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A Physically Based Spatial Expansion Algorithm for Surface Air Temperature and Humidity

DOI: 10.1155/2013/727546

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Abstract:

An algorithm was developed to expand the surface air temperature and air humidity to a larger spatial domain, based on the fact that the variation of surface air temperature and air humidity is controlled jointly by the local turbulence and the horizontal advection. This study proposed an algorithm which considers the advective driving force outside the thermal balance system and the turbulent driving force and radiant driving force inside the thermal balance system. The surface air temperature is determined by a combination of the surface observations and the regional land surface temperature observed from a satellite. The average absolute difference of the algorithm is 0.65 degree and 0.31?mb, respectively, for surface air temperature and humidity expansion, which provides a promising approach to downscale the two surface meteorological variables. 1. Introduction Air temperature and humidity are the most fundamental elements that human beings interact with in the environment. The heat and steam (moisture) that reach the surface (the surface or active surface of soil, vegetation, rocks, and water, generally called the earth’s surface) have complex interaction with the air in the boundary layer, causing new balance and redistribution of heat and steam. The most important and frontier indicator for this new temporal and spatial distribution is the air temperature and humidity at the height of thermometer shelter in weather stations. For a long time, air temperature and humidity are not only the primary items of weather forecast, but also the core input to model the surface sensible heat flux and latent heat flux. The Penman-Monteith equation [1] requires the air temperature and humidity and some other inputs to calculate the evapotranspiration on the basis of energy balance. The spatial distribution of air temperature and humidity depends on the uniformity of the surface energy balance and the intensity of the horizontal advection [2]. So far, the observation means for such important parameters is still limited in very small “point” scale [3]. The spatial representativeness of the air temperature and humidity at the height of thermometer shelter in weather stations is about a few hundred square meters [4, 5]. The density of weather stations varies with each country’s capacity on meteorological observations and the corresponding financial budget. Currently in China, there is approximately one weather station for each administrative county, which means one weather station for average area of 5846 square kilometers (close to 6000 MODIS pixel; counties in

References

[1]  J. L. Monteith, Principles of Environmental Physics, Edward Arnold, London, UK, 1973.
[2]  G. Ali, D. Tetzlaff, C. Soulsby, and J. J. Mcdonnell, “Topographic, pedologic and climatic interactions influencing streamflow generation at multiple catchment scales,” Hydrological Processes, vol. 26, no. 25, pp. 3858–3874, 2012.
[3]  B. Henn, M. S. Raleigh, A. Fisher, and J. D. Lundquist, “A comparison of methods for filling gaps in hourly near-surface air temperature data,” Journal of Hydrometeorology, vol. 14, no. 3, pp. 929–945, 2013.
[4]  C. Daly, “Guidelines for assessing the suitability of spatial climate data sets,” International Journal of Climatology, vol. 26, no. 6, pp. 707–721, 2006.
[5]  J. D. Lundquist, N. Pepin, and C. Rochford, “Automated algorithm for mapping regions of cold-air pooling in complex terrain,” Journal of Geophysical Research D, vol. 113, no. 22, Article ID D22107, 2008.
[6]  E. S. Garcia, C. L. Tague, and J. S. Choate, “Influence of spatial temperature estimation method in ecohydrologic modeling in the Western Oregon Cascades,” Water Resources Research, vol. 49, no. 3, pp. 1611–1624, 2013.
[7]  D. W. Pierce, T. P. Barnett, B. D. Santer, and P. J. Gleckler, “Selecting global climate models for regional climate change studies,” Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 21, pp. 8441–8446, 2009.
[8]  K. A. Eldrandaly and M. S. Abu-Zaid, “Comparison of six GIS-based spatial interpolation methods for estimating air temperature in western saudi arabia,” Journal of Environmental Informatics, vol. 18, no. 1, pp. 38–45, 2011.
[9]  K. Stahl, R. D. Moore, J. A. Floyer, M. G. Asplin, and I. G. McKendry, “Comparison of approaches for spatial interpolation of daily air temperature in a large region with complex topography and highly variable station density,” Agricultural and Forest Meteorology, vol. 139, no. 3-4, pp. 224–236, 2006.
[10]  M. Ninyerola, X. Pons, and J. M. Roure, “Objective air temperature mapping for the Iberian Peninsula using spatial interpolation and GIS,” International Journal of Climatology, vol. 27, no. 9, pp. 1231–1242, 2007.
[11]  T. R. Karl, W. C. Wang, M. E. Schlesinger, R. W. Knight, and D. Portman, “A method of relating general circulation model simulated climate to the observed local climate. Part I: seasonal statistics,” Journal of Climate, vol. 3, no. 10, pp. 1053–1079, 1990.
[12]  D. L. Phillips, J. Dolph, and D. Marks, “A comparison of geostatistical procedures for spatial analysis of precipitation in mountainous terrain,” Agricultural and Forest Meteorology, vol. 58, no. 1-2, pp. 119–141, 1992.
[13]  R. H. Zhang, The Model about the Quantitative Application of Thermal Infrared Remote Sensing and the Base of On-Earth Experiment, China Science Press, 2009.
[14]  R. Zhang, X. Sun, J. Liu, H. Su, X. Tang, and Z. Zhu, “Determination of regional distribution of crop transpiration and soil water use efficiency using quantitative remote sensing data through inversion,” Science in China D, vol. 46, no. 1, pp. 10–22, 2003.

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