A spatiotemporal analysis of the relationship between near-surface air temperature and satellite land surface temperatures using 17 years of data from the ATSR series
A spatiotemporal analysis of the relationship between near-surface air temperature and satellite land surface temperatures using 17 years of data from the ATSR series
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DOI：
10.1002/2017jd026880
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发表时间：
2017-09-16
影响因子：
4.4
通讯作者：
Remedios, John J.
中科院分区：
文献类型：
--
作者：
Good, Elizabeth J.;Ghent, Darren J.;Remedios, John J.
利用17年以上的数据，从空间和时间上描述了卫星陆面温度（LST）与地面2米气温（T-2 m）观测值之间的关系。该分析使用了基于欧洲航天局GlobTemperature项目（http：//www.globtemperature.info/）的沿轨扫描辐射计系列的新的月度LST气候数据记录（CDR）。全球LST-T-2 m差异在位置、土地覆盖、植被覆盖和海拔方面进行了分析，发现所有这些都是重要的影响因素。LST night（类似于当地太阳时晚上10点，只有晴朗的天空）与最小T-2 m（T-min，全天空）密切相关，两个温度通常在+/- 5摄氏度内一致（全球中位数LST night-T-min = 1.8摄氏度，四分位数间距= 3.8摄氏度）。LST日（类似于当地太阳时间上午10点，仅晴朗天空）-最大T-2 m（最大T，全天空）变化较高（全球中位数LST日-T-max = -0.1 ° C，四分位距= 8.1 ° C），因为LST受到日照和表面状况的强烈影响。在热带地区以外，两个温度对的相关性通常>0.9。LST和T-2 m的月度全球和区域异常时间序列是完全独立的数据集，比较非常好。对于地球仪，数据集之间的相关性为0.9，90%的CDR异常落在T-2 m 95%置信限内。在这项研究中提出的结果提出了一个理由，越来越多地使用卫星LST数据在气候和天气科学，无论是作为一个独立的变量，并增加T-2 M数据在气象stations.Plain语言摘要地面温度在陆地上传统上测量气象站。在地球仪的许多地方，观测站非常少，例如非洲和南极洲的大部分地区，导致地表温度数据集出现空白，影响了我们对地表温度如何变化以及极端事件（如热浪）影响的理解。卫星可以提供地球仪的温度观测。然而，卫星测量地表温度（LST;包括树木，建筑物等的最高部分）有多热，而气象站测量表面上方的空气温度（T-2 m）。此外，卫星LST数据可能仅在无云条件下可用。本文介绍了T-2 m和一个新的17年LST数据集之间的比较。它表明，LST和T-2 m往往是密切相关的，特别是在夜间，但确切的关系取决于位置，陆面类型，植被和海拔。时间序列分析表明，LST和T-2 m随时间的变化非常相似;由于这些数据集是独立的，因此对气候变化文献中其他地方报告的T-2 m趋势具有信心。这项研究的结果表明，LST可以有效地增强T-2 m观测的气候和天气科学。
The relationship between satellite land surface temperature (LST) and ground-based observations of 2 m air temperature (T-2m) is characterized in space and time using >17 years of data. The analysis uses a new monthly LST climate data record (CDR) based on the Along-Track Scanning Radiometer series, which has been produced within the European Space Agency GlobTemperature project (http://www.globtemperature.info/).Global LST-T-2m differences are analyzed with respect to location, land cover, vegetation fraction, and elevation, all of which are found to be important influencing factors. LSTnight (similar to 10 P.M. local solar time, clear-sky only) is found to be closely coupled with minimum T-2m (T-min, all-sky) and the two temperatures generally consistent to within +/- 5 degrees C (global median LSTnight-T-min = 1.8 degrees C, interquartile range = 3.8 degrees C). The LSTday (similar to 10 A.M. local solar time, clear-sky only)-maximum T-2m (T-max, all-sky) variability is higher (global median LSTday-T-max = -0.1 degrees C, interquartile range = 8.1 degrees C) because LST is strongly influenced by insolation and surface regime. Correlations for both temperature pairs are typically >0.9 outside of the tropics. The monthly global and regional anomaly time series of LST and T-2m-which are completely independent data sets-compare remarkably well. The correlation between the data sets is 0.9 for the globe with 90% of the CDR anomalies falling within the T-2m 95% confidence limits. The results presented in this study present a justification for increasing use of satellite LST data in climate and weather science, both as an independent variable, and to augment T-2m data acquired at meteorological stations.Plain Language Summary Surface temperatures over land have traditionally been measured at weather stations. There are many parts of the globe where there are very few stations, for example across much of Africa and Antarctica, leading to gaps in surface temperature datasets, affecting our understanding of how surface temperatures are changing, and the impacts of extreme events (e.g. heat waves). Satellites can provide temperature observations across the globe. However, satellites measure how hot the land surface temperature (LST; including the uppermost parts of e.g. trees, buildings) are to touch, whereas weather stations measure the air temperature just above the surface (T-2m). Additionally, satellite LST data may only be available in cloud-free conditions. This paper describes a comparison between T-2m and a new 17-year LST dataset. It demonstrates that LST and T-2m are often strongly related, particularly at night, but the exact relationship depends on location, land surface type, vegetation and elevation. A time-series analysis shows that the change in LST and T-2m with time is remarkably similar; giving confidence in the T-2m trends reported elsewhere in the climate change literature, as these datasets are independent. The results of this study demonstrate that LST can usefully augment T-2m observations in climate and weather science.