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Development of high-resolution daily gridded temperature datasets for the central north region of Egypt

机译:开发埃及中北部的高分辨率每日网格温度数据集

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摘要

This study developed 0.05° × 0.05° land-only datasets of daily maximum and minimum temperatures in the densely populated Central North region of Egypt (CNE) for the period 1981–2017. Existing coarse-resolution datasets were evaluated to find the best dataset for the study area to use as a base of the new datasets. The Climate Prediction Centre (CPC) global temperature dataset was found to be the best. The CPC data were interpolated to a spatial resolution of 0.05° latitude/longitude using linear interpolation technique considering the flat topography of the study area. The robust kernel density distribution mapping method was used to correct the bias using observations, and WorldClim v.2 temperature climatology was used to adjust the spatial variability in temperature. The validation of CNE datasets using probability density function skill score and hot and cold extremes tail skill scores showed remarkable improvement in replicating the spatial and temporal variability in observed temperature. Because CNE datasets are the best available high-resolution estimate of daily temperatures, they will be beneficial for climatic and hydrological studies.
机译:这项研究建立了1981-2017年期间埃及中北部人口稠密地区(CNE)每日最高和最低温度的0.05°×0.05°陆地数据集。对现有的粗分辨率数据集进行了评估,以找到适合研究区域的最佳数据集,以用作新数据集的基础。气候预测中心(CPC)的全球温度数据集被认为是最好的。考虑到研究区域的平坦地形,使用线性插值技术将CPC数据插值到经纬度为0.05°的空间分辨率。鲁棒的核仁密度分布图方法用于使用观测值校正偏差,WorldClim v.2温度气候学用于调整温度的空间变异性。使用概率密度函数技能得分和冷热极端尾部技能得分对CNE数据集进行的验证显示,在复制观测温度的时空变化方面,显着提高。由于CNE数据集是每日温度的最佳可用高分辨率估计,因此它们对于气候和水文研究将是有益的。

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