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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

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

Deadline for manuscript submissions: 20 February 2025 | Viewed by 8230

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Prof. Dr. Yonghong Zhang

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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

Prof. Dr. Xiefei Zhi

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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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Prof. Dr. Donglian Sun

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Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA
Interests: remote sensing natural hazards; surface and atmosphere information
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Dr. Jingyu Wang

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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
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Dr. Wen Huo

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Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China
Interests: disastrous weather and climate; desert boundary layer; observation of sandstorm
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Dr. Fei Ge

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School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China
Interests: weather and climate extremes; climate change; climate dynamics; climate modelling
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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

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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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Understanding the Meteorological Environment in Arid Regions through the Integrative Analyses of Remote Sensing, Ground Observational Stations and Numerical Models in Remote Sensing (15 articles)

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Research

19 pages, 6344 KiB

Open AccessArticle

Evaluation of Fengyun-4B Satellite Temperature Profile Products Using Radiosonde Observations and ERA5 Reanalysis over Eastern Tibetan Plateau

by Yuhao Wang, Xiaofei Wu, Haoxin Zhang, Hong-Li Ren and Kaiqing Yang

Remote Sens. 2024, 16(22), 4155; https://doi.org/10.3390/rs16224155 - 7 Nov 2024

Viewed by 489

Abstract

The latest-generation geostationary meteorological satellite, Fengyun-4B (FY-4B), equipped with the Geostationary Interferometric Infrared Sounder (GIIRS), offers high-spatiotemporal-resolution three-dimensional temperature structures. Its deployment serves as a critical complement to atmospheric temperature profile (ATP) observation in the Tibetan Plateau (TP). Based on radiosonde observation (RAOB) and the fifth-generation ECMWF global climate atmospheric reanalysis (ERA5), this study validates the availability and representativeness of FY-4B/GIIRS ATP products in the eastern TP region. Due to the issue of satellite zenith, this study focuses solely on examining the eastern TP region. Under a clear sky, FY-4B/GIIRS ATP exhibits good consistency with RAOB compared to cloudy conditions, with an average root mean square error (RMSE) of 2.57 K. FY-4B/GIIRS tends to underestimate temperatures in the lower layers while overestimating temperatures in the upper layers. The bias varies across seasons. Except for summer, the horizontal and vertical bias distribution patterns are similar, though there are slight differences in values. Despite the presence of bias, FY-4B/GIIRS ATP maintains a good consistency with observations and reanalysis data, indicating commendable product quality. These results demonstrate that it can play a vital role in augmenting the ATP observation network limited by sparse radiosonde stations in the eastern TP, offering crucial data support for numerical weather prediction, weather monitoring, and related meteorological research in this region. Full article

(This article belongs to the Special Issue Comprehensive Analysis Based on Observation, Remote Sensing, and Numerical Models to Understand the Meteorological Environment in Arid Areas and Their Surrounding Areas)

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29 pages, 9650 KiB

Open AccessArticle

Seasonal Variations in the Rainfall Kinetic Energy Estimation and the Dual-Polarization Radar Quantitative Precipitation Estimation Under Different Rainfall Types in the Tianshan Mountains, China

by Yong Zeng, Lianmei Yang, Zepeng Tong, Yufei Jiang, Abuduwaili Abulikemu, Xinyu Lu and Xiaomeng Li

Remote Sens. 2024, 16(20), 3859; https://doi.org/10.3390/rs16203859 - 17 Oct 2024

Viewed by 653

Abstract

Raindrop size distribution (DSD) has an essential effect on rainfall kinetic energy estimation (RKEE) and dual-polarization radar quantitative precipitation estimation (QPE); DSD is a key factor for establishing a dual-polarization radar QPE scheme and RKEE scheme, particularly in mountainous areas. To improve the understanding of seasonal DSD-based RKEE, dual-polarization radar QPE, and the impact of rainfall types and classification methods, we investigated RKEE schemes and dual-polarimetric radar QPE algorithms across seasons and rainfall types based on two classic classification methods (BR09 and BR03) and DSD data from a disdrometer in the Tianshan Mountains during 2020–2022. Two RKEE schemes were established: the rainfall kinetic energy flux–rain rate (KE_time–R) and the rainfall kinetic energy content–mass-weighted mean diameter (KE_mm–D_m). Both showed seasonal variation, whether it was stratiform rainfall or convective rainfall, under BR03 and BR09. Both schemes had excellent performance, especially the KE_mm–D_m relationship across seasons and rainfall types. In addition, four QPE schemes for dual-polarimetric radar—R(K_dp), R(Z_h), R(K_dp,Z_dr), and R(Z_h,Z_dr)—were established, and exhibited characteristics that varied with season and rainfall type. Overall, the performance of the single-parameter algorithms was inferior to that of the double-parameter algorithms, and the performance of the R(Z_h) algorithm was inferior to that of the R(K_dp) algorithm. The results of this study show that it is necessary to consider different rainfall types and seasons, as well as classification methods of rainfall types, when applying RKEE and dual-polarization radar QPE. In this process, choosing a suitable estimator—KE_time(R), KE_mm(D_m), R(K_dp), R(Z_h), R(K_dp,Z_dr), or R(Z_h,Z_dr)—is key to improving the accuracy of estimating the rainfall KE and R. Full article

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17 pages, 7168 KiB

Open AccessArticle

Evaluating the Prediction Performance of the WRF-CUACE Model in Xinjiang, China

by Yisilamu Wulayin, Huoqing Li, Lei Zhang, Ali Mamtimin, Junjian Liu, Wen Huo and Hongli Liu

Remote Sens. 2024, 16(19), 3747; https://doi.org/10.3390/rs16193747 - 9 Oct 2024

Viewed by 700

Abstract

Dust and air pollution events are increasingly occurring around the Taklimakan Desert in southern Xinjiang and in the urban areas of northern Xinjiang. Predicting such events is crucial for the advancement, growth, and prosperity of communities. This study evaluated a dust and air pollution forecasting system based on the Weather Research and Forecasting model coupled with the China Meteorological Administration Chemistry Environment (WRF-CUACE) model using ground and satellite observations. The results showed that the forecasting system accurately predicted the formation, development, and termination of dust events. It demonstrated good capability for predicting the evolution and spatial distribution of dust storms, although it overestimated dust intensity. Specifically, the correlation coefficient (R) between simulated and observed PM10 was up to 0.85 with a mean absolute error (MAE) of 721.36 µg·m⁻³ during dust storm periods. During air pollution events, the forecasting system displayed notable variations in predictive accuracy across various urban areas. The simulated trends of PM2.5 and the Air Quality Index (AQI) closely aligned with the actual observations in Ürümqi. The R for simulated and observed PM2.5 concentrations at 24 and 48 h intervals were 0.60 and 0.54, respectively, with MAEs of 28.92 µg·m⁻³ and 29.10 µg·m⁻³, respectively. The correlation coefficients for simulated and observed AQIs at 24 and 48 h intervals were 0.79 and 0.70, respectively, with MAEs of 24.21 and 27.56, respectively. The evolution of the simulated PM10 was consistent with observations despite relatively high concentrations. The simulated PM2.5 concentrations in Changji and Shihezi were notably lower than those observed, resulting in a lower AQI. For PM10, the simulation–observation error was relatively small; however, the trends were inconsistent. Future research should focus on optimizing model parameterization schemes and emission source data. Full article

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25 pages, 9842 KiB

Open AccessArticle

Urbanization Effect on Regional Thermal Environment and Its Mechanisms in Arid Zone Cities: A Case Study of Urumqi

by Aerzuna Abulimiti, Yongqiang Liu, Jianping Tang, Ali Mamtimin, Junqiang Yao, Yong Zeng and Abuduwaili Abulikemu

Remote Sens. 2024, 16(16), 2939; https://doi.org/10.3390/rs16162939 - 10 Aug 2024

Cited by 1 | Viewed by 1164

Abstract

Urumqi is located in the arid region of northwestern China, known for being one of the most delicate ecological environments and an area susceptible to climate change. The urbanization of Urumqi has progressed rapidly, yet there is a lack of research on the urbanization effect (UE) in Urumqi in terms of the regional climate. This study investigates the UE of Urumqi (urban built-up area) on the regional thermal environment and its mechanisms for the first time, based on the WRF (Weather Research and Forecasting) model (combined with the Urban Canopy Model, UCM) simulation data of 10 consecutive years (2012–2021). The results show that the UE on surface temperature (Ts) and air temperature at 2 m (T2m) is strong (weak) during the night (daytime) in all seasons, and the UE on these is largest (smallest) in spring (winter). In addition, the maximum UE on both Ts and T2m is present over southern Urumqi in winter, whereas the maximum UE is identified over the northern Urumqi in other seasons. The maximum UE on Ts occurred in northwestern Urumqi at 18 LST (Local Standard Time, i.e., UTC+6) in autumn (reaching 5.2 °C), and the maximum UE on T2m occurred in northern Urumqi at 4 LST in summer (reaching 2.6 °C). Urbanization showed a weak cooling effect during daytime in summer and winter, reflecting the unique characteristics of the UE in arid regions, which are different from those in humid regions. The maximum cooling of Ts occurred in northern Urumqi at 11 LST in summer (reaching −0.4 °C), while that of T2m occurred at 10 LST in northern and northwestern Urumqi in winter (reaching −0.25 °C), and the cooling effect lasted for a longer period of time in summer than in winter. The UE of Urumqi causes the increase of Ts mainly through the influence of net short-wave radiation and geothermal flux and causes the increase of T2m through the influence of sensible heat flux and net long-wave radiation. The UE on the land surface energy balance in Urumqi can be used to explain the seasonal variation and spatial differences of the UEs on the regional thermal environment and the underlying mechanism. Full article

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23 pages, 12258 KiB

Open AccessArticle

Seasonal Variation in Total Cloud Cover and Cloud Type Characteristics in Xinjiang, China Based on FY-4A

by Yong Zeng, Lianmei Yang, Zepeng Tong, Yufei Jiang, Yushu Zhou, Xinyu Lu, Abuduwaili Abulikemu and Jiangang Li

Remote Sens. 2024, 16(15), 2803; https://doi.org/10.3390/rs16152803 - 31 Jul 2024

Viewed by 665

Abstract

In order to deepen the knowledge of the seasonal variation in total cloud cover (TCC) in Xinjiang, China (XJ), a typical arid region, and to broaden the understanding of the seasonal variation in cloud type (CLT) in the region, we used TCC and CLT datasets from the latest generation of the geostationary satellite Fengyun 4A (FY-4A) from 2018 to 2022 to investigate the seasonal variation characteristics of TCC and CLT in XJ. Meanwhile, to verify the accuracy of TCC from FY-4A, ground observation (GROB) TCC datasets from 105 national meteorological stations (NMSs) in XJ and TCC datasets from ERA5 during the same period were used. In addition, the correlation between TCC from FY-4A and meteorological factors from ERA5 was also analyzed in this study. The TCC from FY-4A, GROB, and ERA5 can all well reflect the significant seasonal variation in TCC in XJ, with the highest (lowest) mean TCC and a distribution pattern of high in the southwest (northwest) and low in the northeast (southeast) in spring (fall) in XJ. Although the mean TCC from FY-4A in all four seasons was lower than that from GROB, the two were comparable in spring (44.09% and 47.32%) and summer (42.88% and 43.17%), while there was a significant difference between the two in fall (27.86% and 40.19%) and winter (30.58% and 46.93%) for 105 NMSs in XJ. The TCC from FY-4A was lower (higher) than that from GROB in spring and summer at most NMSs in northern (southern) XJ, while the TCC from FY-4A was lower than that from GROB for the vast majority of NMSs in fall and winter, especially in northern XJ. The seasonal variation in the spatial distribution of different CLTs (clear, water-type, supercooled-type, mixed-type, ice-type, cirrus-type, and overlap-type) from FY-4A exhibited diverse variation characteristics. Water-type (supercooled-water-type) had a high-frequency center of over 30% in the Tarim Basin (Kunlun Mountains) during summer. Mixed-type (ice-type and cirrus-type) had the highest frequency in winter (spring), while overlap-type had the highest frequency in summer. The correlation between TCC and water vapor conditions (total column vertically integrated water vapor, specific humidity at 250 hPa, 500 hPa, and 700 hPa) was positive in XJ. Full article

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19 pages, 3487 KiB

Open AccessArticle

An Improved Remote Sensing Retrieval Method for Elevated Duct in the South China Sea

by Yinhe Cheng, Mengling Zha, Wenli Qiao, Hongjian He, Shuwen Wang, Shengxiang Wang, Xiaoran Li and Weiye He

Remote Sens. 2024, 16(14), 2649; https://doi.org/10.3390/rs16142649 - 19 Jul 2024

Viewed by 774

Abstract

Elevated duct is an atmospheric structure characterized by abnormal refractive index gradients, which can significantly affect the performance of radar, communication, and other systems by capturing a portion of electromagnetic waves. The South China Sea (SCS) is a high-incidence area for elevated duct, so conducting detection and forecasts of the elevated duct in the SCS holds important scientific significance and practical value. This paper attempts to utilize remote sensing techniques for extracting elevated duct information. Based on GPS sounding data, a lapse rate formula (LRF) model and an empirical formula (EF) model for the estimation of the cloud top height of Stratocumulus were obtained, and then remote sensing retrieval methods of elevated duct were established based on the Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data. The results of these two models were compared with results from the elevated duct remote sensing retrieval model developed by the United States Naval Postgraduate School. It is shown that the probability of elevated duct events was 79.1% when the presence of Stratocumulus identified using GPS sounding data, and the trapping layer bottom height of elevated duct well with the cloud top height of Stratocumulus, with a correlation coefficient of 0.79, a mean absolute error of 289 m, and a root mean square error of 598 m. Among the different retrieval models applied to MODIS satellite data, the LRF model emerged as the optimal remote sensing retrieval method for elevated duct in the SCS, showing a correlation coefficient of 0.51, a mean absolute error of 447 m, and a root mean square error of 658 m between the trapping layer bottom height and the cloud top height. Consequently, the encouraging validation results demonstrate that the LRF model proposed in this paper offers a novel method for diagnosing and calculating elevated ducts information over large-scale marine areas from remote sensing data. Full article

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15 pages, 3261 KiB

Open AccessArticle

Validation of Multisource Altimeter SWH Measurements for Climate Data Analysis in China’s Offshore Waters

by Jingwei Xu, Huanping Wu, Xiefei Zhi, Nikolay V. Koldunov, Xiuzhi Zhang, Ying Xu, Yangyang Zhang, Maohua Guo, Lisha Kong and Klaus Fraedrich

Remote Sens. 2024, 16(12), 2162; https://doi.org/10.3390/rs16122162 - 14 Jun 2024

Viewed by 781

Abstract

Climate data derived from long-term, multisource altimeter significant wave height (SWH) measurements are more valuable than those obtained from a single altimeter source. Such data facilitate exploration of long-term air–sea momentum transfer and more comprehensive investigation of weather system dynamics processes over the ocean. Despite the deployment of the first satellite in the Chinese Haiyang-2 (HY-2) series more than 12 years ago, validation and integration of SWH data from China’s offshore waters, derived using Chinese altimeters, have been limited. This study constructed a high-resolution, long-term, multisource gridded SWH climate dataset using along-track data from the HY-2 series, CFOSAT, Jason-2, Jason-3, and Cryosat-2 altimeters. Validation against observations from 31 buoys covering China’s offshore waters indicated that the SWH variances from HY-2A, HY-2B, HY-2C, CFOSAT, and Jason-3 altimeters correlated well with observations, with a temporal correlation coefficient of approximately 0.95 (except HY-2A, correlation: 0.89). These SWH measurements generally showed a robust linear relationship with the buoy data. Additionally, cross-calibration between Jason-3 and the HY-2A, HY-2B, HY-2C, and CFOSAT altimeters also demonstrated a typically linear relationship for SWH > 6.0 m. Using this relationship, the SWH data were linearly corrected and integrated into a 10 d mean, long-term, multisource altimeter gridded SWH dataset. Compared with in situ observations, the merged 10 d mean SWHs are more accurate and closely match the observations, with temporal correlation coefficients improving from 0.87 to 0.90 and bias decreasing from 0.28 to 0.03 m. The merged gridded SWHs effectively represent the local spatial distribution of SWH. This study revealed the importance of observational data in the process of merging and recalibrating long-term multisource altimeter SWH datasets, particularly before their application in specific ocean regions. Full article

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19 pages, 6028 KiB

Open AccessArticle

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://doi.org/10.3390/rs16091596 - 30 Apr 2024

Viewed by 794

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 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

Open AccessArticle

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://doi.org/10.3390/rs16081417 - 17 Apr 2024

Cited by 1 | Viewed by 1081

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

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