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Comprehensive Analysis Based on Observation, Remote Sensing, and Numerical Models to Understand the Meteorological Environment in Arid Areas and Their Surrounding Areas

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 901

Special Issue Editors


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Guest Editor
School of Atmosphere and Remote Sensing, Wuxi University, Wuxi 214105, China
Interests: remote sensing; deep learning; short term precipitation forecast; disaster assessment; environmental monitoring; artificial intelligence

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Guest Editor
School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: numerical weather prediction; climate change; short-term climate prediction; artificial intelligence
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Guest Editor
National Institute of Education—Humanities & Social Studies Education, Nanyang Technological University, Singapore 639672, Singapore
Interests: regional and global climate modelling and applications; severe convective storms and hazards; meteorological instrumentation; land–atmosphere interaction
Special Issues, Collections and Topics in MDPI journals
Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China
Interests: disastrous weather and climate; desert boundary layer; observation of sandstorm
Special Issues, Collections and Topics in MDPI journals
School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China
Interests: weather and climate extremes; climate change; climate dynamics; climate modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Arid and semi-arid areas are mostly located in the hinterland of the Eurasian continent—far from the ocean, with relatively scarce water resources. These regions possess complex terrain, being crisscrossed by mountains and basins and coexisting alongside deserts and oases. Today, such areas are among the most ecologically fragile, and they are especially sensitive to climate responses. Continuous research into the climate and its impact mechanisms in the arid zones is of great scientific significance for developing a deeper understanding of the cause of climatic formation and a better way of predicting climate in arid regions.

Drought is one of the most widespread and severe natural disasters in the world. China is located in a typical monsoon climate zone, and the impact of drought disasters is particularly prominent. A large amount of research has been conducted on the issue of drought internationally, gradually developing from a qualitative and superficial understanding of drought to a quantitative understanding of the objective characteristics and formation mechanisms behind the issue. Based on the international frontiers, hot topics, and development trends of drought research, it is proposed that future drought research must perform strengthen comprehensive experiments in typical drought-prone areas, investigating factors such as the synergistic effects of multiple factors on drought formation, the role of land–air interaction in drought formation and development, the identification, monitoring, and prediction of sudden droughts, and the transformation patterns and non-consistent characteristics shared between various types of droughts. Breakthroughs have been made in key scientific issues such as the role of critical impact periods in agricultural drought development, the complexity of drought responses to climate change, and the scientific assessment of drought disaster risks.

At the same time, as a key area in the upstream of China's weather, the northwest arid region has a significant impact on the occurrence of catastrophic weather events and regional climate change in the northwest and eastern regions of China. Understanding and grasping the characteristics of climate change in arid regions is conducive to providing scientific basis for disaster prevention and mitigation, and reasonable response to climate change. In recent years, many scholars have conducted a series of studies on arid areas, revealing the spatiotemporal characteristics and feedback mechanisms of climate change. However, due to the wide spatial coverage of arid areas, data scarcity has become a major constraint that hinders the further exploration of numerous unresolved issues. The vigorous development of integrated meteorological observation systems that combine spaceborne, airborne, and ground sensors, incorporating the latest progress in remote sensing and numerical modeling, has opened up more research avenues for solving existing and emerging meteorological problems in arid areas.

In this Special Issue, we invite researchers from the fields of meteorology, climatology, ecology, geography, remote sensing, Earth information systems, and environmental science to make innovative contributions to the theoretical, observational, and mode research of the meteorological environment in arid areas at different temporal and spatial scales.

In particular, we encourage studies investigating (but not limited to):

  • Studies on boundary layer structure of heterogeneous underlying surfaces, and the exchange of water, heat and dust in these layers, as well as land surface process characteristic parameters and parametric schemes in arid areas and surrounding area based on observations, remote sensing, and modeling data.
  • Studies on the influencing mechanisms of boundary layers on regional circulation and local weather processes in arid areas by improving the simulation capability of the land surface process model and/or the numerical prediction model.
  • Studies on assessment of climate risks in arid areas and surrounding areas based on observational, remote sensing and reanalysis datasets.
  • Studies on the feedback mechanisms between extreme climate and elements of land–atmosphere systems.

This is the Second Edition of the Special Issue of Remote Sensing, entitled “Understanding the Meteorological Environment in Arid Regions through the Integrative Analyses of Remote Sensing, Ground Observational Stations and Numerical Models”, and experts and scholars in related fields are welcome to submit their original research to this Special Issue.

Prof. Dr. Yonghong Zhang
Prof. Dr. Xiefei Zhi
Prof. Dr. Donglian Sun
Dr. Jingyu Wang
Dr. Wen Huo
Dr. Fei Ge
Guest Editors

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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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–atmosphere interactions
  • regional climate change and extreme weather
  • atmospheric physics and atmospheric environment
  • arid areas and drought
  • dust aerosols
  • greenhouse gases

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Published Papers (2 papers)

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Research

19 pages, 6028 KiB  
Article
Application of Ensemble Algorithm Based on the Feature-Oriented Mean in Tropical Cyclone-Related Precipitation Forecasting
by Jing Zhang and Hong Li
Remote Sens. 2024, 16(9), 1596; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16091596 - 30 Apr 2024
Abstract
Tropical cyclones (TCs) are characterized by robust vortical motion and intense thermodynamic processes, often causing damage in coastal cities as they result in landfall. Accurately estimating the ensemble mean of TC precipitation is critical for forecasting and remains a foremost global challenge. In [...] Read more.
Tropical cyclones (TCs) are characterized by robust vortical motion and intense thermodynamic processes, often causing damage in coastal cities as they result in landfall. Accurately estimating the ensemble mean of TC precipitation is critical for forecasting and remains a foremost global challenge. In this study, we develop an ensemble algorithm based on the feature-oriented mean (FM) suitable for spatially discrete variables in precipitation ensembles. This method can adjust the locations of ensemble precipitation fields to reduce the location-related deviations among ensemble members, ultimately enhancing the ensemble mean forecast skill for TC precipitation. To evaluate the feasibility of the FM in TC precipitation ensemble forecasting, 18 landing TC cases in China from 2019 to 2021 were selected for validation. For precipitation forecasts of the landing TCs with a varying leading time, we conducted a comprehensive quantitative evaluation and comparison of the precipitation forecast skills of the FM and arithmetic mean (AM) algorithms. The results indicate that the field adjustment algorithm in the FM can effectively align with the TC precipitation structure and the location of the ensemble mean, reducing the spatial divergence among precipitation fields. The FM method demonstrates superior performance in the equitable threat score, probability of detection, and false alarm ratio compared with the AM, exhibiting an overall improvement of around 10%. Furthermore, the FM ensemble mean shows a higher pattern of the correlation coefficient with observations and has a smaller root mean square error than the AM ensemble mean, signifying that the FM method can better preserve the characteristics of the precipitation structure. Additionally, an object-based diagnostic evaluation method was used to verify forecast results, and the results suggest that the attribute distribution of FM forecast objects more closely resembles that of observed precipitation objects (including the area, longitudinal and latitudinal centroid locations, axis angle, and aspect ratio). Full article
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25 pages, 7622 KiB  
Article
Analysis of Height of the Stable Boundary Layer in Summer and Its Influencing Factors in the Taklamakan Desert Hinterland
by Guocheng Yang, Wei Shu, Minzhong Wang, Donglei Mao, Honglin Pan and Jiantao Zhang
Remote Sens. 2024, 16(8), 1417; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16081417 - 17 Apr 2024
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Abstract
Stable boundary layer height (SBLH) is an important parameter to characterize the characteristics and vertical structure of the nocturnal lower atmosphere at night. The distribution of SBLH has obvious spatial and temporal differences, and there are many meteorological factors affecting the SBLH, but [...] Read more.
Stable boundary layer height (SBLH) is an important parameter to characterize the characteristics and vertical structure of the nocturnal lower atmosphere at night. The distribution of SBLH has obvious spatial and temporal differences, and there are many meteorological factors affecting the SBLH, but at present, there are few quantitative studies on the effects of near-surface meteorological factors on the SBLH in the desert hinterland. This study was based on GPS sounding balloon data, near-surface meteorological observation data, and ERA5 data from Tazhong Station (TZ) in the Taklamakan Desert (TD) collected in July 2017, 2019, and 2021. The variation characteristics of the SBLH and its relationship with near-surface meteorological factors are described. We quantitatively analyzed the degree of influence of near-surface meteorological factors affecting the SBLH and verified it using a model. The study also elucidates the possible formation mechanism of the SBLH in the TD hinterland. The SBLH in the TD hinterland trended upward in July 2017, 2019, and 2021, which is consistent with the changes in meteorological factors, according to the near-surface meteorological observation and ERA5 data. Therefore, we think that an inherent connection exists between near-surface meteorological factors and the SBLH. The results of correlation analysis show that complex internal connections and interactions exist among the meteorological factors near the ground; some thermal, dynamic, and other meteorological factors strongly correlate with the SBLH. Having established the change in SBLH (ΔSBLH) and in major thermal, dynamic, and other meteorological factors (Δ), the linear regression equation between them revealed that near-surface meteorological factors can affect the SBLH. The dynamic factors have a stronger influence on the ΔSBLH than thermal and other factors. The results of model validation based on the variable importance projection (VIP) also confirmed that the SBLH in the TD hinterland is jointly affected by dynamic and thermal factors, but the dynamic factors have a stronger impact. The mechanism through which the SBLH forms is relatively complex. At night, surface radiative cooling promotes the formation of a surface inversion layer, and low-level jets strengthen wind shear, reducing atmospheric stability. The combined effects of heat and dynamics play an important role in dynamically shaping the SBLH. This study helps us with accurately predicting and understanding the characteristics of the changes in and the factors influencing the SBLH in the TD hinterland, providing a reference for understanding the mechanism through which the SBLH forms in this area. At the same time, it provides a scientific basis for regional weather and climate simulation, meteorological disaster defense, air quality forecasting, and model parameterization improvement. Full article
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