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Accurate air temperature measurements are demanded for climate change research. However, air temperature sensors installed in a screen or a radiation shield have traditionally resisted observation accuracy due to a number of factors, particularly solar radiation. Here we present a novel temperature sensor array to improve the air temperature observation accuracy. To obtain an optimum design of the sensor array, we perform a series of analyses of the sensor array with various structures based on a computational fluid dynamics (CFD) method. Then the CFD method is applied to obtain quantitative radiation errors of the optimum temperature sensor array. For further improving the measurement accuracy of the sensor array, an artificial neural network model is developed to learn the relationship between the radiation error and environment variables. To assess the extent to which the actual performance adheres to the theoretical CFD model and the neural network model, air temperature observation experiments are conducted. An aspirated temperature measurement platform with a forced airflow rate up to 20 m s(-1) served as an air temperature reference. The average radiation errors of a temperature sensor equipped with a naturally ventilated radiation shield and a temperature sensor installed in a screen are 0.42 degrees and 0.23 degrees C, respectively. By contrast, the mean radiation error of the temperature sensor array is approximately 0.03 degrees C. The mean absolute error (MAE) between the radiation errors provided by the experiments and the radiation errors given by the neural network model is 0.007 degrees C, and the root-mean-square error (RMSE) is 0.009 degrees C.