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.对流层映射函数（tropospheric mapping function，MF）在估算电磁波穿过对流层时的延迟方面具有重要作用，尤其在以电磁波为主要测量手段的全球导航卫星系统（GNSS）、甚长基线干涉测量（VLBI）等空间观测技术中至关重要。当前，非气象参数经验模型被广泛用于计算MF参数，此类模型虽便捷，但在气候剧烈变化的区域精度欠佳，可能导致斜路径延迟产生米级误差，并将误差进一步扩散至其他数据产品中。尽管部分权威机构会定期发布MF计算所需的气象参数或现成的MF产品，但这些数据通常存在数天的延迟，或需要特殊授权方可获取。有鉴于此，本文提出了一种基于随机森林（random forest，RF）模型的方法，可基于地面观测数据快速获取MF关键参数。实验结果表明，相较于传统模型，基于RF的方法可显著提升MF参数的计算精度：静力分量的精度提升超过60%，湿分量的精度提升超过20%，且季节性系统偏差几乎被完全消除。最后，本文通过设置不同的样本空间训练了一组RF模型，并针对特征维度与时间跨度的不同组合统计了模型性能，结果表明，在兼顾样本获取难度、训练时长、模型规模、计算效率与精度等要素时，存在最优的折中方案。