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

Abstract

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 halfhourly 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.

An Examination of Temperature Trends at High Elevations Across the Tibetan Plateau: The Use of MODIS LST to Understand Patterns of Elevation‐Dependent Warming

“…The exception is at Bomi (Figure d) where large negative values occur throughout the year, but particularly in summer which is a cloudy time. This is likely due to the erroneous identification of cloud covered pixels as surface temperature, which is known to be a problem in more cloudy regions at night (Zhang, Zhang, Ye, et al, ; Zhang, Zhang, Zhang, et al, ), and can cause differences as big as ‐20 °C. This is clearly unrealistic at night when there is limited or no solar forcing.…”

“…Thus, another reason for bad model fit is probably deficiencies in the original LST data, particularly at night due to the influence of unidentified cloud, erroneously measured as cloud‐free and included in the 8‐day composite LST. This is known to be a common issue (Zhang, Zhang, Ye, et al, ; Zhang, Zhang, Zhang, et al, ), and LST can underestimate T n by as much as 20°C during the warmer part of the year (i.e., the summer monsoon season). Model correction will not remove this error.…”

“…Cloud bias often leads to an average cold bias of LST below T air in high‐latitude regions, since clear periods, especially in winter, are commonly colder than cloudy periods (Westermann et al, ). Cloud contamination is also a known issue in the Tibetan Plateau, particularly at night when clouds can be difficult to differentiate from the ground surface (Zhang, Zhang, Ye, et al, ; Zhang, Zhang, Zhang, et al, ).…”

“…Therefore, there are challenges in converting LST to a realistic measure of air temperature at high elevations (Pepin et al, ). Well‐known issues include contamination by cloud cover (Zhang, Zhang, Ye, et al, ; Zhang, Zhang, Zhang, et al, ), the influence of ephemeral snow cover (Shamir & Georgakakos, ; Williamson et al, ), the role of vegetation in increasing latent heat at the expense of sensible heat (Vancutsem et al, ), and timing and spatial differences between the two measurements (point vs pixel).…”

Estimating High Resolution Daily Air Temperature Based on Remote Sensing Products and Climate Reanalysis Datasets over Glacierized Basins: A Case Study in the Langtang Valley, Nepal

“…The TVX method also needs ground observation and could not be used in ungauged basins and vegetation sparse watersheds. Statistical regression methods usually try to build the relationship between station observed Ta and remotely sensed LST, elevation, NDVI, surface albedo and other explaining variables (such as longitude/latitude) using multi-variable linear regression model or machine learning approaches [25][26][27][28][29][30][31][32][33][34][35]. Using MODIS LST products and observed daily mean temperature from 95 meteorological stations in the Tibetan Plateau, Zhang et al [36] compared the performances of several regression methods, including MLR (multiple linear regression), PLS (partial least squares regression), BPNN (back propagation neural network), SVR (support vector regression), RF (random forests) and CR (Cubist regression), and 15 different combination schemes of the four MODIS observations per day over the Tibet Plateau.…”