TY - JOUR
T1 - Estimating daily average surface air temperature using satellite land surface temperature and top-of-atmosphere radiation products over the Tibetan Plateau
AU - Rao, Yuhan
AU - Liang, Shunlin
AU - Wang, Dongdong
AU - Yu, Yunyue
AU - Song, Zhen
AU - Zhou, Yuan
AU - Shen, Miaogen
AU - Xu, Baiqing
N1 - Publisher Copyright:
© 2019 Elsevier Inc.
PY - 2019/12/1
Y1 - 2019/12/1
N2 - The Tibetan Plateau (TP) has experienced rapid warming in recent decades. However, the meteorological stations of the TP are scarce and mostly located at the eastern and southern parts of the TP where the elevation is relatively low, which increases the uncertainty of regional and local climate studies. Recently, the remotely sensed land surface temperature (LST) has been used to estimate the surface air temperature (SAT). However, the thermal infrared based LST is prone to cloud contamination, which limits the availability of the estimated SAT. This study presents a novel all sky model based on the rule-based Cubist regression to estimate all sky daily average SAT using LST, incident solar radiation (ISR), top-of-atmosphere (TOA) albedo and outgoing longwave radiation (OLR). The model is trained using station data of the Chinese Meteorological Administration (CMA) and corresponding satellite products. The output is evaluated using independent station data with the bias of −0.07 °C and RMSE of 1.87 °C. Additionally, the 25-fold cross validation shows a stable model performance (RMSE: 1.6–2.8 °C). Moreover, the all sky Cubist model increases the availability of the estimated SAT by nearly three times. We used the all sky Cubist model to estimate the daily average SAT of the TP for 2002–2016 at 0.05° × 0.05°. We compared our all sky Cubist model estimated daily average SAT with three existing data sets (i.e., GLDAS, CLDAS, and CMFD). Our model estimation shows similar spatial and temporal dynamics with these existing data sets but outperforms them with lower bias and RMSE when benchmarked against the CMA station data. The estimated SAT data could be very useful for regional and local climate studies over the TP. Although the model is developed for the TP, the framework is generic and may be extended to other regions with proper model training using local data.
AB - The Tibetan Plateau (TP) has experienced rapid warming in recent decades. However, the meteorological stations of the TP are scarce and mostly located at the eastern and southern parts of the TP where the elevation is relatively low, which increases the uncertainty of regional and local climate studies. Recently, the remotely sensed land surface temperature (LST) has been used to estimate the surface air temperature (SAT). However, the thermal infrared based LST is prone to cloud contamination, which limits the availability of the estimated SAT. This study presents a novel all sky model based on the rule-based Cubist regression to estimate all sky daily average SAT using LST, incident solar radiation (ISR), top-of-atmosphere (TOA) albedo and outgoing longwave radiation (OLR). The model is trained using station data of the Chinese Meteorological Administration (CMA) and corresponding satellite products. The output is evaluated using independent station data with the bias of −0.07 °C and RMSE of 1.87 °C. Additionally, the 25-fold cross validation shows a stable model performance (RMSE: 1.6–2.8 °C). Moreover, the all sky Cubist model increases the availability of the estimated SAT by nearly three times. We used the all sky Cubist model to estimate the daily average SAT of the TP for 2002–2016 at 0.05° × 0.05°. We compared our all sky Cubist model estimated daily average SAT with three existing data sets (i.e., GLDAS, CLDAS, and CMFD). Our model estimation shows similar spatial and temporal dynamics with these existing data sets but outperforms them with lower bias and RMSE when benchmarked against the CMA station data. The estimated SAT data could be very useful for regional and local climate studies over the TP. Although the model is developed for the TP, the framework is generic and may be extended to other regions with proper model training using local data.
KW - Land surface temperature
KW - Radiation
KW - Rule-based cubist regression
KW - Surface air temperature
KW - Tibetan Plateau
UR - http://www.scopus.com/inward/record.url?scp=85063506637&partnerID=8YFLogxK
U2 - 10.1016/j.rse.2019.111462
DO - 10.1016/j.rse.2019.111462
M3 - Article
AN - SCOPUS:85063506637
SN - 0034-4257
VL - 234
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
M1 - 111462
ER -