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Atmosphere
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Land Surface Processes: Modeling and Observation
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Land Surface Processes: Modeling and Observation

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: 9 December 2024 | Viewed by 3582

Special Issue Editor

Prof. Dr. Zhiqiu Gao

E-Mail Website
Guest Editor
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Interests: land–air interactions; parameterization and modeling of land surface processes; measurements of land–air interactions
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are happy to invite you to submit your work to this Special Issue of Atmosphere, titled “Land Surface Processes: Modeling and Observation”. The objective of this Special Issue of Atmosphere is to publish original research manuscripts which focus on the application of mathematical and physical methods and artificial intelligence (AI) technology in the modeling and measurement of land surface processes on various scales. We aim to publish papers that relate to (1) novel/improved methods and/or retrieval algorithms of satellite remote sensing to estimate turbulent fluxes from a single, regional point to a global scale; (2) the parameterization of land–air interactions in weather forecasting and regional/global climate prediction; (3) AI technology applications in measurements and modeling of land process; and (4) land surface processes under typical weather environments, namely typhoon, tornado, rainstorm, freezing rain and snow, and wildfire, to benefit the community, open to everybody in need of them. We sincerely encourage submissions from researchers based all around the world, especially the new generation of scientists.

Prof. Dr. Zhiqiu Gao
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • land-air interactions
  • turbulent fluxes
  • measurements
  • modeling
  • AI technology
  • parameterization

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Published Papers (3 papers)

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Research

21 pages, 6001 KiB  
Article
Enhancing Fine Aerosol Simulations in the Remote Atmosphere with Machine Learning
by Mingxinyu Lu and Chloe Yuchao Gao
Atmosphere 2024, 15(11), 1356; https://doi.org/10.3390/atmos15111356 - 12 Nov 2024
Viewed by 448
Abstract
Global aerosol models often underestimate the mass concentration of aerosols in the remote troposphere, as evidenced by aircraft measurements. This study leveraged data from the NASA Atmospheric Tomography Mission (ATom), which provides remote aerosol concentrations, to refine algorithms for simulating these concentrations. Using [...] Read more.
Global aerosol models often underestimate the mass concentration of aerosols in the remote troposphere, as evidenced by aircraft measurements. This study leveraged data from the NASA Atmospheric Tomography Mission (ATom), which provides remote aerosol concentrations, to refine algorithms for simulating these concentrations. Using the GEOS-Chem model, we simulate five fine aerosol types and enhance the simulation results using five machine-learning algorithms: Random Forest, XGBoost, SVM, KNN, and LightGBM, and compare the performance of these algorithms. Additionally, we evaluate the refinement effect of algorithms based on decision trees on a validation dataset. The results demonstrate that GEOS-Chem generally underestimated aerosol mass concentration. Among the tested algorithms, algorithms based on decision trees, particularly the Random Forest algorithm and the LightGBM algorithm, exhibited a superior performance, significantly improving prediction accuracy and computational efficiency in both the training and testing phases, as well as on the validation dataset. Full article
(This article belongs to the Special Issue Land Surface Processes: Modeling and Observation)
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21 pages, 28792 KiB  
Article
Imaging and Interferometric Mapping Exploration for PIESAT-01: The World’s First Four-Satellite “Cartwheel” Formation Constellation
by Tian Zhang, Yonggang Qian, Chengming Li, Jufeng Lu, Jiao Fu, Qinghua Guo, Shibo Guo and Yuxiang Wang
Atmosphere 2024, 15(6), 621; https://doi.org/10.3390/atmos15060621 - 21 May 2024
Cited by 2 | Viewed by 1274
Abstract
The PIESAT-01 constellation is the world’s first multi-baseline distributed synthetic aperture radar (SAR) constellation with a “Cartwheel” formation. The “Cartwheel” formation is a unique formation in which four satellites fly in companion orbits, ensuring that at any given moment, the main satellite remains [...] Read more.
The PIESAT-01 constellation is the world’s first multi-baseline distributed synthetic aperture radar (SAR) constellation with a “Cartwheel” formation. The “Cartwheel” formation is a unique formation in which four satellites fly in companion orbits, ensuring that at any given moment, the main satellite remains at the center, with three auxiliary satellites orbiting around it. Due to this unique configuration of the PIESAT-01 constellation, four images of the same region and six pairs of baselines can be obtained with each shot. So far, there has been no imaging and interference research based on four-satellite constellation measured data, and there is an urgent need to explore algorithms for the “Cartwheel” configuration imaging and digital surface model (DSM) production. This paper introduces an improved bistatic SAR imaging algorithm under the four-satellites interferometric mode, which solves the problem of multi-orbit nonparallelism in imaging while ensuring imaging coherence and focusing ability. Subsequently, it presents an interferometric processing method for the six pairs of baselines, weighted fusion based on elevation ambiguity from different baselines, to obtain a high-precision DSM. Finally, this paper selects the Dingxi region of China and other regions with diverse terrains for imaging and DSM production and compares the DSM results with ICESat-2 global geolocated photon data and TanDEM DSM data. The results indicate that the accuracy of PIESAT-01 DSM meets the standards of China’s 1:50,000 scale and HRTI-3, demonstrating a high level of precision. Moreover, PIESAT-01 data alleviate the reliance on simulated data for research on multi-baseline imaging and multi-baseline phase unwrapping algorithms and can provide more effective and realistic measured data. Full article
(This article belongs to the Special Issue Land Surface Processes: Modeling and Observation)
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17 pages, 10055 KiB  
Article
Simulating the Wind Energy Distribution in the Coastal Hilly Area of the Jiaodong Peninsula Using the Weather Research and Forecasting Model
by Yunhai Song, Sen He, Zhenzhen Zhou, Liwei Wang, Yufeng Yang, Zheng Li and Zhiqiu Gao
Atmosphere 2024, 15(1), 101; https://doi.org/10.3390/atmos15010101 - 13 Jan 2024
Viewed by 1335
Abstract
This study simulated the wind energy density distribution in the Jiaodong Peninsula region using the Weather Research and Forecasting (WRF) Model. The impacts of different boundary-layer and near-surface parameterization schemes on the simulated wind speed and direction were investigated. The results indicate that [...] Read more.
This study simulated the wind energy density distribution in the Jiaodong Peninsula region using the Weather Research and Forecasting (WRF) Model. The impacts of different boundary-layer and near-surface parameterization schemes on the simulated wind speed and direction were investigated. The results indicate that the Yonsei University (YSU) scheme and the Quasi-Normal Scale Elimination (QNSE) scheme performed optimally for wind speed and wind direction. We also conducted a sensitivity test of the simulation results for atmospheric pressure, air temperature, and relative humidity. The statistical analysis showed that the YSU scheme performed optimally, while the MRF and BL schemes performed poorly. Following this, the wind energy distribution in the coastal hilly areas of the Jiaodong Peninsula was simulated using the YSU boundary-layer parameterization scheme. The modeled wind energy density in the mountainous and hilly areas of the Jiaodong Peninsula were higher than that in other regions. The wind energy density exhibits a seasonal variation, with the highest values in spring and early summer and the lowest in summer. In spring, the wind energy density over the Bohai Sea is higher than over the Yellow Sea, while the opposite trend is modeled in summer. Full article
(This article belongs to the Special Issue Land Surface Processes: Modeling and Observation)
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