Shi Liuhua, Liu Pengfei, Kloog Itai, Lee Mihye, Kosheleva Anna, Schwartz Joel
Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA.
John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.
Environ Res. 2016 Apr;146:51-8. doi: 10.1016/j.envres.2015.12.006. Epub 2015 Dec 21.
Accurate estimates of spatio-temporal resolved near-surface air temperature (Ta) are crucial for environmental epidemiological studies. However, values of Ta are conventionally obtained from weather stations, which have limited spatial coverage. Satellite surface temperature (Ts) measurements offer the possibility of local exposure estimates across large domains. The Southeastern United States has different climatic conditions, more small water bodies and wetlands, and greater humidity in contrast to other regions, which add to the challenge of modeling air temperature. In this study, we incorporated satellite Ts to estimate high resolution (1km×1km) daily Ta across the southeastern USA for 2000-2014. We calibrated Ts-Ta measurements using mixed linear models, land use, and separate slopes for each day. A high out-of-sample cross-validated R(2) of 0.952 indicated excellent model performance. When satellite Ts were unavailable, linear regression on nearby monitors and spatio-temporal smoothing was used to estimate Ta. The daily Ta estimations were compared to the NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) model. A good agreement with an R(2) of 0.969 and a mean squared prediction error (RMSPE) of 1.376°C was achieved. Our results demonstrate that Ta can be reliably predicted using this Ts-based prediction model, even in a large geographical area with topography and weather patterns varying considerably.
准确估计时空分辨的近地表气温（Ta）对于环境流行病学研究至关重要。然而，Ta值传统上是从气象站获取的，而气象站的空间覆盖范围有限。卫星地表温度（Ts）测量为跨大区域的局部暴露估计提供了可能性。与其他地区相比，美国东南部具有不同的气候条件、更多的小水体和湿地以及更高的湿度，这增加了气温建模的挑战。在本研究中，我们纳入卫星Ts来估计2000 - 2014年美国东南部高分辨率（1km×1km）的每日Ta。我们使用混合线性模型、土地利用以及每天的单独斜率对Ts - Ta测量值进行校准。样本外交叉验证的高R(2)值为0.952，表明模型性能优异。当无法获取卫星Ts时，利用附近监测器的线性回归和时空平滑来估计Ta。将每日Ta估计值与美国国家航空航天局（NASA）的现代时代回顾性分析研究与应用（MERRA）模型进行比较。实现了良好的一致性，R(2)为0.969，平均平方预测误差（RMSPE）为1.376°C。我们的结果表明，即使在地形和天气模式差异很大的大地理区域，使用这种基于Ts的预测模型也可以可靠地预测Ta。
