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An assessment of spatial models for daily minimum and maximum air temperature

机译：每日最低和最高气温的空间模型评估

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This article explores spatial modeling of daily minimum and maximum air temperatures using data from both ground-based, embedded sensors and remote sensors. Eleven models of min/max air temperature were developed ranging from simple proximity-based models to more complicated models that combine spatial similarity, temporal trends, and remotely sensed observations. These models are compared based on their accuracy, using a case study comprising data from the state of New Jersey. The results show that nearest neighbor and inverse distance weighted models based solely on land-based measurements are superior to models that include remotely sensed land surface temperature even when the gauge network is very sparse.
机译：本文使用来自地面，嵌入式传感器和远程传感器的数据探索每日最低和最高气温的空间模型。从基于接近度的简单模型到结合了空间相似性，时间趋势和遥感观测结果的更复杂的模型，共开发了11种最小/最大气温模型。使用包含新泽西州数据的案例研究，基于模型的准确性对这些模型进行比较。结果表明，即使在轨距网非常稀疏的情况下，仅基于陆基测量的最近邻域和反距离距离加权模型也优于包含遥感地表温度的模型。

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