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

Abstract. Moderate Resolution Imaging Spectroradiometer (MODIS) daytime and nighttime land surface temperature (LST) data are often used as proxies for estimating daily maximum (T_max) and minimum (T_min) 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 (T_air) estimation accuracy. This study comprehensively analyzes the effects of clouds on T_air 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 T_min estimation accuracy. However, for T_max estimation, clouds are mainly found to reduce the estimation accuracy by affecting the essential relationship between T_max and daytime LST. The errors of T_max estimation are obviously larger than those of T_min 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 T_min estimation using MODIS nighttime LST. As a result, the present study reveals the effects of clouds on T_max and T_min estimation through MODIS daytime and nighttime LST, respectively, so as to help improve the T_air estimation accuracy and alleviate the severe air temperature data sparseness issues over the TP.