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Abstract
Land surface temperature plays an important role in surface processes and is a key input for physically based retrieval algorithms of soil moisture and evaporation. This study presents a framework for using independent estimates of land surface temperature from five microwave satellite sensors to improve the accuracy of land surface temperature output from a numerical weather prediction system in an off-line (postprocessing) analysis. First structural differences in timing and amplitude of the temperature signal were addressed. Then satellite observations were assimilated into an auto-regressive error model formulated to estimate errors in the numerical weather prediction output. Errors in daily minimum and amplitude were treated separately. Results of this study provide new insights about potential added benefits of preprocessing and off-line assimilation of microwave remote sensing-based and model-based temperature retrievals. It is shown that the satellite observations may be used to reduce errors in surface temperature particularly for day-time hours. Preprocessing is responsible for the bulk of this reduction in temperature error; data assimilation is shown to further reduce the random temperature error by a few tenths of a Kelvin accounting for a 10% reduction in RMSE.
【土壤磷形态】Jörg Prietzel Alexander Dümig Yanhong Wu Jun Zhou Wantana Klysubun. Synchrotron-based PK-edge XANES spectroscopy reveals rapid changes of phosphorus speciation in the topsoil of two glacier foreland chronosequences.Geochimica et Cosmochimica Acta2013 108: 154–171
Abstract
Phosphorus (P) is a crucial element for life on Earth and the bioavailability of P in terrestrial ecosystems which is dependent on the soil P stock and its speciation may limit ecosystem productivity and succession. In our study for th