"Analysis of Spatial and Temporal Distribution of Middle Clouds in Khuzestan Province"

Document Type : Research Paper

Authors

1 Department of Geography Management Ahv. C. Islamic Azad university. Ahvaz. Iran

2 Department of Geography, Payame Noor Universtiy, PO BOX 19395-3697 Tehran,IRAN

3 Department of Geography Management, Ahv.C., Islamic Azad university, Ahvaz, Iran

10.22034/gp.2025.67295.3406

Abstract

Introduction: Clouds are among the most important climatic elements affecting atmospheric and hydrological systems and play a key role in Earth's energy balance and hydrological cycle. Understanding the spatial and temporal patterns of mid-level clouds is crucial due to their influence on precipitation, temperature, and radiative balance, which is vital for water resources management, agriculture, and weather forecasting.

Objective: This study aimed to investigate the spatial distribution and long-term (30-year, 1990–2020) trends of mid-level clouds in Khuzestan Province and to analyze their variations during warm and cold seasons.

Methodology: Mid-level cloud data were obtained from the ERA5 reanalysis dataset with a spatial resolution of 0.25° for the 30-year period. Analyses included calculating spatial and seasonal averages of cloudiness, examining 30-year time series trends using the Sen’s slope estimator and the non-parametric Mann-Kendall test, and visualizing the data using GIS software.

Findings: Results indicated that mid-level clouds have a higher contribution to total cloudiness during the cold season, with an average cloudiness of about 37% compared to only 0.5% in the warm season. Spatial distribution varied, with the northern and northeastern regions exhibiting the highest cloudiness and the southern and southeastern regions the lowest. Although annual fluctuations were observed over the 30-year period, no statistically significant long-term trend was detected.

Conclusion: Understanding the spatial and temporal patterns of mid-level clouds can support water resource management, agricultural planning, precipitation forecasting, and drought risk reduction. However, limitations such as spatial and temporal errors in reanalysis data and unconsidered short-term fluctuations exist. Future studies could provide more precise analyses using higher-resolution satellite and ground-based observations.

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مقدمه: ابرها از مهم‌ترین عناصر اقلیمی تأثیرگذار بر سامانه‌های جوی و هیدرولوژیکی زمین هستند و نقش کلیدی در بیلان انرژی و چرخه هیدرولوژیک دارند. شناخت الگوهای فضایی و زمانی ابرهای میانی به دلیل تأثیر آن‌ها بر بارش، دما و توازن تابشی، برای مدیریت منابع آب، کشاورزی و پیش‌بینی‌های جوی اهمیت ویژه‌ای دارد.

هدف: این پژوهش با هدف بررسی الگوی توزیع فضایی و روند بلندمدت (۳۰ ساله، 1990–2020) ابرهای میانی در استان خوزستان و تحلیل تغییرات آن‌ها در دوره‌های گرم و سرد سال انجام شد.

روش تحقیق: داده‌های ابرهای میانی از پایگاه بازتحلیل ERA5 با رزولوشن فضایی 25/0 درجه برای دوره ۳۰ ساله دریافت شد. تحلیل‌ها شامل محاسبه میانگین فضایی و فصلی ابرناکی، بررسی روند سری زمانی با استفاده از تخمینگر شیب Sen و آزمون ناپارامتریک من کندال و تجسم داده‌ها با استفاده از نرم‌افزار GIS بود.

یافته‌ها: نتایج نشان داد که ابرهای میانی در دوره سرد سال سهم بیشتری از ابرناکی کل استان دارند، به طوری که میانگین ابرناکی در دوره سرد حدود 37 درصد و در دوره گرم تنها حدود 5/0 درصد بوده است. توزیع فضایی ابرها نیز متفاوت بوده و بخش‌های شمالی و شمال‌شرقی بیشترین میزان ابرناکی و بخش‌های جنوبی و جنوب‌شرقی کمترین میزان را داشته‌اند. روند ۳۰ ساله نشان داد که نوسانات سالانه ابرناکی وجود دارد، اما روند معنی‌دار بلندمدت مشاهده نشد.

نتیجه‌گیری: شناخت الگوهای فضایی و زمانی ابرهای میانی می‌تواند در مدیریت منابع آب، برنامه‌ریزی کشاورزی، پیش‌بینی بارش و کاهش خطر خشکسالی مفید باشد. با این حال، محدودیت‌هایی نظیر خطاهای مکانی و زمانی داده‌های بازتحلیل و عدم لحاظ نوسانات کوتاه‌مدت وجود دارد. پژوهش‌های آینده می‌توانند با داده‌های ایستگاهی و ماهواره‌ای با رزولوشن بالاتر تحلیل دقیق‌تری ارائه دهند.

References

Ahmadi, M., Ahmadi, H., & Dadashi Roudbari, A. (2018). An analysis of the trend and spatial pattern of annual and seasonal cloudiness in Iran. Environmental Hazards Journal, 7(5), 237–254. (In Persian)
Brüning, S. and Tost, H.: (2025) A machine learning-based perspective on deep convective clouds and their organisation in 3D. Part I: Influence of deep convective cores on the cloud life-cycle, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-374,
Dim, J. R., H. Murakami, T. Y. Nakajima, B. Nordell, A. K. Heidinger, and T. Takamura, (2011), The recent state of theclimate: Driving components of cloud-type variability. J. Geophys. Research., 116,1-14.
Fallahi Khoshji, A., Alijani, B., Hejazi Zadeh, Z., & Nasarzadeh, M. H. (2017). Spatial pattern of frequency and intensity changes of rain-bearing low-level clouds in Iran. Environmental Hazards Journal, 6(13), 1–14. (In Persian)
Houshyar, M., Sobhani, B., & Hosseini, S. A. (2018). Projection of maximum temperature changes in Urmia using statistical downscaling of CanESM2 model outputs. Geography and Planning, 22(63), 305–325. https://doi.org/4-15. (In Persian)
Ibraheem, N. T., Al-Zuhairi, M. F., Al-Montaser, Z. N., & Al-Jiboori, M. H. (2025). Investigating the spatial variations of cloud cover properties over Iraq. International Journal of Environmental Sciences, 11(6), 1742. https://theaspd.com/index.php
Jaswal, A. K. (2017). Variability and Changes in Cloud Cover Over India During 1951–2010. (107-127).
Kang, H., Wang, H., Wu, Q., & Zhang, Y. (2025). Detection and Spatiotemporal Distribution Analysis of Vertically Developing Convective Clouds over the Tibetan Plateau and East Asia Using GEO-KOMPSAT-2A Observations. Remote Sensing, 17(8), 1427. https://doi.org/10.3390/rs17081427
Karimi Ahmadabad, M., & Ghasemi, E. (2017). Examining the phenomenon of climate change with a multi-criteria decision-making approach. Geography and Planning, 21(61),
Maleki Morshed, R., Salahi, B., & Saber, M. (2024). Analysis of precipitation changes in northwestern Iran over the coming decades based on GCM models. Geography and Planning, 28(90), 367–389. https://doi.org/10.22034/gp.2024.59552.3212. (In Persian)
Mueller, R., Trentmann, J., Träger-Chatterjee, C., Posselt, R., & Stöckli, R. (2011). The Role of the Effective Cloud Albedo for Climate Monitoring and Analysis. Remote Sensing, 3(11), 2305-2320. https://doi.org/10.3390/rs3112305
Raeispoor, K., & Razmi, R. (2020). Estimating cloudiness over Iran using MISR multi-angle imaging spectroradiometer products. Iranian Journal of Water Resources Research, 16(36), 257–271. (In Persian)
Raichijk, C. (2012). Observed trends in sunshine duration over South America. International Journal of Climatology, 32(5), 669-680
Shamohammadi, Z., Yarahmadi, D., & Mirhashemi, H. (2024). Temporal and spatial analysis of low-level cloud cover over Iran. Climate Change and Weather Hazards, 3(5), e714759. https://doi.org/10.22034/e714759 (In Persian)
Wang, P. K. (2013), Physics and dynamics of clouds and precipitation. Cambridge University Press
Wenjing, Z., Ning, Z., & Jianning, S. (2014). Spatiotemporal Variations of Cloud Amount over the Yangtze River Delta, China. Journal of Meteorological Research, 28(3), 371-380.
World Meteorological Organization. (2008). Guide to meteorological instruments and methods of observation. Secretariat of the World Meteorological Organizatio
Wu, Y.; Gao, J.; Zhao, A. (2024). Cloud properties and dynamics over the Tibetan Plateau—A review. Earth-Sci. Rev. 248, 104633.
Xie, X., Ma, L., Yao, J., & Mao, W. (2025). Spatiotemporal Variability of Cloud Parameters and Their Climatic Impacts over Central Asia Based on Multi-Source Satellite and ERA5 Data. Remote Sensing, 17(15), 2724. https://doi.org/10.3390/rs17152724
Yousefi, M., Saligheh, M., & Karbalaei-Darayi, A. (2024). Analysis of cloud cover changes during the cold season in southeastern Iran. Climate Change Research, 5(20), 49–64. https://doi.org/10.30488/­ccr.­2024.­470082.1233 (In Persian)
Zarei, Y., Mohammadkhorshiddoust, A., Rezaei Banafshah, M., & Rostamzadeh, H. (2023). Assessing the effects of global climate change on climatic elements of temperature and precipitation in different climatic regions of Iran using RCP scenarios. Geography and Planning, 27(83), 63–71. https://doi.org/10.22034/gp.2023.10791. (In Persian)
Zhou, C., Zelinka, M. & Klein, S. Impact of decadal cloud variations on the Earth’s energy budget. Nature Geosci 9, 871–874 (2016). https://doi.org/10.1038/ngeo2828

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