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Identifying Sky Conditions in Iran from MODIS Terra and Aqua Cloud Products

Khodakaram HATAMI BAHMANBEIGLOU Saeed MOVAHEDI

Khodakaram HATAMI BAHMANBEIGLOU, Saeed MOVAHEDI. Identifying Sky Conditions in Iran from MODIS Terra and Aqua Cloud Products[J]. 中国地理科学, 2017, 27(5): 800-809. doi: 10.1007/s11769-017-0908-4
引用本文: Khodakaram HATAMI BAHMANBEIGLOU, Saeed MOVAHEDI. Identifying Sky Conditions in Iran from MODIS Terra and Aqua Cloud Products[J]. 中国地理科学, 2017, 27(5): 800-809. doi: 10.1007/s11769-017-0908-4
Khodakaram HATAMI BAHMANBEIGLOU, Saeed MOVAHEDI. Identifying Sky Conditions in Iran from MODIS Terra and Aqua Cloud Products[J]. Chinese Geographical Science, 2017, 27(5): 800-809. doi: 10.1007/s11769-017-0908-4
Citation: Khodakaram HATAMI BAHMANBEIGLOU, Saeed MOVAHEDI. Identifying Sky Conditions in Iran from MODIS Terra and Aqua Cloud Products[J]. Chinese Geographical Science, 2017, 27(5): 800-809. doi: 10.1007/s11769-017-0908-4

Identifying Sky Conditions in Iran from MODIS Terra and Aqua Cloud Products

doi: 10.1007/s11769-017-0908-4
基金项目: Under the auspices of Faculty of Geographical Science and Planning,University of Isfahan,Doctoral Climatology Project (No.168607/94)
详细信息
    通讯作者:

    Saeed MOVAHEDI,E-mail:S.movahedi@geo.ui.ac.ir

Identifying Sky Conditions in Iran from MODIS Terra and Aqua Cloud Products

Funds: Under the auspices of Faculty of Geographical Science and Planning,University of Isfahan,Doctoral Climatology Project (No.168607/94)
More Information
    Corresponding author: Saeed MOVAHEDI,E-mail:S.movahedi@geo.ui.ac.ir
  • 摘要: Clouds can influence climate through many complex interactions within the hydrological cycle. Due to the important effects of cloud cover on climate, it is essential to study its variability over certain geographical areas. This study provides a spatial and temporal distribution of sky conditions, cloudy, partly cloudy, and clear days, in Iran. Cloud fraction parameters were calculated based on the cloud product (collection 6_L2) obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on board the Terra (MOD06) and Aqua (MYD06) satellites. The cloud products were collected daily from January 1, 2003 to December 31, 2014 (12 years) with a spatial resolution of 5 km×5 km. First, the cloud fraction data were converted into a regular geographic coordinate network over Iran. Then, the estimations from both sensors were analyzed. Results revealed that the maximum annual frequency of cloudy days occurs along the southern shores of the Caspian Sea, while the minimum annual frequency occurs in southeast Iran. On average, the annual number of cloudy and clear-sky days was 88 and 256 d from MODIS Terra, as compared to 96 and 244 d from MODIS Aqua. Generally, cloudy and partly cloudy days decrease from north to south, and MODIS Aqua overestimates the cloudy and partly cloudy days compared to MODIS Terra.
  • [1] Alijani B, 1995. Climate of Iran. Tehran: Payame Noor Publications, 236.
    [2] Amiri M J, Eslamian S S, 2010. Investigation of climate change in Iran. Journal of Environmental Science and Technology, 3(4): 208–216. doi:  10.3923/jest.2010.208.216
    [3] Bannayan A M, Mohamadian A, Alizadeh A, 2011. On climate variability in north east of Iran. Journal of Water and Soil, 24(1): 118–131.
    [4] Bostan D C, Manea E F, Stefan S, 2015. Total and partial cloudiness distribution in eastern Romania. Romanian Reports in Physics, 67(3): 1117–1127.
    [5] Butt N, New M, Lizcano G et al., 2009. Spatial patterns and recent trends in cloud fraction and cloud-related diffuse radiation in Amazonia. Journal of Geophysical Research: Atmospheres, 114(D21): D21104. doi:  10.1029/2009JD012217
    [6] Calbo J, Sabburg J, 2008. Feature extraction from whole-sky ground-based images for cloud-type recognition. Journal of Atmospheric and Oceanic Technology, 25(1): 3–14. doi: 10.1175/2007JTECHA959.1
    [7] Chen L, Yan G J, Wang T X et al., 2012. Estimation of surface shortwave radiation components under all sky conditions:modeling and sensitivity analysis. Remote Sensing of Environment, 123: 457–469. doi:  10.1016/j.rse.2012.04.006
    [8] Chen T M, Guo J P, Li Z Q et al., 2016. A CloudSat perspective on the cloud climatology and its association with aerosol perturbations in the vertical over Eastern China. Journal of the Atmospheric Sciences, 73(9): 3599–3616. doi:  10.1175/JASD-15-0309.1.
    [9] Congren C, 2013. Spatial and Temporal Variation of Cloud Free Days in Sweden. Gothenburg: University of Gothenburg, 1–26.
    [10] Dai A G, Trenberth K E, Karl T R, 1999. Effects of clouds, soil moisture, precipitation, and water vapor on diurnal temperature range. Journal of Climate, 12(8): 2451–2473. doi: 10.1175/1520-0442(1999)012<2451:EOCSMP>2.0.CO;2
    [11] Davy R, Esau I, 2016. Differences in the efficacy of climate forcings explained by variations in atmospheric boundary layer depth. Nature Communications, 7: 11690. doi: 10.1038/ ncomms11690
    [12] Eastman R, Warren G S, 2010. Interannual variations of arctic cloud types in relation to sea ice. Journal of Climate, 23(15):4216–4223. doi:  10.1175/2010JCLI3492.1
    [13] Filipiak J, Mi?tus M, 2009. Spatial and temporal variability of cloudiness in Poland, 1971–2000. International Journal of Climatology, 29(9): 1294–1311. doi:  10.1002/joc.1777
    [14] Garcia P, Benarroch A, Riera J M, 2008. Spatial distribution of cloud cover. International Journal of Satellite Communications and Networking, 26(2): 141–155. doi:  10.1002/sat.899
    [15] Griggs J, Bamber J, 2008. Assessment of cloud cover characteristics in satellite datasets and reanalysis products for Greenland. Journal of Climate, 21(9): 1837–1849. doi:  10.1175/2007JCLI1570.1
    [16] Guo J P, Zhang X Y, Wu Y R et al., 2011. Spatio-temporal variation trends of satellite-based aerosol optical depth in China during 1980–2008. Atmospheric Environment, 45(37): 6802– 6811. doi:  10.1016/j.atmosenv.2011.03.068
    [17] Guo J P, Deng M J, Lee S S et al., 2016a. Delaying precipitation and lightning by air pollution over the Pearl River Delta. Part I: Observational analyses. Journal of Geophysical Research:Atmospheres, 121(11): 6472–6488. doi:  10.1002/2015JD023257
    [18] Guo J P, Miao Y C, Zhang Y et al., 2016b. The climatology of planetary boundary layer height in China derived from radiosonde and reanalysis data. Atmospheric Chemistry and Physics, 16(20): 13309–13319. doi:  10.5194/acp-16-13309-2016
    [19] Hubanks P, Platnick S, King M et al., 2015. MODIS atmosphere L3 gridded product algorithm theoretical basis document(ATBD) & users guide. ATBD reference number ATBDMOD-30, NASA. Available at: https://modis-atmos.gsfc.nasa.gov/_docs/L3_ATBD_C6_2015_05_06.pdf. Cited 6 May 2015
    [20] Javanmard S, Yatagai A, Nodzu M I et al., 2010. Comparing high-resolution gridded precipitation data with satellite rainfall estimates of TRMM_3B42 over Iran. Advances in Geosciences, 25: 119–125. doi:  10.5194/adgeo-25-119-2010
    [21] King N J, Bower K N, Crosier J et al, 2013. Evaluating MODIS cloud retrievals with in situ observations from VOCALS-Rex. Atmospheric Chemistry and Physics, 13(1): 191–209. doi: 10. 5194/acp-13-191-2013
    [22] Kong H J, Kim J T, 2013. A Classification of real sky conditions for Yongin, Korea. In: Hakansson A et al. (eds). Sustainability in Energy and Buildings. Smart Innovation, Systems and Technologies, vol 22. Berlin, Heidelberg: Springer.
    [23] Koren I, Dagan G, Altaratz O, 2014. From aerosol-limited to invigoration of warm convective clouds. Science, 344(6188):1143–1146. doi:  10.1126/science.1252595
    [24] Kotarba A Z, 2009. A comparison of MODIS-derived cloud amount with visual surface observations. Atmospheric Research, 92(4): 522–530. doi:  10.1016/j.atmosres.2009.02.001
    [25] Kumar S V V A, Babu K N, Shukla A K, 2015. Comparative analysis of chlorophyll-a distribution from SEAWIFS, MODIS-AQUA, MODIS-TERRA and MERIS in the Arabian Sea. Marine Geodesy, 38(1): 40–57. doi: 10.1080/01490419. 2014.914990.
    [26] Li Z, Cribb M C, Chang F L et al., 2004. Validation of MODISretrieved cloud fractions using whole sky imager measurements at the three ARM sites. Proceedings of the 14th ARM Science Team Meeting. Albuquerque, New Mexico: ARM.
    [27] Li Z Q, Lau W K M, Ramanathan V et al., 2016. Aerosol and monsoon climate interactions over Asia. Reviews of Geophysics, 54(4): 866–929. doi:  10.1002/2015RG000500
    [28] Menzel W P, Frey R A, Baum B A, 2013. Cloud Top Properties and Cloud Phase Algorithm Theoretical Basis Document, Collection 006 Update. Space Science and Engineering Center, 1–70.
    [29] Menzel W P, Frey R A, Zhang H et al., 2008. MODIS global cloud-top pressure and amount estimation: algorithm description and results. Journal of Applied Meteorology and Climatology, 47(4): 1175–1198. doi:  10.1175/2007JAMC1705.1
    [30] Nakamura H, Oki M, Hayashi Y, 1985. A study on the estimation of the relative frequency of occurrences of the Clear Sky, the Intermediate Sky and the Overcast Sky in Japan. Journal of Light & Visual Environment, 9(2): 22–31. doi:  10.2150/jlve.9.2_22
    [31] Pincus R, Platnick S, Ackerman S A et al., 2012. Reconciling simulated and observed views of clouds: MODIS, ISCCP, and the limits of instrument simulators. Journal of Climate, 25(13): 4699–4720. doi:  10.1175/JCLI-D-11-00267.1
    [32] Ramanathan V, Crutzen P J, Kiehl J T et al., 2001. Aerosols, climate, and the hydrological cycle. Science, 294(5549): 2119– 2124. doi:  10.1126/science.1064034
    [33] Schiffer R A, Rossow W B, 1983. The international satellite cloud climatology project (ISCCP): the first project of the World Climate Research Programme. Bulletin of the American Mete-orological Society, 64(7): 779–784. doi:  10.1175/1520-0477(1991)072<0002:ICDP>2.0.CO;2
    [34] Spena A D, D’Angiolini G, Strati C et al., 2010. First correlations for solar radiation on cloudy days in Italy. In: ASME-ATI-UIT Conference on Thermal and Environmental Issues in Energy Systems. Sorrento, Italy.
    [35] Tang Q H, Leng G Y, 2013. Changes in cloud cover, precipitation, and summer temperature in North America from 1982 to 2009. Journal of Climate, 26(5): 1733–1744. doi:  10.1175/JCLI-D-12-00225.1
    [36] U.S. Department of Commerce/National Oceanic and Atmospheric Administration, 1995. Surface Weather Observations and Reports. Federal Meteorological Handbook No.1. OFCM U.S. Department of Commerce/NOAA, 94. Washington, D.C:U.S. Department of Commerce/National Oceanic and Atmospheric Administration.
    [37] Wang F, Guo J P, Wu Y R et al., 2014. Satellite observed aerosol-induced variability in warm cloud properties under different meteorological conditions over eastern China. Atmospheric Environment, 84: 122–132. doi: 10.1016/j.atmosenv.2013.11. 018
    [38] Wang F, Guo J P, Zhang J H et al., 2015. Multi-sensor quantification of aerosol-induced variability in warm clouds over eastern China. Atmospheric Environment, 113: 1–9. doi: 10.1016/j. atmosenv.2015.04.063
    [39] Warren S G, Eastman R M, Hahn C J, 2007. A survey of changes in cloud cover and cloud types over land from surface observations, 1971–96. Journal of Climate, 20(4): 717–738. doi: 10. 1175/JCLI4031.1
    [40] Wilson A M, Jetz W, 2016. Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions. PLoS Biology, 14(3): e1002415. doi: 10. 1371/journal.pbio.1002415
    [41] Wu Jian, Liu Jia, 2013. Variations of cloud fraction over East Asia under global warming conditions in the past 20 years. Journal of Tropical Meteorology, 19(2): 171–180. doi:  1006-8775(2013)02-0171-10
    [42] Wylie D, Jackson D L, Menzel W P et al., 2005. Trends in global cloud cover in two decades of HIRS observations. Journal of Climate, 18(15): 3021–3031. doi:  10.1175/JCLI3461.1
    [43] Xia X, 2012. Significant decreasing cloud cover during 1954– 2005 due to more clear-sky days and less overcast days in China and its relation to aerosol. Annales Geophysicae, 30(3):573–582. doi:  10.5194/angeo-30-573-2012
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Identifying Sky Conditions in Iran from MODIS Terra and Aqua Cloud Products

doi: 10.1007/s11769-017-0908-4
    基金项目:  Under the auspices of Faculty of Geographical Science and Planning,University of Isfahan,Doctoral Climatology Project (No.168607/94)
    通讯作者: Saeed MOVAHEDI,E-mail:S.movahedi@geo.ui.ac.ir

摘要: Clouds can influence climate through many complex interactions within the hydrological cycle. Due to the important effects of cloud cover on climate, it is essential to study its variability over certain geographical areas. This study provides a spatial and temporal distribution of sky conditions, cloudy, partly cloudy, and clear days, in Iran. Cloud fraction parameters were calculated based on the cloud product (collection 6_L2) obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on board the Terra (MOD06) and Aqua (MYD06) satellites. The cloud products were collected daily from January 1, 2003 to December 31, 2014 (12 years) with a spatial resolution of 5 km×5 km. First, the cloud fraction data were converted into a regular geographic coordinate network over Iran. Then, the estimations from both sensors were analyzed. Results revealed that the maximum annual frequency of cloudy days occurs along the southern shores of the Caspian Sea, while the minimum annual frequency occurs in southeast Iran. On average, the annual number of cloudy and clear-sky days was 88 and 256 d from MODIS Terra, as compared to 96 and 244 d from MODIS Aqua. Generally, cloudy and partly cloudy days decrease from north to south, and MODIS Aqua overestimates the cloudy and partly cloudy days compared to MODIS Terra.

English Abstract

Khodakaram HATAMI BAHMANBEIGLOU, Saeed MOVAHEDI. Identifying Sky Conditions in Iran from MODIS Terra and Aqua Cloud Products[J]. 中国地理科学, 2017, 27(5): 800-809. doi: 10.1007/s11769-017-0908-4
引用本文: Khodakaram HATAMI BAHMANBEIGLOU, Saeed MOVAHEDI. Identifying Sky Conditions in Iran from MODIS Terra and Aqua Cloud Products[J]. 中国地理科学, 2017, 27(5): 800-809. doi: 10.1007/s11769-017-0908-4
Khodakaram HATAMI BAHMANBEIGLOU, Saeed MOVAHEDI. Identifying Sky Conditions in Iran from MODIS Terra and Aqua Cloud Products[J]. Chinese Geographical Science, 2017, 27(5): 800-809. doi: 10.1007/s11769-017-0908-4
Citation: Khodakaram HATAMI BAHMANBEIGLOU, Saeed MOVAHEDI. Identifying Sky Conditions in Iran from MODIS Terra and Aqua Cloud Products[J]. Chinese Geographical Science, 2017, 27(5): 800-809. doi: 10.1007/s11769-017-0908-4
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