Applied Research in Geographical Sciences

Applied Research in Geographical Sciences

Modeling of spatio-temporal of albedo over Iran

Authors
Zahra Hejazizadeh 1
Meysam Toulabi Nejad 2
Alireza Rahimi 3
nasrin bazmi 4
Atefeh Bosak 5
1 Professor of climatology , kharazmi university
2 phd student of climatology , kharazmi university
3 Associate Professor, university of kashan
4 phd student of climatology, NOOR AZAD university
5 msc student of climatology, kharazmi university
Abstract
The aim of this study is modeling spatiotemporal variations of albedo. This study was conducted using simultaneous effects of several components, such as wetness of surface layer of soil, cloudiness, topography and vegetation density (NDVI), using MEERA2 model with a resolution of 50 in 50 km during 2000-2010 in Iran. The results of spatial analysis of albedo values in Iran showed that the highest value is in 44 to 45 degrees of east longitude about 2.8 to 3.3 and the lowest value of albedo is also in 52 to 53 degrees of east longitude, that is, the eastern slopes of the Zagros Mountains, have been recorded at 1 to 1.5 units. In terms of provincial rank, the largest albedo is about 0.25 units in Ilam province and the Fars province is ranked next about 0.24 units. The lowest amount of albedo also in the Gilan provinces and in next Mazandaran province are about 0.19 and 0.18 respectively. In addition, the results of temporal analysis in seasonal scale showed that the highest albedo in Iran in winter was 0.26 and its lowest amount was recorded in spring with 0.23 units. In general, according to the factors used, it can be said that the western and central parts of the country have a highest albedo, and the north and northwest regions of the country have a lowest albedo.
Keywords
albedo
spatio-temporal analysis
MERRA2 model
Iran
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10) Bosilovich, M. G., R. Lucchesi, and M. Suarez,(2016). MERRA-2: File Specification. GMAO Office Note. 9 (1.1), pp73.
11) Coakley, J. A.( 2003). Reflectance and albedo, surface. Encyclopedia of Atmos. Sci., 1914- 1923.
12) Chiacchio, M., F. Solmon, F. Giorgi, P. Stackhouse Jr., and M. Wild ,(2015), Evaluation of the radiation budget with a regional climate model over Europe and inspection of dimming and brightening, J. Geophys. Res. Atmos., 120: 1951–1971, doi:10.1002/2014JD022497.
13) da Silva, A. M., C. A. Randles, V. Buchard, A. Darmenov, P. R. Colarco, and R. Govindaraju, (2015). File Specification for the MERRA Aerosol Reanalysis (MERRAero). GMAO Office Note No. 7 .pp63.
14) Hartmann. D. L., Ramanthan V.A,. Berroir. G, (1986), Earth Radiation Budget Data and Climate Research, REVIEWS OF GEOPHYSICS, 24(2): 439-468.
15) He, T.(2012). Estimating land surface albedo from satellite data. Doctor of Philosophy dissertation, Faculty of the Graduate School of the University of Maryland, College Park, pp54.
16) Jackson RD, Moran MS, Gay LW and Raymond LH.( 1987). Evaluating evaporation from field crops using airborne radiometry and ground-based meteorological data. Irrigation Science, 8(2): 81-90.
17) Kim, D., and V. Ramanathan ,(2008), Solar radiation budget and radiative forcing due to aerosols and clouds, Jurnal of Geophysical Research, 113:1-34, D02203, doi:10.1029/2007JD008434.
18) Liang S. (2001). Narrowband to broadband conversions of land surface albedo I: Algorithms. Remote Sensing of Environment, 76(2): 213-238.
19) Liang S, Fang H, Morisette JT, Chen M, Shuey CJ, Walthall CL and Daughtry CS. (2002). Atmospheric correction of Landsat ETM+ land surface imagery. II. Validation and applications. Geoscience and Remote Sensing, IEEE Transactions on, 40(12): 2736-2746.
20) Maykut, G., and N. Untersteiner. ,(1971). Some results from a time-dependent thermodynamic model of Arctic sea ice. Journal of Geophysical Research , 76(6): 1550-1575.
21) Maykut, G. (1986). The surface heat and mass balance, In The geophysics of sea ice, edited by N. Untersteiner. New York: Plenium.
22) Molod, A., L. Takacs, M. Suarez, J. Bacmeister, I.-S. Song, and A. Eichmann,(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, NASA TM—2012-104606, Vol. 28, 117 pp.
23) Molod, A., Takacs, L., Suarez, M., and Bacmeister, J., 2014: Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA-2, Geosci. Model Dev. Discuss.,7:7575-7617, doi:10.5194/gmdd-7-7575-2014.
24) Rienecker and Coauthors,.(2011). MERRA - NASA's Modern-Era Retrospective Analysis for Research and Applications. J. Climate, 24: 3624-3648, doi:10.1175/JCLI-D-11-00015.1.
25) Perovich, D.K .(1998). Sea ice motion in response to geostrophic winds. Journal of Geophysical Research, 87(8): 5845-5852.
26) Stephens, G. L., D. O’Brien, P. J. Webster, P. Pilewski, S. Kato, and J.-l. Li ,(2015), The albedo of Earth, Rev. Geophys., 53:1-23, doi:10.1002/2014RG000449.
27) Tasumi, M., Allen, R. G., Trezza, R. (2008). Atsurface reflectance and albedo from satellite for operational calculation of land surface energy balance. J. Hydrologic Eng., 13(2):51-63.
28) Wielicki, B. A., Wong, T., Loeb, N., Minnis, P., Priestley, K., Kandel, R.(2005). Changes in Earth's albedo measured by satellite. Sci., 308(5723): 825- 825.
29) Wu, W.-S., R.J. Purser and D.F. Parrish,(2002), Three-dimensional variational analysis with spatially inhomogeneous covariances. Mon. Wea. Rev., 130: 2905-2916.
30) Ying Qu ,. Shunlin Liang,. Qiang Liu,. Xijia Li,. Youbin Feng,. Suhong Liu. (2016). Estimating Arctic sea-ice shortwave albedo from MODIS data.2016. Remote Sensing of Environment, 186:32–46, doi:10.1016/j.rse.2016.08.015.