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صحتسنجی ماسک ابر سنجندة مادیس با معرفی ماسک ابر ناحیه‌ای براساس داده‌های سنجندة AVHRR | ||
| پژوهش های جغرافیای طبیعی | ||
| مقاله 5، دوره 51، شماره 3، مهر 1398، صفحه 447-468 اصل مقاله (2.25 M) | ||
| نوع مقاله: مقاله کامل | ||
| شناسه دیجیتال (DOI): 10.22059/jphgr.2019.235820.1007068 | ||
| نویسندگان | ||
| الهام قاسمی‌فر1؛ منوچهر فرج‌زاده اصل* 2؛ یوسف قویدل رحیمی3؛ عباسعلی علی‌اکبری بیدختی4 | ||
| 1دانش‌آموختۀ دکتری اقلیم شناسی ماهواره ای، دانشکدة علوم انسانی، دانشگاه تربیت مدرس، تهران، ایران | ||
| 2استاد آب‌وهواشناسی دانشکدة علوم انسانی، دانشگاه تربیت مدرس، تهران، ایران | ||
| 3استادیار اقلیم شناسی دانشکدة علوم انسانی، دانشگاه تربیت مدرس، تهران، ایران | ||
| 4استاد گروه آموزشی فیزیک فضا، مؤسسة ژئوفیزیک، دانشگاه تهران، ایران | ||
| چکیده | ||
| از داده‌های سنجندة AVHRR برای آشکارسازی ناحیه‌ای ابر در دو منطقة ایران با ویژگیهای متفاوت جغرافیایی و جوی بهمنظور مقایسه با داده‌های ماسک ابر مادیس (MOD35) استفاده شده است. بدین‌منظور، پنج تاریخ دارای بالاترین و پایینترین آنومالی ابرناکی طی بازة زمانی 2001-2015 متناسب با گذر ماهوارههای ترا و نوآ انتخاب شد. پنج آزمون آستانهگذاری طیفی مختلف استفاده شد. ابتدا پس از تشخیص پیکسلهای برفی، این پیکسلها از ادامة کار حذف شدند. سپس، از سه آزمون بهمنظور تشخیص پیکسلهای ابرناک بهره برده شد؛ این آزمونها عبارت است از: آزمون بازتاب کانال مرئی؛ آزمون نسبت بازتاب مادون قرمز نزدیک/ مرئی؛ و آزمون دمای درخشندگی جو بدون ابر. آزمون آخر نشان داد، طی تاریخهای مورد بررسی، با توجه به دمای سطحی، دمای درخشندگی قابل تقسیم به دو طبقة اصلی است. هنگامی که دمای سطحی بیشتر از 5 درجة سانتیگراد است، جو صاف بهطور متوسط 1 درجة سانتیگراد سردتر از جو ابری است و برعکس؛ هنگامی که دمای سطحی کمتر از 5 درجة سانتیگراد باشد، جو صاف بهطور متوسط 6 درجة سانتیگراد گرمتر است. نتایج حاصل از صحتسنجی این الگوریتم با ارزیابی داده‌های ایستگاهی و محصول ماسک ابر سنجندة مادیس دقت بالای 90درصد را نیز در برخی از تاریخها آشکار کرد. | ||
| کلیدواژه‌ها | ||
| دمای درخشندگی جو بدون ابر؛ ماسک ابر ناحیه ای؛ مدل انتقال تابش؛ AVHRR؛ MOD35 | ||
| عنوان مقاله [English] | ||
| Validating MODIS Cloud Mask Based on a Regional Cloud Mask of AVHRR | ||
| نویسندگان [English] | ||
| Elham Ghasemifar1؛ Manuchehr Farajzadeh Asl2؛ Yousef Ghavidel Rahimi3؛ Abbas Ali Aliakbari Bidokhti4 | ||
| 1PhD Candidate in Satellite Climatology, Department of Physical Geography, Tarbiat Modares University, Tehran, Iran | ||
| 2Professor of Climatology, Department of Physical Geography, Tarbiat Modares University, Tehran, Iran | ||
| 3Assistant Professor of Climatology, Department of Physical Geography, Tarbiat Modares University, Tehran, Iran | ||
| 4Professor of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran | ||
| چکیده [English] | ||
| Introduction Cloud plays an important role in the study of radiation balance and greenhouse gases due to existing water vapor as the greenhouse gases of the atmosphere. Clouds can reflect sun radiated in the top of the atmosphere based on their thickness and density. Iran country has different regions in regards to cloudiness. For example, the north of Iran has a cloudy sky in most days of the year, while cloudy conditions are low in the central regions of Iran. One of the most important datasets for cloud detection is satellite data. The two main sensors that can be used in meteorology are Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard Terra and Aqua Platforms and Advanced Very High Resolution Radiometer (AVHRR) aboard National Oceanic and Atmospheric Administration (NOAA) that were used in this paper. Various cloud detection algorithms are applied to different satellite observations over various land surfaces. AVHRR level-1 data is used to introduce a regional cloud detection scheme for two regions in Iran which have different geographical characteristics. Most cloud mask algorithms were developed globally using different spectral tests and have not examined and validated at regional scale. For example, MODIS cloud mask (MCM) algorithm can be applied in five group tests in visible, near infrared, and thermal spectral regions. The goal of the study is to validate the MCM based on AVHRR data and radiative transfer model simulation of clear sky brightness temperature (BT). Materials and methods We have executed the analysis in two regions of Guilan, located in southern coasts of Caspian Sea with maritime climate as well as in Khohgiluyeh Va Boyerahmad in west Iran with mountainous climate. We have employed five spectral threshold tests for the regions. Radiative Transfer for Television and Infrared Observation Satellite (TIROS) Operational (RTTOV) simulation of clear sky BT was also applied for clear sky BT thresholding. Snow have detected by NearIR(1.6) /VIS(0.6) test ranging values below and equal to 0.2 with a BT test at 12 ranging -8.16 to 11.84 degrees Celsuis. The reflectance test was also performed. Fourth test was a NearIR (0.9)/VIS (0.6) ranging from 0.6 to 1.3 over cloudy area and finally a clear sky BT test was applied to the areas. Results and discussion These all tests were applied to five dates with the highest positive anomalies (Dec 2003, Oct 2006, Jan 2008 and Nov2011) and the highest negative anomaly (Dec 2010) during 2001-2015. The results showed that although an extended area have values below and equal to 0.2, BT test for cloudy region have values below -8.16 degrees Celsuis. According to the results of the fourth test, the test indicated the values between 0.6 and 1.3 over Guilan and 0.7 and 1.3 over Khohgiluyeh Va Boyerahmad fot detecting the clouds. The fifth test revealed interesting results due to different properties over low and high skin temperature area so that an area with values of skin temperature higher than 5 degrees Celsuis experienced clear sky BT colder than cloudy sky temperature and vice versa. This test showed that the values of T12μm below -2.5 C can detect clouds in both cases (skin temperature below 5 C and greater than 5 C) over Khohgiluyeh Va Boyerahmad. The values of 5 and -13.68 C can be used for the thresholding over the area with skin temperature greater and below 5 C on Guilan province, respectively. The results demonstrated that these tests can well detect clouds over more than 90 percent of the areas. With applying these tests over 90 and 91 points of Guilan and Khohgiluyeh Va Boyerahmad, the results showed good agreement between new cloud mask and MCM and weather stations total cloud cover data. Conclusion This study has addressed the cloud mask properties by MCM using AVHRR imagery data and weather stations statistic data. Results of the radiative transfer model revealed interesting effects on cloud detection. is the results of this research, as the first attempt to introduce a regional cloud mask over Iran, suggested the use of SEVIRI data with higher temporal resolution over two regions. The radiative transfer simulation results can compare the radiative transfer model in this research. It is recommended to use more geographic areas and more different dates for such researches. | ||
| کلیدواژه‌ها [English] | ||
| cloud detection, AVHRR, RTTOV, Guilan, Khohgiluyeh Va Boyerahmad | ||
| مراجع | ||
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قاسمی‌فر، الف.؛ فرج‌زاده، م.؛ قویدل رحیمی، ی. و علی‌اکبری بیدختی، ع.ع. (1397). بررسی تغییرات فضایی- زمانی ابرناکی بر پایة ویژگیهای جغرافیایی و داده‌های سنجش از دور در ایران، فیزیک زمین و فضا، 44(1): 103-124. علیجانی،ب.(1387). آب و هوای ایران،چاپ هشتم، دانشگاه پیام نور، تهران. 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. Ackerman, S.A.; Strabala, K.I.; Menzel, W.P.; Frey, R.A.; Moeller, C.C. and Gumley, L.E. (1998). Discriminating clear sky from clouds with MODIS, Journal of geophysical research, 103(D24): 32,141-32,157. Ackerman, S.A.; Strabala, K.I.; Menzel, W.P.; Frey, R.A.; Moeller, C.C.; Gumley, L.E.; Baum, B.; Schaaf, C. and Riggs, G. (1997). Discriminating clear-sky from cloud with modis algorithm theoretical basis document (mod35), V3.2. Alijani, B. and Harman, J.R. (1985). Synoptic Climatology of Precipitation in Iran, Annals of the association of American geographers, 75(3): 404-416. Azorin-Molina, C.; Baena-Calatrava, R.; Echave-Calvo, I.; Connell, B.H.; Vicente-Serrano, S.M. and Lopez-Moreno, J.L. (2013). A daytime over land algorithm for computing AVHRR convective cloud climatologies for the Iberian Peninsula and the Balearic Islands: Int. J. Climatol, 33: 2113-2128. Aune, R.M.; Frey, R. and Wetzel-Seemann, S. (2003). Evaluating Water Vapor and Cloud Retrievals from MODIS Over Antarctica, 12th Conference on Satellite Meteorology and Oceanography, February 2003. Bauer, P. (2002). Microwave radiative transfer modeling in clouds and precipitation. Part I: Model description: NWP SAF Rep, NWPSAF-EC-TR-005, 27 pp. Bennartz, R., Greenwald, T.(2011). Current problems in scattering radiative transfer modelling for data assimilation,quarterly journal of theroyal meteorological society.,137(661): Pages 1952-1962. Bley, S. and Deneke, H. (2013). A threshold-based cloud mask for the high-resolution visible channel of Meteosat Second Generation SEVIRI, Atmos. Meas. Tech., 6: 2713-2723, doi:10.5194/amt-6-2713-2013. Chen, Y.; Han, Y.;Van Delst, P. and Weng, F. (2013). Assessment of Shortwave Infrared Sea Surface Reflection and Nonlocal Thermodynamic Equilibrium Effects in the Community Radiative Transfer Model Using IASI Data, Journal of atmospheric and oceanic technology, 30: 2152-2160, DOI: 10.1175/JTECH-D-12-00267.1. Chen, X.; Wei, H.; Yang, P.; Jin, Z. and Baum, B.A. (2011). An efficient method for computing atmospheric radiances in clear-sky and cloudy conditions, Journal of Quantitative Spectroscopy & Radiative Transfer, 112: 109-118, doi:10.1016/j.jqsrt.2010.08.013. Chen, Y.; Weng, F.; Han, Y. and Liu, Q. (2008). Validation of the Community Radiative Transfer Model by using CloudSat data, Journal of geophysical research, 113: D00A03, doi:10.1029/2007JD009561. Ding, S.; Yang, P.; Weng, F.; Liu, Q.; Han, Y.; Van Delst, P.; Li, J. and Baum, B. (2011). Validation of the community radiative transfer model, Journal of Quantitative Spectroscopy & Radiative Transfer, 112: 1050-1064, doi:10.1016/j.jqsrt.2010.11.009. Ding, W.Y.; Wan, Q.L.; Zhang, C.Z.; Chen, ZT. and Huang, YY. (2010). MODIS brightness temperature data assimilation under cloudy conditions: methods and ideal tests, Journal of tropical meteorology,16(4): 313-324, doi: 10.3969/j.issn.1006-8775.2010.04.002. Derrien, M.; Farki, B.; Harang, L.; Le Gl´eau, H.; Noyalet, A.; Pochic, D. and Sairouni, A. (1993). Automatic cloud detection applied to NOAA-11/AVHRR imagery, Remote Sensing of Environment, 46(3): 246-267. Da, C. (2014). An Objective Regional Cloud Mask Algorithm for GOES Infrared Imager with Regime-Dependent Thresholds for Direct Radiance Assmilation, A Thesis submitted to the Department of Earth, Oceanic and Atmospheric Sciences in partial fulfillment of the requirements for the degree of Master of Science,Florida State University. Dybbroe, A.; Karlsson, K.G. and Thoss, A. (2005). NWCSAF AVHRR Cloud Detection and Analysis Using Dynamic Thresholds and Radiative Transfer Modeling. Part I: Algorithm Description, Journal of applied meteorology, 44: 39-54. EUMETSAT (2011). AVHRR Level 1b Product Guide, EUM/OPS-EPS/MAN/04/0029, v3A, 21 January 2011. ECMWF (European Centre for Medium-Range Weather Forecasts) (2015). PART IV: PHYSICAL PROCESSES, IFS DOCUMENTATION -Cy41r1 Operational implementation, 12 May 2015. Eyre, J.R., and Woolf, H.M. (1988). Transmittance of atmospheric gases in the microwave region: a fast model, Applied Optics, 27(15): 3244-3249. Eyre, J.R. and Watts, P.D. (1987). A sequential estimation approach to cloud clearing for satellite temperature sounding, Q. JI R. met. Soc. Farajzadeh, M.; Karimi Ahmadabad, M.; Ghaemi, H. and Mobasheri, M.R. (2007). Studying the moisture flux over west of Iran: a case study of January 1 to 7, 1996 Rain Storm, Journal of applied sciences, 7(20): 3023-3030. Ghasemifar, E.; Farajzadeh, M.; Perry, M.C.; Ghavidel Rahimi,Y. and Bidokhti, A.A. (2017). Analysis of spatiotemporal variations of cloud fraction based on geographic characteristics over Iran, Theor Appl Climatol, https://doi.org/10.1007/s00704-017-2308-1. Ghasemifar, E.; Mohammadi, C. and Farajzadeh, M. (2018). Spatiotemporal analysis of snow cover in Iran based on topographic characteristics,Theoretical and Applied Climatology, https://doi.org/10.1007/s00704-018-2690-3. Hocking, J. ; Rayer, P. Rundle, D.; Saunders, R.,Matricardi , M.; Geer, A. and Brunel, P.; Vidot, J.(2014). RTTOV v11 Users Guide,V1.3, Doc ID: NWPSAF-MO-UD-028. Hocking, J.; Francis, P.N. and Saunders, R. (2011). Cloud detection in Meteosat Second Generation imagery at the Met Office, Meteorol. Appl., 18: 307-323 . Hocking, J.; Francis, P. and Saunders, R. (2009). The use of ancillary surface data products in the office SEVIRI cloud mask, EUMETSAT Meteorological satellite conference, At Bath, United Kingdom. Han, W. and Dong, P. (2012). Study and comparison of simulation of satellite microwave observations in cloudy and rainy areas using RTTOV and CRTM: In. Proc, 18th International TOVS Study Conference, 94-95. Hutchison, K.D. (1999). Application of AVHRR/3 Imagery for the Improved Detection of Thin Cirrus Clouds and Specification of Cloud-Top Phase, DOI: http://dx.doi.org/10.1175/1520-0426(1999)0162.0.CO;2, http://journals.ametsoc.org/doi/full/10.1175/1520-0426%281999%29016%3C1885%3AAOAIFT%3E2.0.CO%3B2. Han, Y.; Van Delst, P.; Liu, Q.; Weng, F.; Yan, B.; Treadon, R. and Derber, J. (2006). NOAA Technical Report NESDIS 122, JCSDA Community Radiative Transfer Model (CRTM) - Version 1. Hall, D.K.; Riggs, G.A. and Salomonson, V.V. (1995). Development of methods for mapping global snow cover using Moderate Resolution Imaging Spectroradiometer data, Remote Sensing of Environment, 54(2): 127-140. Imai, T. and Yoshida, R. (2016). Algorithm Theoretical Basis for Himawari-8 Cloud Mask Product, Meteorological satellite center technical note, 61. Joint Center for Satellite Data Assimilation (JCSDA), June 20, 2013, rev29365.CRTM: v2.1.3 User Guide. Kim, M.J.; English, S.; Bauer, P.; Geer, A. and Okamoto, K. (2008). Comparison of progress in assimilating cloud-affected microwave radiances at NCEP, ECMWF, JMA and the Met Office, NWP SAF Satellite Application Facility for Numerical Weather Prediction Visiting Scientist mission report Document NWPSAF-MO-VS-037, Version 1.0. Kotarba, AZ. (2016.) Regional high-resolution cloud climatology based on MODIS cloud detection data, Int J Climatol, 36(8): 3105-3115. https://doi.org/10.1002/joc.4539. Kim, J.H.; Kang, J.H. and Lee, S. (2014). A comparison of observed and simulated brightness temperatures from two radiative transfer models of RTTOV and CRTM, Jour. Korean Earth Science Society, 35(1):19-28, http://dx.doi.org/10.5467/JKESS.2014.35.1.19. Klüser, L.; Killius, N. and Gesell, G. (2015). APOLLO_NG – a probabilistic interpretation of the APOLLO legacy for AVHRR heritage channels, Atmospheric measurment techniques, 8: 4155-4170. doi:10.5194/amt-8-4155-2015. Kriebel, K.T.; Gesell, G.; Kästner, M. and Mannstein, H. (2003). The cloud analysis tool APOLLO: improvements and validations, Int. J. Remote Sens., 24: 2389-2408. Krieble, K.T. (1989). Cloud properties derived from AVHRR data, ECMWF/EUMETSAT Workshop on The Use of Satellite Data in Operational Numerical Weather Prediction: 1989-1993, 9-12 May 1989, Conference location: Shinfield Park, Reading. publisher: ECMWF. Kim, J.H.; Kang, J.H.; Chun, H.W. and Lee, S. (2013). Comparison of simulated radiance fields using RTTOV and CRTM at microwave frequencies in kops framework: 2013 EUMETSAT and 19th AMS Conference, Vienna, Austria, 16-20 Sep 2013. Lievens, H.; Al Bitar, A.; Verhoest, NEC.; Cabot, F.; De Lannoy, G.J.M.; Drusch, M.; Dumedah, G.; Hendricks Franssen, H.J.; Kerr, Y.; Tomer, S.K.; Martens, B.; Merlin, O.; Pan, M.; Van den Berg, M.J.; Vereecken, H.; Walker, J.P.; Wood, E.F. and Pauwels, V.R.N. (2015). Optimization of a Radiative Transfer Forward Operator for Simulating SMOS Brightness Temperatures over the Upper Mississippi Basin, Journal of Hydrometeorology, 16(3): 1109-1134, DOI: http://dx.doi.org/10.1175/JHM-D-14-0052.1. Liu, Q.; Van Delst, P.; Zhu, T.; Weng, F. and Boukabara, S.A. (2015). Community Radiative Transfer Model (CRTM) For Cloud Radiance Simulation: Joint 20th American Meteorological Society (AMS) Satellite Conference, 11th AMS Annual Symposium on New Generation Operational Environmental Satellite Systems, and 3rd AMS Symposium on the Joint Center for Satellite Data Assimilation (JCSDA), 4-8 January 2015, Phoenix, Arizona. Liang, X.M.; Ignatov, A.; Kihai1, Y.; Heidinger, A.; Han, Y. and Chen, Y. (2008). Validation of the Community Radiative Transfer Model (CRTM) against AVHRR Clear-Sky Processor for Oceans (ACSPO) Nighttime Radiances for improved cloud detection and physical SST retrievals: 88th AMS Annual Meeting, 5th GOES Users' Conference, 20-24 January 2008, New Orleans, LA. Llewellyn-Jones, D.T.; Minnett, P.J.; Saunders, R.W. and Zavody, A.M. (1984). Satellite multichannel infrared measurements of sea surface temperature of the N.E. Atlantic Ocean using AVHRR/2, Q. JI R. met. Soc., 110: 613. Lacis, A.A. and Hansen, J.E. (1974). A parameterization for the absorption of solar radiation in the earth’s atmosphere, Journal of the atmospheric sciences, 31: 118-133. Prata, F. (2013). Detecting and Retrieving Volcanic Ash from SEVIRI Measurements Algorithm Theoretical Basis Document, v1.0 DRAFT of 22 May 2013. Maher, A.I.; Treitz, P.M. and Ferguson, M.A.D. (2012). Can Landsat data detect variations in snow cover within habitats of arctic ungulates?, Wildl. Biol., 18: 75-87, DOI: 10.2981/11-055. McGinnis, D.F. and Tarpley, J.D. (1985). Vegetation cover mapping from NOAA/AVHRR. Adv, Space Res., 5: 359. McClain, E.P.; Pichel, W.G. and Walton, C.C. (1985). Comparative performance of AVHRR based multichannel sea surface temperatures, J. geophys. Res., 90: C6, 11587. Niu, J.; Yang, P.; Huang, H.L.; Davies, J.E.; Li, J.; Baum, B.A. and Hu, Y.X. (2007). A fast infrared radiative transfer model for overlapping clouds, Journal of Quantitative Spectroscopy & Radiative Transfer, 103: 447-459. Olesen, F.S. and Grassl, H. (1985). Cloud detection and classification over oceans at night with NOAA-7, Int. J. remote Sensing, 6: 1435. Oku, Y. and Ishikawa, H. (2004). Estimation of land surface temperature over the Tibetan Plateau using GMS data, Journal of Applied Meteorology, 43(4): 548-561. Petrenko, B.; Ignatov, A.; Kihal, Y. and Heidinger, A. (2010). Clear-Sky Mask for the Advanced Clear-Sky Processor for Oceans, Journal of atmospheric and oceanic technology, 27: 1909-1623. DOI: 10.1175/2010JTECHA1413.1. Pinker, R.T. (1985). Determination of surface albedo from satellites, Adv. Space Res., 5: 333. Price, J.C. (1984). Land surface temperature measurements from the split window channels of the NOAA-7 AVHRR, J. geophys. Res., 89: 7231. Pilewskie, P. and Twomey, S. (1987). Cloud phase determination by reflectance measurements near 1.6 and 2.2 μm, J. Atmos.Sci., 44: 3419-3420. Parajka, J. and Blöschl, G. (2008). Spatio-temporal combination of MODIS images – potential for snow cover mapping, Water Resources Research, 44: W03406. Rosenkranz, P.W. and Barnet, C.D. (2006). Microwave radiative transfer model validation, Journal of geophysical research, 111, D09S07, doi:10.1029/2005JD006008. Rossow, W.B. and Schiffer, R.A. (1991). Isccp cloud data product, Bulletin of the American meteorological society, 72(1): 1-20. Roebeling,R.A.; Feijt, A.J. and Stamme, S.P. (2006). Cloud property retrievals for climate monitoring: Implications of differences between Spinning Enhanced Visible and Infrared Imager (SEVIRI) on METEOSAT-8 and Advanced Very High Resolution Radiometer (AVHRR) on NOAA-17, Journal of Geophysical Research, Vol. 111, D20210, doi:10.1029/2005JD006990. Rossow, W.B. and Schiffer, R.A. (1999). Advances in understanding clouds from ISCCP, Bulletin of the American Meteorological Society, 80(11): 2260-2287. Spurr, R.J.D.; Kurosu, T.P. and Chance, K.V. (2001). A linearized discrete ordinate radiative transfer model for atmospheric remote-sensing retrieval, Journal of Quantitative Spectroscopy & Radiative Transfer, 68: 689-735. Saunders, R.W. and Kriebel, K.T. (1988). An improved method for detecting clear sky and cloudy radiances from AVHRR data, International Journal of Remote Sensing, 9(1): 123-150, DOI: 10.1080/01431168808954841. Takashima, T. (1975). A new approach of the adding method for the computations of emergent radiation of an inhomogeneous plane-parallel planetary atmosphere, Astrophysics and space science, 36: 319-328. Troj´akova, A. and M´ajek, M. (2011). Assimilation of SEVIRI data, report from LACE stay in Budapest, Scientific supervisor: Roger Randriamampianina. Thompson, S.D. (2004). Evaluation of a microwave radiative transfer model for calculating satellite brightness temperature, MS thesis, Department of Electrical and Computer Engineering, university of central Florida. Trepte, Q.; Arduini, R.F.; Chen, Y.; Sun-Mack, S.; Minnis, P.; Spangenberg, D.A. and Doelling, D.R. (2001). Development of a daytime polar cloud mask using theoretical models of nearinfrared bidirectional reflectance for ARM and CERES, AMS 6th Conference on Polar Meteorology and Oceanography, 4-18 May 2001San Diego, CA, 242-245. Wiscombe, W. and Warren, S.G. (1980). A model for the spectral albedo of snow. I: Pure snow, J. Atmos. Sci., 37: 2712-2733. Weng, F. (2007). Advances in Radiative Transfer Modeling in Support of Satellite Data Assimilation, Journal of the atmospheric sciences-Special Section, 64: 3799-3807. Wang, X.; Li, W.; Zhu, Y. and Zhao, B. (2013). Improved cloud mask algorithm for FY-3A/VIRR data over the northwest region of China, Atmos. Meas. Tech., 6: 549-563, doi:10.5194/amt-6-549-2013. Zou, X. and Da, C. (2014). An objective regional cloud mask algorithm for GOES infrared imager radiance assimilation, J. Geophys. Res. Atmos., 119, 6666-6680, doi:10.1002/2014JD021455. Zou, X.; Zhuge, X. and Weng, F. (2016). Characterization of Bias of Advanced Himawari Imager Infrared Observations from NWP Background Simulations Using CRTM and RTTOV, Journal of the atmospheric and oceanic technology, 33: 2553-2567. DOI: 10.1175/JTECH-D-16-0105.1. | ||
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آمار تعداد مشاهده مقاله: 518 تعداد دریافت فایل اصل مقاله: 359 |
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