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Parameterization of air temperature in high temporal and spatial resolution from a combination of the SEVIRI and MODIS instruments

机译:结合SEVIRI和MODIS仪器以高时空分辨率对空气温度进行参数化

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Some applications, e.g. from traffic or energy management, require air temperature data in high spatial and temporal resolution at two metres height above the ground (T_(2m)). sometimes in near-real-time. Thus, a parameterization based on boundary layer physical principles was developed that determines the air temperature from remote sensing data (SEVIRI data aboard the MSG and MODIS data aboard Terra and Aqua satellites). The method consists of two parts. First, a downscaling procedure from the SEVIRI pixel resolution of several kilometres to a one kilometre spatial resolution is performed using a regression analysis between the land surface temperature (LST) and the normalized differential vegetation index (NDVI) acquired by the MODIS instrument. Second, the lapse rate between the LST and T_(2m) is removed using an empirical parameterization that requires albedo, down-welling surface short-wave flux, relief characteristics and NDVI data. The method was successfully tested for Slovenia, the French region Franche-Comte and southern Germany for the period from May to December 2005, indicating that the parameterization is valid for Central Europe. This parameterization results in a root mean square deviation RMSD of 2.0 K during the daytime with a bias of -0.01 K and a correlation coefficient of 0.95. This is promising, especially considering the high temporal (30 min) and spatial resolution (1000 m) of the results.
机译:一些应用,例如交通或能源管理方面的要求,需要在距地面两米高的地方(T_(2m))以高时空分辨率获得气温数据。有时是近实时的。因此,开发了一种基于边界层物理原理的参数化技术,该技术可根据遥感数据(Terra和Aqua卫星的MSG数据和SEDIS数据,MSG的SEVIRI数据)确定气温。该方法包括两个部分。首先,使用MODIS仪器获取的地表温度(LST)和归一化植被指数(NDVI)之间的回归分析,执行了从几公里的SEVIRI像素分辨率到一公里的空间分辨率的缩小过程。其次,使用需要反照率,井下表面短波通量,浮雕特性和NDVI数据的经验参数化来消除LST和T_(2m)之间的通过率。该方法已于2005年5月至2005年12月在斯洛文尼亚,法国弗朗什-孔德地区和德国南部成功进行了测试,表明该参数设置对中欧有效。该参数化导致白天的均方根偏差RMSD为2.0 K,偏差为-0.01 K,相关系数为0.95。这是有希望的,尤其是考虑到结果的高时间(30分钟)和空间分辨率(1000 m)时。

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