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DOI | 10.1002/joc.4705 |
Modelling spatio-temporally resolved air temperature across the complex geo-climate area of France using satellite-derived land surface temperature data | |
Kloog, Itai1; Nordio, Francesco2; Lepeule, Johanna2,3,4,5; Padoan, Andrea6; Lee, Mihye2; Auffray, Annick7; Schwartz, Joel2 | |
2017 | |
发表期刊 | INTERNATIONAL JOURNAL OF CLIMATOLOGY
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ISSN | 0899-8418 |
EISSN | 1097-0088 |
出版年 | 2017 |
卷号 | 37期号:1 |
文章类型 | Article |
语种 | 英语 |
国家 | Israel; USA; France; Italy |
英文摘要 | Climate change has focused attention on the effects of changing temperature, particularly the effect on human health. Thus, robust and accurate spatially and temporally resolved air temperature (T-a) data are of particular importance in the field of epidemiology and public health. However, most health studies to date have matched people to the nearest monitor. In this study, we aimed to develop a robust satellite-based spatio-temporally resolved T-a estimation model across the complex geo-climatic regions of France resulting in daily high-resolution 1 km predicted air temperature (T-ap) estimations. We use a daily calibration approach using a series of processes to generate daily T-ap for every day across the entire study area and period. First, we start by calibrating MODIS (Moderate Resolution Imaging Spectroradiometer) satellite-gridded surface temperature (T-s) data against T-a collected within 1 km of the T-s centroid. The calibration stage adjusted for spatio-temporal predictors, as done in environmental exposure assessment methods such as land use regressions. Second, to estimate T-ap when no T-s data are available we fit a second model which uses the association of predicted grid cells T-ap values (based on satellite T-s) with surrounding T-a monitors and the association with values in neighbouring grid cells. Out-of-sample tenfold cross-validation was used to quantify the accuracy of our predictions. Our model performance was excellent for both days with available T-s and days without T-s observations (overall mean out-of-sample R-2 = 0.95 for both stages). In conclusion, we demonstrate how T-s can be used reliably to predict daily T-ap at high-resolution across France for use in studies looking at the effects of fine resolution Ta exposure on various health outcomes. |
英文关键词 | MODIS surface temperature air temperature exposure error epidemiology |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000392415700022 |
WOS关键词 | AMBIENT-TEMPERATURE ; NORTHEASTERN USA ; BIRTH OUTCOMES ; FRENCH CITIES ; MORTALITY ; MASSACHUSETTS ; PRODUCTS ; WEIGHT ; SEASON |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/37492 |
专题 | 气候变化 |
作者单位 | 1.Ben Gurion Univ Negev, Dept Geog & Environm Dev, POB 653, Beer Sheva, Israel; 2.Harvard Sch Publ Hlth, Dept Environm Hlth, Exposure Epidemiol & Risk Program, Boston, MA USA; 3.Univ Grenoble Alpes, Inst Albert Bonniot, Team Environm Epidemiol Appl Reprod & Resp Hlth, Grenoble, France; 4.INSERM, Inst Albert Bonniot, Team Environm Epidemiol Appl Reprod & Resp Hlth, Grenoble, France; 5.CHU Grenoble, Inst Albert Bonniot, Team Environm Epidemiol Appl Reprod & Resp Hlth, Grenoble, France; 6.Univ Padua, Dept Med DIMED, Padua, Italy; 7.Meteo France, DIRCE ECP Clim, Bron, France |
推荐引用方式 GB/T 7714 | Kloog, Itai,Nordio, Francesco,Lepeule, Johanna,et al. Modelling spatio-temporally resolved air temperature across the complex geo-climate area of France using satellite-derived land surface temperature data[J]. INTERNATIONAL JOURNAL OF CLIMATOLOGY,2017,37(1). |
APA | Kloog, Itai.,Nordio, Francesco.,Lepeule, Johanna.,Padoan, Andrea.,Lee, Mihye.,...&Schwartz, Joel.(2017).Modelling spatio-temporally resolved air temperature across the complex geo-climate area of France using satellite-derived land surface temperature data.INTERNATIONAL JOURNAL OF CLIMATOLOGY,37(1). |
MLA | Kloog, Itai,et al."Modelling spatio-temporally resolved air temperature across the complex geo-climate area of France using satellite-derived land surface temperature data".INTERNATIONAL JOURNAL OF CLIMATOLOGY 37.1(2017). |
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