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How Accurately Can the Air Temperature Lapse Rate Over the Tibetan Plateau Be Estimated From MODIS LSTs?
Author(s) -
Zhang Hongbo,
Zhang Fan,
Zhang Guoqing,
Che Tao,
Yan Wei
Publication year - 2018
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1002/2017jd028243
Subject(s) - lapse rate , moderate resolution imaging spectroradiometer , environmental science , downscaling , plateau (mathematics) , air temperature , atmospheric sciences , climatology , meteorology , precipitation , geology , satellite , geography , mathematics , mathematical analysis , engineering , aerospace engineering
The air temperature lapse rate (TLR) is a key parameter for interpolating air temperature data in high mountainous regions such as the Tibetan Plateau (TP). The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) has been frequently used for estimating air temperature during the past decade, but its performance in estimating the TLR in the TP has seldom been investigated. This study employed two methods in estimating the TLR based on MODIS LSTs compared with the “observed” TLR derived from 86 stations across the TP. The two methods include a method for directly computing the lapse rate from MODIS LST (DFM) and a second method for calculating the lapse rate of estimated air temperatures based on air temperature estimation from MODIS LST (TEM). The results show that the MODIS LST‐estimated TLR for daily mean air temperature ( T mean) using both DFM and TEM is more accurate than that for daily minimum and maximum air temperatures. When using MODIS nighttime LSTs, both DFM and TEM show acceptable accuracies for estimating the TLR of T mean with averaged root‐mean‐square deviations of 0.21 and 0.19 °C/100 m, respectively. The spatial and seasonal patterns of MODIS LST‐estimated TLRs of T mean from both DFM and TEM are found to be highly consistent with the observed TLRs. This study can help alleviate the data‐sparse problem in downscaling air temperature or hydrological modeling studies in ungauged areas, especially for the western TP where station data are extremely scarce.