پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 158 |
| تعداد شماره‌ها | 6,225 |
| تعداد مقالات | 67,685 |
| تعداد مشاهده مقاله | 114,994,647 |
| تعداد دریافت فایل اصل مقاله | 89,664,726 |
برآورد ماهوارهای بخار آب قابل بارش (PWV) در جو ایران و تحلیل همبستگی مکانی آن با فراسنج‌های آب و هواشناختی | ||
| تحقیقات آب و خاک ایران | ||
| دوره 51، شماره 10، دی 1399، صفحه 2543-2557 اصل مقاله (1.44 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2020.303404.668630 | ||
| نویسنده | ||
| کوهزاد رئیس پور* | ||
| گروه جغرافیا، دانشکده علوم انسانی، دانشگاه زنجان، زنجان، ایران. | ||
| چکیده | ||
| بخار آب قابل بارش یکی از کمیت‌های مهمِ هواشناسی، فیزیک جو، هیدرولوژی و تغییرات اقلیمی است که برآورد آن در پیشبینی مقدار بارش، وقوع سیلاب و سایر پارامترهای هیدرولوژیکی مفید میباشد. امروزه از تصاویر ماهوارهای به‌طور گسترده‌ای برای برآورد بخار آب قابل بارش و تحلیل همبستگی آن با سایر فراسنج‌های آب و هواشناختی استفاده می‌شود. هدف از پژوهش حاضر برآورد مقدار بخار آب قابل بارش و بررسی رابطه‌ی آن با شش متغیر اقلیمی از قبیل دما، فشار، رطوبت نسبی، درصد ابرناکی، بارش و سرعت باد در گستره‌ی جغرافیایی ایران با استفاده از داده‌های ماهواره مبنا میباشد. داده‌های مورد استفاده با گام‌های زمانی ماهانه و مکانی° 1°×1 در گستره‌ی اقلیمی جو ایران برای دوره‌ی داده‌برداری 2019 – 2003 انتخاب گردید. برای بررسی رابطه‌ی بین بخار آب قابل بارش با متغیرهای اقلیمی، از ضریب همبستگی پیرسون استفاده شد. داده‌های رقومی استخراج شده پس از کنترل کیفی و پیش‌پردازش، توسط نرم‌افزارهای تخصصی از قبیلENVI، ArcGIS و Grads برای ساخت لایه‌های رستری بر اساس مرز جغرافیایی کشور ایران بهکار گرفته‌شد. بر اساس نتایج، میانگین آب قابل بارش در جو ایران mm 7/12 است که در مقایسه با میانگین جهانی (mm 6/21)، کم بودن مقدار آب قابل بارش در جو ایران را نشان می دهد. از سویی دیگر مقدار آب قابل بارش در جو ایران از توزیع زمانی و مکانی همگنی برخوردار نیست. بهطوری که بیشترین مقدار آن در ناحیه‌ی ساحلی جنوب و شمال و کمترین مقدار آن بر فراز سلسله جبال زاگرس، بخش‌هایی از شمال شرق و شرق ایران و در اولویت بعدی در نواحی بیابانی ایران مرکزی متمرکز است. میانگین همبستگی آماری (پیرسون) بین pwv با دما (R= %86)، با فشار (R= - %89)، با رطوبت نسبی (R= - %88)، با ابرناکی (R= - %32)، با بارش (R= - %64) و سرعت باد (R= %67) بوده‌است. | ||
| کلیدواژه‌ها | ||
| برآورد ماهوارهای؛ آب قابل بارش؛ سنجنده AIRS؛ همبستگی پیرسون؛ اتمسفر ایران | ||
| عنوان مقاله [English] | ||
| Satellite Estimation of Precipitable Water Vapor (PWV) in Iran Atmosphere of Iran and the Analysis of its Spatial Correlation with Meteorological Variables | ||
| نویسندگان [English] | ||
| Koohzad Raispour | ||
| Department of Geography, Faculty of Humanities, University of Zanjan , Zanjan, Iran. | ||
| چکیده [English] | ||
| Precipitable Water Vapor (PWV) is one of the most important quantities in meteorology, atmospheric physics, hydrology and climate change studies, which its estimation is useful in predicting precipitation, flood occurrence and other hydrological parameters. Today, satellite imagery is widely used to estimate PWV and analyze its correlation with other meteorological Variables. The objective of this study was to estimate the amount of PWV and to investigate its relationship with six climatic variables such as; temperature, pressure, relative humidity, cloudiness ratio, precipitation and wind speed in the geographical area of Iran using satellite-based data.The proposed data with monthly time steps and 1°*1° spatial resolution were selected in the climatic range of Iran's atmosphere for the period of 2003-2019. Pearson correlation coefficient was used to investigate the relationship between PWV and the above mentioned climatic variables. Digital data extracted after qualitative control and pre-processing were used by specialized software such as ENVI, ArcGIS and Grads to build raster layers based on the geographical boundary of Iran. According to the results, the average PWV in the atmosphere of Iran is 12.7 mm, which shows a lower amount as compared to the global average (21.6 mm). On the other hand, the amount of PWV in the Atmosphere of Iran does not have a temporal and spatial homogeneous distribution. So that the highest amount of PWV is concentrated in the coastal area of south and north and the lowest amount is concentrated over the Zagros mountain range, parts of northeast and east of Iran and in the next priority in the desert areas of central Iran. The Pearson correlation coefficients between PWV and the meteorlogical variables were 86% for air temperaure, - 89% for pressure, - 88% for relative humidity, - 32% for cloudiness ratio, - 64% for precipitation and 67% for wind speed. | ||
| کلیدواژه‌ها [English] | ||
| Satellite Estimation, Precipitable Water, AIRS Sensor, Pearson Correlation, Atmosphere of Iran | ||
| مراجع | ||
|
Adeyemi, B., and Joerg, S. (2012). Analysis of water vapor over Nigeria using radiosonde and satellite data. J. Appl. Meteorol. Climatol. 51(10):1855-1866. Alraddawi , D., Keckhut, P., Sarkissian, A., and Abdanour Irbah., O. (2017). Enhanced MODIS Atmospheric Total Water Vapour Content Trends in Response to Arctic Amplification. Atmosphere. 8(12): 241. Asakereh, H., and Dostkamian, M. (2015). Investigation of the role of spatial factors on distribution - Distribution of rainfall maximum water in Iran. Journal of Applied Research in Geographical Sciences. 15(32):7 - 24. (In Farsi). Asakereh, H., Doostkamian, M., and Sadrafshary, S. (2015). Anomalies and cycles of precipitable water over Iran in recent decades. Arabian Journal of Geosciences. 8(11): 9569-9576. Aumann, H. H., and Coauthors, B. (2003). AIRS/AMSU/HSB on the Aqua mission: Design, science objectives, data products, and processing. IEEE Trans. Geosci. Remote Sens. 41:253–264. Bayat, A., and Mashhadi Zadeh, S. (2019). Analysis of spatial and temporal correlation between AIRS precipitable water vapor and data of 29 synoptic stations of Iran: Journal of Applied Research in Geographical Sciences. 19(35): 19-32. (In Farsi). Bedka, S., Knuteson, R., Revercomb, H., Tobin, D., and Turner, D. (2010). An assessment of the absolute accuracy of the Atmospheric Infrared Sounder v5 precipitable water vapor product at tropical, midlatitude, and arctic ground‐truth sites: September 2002 through August 2008. Journal of Geophysical Research: Atmospheres.115(17): 241-256. Benevides, P., Catalao, J., and Miranda, P.M.A. (2015). On the inclusion of GPS precipitable water vapor in the nowcasting of rainfall. Nat. Hazards Earth Syst. 5 (21): 2605–2616. Bengtsson, L., Hagemann, S., and Hodges, K. I.(2004). Can climate trends be calculated from reanalysis data?. J. Geophys. Res.-Atmos.109:1024-1032. Bennitt, G. V., and Jupp, A. (2012). Operational assimilation of GPS zenith total delay observations into the met office numerical weather prediction models.Weather Rev. 140: 2706–2719. Bevis, M., Businger, S., Herring, T. A., Rocken, C., Anthes, R. A., and Ware, R. H. (1992). GPS eteorology: Remote sensing of atmospheric water vapor using the global positioning system. J. Geophys. Res.-Atmos. 97 (8):15787–15801. Bock, O., Keil, C., Richard, E., Flamant, C., and Bouin, M. N. (2005). Validation of precipitable water from ECMWF model analyses with GPS and radiosonde data during the MAP SOP. Q. J. Roy. Meteor. Soc. 131:3013–3036. Bolsenga, S. J. (1965). The relationship between total atmospheric water vapor and surface dew point on a mean daily and hourly basis: J. Appl. Meteorol. 4:430–432. Bretherton, H.S., Matthew, E.P., and Larissa, E.B. (2004). Relationships between Water Vapor Path and Precipitation over the Tropical Oceans. J. Climate. 17:1517-1528. Campmany, E., Bech, J., Rodríguez-Marcos, J., Sola, Y., and Lorente, J. (2010). A comparison of total precipitable water measurements from radiosonde and sunphotometers. Atmospheric Research. 97: 385-392. Campos-Arias, P., Esquivel-Hernández, G., Valverde-Calderon, J.F., Rodríguez-Rosales, S., Moya-Zamora, J., Sanchez-Murillo, R., and Boll, J.(2019). GPS Precipitable Water Vapor Estimations over Costa Rica: A Comparison against Atmospheric Sounding and Moderate Resolution Imaging Spectrometer (MODIS). Climate. 7: 63-76. Chahine, M. T., and Coauthors, F. (2006) AIRS: Improving weather forecasting and providing new data on greenhouse gases. Bull. Amer. Meteor. Soc. 87:911–926. Chang-Geun, P., Kyoung-Min, R., and Jungho, C. (2012). Radiosonde sensors bias in precipitable water vapor from comparisons with global positioning system measurements: J. Astron. Space Sci. 29 (3):295-303. Chen, B., and Liu, Z. (2016). Global water vapor variability and trend from the latest 36-year (1979 to 2014) data of ECMWF and NCEP reanalyses, radiosonde, GPS, and microwave satellite. JGR Atmospheres. 121(19): 11,442-11,462. Dehghani, T., Saligheh, M., and Alijani, B. (2015). The effect of climate change on rainfall in the northern coasts of the Persian Gulf. Applied Research in Geographical Sciences. 18 (49): 75 - 91. (In Farsi). Diedrich, H., Wittchen, F., Preusker, R., and Fischer, J.(2016). Representativeness of total column water vapour retrievals from instruments on polar orbiting satellites. Atmos. Chem. Phys.16: 8331–8339. Elgered, G., and Jarlemark, P. O. (1998).Ground-based microwave radiometry and long-term observations of atmospheric water vapor: Radio Sci. 33(7):707–717. Fishbein, E., S. Y. Lee, E. Manning, E. Maddy, and W. W. McMillan. (2007). AIRS/AMSU/HSB version 5 level 2 product levels, layers and trapezoids. User Doc., Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 11 pp. Fujibe, F.(2016). Annual variation of extreme precipitation intensity in Japan: Assessment of the validity of Clausius-Clapeyron scaling in seasonal change. SOLA.12:106–110. Fujita, M., and Sato, T.(2017). Observed behaviours of precipitable water vapour and precipitation intensity in response to upper air profiles estimated from surface air temperature. Sci Rep. 7(3-4): 4233. Fujita, M., Wada, A., Iwabuchi, T., and Rocken, C.(2012). GPS Precipitable water vapor dataset for climate science, Proceedings of the 25th International Technical Meeting of The Satellite Division of the Institute of Navigation. 19:3454–3458. Gao B.C and Kaufman, Y.J. (2002).The MODIS Near-IR Water Vapor Algorithm”; IEEE Transaction on Geosciences and Remote Sensing. 56: 2123-2134. Gao, B.C., and Kaufman, Y.J.(2003). Water vapor retrievals using moderate resolution imaging spectroradiometer (MODIS) near-infrared channels. J. Geophys. Res. Atmos. 108:1007–1021. Gendt, G., Dick, G., Reigber, C., Tomassini, M., Liu, Y., and Ramatschi, M.(2004). Near real time GPS water vapor monitoring for numerical weather prediction in Germany: J. Meteor. Soc. Jpn. 82:361–370. Gettelman, A., and Coauthors. (2004).Validation of Aqua satellite data in the upper troposphere and lower stratosphere with in-situ aircraft instruments. Geophys. Res. Lett. 31: 22107. Gettelman, A., W. D. Collins, E. J. Fetzer, A. Eldering, F. W. Irion, P. B. Duffy, and G. Bala.(2006). Climatology of upper-tropospheric relative humidity from the Atmospheric Infrared Sounder and implications for climate. J. Climate. 19: 6104–6121. Ghasemi, A. R. (2012). Modeling the temporal and spatial changes of cloud cover, with emphasis on rainy days in Iran. PhD thesis in Natural Geography, Climatology. Tabriz University. (In Farsi). Gui, K., Che, H., Chen, Q., Zeng, Z., Liu, H., Wang, Y., Zheng, Y., Sun, T., Liao, T., and Wang, H. (2017). Evaluation of radiosonde, MODIS-NIR-Clear, and AERONET precipitable water vapor using IGS ground-based GPS measurements over China. Atmos. Res, 197: 461–473. Hagan, D. E., Webster, C. R., Farmer, C. B., May, R. D., Herman, R. L., Weinstock, E. M., and Newman, P. A.(2004).Validating AIRS upper atmosphere water vapor retrievals using aircraft and balloon in situ measurements: Geophysical research letters. 31(21): 217-242. Hausmann, P., Sussmann, R., Trickl, T., and Schneider, M. (2017). A decadal time series of water vapor and D=H isotope ratios above Zugspitze: transport patterns to central Europe. Atmos.Chem. Phys. 17: 7635–7651. Heise, S., Dick, G., Gendt, G., Schmidt, T., and Wickert, J.(2009). Integrated water vapor from IGS ground-based GPS observations: initial results from a global 5-min data set: Ann. Geophys. 27: 2851–2859. Inness, A., Inness, A., Ades, M., Agustipanareda, A., Barre, J., Benedictow, A., Blechschmidt., A.M., and Dominguez, J.J.(2019). The CAMS reanalysis of atmospheric composition. Atmospheric Chemistry Physic.9:3515-3556. Jade, S., and Vijayan, M. (2008). GPS-based atmospheric precipitable water vapor estimation using meteorological parameters interpolated from NCEP global reanalysis data: J. Geophys. Res.-Atmos. 113(10):1029. Jiang, J., Zhou, T., and Zhang, W.(2019). Evaluation of Satellite and Reanalysis Precipitable Water Vapor Data Sets Against Radiosonde Observations in Central Asia. Earth and Space Science. 10:1029. Kanemaru, K., and Masunaga, H. A.(2013). satellite study of the relationship between sea surface temperature and column water vapor over tropical and subtropical oceans. J. Climate. 26: 4204–4218. Kleespies T.J., McMillin, L.M. (1990). Retrieval of precipitable water from bservations in the split window over varying surface temperatures. Journal of Applied Meteorology. 29: 1236-1247. Kumar, S., Allan, R.P., Zwiers, F., Lawrence, D.M., and Dirmeyer, P.A.(2015). Revisiting trends in wetness and dryness in the presence of internal climate variability and water limitations over land. Geophys. Res. Lett. 42:10867–10875. Lambrigtsen, B. H. (2003). Calibration of the AIRS microwave instruments. IEEE Trans. Geosci. Remote Sens. 41: 369–378. Lambrigtsen, B. H., and Lee, S. Y.(2003).Coalignment and synchronization of the AIRS instrument suite. IEEE Trans. Geosci. Remote Sens. 41: 343–351. Lambrigtsen, B. H., E. J. Fetzer, E. Fishbein, S. Y. Lee, and T. Pagano. (2004). AIRS-the Atmospheric Infrared Sounder. Proc. Int. Geoscience and Remote Sensing Symp. 3: 2204–2207. Le Marshall, J., and Coauthors, M. (2006). Improving global analysis and forecasting with AIRS. Bull. Amer. Meteor. Soc. 87: 891–894. Lee, MI., Schubert, SD., Suarez, MJ., Held, IM., Lau, NC., Plushy, JJ., Kumar, A., Kim, HK., and Schema, J.K.E. (2007). An analysis of the warm-season diurnal cycle over the continental United States and northern Mexico in general circulation models. J. Hydrometeor. 8(3): 344–366. Lenderink, G., and van Meijgaard, E. V. (2010). Linking increases in hourly precipitation extremes to atmospheric temperature and moisture changes: Environ. Res. Lett. 5: 136 -148. Li, X., Tan, H., Dick, G., Wickert, J., and Schuh, H. (2018). Real-time sensing of precipitable water vapor from BeiDou observations: Hongkong and CMONOC networks. J. Geophys. Res. Atmos. 123: 212-221. Loriaux, J. M., Lenderink, G., Roode, S. R. D., and Siebesma, A. P. (2013). Understanding convective xtreme precipitation scaling using observations and an entraining plume model: J. Atmos. Sci. 70:3641–3655. Lu, N., Qin, J., Yang, K., Gao, Y., Xu, X., and Koike, T. (2011). On the use of GPS measurements for moderate resolution imaging spectrometer precipitable water vapor evaluation over southern Tibet: J. Geophys. Res.10: 116, 1–7. Maghrabi, A., and Al Dajani, H.M. (2012). Estimation of precipitable water vapour using vapour pressure and air temperature in an arid region in central Saudi Arabia. J. Assoc. Arab Uni. Basic Appl. Sci. 14:1-8. McNally, A. P., P. D. Watts, J. A. Smith, R. Engelen, G. A. Kelly, J. N. Thépaut, and M. Matricardi. (2006) The assimilation of AIRS radiance data at ECMWF. Quart. J. Roy. Meteor. Soc. 132: 935–957. Mobasheri, M., Purbagher, S.,Kordi, M., Farajzadeh, M., and Sadeghi Naeini, A. (2008) .Improvement of remote sensing techniques in TPW assessment using radiosonde data.Journal of Applied Sciences. 8: 480-488. (In Farsi). Mobashri, M. R., Pourbagher Kurdi, S. M., Farajzadeh Asl, M., and Sadeghi Naeini, A.(2010). Estimation of total precipitation using MODIS satellite images and radio data (study area: Tehran region). Journal of Humanities Teacher. 65(2):107 - 126. (In Farsi). Mohammadiyah, A., Memarian, M. H., Azadi, M., and Reyhani Parvari, M.(2014). Investigation of WRF model forecasts for rainwater and its relationship with rainfall estimation using Tehran radar data. Iranian Geophysical Journal. 3(22):1- 13. (In Farsi). Molod, A., Takacs, L., Suarez, M., Bacmeister, J., Song, I.S and Eichmann, A.(2012). The GEOS-5 Atmospheric General Circulation Model: Mean Climate and Development from MERRA to Fortuna; NASA Technical Report Series on Global Modeling and Data Assimilation.Greenbelt. 117: 28-45. Moteki, N., and Kondo, Y.(2013).A new theoretical method for calculating temperature and water vapor saturation ratio in an expansion cloud chamber. J. Geophys. Res. Atmos. 118: 6627- 6633. Ning, T., Wickert, J., Deng, Z., Heise, S., Dick, G., Vey, S., and Schone, T.(2016). Homogenized time series of the atmospheric water vapor content obtained from the GNSS reprocessed data. J. Climate. 29:2443–2456. Oluwasesan, A., Falaiye, J., Abimbola- Rachel ,T., and Pinker, D.(2018). Multi-Technique Analysis of Precipitable Water Vapor Estimates in the sub-Sahel West Africa. Heliyon. 4 :756-768. Pagano, T. S., Aumann, H. H., Hagan, D., and Overoye, K. (2003). Prelaunch and in‐flight radiometric calibration of the Atmospheric Infrared Sounder (AIRS), IEEE Trans. Geosci. Remote Sens. 41:265– 273. Parkinson, C. L. (2003). Aqua: an earth-observing satellite mission to examine water and other climate variables. IEEE Transactions on Geoscience and Remote Sensing.41: 173-183. Plantinch S., King M. D., Ackerman S. A., Menzel W. P., Baum B. A., Riedi J.C., and Fery R.A. (2003). The MODIS products: Algorithms and examples from Terra. IEEE Transaction on Geoscience and RemoteSensing. 41:241-257. Prasad, A. K., and Singh, R. P.(2009). Validation of MODIS Terra, AIRS, NCEP/DOE AMIP‐II Reanalysis‐2, and AERONET Sun photometer derived integrated precipitable water vapor using ground‐based GPS receivers over India. Journal of Geophysical Research. Atmospheres. 114:1232-1245. Raja, M. R. V., Gutman, S. I., Yoe, J. G., McMillin, L. M., and Zhao, J.(2008). The validation of AIRS retrievals of integrated precipitable water vapor using measurements from a network of ground-based GPS receivers over the contiguous United States. Journal of Atmospheric and Oceanic Technology. 25(3):416-428. Randles,C.A., Dasilva, A., Buchard, V., Colarco, P.R, Darmenov, A.S., Govindaraju, R.C., Smirnov, A., Ferrare, R.A., Hair, J.W. and Shinozuka, Y.(2017). The MERRA-2 Aerosol Reanalysis, 1980-onward, Part I: System Description and Data Assimilation Evaluation. Journal Climatology. 30: 6823-6850. Rasooli, A. A., Jahanbakhsh, S., and Ghasemi, A. R. (2014). Investigation of the relationship between important parameters of cloud and daily rainfall in Iran: Journal of Geographical Research. 29 (1): 23-42. Sadeghi Hosseini, S. A., Hjam, S., and Tophangsaz, P. (2005). The relationship between cloudy rainwater and observed rainfall in Tehran region. Journal of Earth and Space Physics. 31(2): 13 -21. (In Farsi). Sharifi, M.A., Khaniani, A.S., and Joghataei, M.(2015). Comparison of GPS precipitable water vapor and meteorological parameters during rainfalls in Tehran: Meteorol. Atmos. Phys.127 (6): 701-710. Sherwood, S.C., Roca, R.,Weckwerth, T.M., and Andronova, N.G.(2010). Tropospheric Water Vapor, Convection, and Climate. Rev. Geophys. 48 (15): 2001-2017. Sun, L., Shen, B., and Sui, B.(2010). A study on water vapor transport and budget of heavy rain in northeast China: Advances in Atmospheric Sciences. 27 ( 6): 1399–1414. van der Ent, R.J., and Tuinenburg, O.A.(2017). The residence time of water in the atmosphere revisited., Hydrol. Earth Syst. Sci. 21: 779–790. Vaquero-Martínez, J., Anton, M., Ortiz de Galisteo, J., Cachorro, V., Costa, M., Roman, R., and Bennouna, Y.(2017). Validation of MODIS integrated water vapor product against reference GPS data at the Iberian Peninsula: Int. J. Appl. Earth Obs, 63: 214–221. Vaquero-Martinez, J., Anton, M., Ortiz de Galisteo, J.P., Cachorro, V.E., Alvarez-Zapatero, P., Roman, R., Loyola, D., Costa, M.J., Wang, H., and Gonzalez Abad, G.(2018). Inter-comparison of integrated water vapor from satellite instruments using reference GPS data at the Iberian Peninsula. Remote Sens. Environ. 204: 729–740. Vermote, E. F., Saleous, N. Z. El., and Justice, C.O. (2002).Atmospheric correctionof MODIS data in the visible to middle infrared. First results, Remote Sensing Environ. 83: 1236-1248. Wang, H.,Wei, M., Li, G., Zhou, S., and Zeng, Q.(2013). Analysis of precipitable water vapor from GPS measurements in Chengdu region: Distribution and evolution characteristics in autumn. Adv. Space Res, 52: 656–667. Wang, J., and Dai, A. (2016). Global Water Vapor Trend from 1988 to 2011 and Its Diurnal Asymmetry Based on GPS, Radiosonde, and Microwave Satellite Measurements. J. Climate. 29 (14): 5205–5222. Wang, W., Sun, X., Zhang, R., and Su, H. (2006).Multi-layer perceptron neural network based algorithm for estimating precipitable water vapour from MODIS NIR Data. International Journal of Remote Sensing.27(3): 789-796. Willoughby, A.A., Adimula, I.A., Aro, T.O., and Owolabi, I.E.(2008). Analysis of radiosonde data on tropospheric water vapor in Nigeria. J. Phys. 20 (2): 299-308. Wu, W.S., Purser, R.J. and Parrish, D.F.(2002). Three-dimensional variational analysis with spatially inhomogeneous covariances. Monthly Weather Review. 130: 2905-2916. You, Q., Jiang, Z., Bao, Y., Pepin, N., and Fraedrich, K. (2016). Trends in upper tropospheric water vapour over the Tibetan Plateau from remote sensing Int. J. Climatol. 36: 4862-4874. Zhang, X., Li, M., and Sun, T.(2013). Spatiotemporal variation of water vapor in upper troposphere over Central Asia based on AIRS satellite retrieval (in Chinese). Arid Zone Research. 30(6): 951– 957. Zhang, Y., Wang, D., Zhai, P., and Gu, G.(2012). Applicability of AIRS monthly mean atmospheric water vapor profiles over the Tibetan Plateau region. Journal of Atmospheric and Oceanic Technology. 29 (11):1617–1628.
| ||
|
آمار تعداد مشاهده مقاله: 567 تعداد دریافت فایل اصل مقاله: 342 |
||