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Advances in Methods for the Investigation of the Atmospheric Water...
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Advances in Methods for the Investigation of the Atmospheric Water Cycle

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Biosphere/Hydrosphere/Land–Atmosphere Interactions".

Deadline for manuscript submissions: 20 January 2026 | Viewed by 2903

Special Issue Editor

Dr. Lifeng Wu

E-Mail Website
Guest Editor
School of Hydraulic and Ecological Engineering, Nanchang Institute of Technology, Nanchang 330099, China
Interests: irrigation and water management; artificial intelligence; fertigation; water resources management; agricultural meteorology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The atmospheric water cycle is a crucial component of Earth, playing an important role in understanding climate change, weather patterns, and water resource management. It involves the processes of evaporation, condensation, and precipitation and the transfer of water between the atmosphere and the surface, serving as a fundamental process within the Earth's climate system. With the continuous advancement of science and technology, new research methods and techniques are emerging, providing richer perspectives and tools for studying the atmospheric water cycle. Under the dual influence of climate change and human activities, it has become increasingly important to gain a deeper understanding of the dynamic changes in the water cycle. In recent years, advancements in artificial intelligence, numerical weather forecasting, and remote sensing technologies have led to a more profound understanding of the water cycle, making it essential to review cutting-edge methods. This Special Issue aims to gather current frontier research findings in the field of atmospheric water cycle studies, facilitating academic exchange and collaboration and providing theoretical support and technical guidance to address global water resource challenges.

Major Submission Directions

This Special Issue welcomes submissions on, but not limited to, the following topics:

  • Novel remote sensing instruments and data processing methods;
  • Monitoring and analysis methods for water vapor transport;
  • High-resolution simulations of the atmospheric water cycle;
  • Applications of machine learning and artificial intelligence in optimizing water cycle models;
  • Changes in the water cycle under different climate scenarios due to climate change;
  • Impacts of extreme weather events on water resources;
  • Quantitative methods for the dynamic relationships between precipitation, evaporation, soil moisture, and vegetation;
  • Assessment methods for the impacts of climate change on transboundary water resources.

Dr. Lifeng Wu
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

  • advanced computing methods
  • machine learning
  • deep learning
  • numerical weather prediction
  • land surface model
  • soil–plant–atmosphere continuum
  • precipitation
  • evapotranspiration

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

17 pages, 1235 KiB  
Article
Bias Correction Methods Applied to Satellite Rainfall Products over the Western Part of Saudi Arabia
by Ibrahim H. Elsebaie, Atef Q. Kawara, Raied Alharbi and Ali O. Alnahit
Atmosphere 2025, 16(7), 772; https://doi.org/10.3390/atmos16070772 - 24 Jun 2025
Viewed by 394
Abstract
Accurate rainfall data with good spatial–temporal distribution remain a challenge worldwide, particularly in arid regions such as western Saudi Arabia, where variability critically influences water resource management and flood mitigation. This study evaluates five satellite-based rainfall products—GPM, GPCP, CHIRPS, PERSIANN-CDR and PERSIANN—against observed [...] Read more.
Accurate rainfall data with good spatial–temporal distribution remain a challenge worldwide, particularly in arid regions such as western Saudi Arabia, where variability critically influences water resource management and flood mitigation. This study evaluates five satellite-based rainfall products—GPM, GPCP, CHIRPS, PERSIANN-CDR and PERSIANN—against observed monthly rainfall at 28-gauge stations, using the correlation coefficient (CC), root mean square error (RMSE), relative bias (RB) and mean absolute error (MAE). Among uncorrected products, GPM achieved the highest mean CC (0.52), and lowest RMSE (17.0 mm) and MAE (9.18 mm) compared with CC = 0.39 (RMSE 19.9 mm) for GPCP, CC = 0.20 (RMSE 21.6 mm) for CHIRPS, CC = 0.43 (RMSE 19.2 mm) for PERSIANN-CDR and CC = 0.26 (RMSE 57.3 mm) for PERSIANN. Four bias correction methods—linear scaling, nonlinear adjustment, quantile mapping and artificial neural networks (ANN)—were applied. The ANN reduced GPM’s RMSE by 19% to 13.8 mm, increased CC to 0.59, lowered RB to 2.5% and achieved an MAE of 6.89 mm. These results demonstrate that GPM, particularly when bias-corrected via ANN, provides a dependable rainfall dataset for hydrological modeling and flood risk assessment in arid environments. Full article
(This article belongs to the Special Issue Advances in Methods for the Investigation of the Atmospheric Water Cycle)
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23 pages, 6031 KiB  
Article
Assessment of the PPP-AR Strategy for ZTD and IWV in Africa: A One-Year GNSS Study
by Moustapha Gning Tine, Pierre Bosser, Ngor Faye, Lila Jean-Louis and Mapathé Ndiaye
Atmosphere 2025, 16(6), 741; https://doi.org/10.3390/atmos16060741 - 17 Jun 2025
Viewed by 462
Abstract
With the increasing demand for near real-time atmospheric water vapor monitoring, this study evaluates the performance of the open-source PRIDE PPP-AR software (version 3.0.5) for retrieving Zenith Total Delay (ZTD) and Integrated Water Vapor (IWV) over the African continent over a one-year period. [...] Read more.
With the increasing demand for near real-time atmospheric water vapor monitoring, this study evaluates the performance of the open-source PRIDE PPP-AR software (version 3.0.5) for retrieving Zenith Total Delay (ZTD) and Integrated Water Vapor (IWV) over the African continent over a one-year period. PRIDE PPP-AR is compared with established PPP-AR and PPP solutions, including CSRS-PPP, IGN-PPP, and NGL and using GipsyX, ERA5, and IGS products as references. A robust methodology combining time series processing and statistical evaluation was adopted. Multiple tools were leveraged to ensure a comprehensive performance analysis of GNSS data from seven stations in Africa, where such studies remain scarce. The results show that PRIDE PPP-AR achieves ZTD accuracy comparable to GipsyX (RMSE < 6 mm, R2 ≈ 0.99) and performs at a similar level to NGL and CSRS-PPP. Compared to the other solutions, PRIDE PPP-AR has an accuracy similar to CSRS-PPP and NGL, but slightly better than IGN-PPP, in line with ERA5 and IGS references. For IWV retrieval, comparisons with ERA5 indicate RMSE values of about 1.5 to 2.7 kg/m2, depending on station location and climatic conditions. IWV variability tends to increase towards the equator, where the recorded fluctuations are higher than in subtropical zones. In addition, collocated radiosonde (RS) measurements in Abidjan confirm good agreement, further validating the reliability of the software. This study highlights the potential of GNSS meteorology, in providing reliable spatiotemporal IWV monitoring and indicates that the PRIDE PPP-AR is ready for the high precision meteorological applications in African regions. These results offer promising prospects for spatiotemporal studies through African multi-GNSS networks and the PRIDE PPP-AR approach. Full article
(This article belongs to the Special Issue Advances in Methods for the Investigation of the Atmospheric Water Cycle)
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18 pages, 7773 KiB  
Article
Expanding Lake Area on the Changtang Plateau Amidst Global Lake Water Storage Declines: An Exploration of Underlying Factors
by Da Zhi, Yang Pu, Chuan Jiang, Jiale Hu and Yujie Nie
Atmosphere 2025, 16(4), 459; https://doi.org/10.3390/atmos16040459 - 16 Apr 2025
Viewed by 392
Abstract
The remarkable expansion of lake areas across the Changtang Plateau (CTP, located in the central Tibetan Plateau) since the late 1990s has drawn considerable scientific interest, presenting a striking contrast to the global decline in natural lake water storage observed during the same [...] Read more.
The remarkable expansion of lake areas across the Changtang Plateau (CTP, located in the central Tibetan Plateau) since the late 1990s has drawn considerable scientific interest, presenting a striking contrast to the global decline in natural lake water storage observed during the same period. This study systematically investigates the mechanisms underlying lake area variations on the CTP by integrating glacierized area changes derived from the Google Earth Engine (GEE) platform with atmospheric circulation patterns from the ERA5 reanalysis dataset. Our analysis demonstrates that the limited glacier coverage on the CTP exerted significant influence only on glacial lakes in the southern region (r = −0.65, p < 0.05). The widespread lake expansion across the CTP predominantly stems from precipitation increases (r = 0.74, p < 0.01) associated with atmospheric circulation changes. Enhanced Indian summer monsoon (ISM) activity facilitates anomalous moisture transport from the Indian Ocean to the southwestern CTP, manifesting as increased specific humidity (Qa) in summer. Simultaneously, the weakened westerly jet stream reinforces moisture convergence across the CTP, driving enhanced annual precipitation. By coupling glacier coverage variations with atmospheric processes, this research establishes that precipitation anomalies rather than glacial meltwater primarily govern the extensive lake expansion on the CTP. These findings offer critical insights for guiding ecological security strategies and sustainable development initiatives on the CTP. Full article
(This article belongs to the Special Issue Advances in Methods for the Investigation of the Atmospheric Water Cycle)
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22 pages, 40986 KiB  
Article
Modeling Short-Term Drought for SPEI in Mainland China Using the XGBoost Model
by Fanchao Zeng, Qing Gao, Lifeng Wu, Zhilong Rao, Zihan Wang, Xinjian Zhang, Fuqi Yao and Jinwei Sun
Atmosphere 2025, 16(4), 419; https://doi.org/10.3390/atmos16040419 - 4 Apr 2025
Cited by 1 | Viewed by 728
Abstract
Accurate drought prediction is crucial for optimizing water resource allocation, safeguarding agricultural productivity, and maintaining ecosystem stability. This study develops a methodological framework for short-term drought forecasting using SPEI time series (1979–2020) and evaluates three predictive models: (1) a baseline XGBoost model (XGBoost1), [...] Read more.
Accurate drought prediction is crucial for optimizing water resource allocation, safeguarding agricultural productivity, and maintaining ecosystem stability. This study develops a methodological framework for short-term drought forecasting using SPEI time series (1979–2020) and evaluates three predictive models: (1) a baseline XGBoost model (XGBoost1), (2) a feature-optimized XGBoost variant incorporating Pearson correlation analysis (XGBoost2), and (3) an enhanced CPSO-XGBoost model integrating hybrid particle swarm optimization with dual mechanisms of binary feature selection and parameter tuning. Key findings reveal spatiotemporal prediction patterns: temporal-scale dependencies show all models exhibit limited capability at SPEI-1 (R2: 0.32–0.41, RMSE: 0.68–0.79) but achieve progressive accuracy improvement, peaking at SPEI-12 where CPSO-XGBoost attains optimal performance (R2: 0.85–0.90, RMSE: 0.33–0.43) with 18.7–23.4% error reduction versus baselines. Regionally, humid zones (South China/Central-Southern) demonstrate peak accuracy at SPEI-12 (R2 ≈ 0.90, RMSE < 0.35), while arid regions (Northwest Desert/Qinghai-Tibet Plateau) show dramatic improvement from SPEI-1 (R2 < 0.35, RMSE > 1.0) to SPEI-12 (R2 > 0.85, RMSE reduction > 52%). Multivariate probability density analysis confirms the model’s robustness through enhanced capture of nonlinear atmospheric-land interactions and reduced parameterization uncertainties via swarm intelligence optimization. The CPSO-XGBoost’s superiority stems from synergistic optimization: binary particle swarm feature selection enhances input relevance while adaptive parameter tuning improves computational efficiency, collectively addressing climate variability challenges across diverse terrains. These findings establish an advanced computational framework for drought early warning systems, providing critical support for climate-resilient water management and agricultural risk mitigation through spatiotemporally adaptive predictions. Full article
(This article belongs to the Special Issue Advances in Methods for the Investigation of the Atmospheric Water Cycle)
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14 pages, 5120 KiB  
Article
An Enhanced Neural Network Forecasting System for July Precipitation over the Middle-Lower Reaches of the Yangtze River
by Wenyan Liu and Xiangjun Shi
Atmosphere 2025, 16(3), 272; https://doi.org/10.3390/atmos16030272 - 26 Feb 2025
Viewed by 469
Abstract
Forecasting July precipitation using prophase winter sea surface temperature through a nonlinear machine learning model remains challenging. Given the scarcity of observed samples and more attention should be paid to anomalous precipitation events, the shallow neural network (NN) and several improving techniques are [...] Read more.
Forecasting July precipitation using prophase winter sea surface temperature through a nonlinear machine learning model remains challenging. Given the scarcity of observed samples and more attention should be paid to anomalous precipitation events, the shallow neural network (NN) and several improving techniques are employed to establish the statistical forecasting system. To enhance the stability of predicted precipitation, the final output precipitation is an ensemble of multiple NN models with optimal initial seeds. The precipitation data from anomalous years are amplified to focus on anomalous events rather than normal events. Some artificial samples are created based on the relevant background theory to mitigate the problem of insufficient sample size for model training. Sensitivity experiments indicate that the above techniques could improve the stability and interpretability of the forecasting system. Rolling forecasts further indicate that the forecasting system is robust and half of the anomalous events can be successfully predicted. These improving techniques used in this study can be applied not only to the precipitation over the middle-lower reaches of the Yangtze River but also to other climate events. Full article
(This article belongs to the Special Issue Advances in Methods for the Investigation of the Atmospheric Water Cycle)
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