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Xinjiang University (2021)

The Characteristic of Surface Radiation and Energy Variation with Usability of Multi-sources Data over the Gobi Region in Xinjiang

赵雪赏

Titre : The Characteristic of Surface Radiation and Energy Variation with Usability of Multi-sources Data over the Gobi Region in Xinjiang

Auteur : 赵雪赏

Grade : Master 2021

Université : Xinjiang University

Résumé partiel
This paper’s characteristics of the surface radiation and energy flux budget of the typical black Gobi desert in the Hami area of eastern Xinjiang under different time scales and weather conditions were analyzed by the radiation observation system and the open-path eddy covariance system data from the Hongliuhe Gobi Land-Atmosphere Interaction Observation Station.Combined ground-based data to quantitatively evaluate the applicability of CERES satellite dataset and ERA-5,MERRA2,JRA-55 reanalysis dataset,and finally used ERA-5 data to analyze the characteristics of solar radiation distribution on the black Gobi desert area.The following results are obtained :(1)In 2018,the radiation budget of the Hongliuhe area was mainly downward solar radiation and upward longwave radiation.Upward shortwave radiation was the smallest,and downward shortwave radiation was the largest.The trends of the two were the same.After sunrise,they gradually increased with the increase of the solar elevation angle,the peak value appear at 12 p.m.The maximum value of the downward shortwave radiation is 1104.3 W·m-2.The upward and downward longwave radiation has the same changing trend,lagging the shortwave radiation by about 1.5 hours,of which the minimum annual average daily difference of the downward shortwave radiation is only 24.68 W·m-2.The annual variation of the surface albedo in the Hongliuhe area is the highest in December(0.56)and the lowest in August(0.23).On a daily scale,the surface albedo is highest in the morning and evening,and the lowest at noon.The average annual surface albedo in 2018 was 0.29.

Mots clés : Radiation budget ;turbulent flux ;CERES dataset ;Reanalysis data ;

Présentation (CNKI)

Page publiée le 13 mars 2022