Articles | Volume 16, issue 21
Atmos. Chem. Phys., 16, 13681–13696, 2016
Atmos. Chem. Phys., 16, 13681–13696, 2016

Research article 04 Nov 2016

Research article | 04 Nov 2016

Evaluation of cloud effects on air temperature estimation using MODIS LST based on ground measurements over the Tibetan Plateau

Hongbo Zhang1,2, Fan Zhang1,2, Guoqing Zhang1,2, Xiaobo He3, and Lide Tian1,2 Hongbo Zhang et al.
  • 1Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
  • 2CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing, China
  • 3Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, China

Abstract. Moderate Resolution Imaging Spectroradiometer (MODIS) daytime and nighttime land surface temperature (LST) data are often used as proxies for estimating daily maximum (Tmax) and minimum (Tmin) air temperatures, especially for remote mountainous areas due to the sparseness of ground measurements. However, the Tibetan Plateau (TP) has a high daily cloud cover fraction (> 45 %), which may affect the air temperature (Tair) estimation accuracy. This study comprehensively analyzes the effects of clouds on Tair estimation based on MODIS LST using detailed half-hourly ground measurements and daily meteorological station observations collected from the TP. It is shown that erroneous rates of MODIS nighttime cloud detection are obviously higher than those achieved in daytime. Large errors in MODIS nighttime LST data were found to be introduced by undetected clouds and thus reduce the Tmin estimation accuracy. However, for Tmax estimation, clouds are mainly found to reduce the estimation accuracy by affecting the essential relationship between Tmax and daytime LST. The errors of Tmax estimation are obviously larger than those of Tmin and could be attributed to larger MODIS daytime LST errors that result from higher degrees of LST heterogeneity within MODIS pixel compared to those of nighttime LST. Constraining MODIS observations to non-cloudy observations can efficiently screen data samples for accurate Tmin estimation using MODIS nighttime LST. As a result, the present study reveals the effects of clouds on Tmax and Tmin estimation through MODIS daytime and nighttime LST, respectively, so as to help improve the Tair estimation accuracy and alleviate the severe air temperature data sparseness issues over the TP.

Short summary
Based on MODIS LST, clouds are believed to affect Tair estimation; however, understanding of the cloud effect on the Tair–LST relationship remains limited. Our paper reveals the subtle influence of clouds that affects Tmin and Tmax estimation in clearly different ways. The results contribute to better understanding of cloud effects and more accurate estimation of Tair using satellite LST.
Final-revised paper