پژوهشی بر ابرهای خزری (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
در این مطالعه به شناسایی ابرهای خزری که مابین ساحل جنوبی دریای خزر و کوهستان البرز شکل می گیرد، پرداخته شد. به این منظور در طول دوره آماری 10 ساله (2020-2010) که ابرهای خزری 279 روز رخداد داشته اند، از داده های مشاهداتی، تصاویر سنجنده مودیس، داده های بازکاوی شده ERA5، NCEP/NCAR و مدل HYSPLIT استفاده شد. یافته ها نشان داد که ابرهای خزری در فصل تابستان (1/16 روز) با داشتن بیشینه رخداد، تیپ غالب ابرهای تابستانه منطقه ای خزری می باشند که تحت شرایط خاص محیطی و اقلیم سینوپتیک حاکم بر منطقه شکل می گیرند. این ابرها اغلب به صورت ابرهای پایین از نوع ابرهای استراتوس و ابرهای میانی از نوع ابر آلتوکومولوس مشاهده می شوند. بیشینه سهم بارش های سالانه ابرهای خزری در منطقه بیش از 80 میلی متر بوده و بیشینه مقدار آن به ترتیب در فصل های تابستان و پاییز رخ می دهد. در این میان تأثیر مشترک پارامترهای ابر بر بارش ابرهای خزری 57 درصد می باشد. بررسی نقشه های همدیدی نشان داد که با استقرار هسته پرفشار در شمال دریای خزر شرایط مساعدی را برای جریان باد و انتقال رطوبت دریای خزر به سمت سواحل جنوبی فراهم کرده، به طوری که توده هوای مرطوب از طریق صعود اورگرافیکی منجر تشکیل ابر در منطقه می شود. مدل HYSPLIT انتقال رطوبت از روی دریای خزر به منطقه مطالعاتی را تأیید کرد.Research on the Caspian Clouds
Extended Introduction What is known as a cloud is actually the accumulation of water vapor particles in the atmosphere around the nuclei of their density and cooling (Ghasemi, 2012). In this study, we will study and identify the clouds that are formed in terms of spatial distribution between the southern coast of the Caspian Sea to the Alborz Mountains and in terms of temporal distribution in all seasons, especially in summer. It seems that these clouds were different in terms of atmospheric formation mechanism and are formed under special environmental conditions of the Caspian coast. Therefore, the main purpose of this study will be to identify and study these clouds. For this purpose, 279 cloud days were selected for study. Materials and methods This study uses type, amount and height of low, medium clouds, including hourly data (00, 03, 06, 09, 12, 15, 18 and 21 UTC) and daily precipitation of 13 meteorological stations in the study area, for selected samples, were received from the Iran Meteorological Organization (IMO). The characteristics of the physical parameters of the cloud Included CTT, CTH, CER, COT and CWP were obtained from level 2 MODIS (MOD06 TERRA and MYD06 Aqua) with a resolution of 1 km. Upper atmosphere data were obtained from ERA5 at a resolution of 0.25° × 0.25 °. Which includes geopotential height, u- wind, v- wind, specific humidity and omega levels of 1000 to 500 hPa isobaric. Ground surface data (SLP, U-wind and V-wind 10m) were obtained from the NCEP/NCAR database and its circulation patterns were drawn in GRADS. HYSPLIT model and the backward method was used to identify the source of moisture. In this study, Global Data AssimilationSystem (GDAS 1°) meteorological data provided by NOAA HYSPLIT model were used to calculate the backward paths for altitudes of 50, 500 and 1000 m above the ground. First, the frequency percentage of the type and height of different layers of clouds were calculated. The average seasonal and monthly occurrence of Caspian clouds were calculated. The average seasonal and annual rainfall of Caspian clouds were calculated. The relationship between precipitation and cloud parameters was investigated by multivariate regression Result and discussion During the 10-year statistical period (2020-2010), 279 cases (days) of the occurrence of Caspian clouds were identified. The research findings showed that the highest average monthly frequency of Caspian clouds occurs in August until its lowest occurrence in November to April. The maximum seasonal frequency of days with Caspian clouds occurs in summer with 16.1 days. These clouds are mainly in the form of low- and middle-level clouds in the region with their most common types being Stratus and Altocumulus. The analysis of rainfall rainfall from Caspian clouds indicates the annual rainfall of Caspian clouds in the region and in most stations more than 80 mm, and its highest amount occurs in summer and autumn chapters, respectively. Spatial distribution the average rainfall derived from Caspian clouds showed that its maximum is on the annual scale and summer and autumn seasons in the southwest and west of the region; but in the spring, it is placed in limited parts of the south. By applying the multivariate regression model, it was found that cloud parameters may predict 57% of the rainfall changes in Caspian clouds. Examination of the synoptic patterns shows that high-pressure settlement in the north of the Caspian Sea provides favorable conditions for wind flow and moisture transfer of the Caspian Sea to its southern coast. So that with the encounter of the humid air mass to the Alborz mountain range, it leads to orographic lift and formation of clouds and rain in the region. The HYSPLIT model indicates that the source of moisture for the formation of Caspian clouds is largely from the Caspian Sea. Conclusion The average frequency of the occurrence of Caspian clouds in August to stamp is more than spring and winter months. The average number of summer and autumn, as well as the average rainfall of Caspian clouds in the summer and autumn, is more than other seasons. These clouds are mainly in the form of low- and middle-level clouds in the region with their most common types being Stratus and Altocumulus. By applying the multivariate regression model, it was found that cloud parameters may predict 57% of the rainfall changes in Caspian clouds. Examination of the synoptic patterns shows that high-pressure settlement in the north of the Caspian Sea provides favorable conditions for wind flow and moisture transfer of the Caspian Sea to its southern coast. So that with the encounter of the humid air mass to the Alborz mountain range, it leads to orographic lift and formation of clouds and rain in the region. The HYSPLIT model confirmed the moisture transfer from the Caspian Sea to the study area.
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