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近日,中国地质大学环境学院大气科学系任国玉教授团队在全球极端气候检测领域的研究工作取得重要进展,相关研究成果发表在大气科学领域权威期刊《Journal of Climate》(IF = 4.66)。该研究基于我国第一套全球日气温数据集建立了1951-2015年全球陆地极端气温平均时间序列,填补了我国在全球尺度极端气温变化检测领域的研究空白。
We investigated the usability of the method of local linear models (LLM), multilayer perceptron neural network (MLP NN) and radial basis function neural network (RBF NN) for the construction of tempor...
We quantify the long-term predictability of global mean daily temperature data by means of the Rényi entropy of second order K2. We are interested in the yearly amplitude fluctuations of the temperatu...
Complexity and predictability of daily precipitation in a tropical semi-arid region (Ceará State, Brazil) is assessed by applying entropy concepts. Precipitation regimes in that region depend on sever...
We present a near global statistics on the correlation properties of daily temperature records. Data from terrestrial meteorological stations in the Global Daily Climatology Network are analyzed by me...
This study originated from recent results reported in literature, which support the existence of long-range (power-law) persistence in atmospheric temperature fluctuations on monthly and inter-annual ...
Extreme Distribution Functions for Daily and Monthly Precipitation in Finland.
This paper presents an inverse method for inferring trace gas fluxes at high temporal (daily) and spatial (model grid) resolution from continuous atmospheric concentration measurements. The method is ...
An Analysis of Covariance (ANCOVA) was used to derive the influence of the meteorological variability on the daily maximum ozone concentrations at 12 low-elevation sites north of the Alps in Switzerl...

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