard Space Flight Center GreenbeltMD United States West Virginia University MorgantownWV United States Laboratory for Atmospheric and Space Physics University of Colorado BoulderCO United States Space Environment Technologies Pacific PalisadesCA United States Institute for Space-Earth Environmental Research Nagoya University Nagoya4648601 Japan Institute for Advanced Research Nagoya University Nagoya4648601 Japan Uk Solar System Data Centre Space Physics and Operations Division Ral Space Science and Technology Facilities Council Rutherford Appleton Laboratory Harwell Oxford Oxfordshire DidcotOX11 0QX United Kingdom Nishina Center Riken Wako 3510198 Japan

Satellites, crewed spacecraft and stations in low-Earth orbit (LEO) are very sensitive to atmospheric drag. A satellite's lifetime and orbital tracking become increasingly inaccurate or uncertain during magnetic storms. Given the planned increase of government and private satellite presence in LEO, the need for accurate density predictions for collision avoidance and lifetime optimization, particularly during extreme events, has become an urgent matter and requires comprehensive international collaboration. Additionally, long-term solar activity models and historical data suggest that solar activity will significantly increase in the following years and decades. In this article, we briefly summarize the main achievements in the research of thermosphere response to extreme magnetic storms occurring particularly after the launching of many satellites with state-of-the-art accelerometers from which high-accuracy density can be determined. We find that the performance of an empirical model with data assimilation is higher than its performance without data assimilation during all extreme storm phases. We discuss how forecasting models can be improved by looking into two directions: first, to the past, by adapting historical extreme storm datasets for density predictions, and second, to the future, by facilitating the assimilation of large-scale thermosphere data sets that will be collected in future events. Therefore, this topic is relevant to the scientific community, government agencies that operate satellites, and the private sector with assets operating in LEO. © 2021, CC BY.

关键词： Orbits

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Regulation effect of planted forests on land surface temperature in China

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Agricultural and Forest Meteorology 2025年 372卷

作者： Zhijiang Zhang Kai Yang Lunche Wang Kai Cheng Chao Chen Chao Ding School of Geography Science and Geomatics Engineering Suzhou University of Science and Technology Suzhou 215009 China SuZhou Key Laboratory of Spatial Information Intelligent Technology and Application Suzhou University of Science and Technology Suzhou 215009 China School of Geography and Information Engineering China University of Geosciences Wuhan 430074 China Institute of Remote Sensing and Geographic Information System School of Earth and Space Sciences Peking University Beijing 100871 China Institute of Ecology College of Urban and Environmental Sciences Peking University Beijing 100871 China Department of Geographic Science Faculty of Arts and Sciences Beijing Normal University Zhuhai 519087 China

Afforestation has been regarded as a potentially effective strategy for the mitigation of climate warming. China has the largest planted forest area worldwide benefited by a series of large-scale afforestation and reforestation project since the 1970s. The planted forests (PF) have been widely reported as an enduring carbon sink. However, the climatic regulation effect of PF on climate through biophysical process remains unclear. To this end, we utilized satellite observations to quantify the impact of the conversion of grasslands (GRA), croplands (CRO) and natural forests (NF) to PF on land surface temperature (LST) between 2003 and 2012 and illustrated the underlying biophysical mechanisms based on a revised Intrinsic Biophysical Mechanism (IBM) method. The results indicated that afforestation of PF caused negligible daily cooling effect (−0.005 ± 0.002 K) compared with NF while induced obvious daily cooling effect compared with GRA (−0.253 ± 0.004 K) and CRO (−0.162 ± 0.002 K). We also demonstrated that the revised IBM attribution method is applicable in interpreting the biophysical mechanism of PF on LST. Attribution analysis revealed that the surface roughness and Bowen ratio dominated the cooling effect as the surface became rougher and more evaporative cooling after converting NF, GRA, or CRO to PF. The contributions of albedo were less stark compared with other components and presented a weak warming effect. Furthermore, the atmospheric feedback was proved that have a pronounced impact on LST and should be included in the attribution of LST change. This study focuses on the potential of PF afforestation to provide local climate adaptation benefits, filling the gaps of previous studies involved to biophysical effect failure in distinguishing NF and PF, and provides new guidance for local climate impact evaluation when implementing forestry projects.

关键词： Planted forests Surface temperature Biophysical effect Intrinsic biophysical mechanism Climate mitigation

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Enhancing global agricultural monitoring system for climate-smart agriculture

Climate Smart Agriculture

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Climate Smart Agriculture 2025年第1期2卷

作者： Le Yu Zhenrong Du Xiyu Li Jinhui Zheng Qiang Zhao Hui Wu Duoji weise Yuanzhen Yang Quan Zhang Xinyue Li Xiaorui Ma Xiaomeng Huang Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing China Ministry of Education Ecological Field Station for East Asian Migratory Birds Beijing China Tsinghua University (Department of Earth System Science)- Xi'an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping Beijing China School of Information and Communication Engineering Dalian University of Technology Dalian China Geothermal Geological Brigade Tibet Autonomous Region Geological and Mineral Exploration and Development Bureau Lhasa China Key Laboratory of Environment Change and Resources Use in Beibu Gulf Ministry of Education Nanning Normal University Nanning China Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation Nanning Normal University Nanning China

Global agricultural monitoring systems face unprecedented challenges due to intensifying climate change. This paper reviews the advancements in existing global agricultural monitoring systems, highlighting deficiencies in addressing extreme weather events, data integration, and real-time analysis. To overcome these limitations, we introduce the Earth system Model-Coupled Global Agricultural Monitoring system (ESM-GAMS), an advanced framework that combines satellite and near-surface remote sensing, artificial intelligence-driven modeling, supercomputing, and crop model to enhance the accuracy and timeliness of crop monitoring and yield predictions under diverse climate scenarios. By integrating multi-source remote sensing data, ESM-GAMS mitigates delays caused by satellite revisit cycles and weather interference, enabling near real-time monitoring with results available at hourly or minute-level intervals. Additionally, the system demonstrated high accuracy in yield simulations under extreme weather, with the improved WOFOST model achieving robust R 2 values ranging from 0.55 to 0.77, indicating its reliability in predicting yields across diverse conditions. ESM-GAMS not only enables detailed daily monitoring of crop growth, but also provides early-warning capabilities for extreme weather and its impact on prediction. By optimizing resource allocation, supporting climate resilience, and enabling global data computing, ESM-GAMS represents a further step toward achieving climate-smart agriculture.

关键词： Agricultural monitoring Earth system model Remote sensing Crop model

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Nonlinear electronic stopping of negatively-charged particles in liquid water

arXiv

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arXiv 2023年

作者： Koval, Natalia E. Da Pieve, Fabiana Gu, Bin Muñoz-Santiburcio, Daniel Kohanoff, Jorge Artacho, Emilio CIC Nanogune BRTA Tolosa Hiribidea 76 Donostia-San Sebastián Spain Royal Belgian Institute for Space Aeronomy Brussels Belgium Atomistic Simulation Centre Queen's University Belfast BelfastBT7 1NN United Kingdom Department of Physics Nanjing University of Information Science and Technology Nanjing210044 China Instituto de Fusión Nuclear "Guillermo Velarde" Universidad Politécnica de Madrid Spain CIC Nanogune BRTA and DIPC Tolosa Hiribidea 76 San Sebastián20018 Spain Theory of Condensed Matter Cavendish Laboratory University of Cambridge J. J. Thomson Avenue CambridgeCB3 0HE United Kingdom Ikerbasque Basque Foundation for Science Bilbao48011 Spain

We present real-time time-dependent density-functional-theory calculations of the electronic stopping power for negative and positive projectiles (electrons, protons, antiprotons and muons) moving through liquid water. After correction for finite mass effects, the nonlinear stopping power obtained in this work is significantly different from the previously known results from semi-empirical calculations based on the dielectric response formalism. Linear-nonlinear discrepancies are found both in the maximum value of the stopping power and the Bragg peak's position. Our results indicate the importance of the nonlinear description of electronic processes, particularly for electron projectiles, which are modeled here as classical point charges. Our findings also confirm the expectation that the quantum nature of the electron projectile should substantially influence the stopping power around the Bragg peak and below. Copyright © 2023, The Authors. All rights reserved.

关键词： Projectiles

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Re-calibration of the Sunspot Number: Status Report

arXiv

引用

arXiv 2023年

作者： Clette, F. Lefèvre, L. Chatzistergos, T. Hayakawa, H. Carrasco, V.M. Arlt, R. Cliver, E.W. de Wit, T. Dudok Friedli, T. Karachik, N. Kopp, G. Lockwood, M. Mathieu, S. Muñoz-Jaramillo, A. Owens, M. Pesnell, D. Pevtsov, A. Svalgaard, L. Usoskin, I.G. van Driel-Gesztelyi, L. Vaquero, J.M. World Data Center SILSO Royal Observatory of Belgium 3 avenue Circulaire Brussels1180 Belgium Max Planck Institute for Solar System Research Justus-von-Liebig-weg 3 Göttingen37077 Germany Institute for Space-Earth Environmental Research Nagoya University Nagoya4648601 Japan Institute for Advanced Research Nagoya University Nagoya4648601 Japan Space Physics and Operations Division RAL Space Science and Technology Facilities Council Rutherford Appleton Laboratory Harwell Oxford Didcot OxfordshireOX11 0QX United Kingdom Nishina Centre Riken Wako3510198 Japan Departamento de Física Universidad de Extremadura BadajozE-06006 Spain Leibniz-Institut f. Astrophysik Potsdam An der Sternwarte 16 Potsdam14482 Germany National Solar Observatory 3665 Discovery Drive 3rd Floor BoulderCO80303 United States LPC2E CNRS CNES University of Orléans Orléans45067 France International Space Science Instititute Hallerstrasse 6 Bern3012 Switzerland Rudolf Wolf Society Ahornweg 29 Belp3123 Switzerland Laboratory for Atmospheric and Space Physics University of Colorado 3665 Discovery Dr. BoulderCO80303 United States Department of Meteorology University of Reading Earley Gate PO Box 243 ReadingRG6 6BB United Kingdom ISBA/LIDAM UCLouvain Pl. de l'Université 1 Ottignies-Louvain-la-Neuve1348 Belgium Division of Solar System Science Southwest Research Institute BoulderCO United States Code 671 NASA Goddard Space Flight Center GreenbeltMD20771 United States Hansen Experimental Physics Lab Stanford University StanfordCA94305 United States Space Physics and Astronomy Research Unit Sodankylä Geophysical Observatory Univeristy of Oulu Oulu90014 Finland University College London Mullard Space Science Laboratory Holmbury St. Mary Dorking SurreyRH5 6NT United Kingdom Observatoire de Paris Université PSL CNRS Sorbonne Université Univ. Paris Diderot Sorbonne Paris Cité 5 place Jules Janssen MeudonF-92195 France Konkoly Observatory Research Centre f

We report progress on the on-going recalibration of the Wolf sunspot number (SN) and Group sunspot number (GN) following the release of version 2.0 of SN in 2015. This report constitutes both an update of the efforts reported in the 2016 Topical Issue of Solar Physics and a summary of work by the International space science Institute (ISSI) International Team formed in 2017 to develop optimal SN and GN re-construction methods while continuing to expand the historical sunspot number database. Significant progress has been made on the database side while more work is needed to bring the various proposed SN and (primarily) GN reconstruction methods closer to maturity, after which the new reconstructions (or combinations thereof) can be compared with (a) "benchmark" expectations for any normalization scheme (e.g., a general increase in observer normalization factors going back in time), and (b) independent proxy data series such as F10.7 and the daily range of variations of Earth’s undisturbed magnetic field. New versions of the underlying databases for SN and GN will shortly become available for years through 2022 and we anticipate the release of next versions of these two time series in 2024. © 2023, CC BY-NC-SA.

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Corrigendum to 'Transition in air pollution, disease burden and health cost in China: A comparative study of long-term and short-term exposure' [Environmental Pollution 277 (2021) Start page 4-End page 4/116770]

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Environmental pollution (Barking, Essex : 1987) 2023年 329卷 121714页

作者： Jun Liu Hao Yin Xiao Tang Tong Zhu Qiang Zhang Zhu Liu XiaoLong Tang HongHong Yi Department of Environmental Engineering School of Energy and Environmental Engineering University of Science and Technology Beijing Beijing 100083 PR China Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants University of Science and Technology Beijing Beijing 100083 PR China Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System Science Tsinghua University Beijing 100084 PR China. Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System Science Tsinghua University Beijing 100084 PR China School of Population and Public Health The University of British Columbia Vancouver BC Canada Energy and Resources Group University of California Berkeley CA USA. State Key Laboratory of Atmospheric Boundary Physics and Atmospheric Chemistry Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 PR China. State Key Joint Laboratory of Environmental Simulation and Pollution Control College of Environmental Sciences and Engineering Peking University Beijing 100871 PR China. Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants University of Science and Technology Beijing Beijing 100083 PR China. Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants University of Science and Technology Beijing Beijing 100083 PR China. Electronic address: yihonghong@***.

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Composition and thermal properties of Ganymede's surface from JWST/NIRSpec and MIRI observations

arXiv

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arXiv 2023年

作者： Bockelée-Morvan, D. Lellouch, E. Poch, O. Quirico, E. Cazaux, S. de Pater, I. Fouchet, T. Fry, P.M. Rodriguez-Ovalle, P. Tosi, F. Wong, M.H. Boshuizen, I. de Kleer, K. Fletcher, L.N. Meunier, L. Mura, A. Roth, L. Saur, J. Schmitt, B. Trumbo, S.K. Brown, M.E. O'Donoghue, J. Orton, G.S. Showalter, M.R. LESIA Observatoire de Paris Université PSL Sorbonne Université Université Paris Cité CNRS Meudon92195 France Univ. Grenoble Alpes CNRS IPAG Grenoble38000 France Faculty of Aerospace Engineering Delft University of Technology Delft Netherlands Leiden Observatory Leiden University P.O. Box 9513 Leiden28 NL 2300 RA Netherlands Department of Astronomy University of California 22 BerkeleyCA94720 United States Department of Earth and Planetary Science University of California 22 BerkeleyCA94720 United States University of Wisconsin MadisonWI53706 United States Rome00133 Italy Division of Geological and Planetary Sciences Caltech PasadenaCA91125 United States School of Physics and Astronomy University of Leicester University Road LeicesterLE1 7RH United Kingdom Space and Plasma Physics KTH Royal Institute of Technology Stockholm Sweden Institute of Geophysics and Meteorology University of Cologne Albertus Magnus Platz Cologne50923 Germany Cornell Center for Astrophysics and Planetary Science Cornell University IthacaNY14853 United States Department of Solar System Science JAXA Institute of Space and Astronautical Science Japan Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States SETI Institute Mountain ViewCA94043 United States

Context. We present the first spectroscopic observations of Ganymede by the James Webb space Telescope undertaken in August 2022 as part of the proposal "ERS observations of the Jovian system as a demonstration of JWST's capabilities for Solar system science". Aims. We aimed to investigate the composition and thermal properties of the surface, and to study the relationships of ice and non-water-ice materials and their distribution. Methods. NIRSpec IFU (2.9-5.3 µm) and MIRI MRS (4.9-28.5 µm) observations were performed on both the leading and trailing hemispheres of Ganymede, with a spectral resolution of ∼ 2700 and a spatial sampling of 0.1 to 0.17" (while Ganymede size was ∼1.68"). We characterized the spectral signatures and their spatial distribution on the surface. The distribution of brightness temperatures was analyzed with standard thermophysical modelling including surface roughness. Results. Reflectance spectra show signatures of water ice, CO2 and H2O2. An absorption feature at 5.9 µm, with a shoulder at 6.5 µm, is revealed, and is tentatively assigned to sulfuric acid hydrates. The CO2 4.26-µm band shows latitudinal and longitudinal variations in depth, shape and position over the two hemispheres, unveiling different CO2 physical states. In the ice-rich polar regions, which are the most exposed to Jupiter's plasma irradiation, the CO2 band is redshifted with respect to other terrains. In the boreal region of the leading hemisphere, the CO2 band is dominated by a high wavelength component at ∼ 4.27 µm, consistent with CO2 trapped in amorphous water ice. At equatorial latitudes (and especially on dark terrains) the observed band is broader and shifted towards the blue, suggesting CO2 adsorbed on non-icy materials, such as minerals or salts. Maps of the H2O Fresnel peak area correlate with Bond albedo maps and follow the distribution of water ice inferred from H2O absorption bands. Amorphous ice is detected in the ice-rich polar regions, and is especially a

关键词： Carbon dioxide

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Exploring Burnt Area Delineation with Cross-Resolution Mapping: A Case Study of Very High and Medium-Resolution Data

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Sensors (Basel, Switzerland) 2025年第10期25卷 3009页

作者： Sai Balakavi Vineet Vadrevu Kristofer Lasko Universities Space Research Association (USRA) Science and Technology Institute Huntsville AL 35805 USA. Earth System Science Center University of Alabama at Huntsville Huntsville AL 35805 USA. James Clemens High School Madison AL 35756 USA. Geospatial Research Laboratory Engineer Research and Development Center US Army Corp of Engineers Alexandria VA 22315 USA.

Remote sensing is essential for mapping and monitoring burnt areas. Integrating Very High-Resolution (VHR) data with medium-resolution datasets like Landsat and deep learning algorithms can enhance mapping accuracy. This study employs two deep learning algorithms, UNET and Gated Recurrent Unit (GRU), to classify burnt areas in the Bandipur Forest, Karnataka, India. We explore using VHR imagery with limited samples to train models on Landsat imagery for burnt area delineation. Four models were analyzed: (a) custom UNET with Landsat labels, (b) custom UNET with PlanetScope-labeled data on Landsat, (c) custom UNET-GRU with Landsat labels, and (d) custom UNET-GRU with PlanetScope-labeled data on Landsat. Custom UNET with Landsat labels achieved the best performance, excelling in precision (0.89), accuracy (0.98), and segmentation quality (Mean IOU: 0.65, Dice Coefficient: 0.78). Using PlanetScope labels resulted in slightly lower performance, but its high recall (0.87 for UNET-GRU) demonstrating its potential for identifying positive instances. In the study, we highlight the potential and limitations of integrating VHR with medium-resolution satellite data for burnt area delineation using deep learning.

关键词： GRU PlanetScope UNET burnt areas deep learning satellite remote sensing very high resolution (VHR)

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Correction to: Near‑global summer circulation response to the spring surface temperature anomaly in Tibetan Plateau –– the GEWEX/LS4P first phase experiment

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Climate Dynamics 2024年第6期62卷 5743-5744页

作者： Zhang, Yang Pan, Yan Xue, Yongkang Diallo, Ismaila Zeng, Xubin Li, Shuting Neelin, J. David Lau, William K. M. Boone, Aaron A. Vitart, Frederic Yao, Tandong Tang, Qi Sato, Tomonori Koo, Myung-Seo Ardilouze, Constantin Saha, Subodh K. Yang, Jing Materia, Stefano Lin, Zhaohui Qi, Xin Qin, Yi Nakamura, Tetsu Nobre, Paulo Peano, Daniele Senan, Retish Takaya, Yuhei Wang, Hailan Zhang, Hongliang Zhan, Yanling Zhao, Mei Mechoso, Carlos R Bao, Qing Bottino, Marcus Jorge Hong, Songyou Lin, Yanluan Xie, Shaocheng Pan, Xiaoduo Nayak, Hara Prasad Chou, Sin Chan Guo, Weidong School of Atmospheric Sciences Nanjing University Nanjing China Duke University Durham USA University of California ? Los Angeles Los Angeles USA Pennsylvania State University University Park USA University of Arizona Tucson AZ USA Nanjing China Earth System Science Interdisciplinary Center Universityof Maryland College Park USA CNRM Universit? de Toulouse M?t?o-France CNRS Toulouse France European Centre for Medium-Range Weather Forecasts Reading UK Institute of Tibetan Plateau Research Chinese Academy of Sciences (CAS) Beijing China Lawrence Livermore National Laboratory Livermore USA Hokkaido University Sapporo Japan Korea Institute of Atmospheric Prediction Systems Seoul South Korea Indian Institute of Tropical Meteorology Pune India Beijing Normal University Beijing China Barcelona Supercomputing Centre Barcelona Spain Institute of Atmospheric Physics CAS Beijing China Pacific Northwest National Laboratory Richland USA National Institute for Space Research (INPE) São José dos Campos Brazil Climate Simulation and Prediction Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici Bologna Italy Meteorological Research Institute Japan MeteorologicalAgency Tsukuba Japan National Center for Environmental Prediction College Park USA Center for Earth System Modeling and Prediction CMA Beijing China Bureau of Meteorology Melbourne Australia LASG Institute of Atmospheric Physics CAS Beijing China NOAA/ESRL and University of Colorado/CIRES Boulder USA Tsinghua University Beijing China

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