بررسی تغییرات فضایی- زمانی ابرناکی بر پایۀ ویژگی‌های جغرافیایی و داده‌های سنجش از دور در ایران

نویسندگان

1 دانشجوی دکتری، گروه جغرافیای طبیعی، دانشکده علوم انسانی، دانشگاه تربیت مدرس، تهران، ایران

2 استاد، گروه جغرافیای طبیعی، دانشکده علوم انسانی، دانشگاه تربیت مدرس، تهران، ایران

3 دانشیار، گروه جغرافیای طبیعی، دانشکده علوم انسانی، دانشگاه تربیت مدرس، تهران، ایران

4 استاد، گروه فیزیک فضا، موسسه ژئوفیزیک دانشگاه تهران، ایران

چکیده

تاکنون، تحلیل تغییرات فضایی- زمانی ابرناکی بر پایه ویژگی‌های جغرافیایی(عرض جغرافیایی، توپوگرافی و پوشش گیاهی) در کشور وسیعی نظیر ایران از سوی پژوهشگران آب‌و‌هواشناسی بررسی نشده است. هدف پژوهش حاضر فراهم‌کردن نمای کلی از درصد ابرناکی بر پایۀ این ویژگی‌های جغرافیایی طی دورۀ 2001-2015 (ترا) و 2002-2015 (آکوا) برای صبح‌هنگام و عصرهنگام است. ارزیابی داده‌های ماهانۀ سنجندۀ مادیس شامل دو ماهوارۀ ترا (صبح‌هنگام) و آکوا (عصرهنگام) با استفاده از داده‌های بازتحلیل ERA-interim و ایستگاهی انجام گرفت. نتایج، دقت بیشتر از 80 درصد را برای ماه‌های سرد سال و صبح‌هنگام با استفاده از ضرایب تعیین پلونومیال نشان داد، در صورتی که این دقت برای ماه‌های گرم به‌ویژه ژوئن و ژوئیه بسیار کم بود. نتایج حاصل از بررسی ماهانۀ درصد ابرناکی، ابرناکی بیشتر از 65 درصد را برای ماه‌های سرد به‌ویژه فوریه و ژانویه و 7 تا 25 درصد را در ماه‌های ژوئن تا سپتامبر نمایش داده است. نتایج بررسی عرض جغرافیایی نشان داد که با حرکت به سمت عرض‌های جغرافیایی پایین از مقدار ابرناکی در همۀ فصول به جز تابستان کاسته شده است. در این فصل نقش سیستم مونسون، این الگو را متفاوت کرد و بیشترین ابرناکی در نواحی جنوب­شرق و سواحل شمالی کشور قابل مشاهده بود. وادا‌شت‌های توپوگرافی نقش بسیار مهم همرفت دامنه‌ای را در فصول پاییز و بهار آشکار کرد که بیشترین ابرناکی در محدودۀ با میانگین ارتفاع 500 تا 1000 متر ثبت شده بود. پوشش گیاهی همبستگی مثبت و ناکاملی (به‌طور مکانی) را با درصد ابرناکی نشان داد. تغییرات زمانی ابرناکی با استفاده از مقدار انحراف معیار، بیشترین پراکندگی در درصد ابرناکی را در فصل پاییز و در صبح هنگام نشان داد. در تغییرات زمانی ماهانۀ درصد ابرناکی روند قابل ملاحظه‌ای دیده نشد و تنها ماه دسامبر بیشترین روند کاهشی سالانه را با مقدار 2 تا 3 درصد ابرناکی، طی دورۀ مطالعاتی نشان داد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Analysis of spatiotemporal variations of cloud fraction based on Geographic characteristics in Iran

نویسندگان [English]

  • elham Ghasemifar 1
  • Manouchehr Farajzadeh 2
  • Yousef Ghavidel Rahimi 3
  • Abbas Ali Ali-Akbari Bidokhti 4
1 Ph.D. Student, Department of Physical Geography, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran
2 Professor, Department of Physical Geography, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran
3 Associate Professor, Department of Physical Geography, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran
4 Professor, Department of Space Physics, Institute of Geophysics, University of Tehran, Iran
چکیده [English]

Clouds cover major portion of the earth’s surface and play an important role in climatic system. Clouds affect the radiation energy balance of the earth’s climate system by absorbing or scattering solar radiation and long wave radiation and emitting thermal radiation. Cloud properties are closely related to cloud cover patterns, a shift in cloud regime would result in changes in cloud fraction and the cloud microphysical properties and both of these (cloud fraction and cloud microphysics) influence the radiation forcing (Rapp, 2015). Clouds have a strong effect on precipitation distribution, tropospheric temperature profile, climate change, radiation budget, global hydrology budget. Thus, they have an important influence on global climate. The purpose of this research is the study of this variety of cloud fraction in Iran during all months over 2001-2015 with respect to the latitude, topographic forcing, and vegetation cover. Latitude, altitude, slope, aspect and vegetation are geographic characteristics in an area which determine and control many climatic parameters such as temperature, precipitation and etc. Analysis of spatiotemporal variations of cloud fraction based on the characteristics in a vast country like Iran has not been considered by researcher. Satellite imagery is one of the most efficient data source to monitor cloudiness. The spatial and temporal variation of cloud type Ping as deep cloud (Ping et al., 2014), stratospheric clouds (Pitt et al., 2007) and different cloud type (Halladay et al., 2012) have been studied by researchers over the world. Some researchers consider relation between cloud fraction and climate and geographic parameters e.g. Sato et al., 2007. Some other researchers reviewed cloudiness studies e.g. Bromwich et al., 2012. Iran is located between 24.5 to 39.5 north latitude and has topographic range between -28 to 5595m. NDVI value reaches a maximum value in June (0.897) during 2001-2015 time period. This research uses DEM 30 meter and Normalized Difference Vegetation Index in order to analyzie the effets of geographic parameters on cloud fraction. Monthly mean values of cloud fraction are extracted from MOD08/MYD08 MODIS products. We have then validated accuracy of MODIS mean monthly of cloud fraction aboard the Terra and Aqua using ERA-Interim and station data. Results show that there is a good agreement between them but the data is more accurate in cold month, and in the mornings, so that, polynomial coefficients of determination are higher against the stations data and in the morning times due to hourly stations weather data which corresponded to the satellites overpass. The geographic characteristics results showed that cloud fraction increases with increase in latitude except summer seasons due to monsoon system. In order to showing topographic forcing on cloud fraction, this parameter is divided into intervals of 15% for each months and then altitude, slope and aspect that were extracted for each interval. Topographic forcing presents the interesting role of slopped convection in mountain area in average elevation (500-1500 meter) over spring and autumn. Vegetation also has nearly direct relation with cloud fraction. Investigation of temporal variations of cloud fraction showed that the maximum value of STD is obtained in autumn for both satellites. Furthermore, significant trend was not observed in many months, but month of December showed decreasing trend by 2 to 3 annually. This research is the first attempt in the field of cloud climatology in recent decades and further analysis are needed to show the ongoing climate change effects on cloud climatology in this region. Study of cloud vertical profiles can be the next research in this field.

کلیدواژه‌ها [English]

  • Spatial and Temporal Variations
  • remote sensing data
  • geographic characteristics
  • Iran

مراجع

رسولی، ع.، جهانبخش، س. و قاسمی، الف.، 1392، بررسی تغییرات زمانی و مکانی مقدار پوشش ابر در ایران، فصلنامی تحقیقات جغرافیایی، 288، شماره3، 102-85.
موسوی‌بایگی، م. و اشرف، ب.،1390، شناسایی مناطق با کمترین میزان ابرناکی به منظور پهنه بندی نواحی پرتابش کشور، مجله آب و خاک، 25، شماره 3، 665-675.
Ackerman, S. A., Frey, R. A., Strabala, K. I., Liu, Y., Gumley, L. E. and Menzel, W. P., 2010, discriminating clear-sky from cloud with modis algorithm theoretical basis document (mod35).V.6.1.
Barja, B. and Antuna, J. C., 2010, Cirrus clouds physical and spatiotemporal features in the Wider Caribbean, Atmósfera, 23(2), 185-196.
Bromwich, D. H., Nicolas, J. P., Hines, K. M., Kay, J. E., Key, E. L., Lazzara, M. A., Lubin, D., McFarquhar, G. M., Gorodetskaya, I. V., Grosvenor, D. P., Lachlan‐Cope, T. and van Lipzig, N. P. M., 2012, Tropospheric clouds in Antarctica, Reviews of Geophysics, 50, RG1004, 8755-1209/12/2011RG000363.
Banta, R. M., 1998, The role of mountain flows in making clouds. Chapter 9 of Atmospheric processes over complex terrain, Meteorol. Monogr, 23, American Meteorological Society.
Carleton, A. M., Travis, D., Arnold, D. and Brinegar, R., 1994, climatic-scale vegetation-cloud interaction during drought using satellite data, international journal of climatology,14:593-623, doi: 10. 1002/ joc. 3370140602.
Cziczo, D. J., Froyd, K. D., Hoose, C., Jensen, E. J., Diao, M., Zondlo, M. A., Smith, J. B., Twohy, C. H. and Murphy, D. M., 2013, Clarifying the Dominant Sources and Mechanisms of Cirrus Cloud Formation.Science. 340 (6138): 1320-1324, doi: 10.1126/science.1234145.
Chernokulsky, A. and Mokhov, I. I., 2012, Climatology of Total Cloudiness in the Arctic: An Intercomparison of Observations and Reanalyses, Advances in Meteorology, Volume 2012, Article ID 542093, 15 pages, 2012. doi:10.1155/2012/542093.
Didier, N., 2015, Comparison of Spatial and Temporal Cloud Coverage Derived from CloudSat, CERES, ISCCP and Their Relationship with Precipitation Over Africa, American Journal of Remote Sensing, 3(2), 17-28, doi: 10.11648/j.ajrs.20150302.11.
Evans, J. P., Smith, R. B. and Oglesby, R. J., 2004, Middle east climate simulation and dominant precipitation processes, International journal of climatology, 24, 1671–1694, doi: 10.1002/joc.1084.
Forsythe, N., Hardy, A. J., Fowler, H. J., Blenkinsop, S., Kilsby, C. G., Archer, D. R. and Hashmi, M. Z., 2015, A Detailed Cloud Fraction Climatology of the Upper Indus Basin and Its Implications for Near-Surface Air Temperature, Journal of climate, 28:3537-3556,doi: 10.1175/JCLI-D-14-00505.1.
Halladay, K., Malhi, Y. and New, M., 2012, Cloud frequency climatology at the Andes/ Amazon transition: 1. Seasonal and diurnal cycles. Journal of geophysical research, 117, D23102, doi:10.1029/2012JD017770.
Iacobellis, S. F. and Cayan, D. R., 2013, The variability of California summertime marine stratus: Impacts on surface air temperatures, Journal of geophysical research, 118:1–18, doi:10.1002/jgrd.50652.
King, M. D, Platnick, S., Menzel, W. P., Ackerman, S. A. and Hubanks, P. A., 2013, Spatial and Temporal Distribution of Clouds Observed by MODIS Onboard the Terra and Aqua Satellites,IEEE transactions on geoscience and remote sensing, 51(7): 3826-3852, 10.1109/TGRS.2012.2227333.
King, M. D., Menzel, W. P., Kaufman, Y. J., Tanré, D., Gao, B. C., Platnick, S., Ackerman, S. A., Remer, L. A., Pincus, R. and Hubanks, P. A., 2003, “Cloud and aerosol properties, precipitable water, and profiles of temperature and water vapor from MODIS,” IEEE Trans. Geosci. Remote Sens., vol. 41,no. 2, pp. 442–458.
Kirchg¨aßner, A., 2010, An analysis of cloud observations from Vernadsky, Antarctica. International journal of climatology, 30: 1431–1439.
Klitch, M. A., Weaver, J. F., Kelly, F. P. and VonderHaar, T. H., 1985, Convective cloud climatologies constructed from satellite imagery. Monthly Weather Review, 113(3):326–337.
Liou, K. N., 1992, Radiation and Cloud Processes in the Atmosphere:Theory, Observation, and Modeling. Oxford University Press,487 pp.
Liu, R. and Liu,Y., 2013, Generation of new cloud masks from MODIS land surface reflectance products, Remote Sensing of Environment, 133, pp. 21–37.
Matuszko, D. and Weglarczykb, S., 2014, Effect of cloudiness on long-term variability in air temperature in Krakow, International journal of climatology, 34: 145–154.
Pitts, M. C., Thomason, L. W., Poole, L. R. and Winker, D. M., 2007, Characterization of Polar Stratospheric Clouds with spaceborne lidar: CALIPSO and the 2006 Antarctic season. Atmos. Chem. Phys, 7: 5207–5228.
Peng, J., Zhang, H. and Li, Z. Q., 2014, Temporal and Spatial Variationss of Global Deep Cloud Systems Based on CloudSat and CALIPSO Satellite Observations. Advances in atmospheric sciences, 31: 593–603, doi: 10.1007/s00376-013-3055-6.
Sato, T., Kimura, F. and Hasegawa, A. S., 2007, Vegetation and topographic control of cloud activity over arid/semiarid Asia, Journal of geophysical research, 112, D24109, doi:10.1029/2006JD008129.
Stengel, M. S., Kniffka, A. K., Meirink, J. F. M., Lockhoff, M. L., Tan, J. T. and Hollmann, R. H., CLAAS: the CM SAF cloud property data set using SEVIRI, 2014, Atmos. Chem. Phys, 14: 4297–4311.
Stull, R. B., 1988, An introduction to boundary layer meteorology, Kluwer academic publications.
Wang, X. and Key, J. R., 2003, Recent Trends in Arctic Surface, Cloud, and Radiation Properties from Space. Science, 299:1725-1727.
Xie, Y. and Liu,Y., 2013, A new approach for simultaneously retrieving cloud albedo and cloud fraction from surface-based shortwave radiation measurements, Environ. Res. Lett. 8 (2013) 044023 (9pp) doi:10.1088/1748-9326/8/4/044023.
Yadav, A. K., 2016, On the relationship between Iran surface temperature and northwest India summer monsoon rainfall. International journal of climatology, published online in Wiley online library, Doi:10.1002/joc.4648.
Yang, P. and Baum, B. A., 2015, Encyclopedia of Atmospheric Sciences 2nd Edition, 5, 116-127, http://dx.doi.org/10.1016/B978-0-12-382225-3.00503-X.
فیزیک زمین و فضا
دوره 44، شماره 1
اردیبهشت 1397
صفحه 103-124

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