Estimation of Day-Time Seeing Changes at Huairou Solar Observing Station Based on Neural Networks from 1989 to 2010

Seeing is a key factor affecting the image quality of astronomical observations and can be quantitatively described by the Fried parameter r0. The larger the r0 value (in unit of cm), the better the seeing conditions. Currently, daytime seeing measurements are primarily conducted using the Solar Differential Image Motion Monitor (SDIMM) or the spectral ratio method. In this work, we propose a neural network model for estimating daytime r0. The experimental results of the training set and the test set show that this model can currently estimate r0 with an accuracy exceeding 99%. Using this model, we estimate the r0 of the Huairou Solar Observing Station (HSOS) in 22 consecutive years from 1989 to 2010. The median r0 of HSOS in 22 consecutive years was around 2.5 cm, and the best seeing condition was in April and September of one year. This result confirmed the long-term stability of seeing conditions. In addition, we conducted an error analysis comparing the seeing measured by SDIMM and the results obtained by the spectral ratio method both under domeless and domed conditions. The results indicate a significant correlation between the SDIMM results and the spectral ratio method results, with first-order fitting coefficients of 2.2 and 2.9, respectively.