Code and data for random forests model in mapping function parameter calculation

The tropospheric mapping function (MF) plays an important role in estimating the delay of electromagnetic waves when they traveling through the troposphere, especially for the space observing technologies such as GNSS and VLBI, where electromagnetic waves serve as the main measuring means. At present, the non-meteorological parameter empirical models are widely used to calculate MF parameters, convenient yet less accurate for areas suffering from drastic climate changes, which may lead to even meter-level errors for slant path delays and further diffuse errors to other data products. Although, there are authoritative institutions that regularly release meteorological parameters which are essential to MF calculation or ready-made MF products, they usually hold a delay of several days or require special authorization. In view of this, we proposed a method based on the random forest (RF) model to obtain MF key parameters rapidly based on surface observations. Experiments have shown that compared with traditional models, the RF method could significantly raise the accuracy of MF parameters, with an improvement of over 60% for the hydrostatic component and over 20% for the wet part. Seasonal system deviations were almost eliminated. In the end, a set of RF models were trained by setting different sample spaces, and the performances were counted under different combinations of feature dimensions and time spans, which turned out that an optimal compromise may exist when difficulty in sample data obtaining, training time, model size, computational efficiency, accuracy, etc. were taken into consideration.