基于等维新息 GM(2,1) 的大气加权平均温度模型

大气加权平均温度是对流层的一个重要参数, 对全球导航卫星系统(Global Navigation Satellite System, GNSS)水汽反演至关重要。文章采用 GM(2,1) 灰色模型结合一阶弱化算子对大气加权平均温度进行拟合和预测, 基于 2018 年中国不同区域探空站日均大气加权平均温度进行建模分析。结果表明: 在少量可用数据的情况下, GM(2,1) 具有较好的建模预测能力, 相对误差不超过 5%, 未来 2 d 的预测值相对误差均小于 2%; 与 Bevis 模型相比, GM(2,1) 对大气加权平均温度建模也更具优势, 且不需要实测的气象参数。该研究为 GM(2,1) 灰色模型应用于 GNSS 水汽反演、天气预报等提供借鉴。

The atmospheric weighted mean temperature is an important parameter of the troposphere, which is crucial for Global Navigation Satellite System (GNSS) water vapor inversion. This study proposes an approach of using the GM(2,1) gray model combined with a first-order weakening operator to fit and predict the atmospheric weighted mean temperature. By modeling and analyzing the daily atmospheric weighted mean temperature data from sounding stations in different regions of China in 2018, it is shown that the GM(2,1) model has good modeling and predictive capabilities with a relative error of less than 5% even with limited data availability. Moreover, the relative error of the predicted values for the next two days is less than 2%. Compared with the Bevis model, the GM(2,1) model has advantages in modeling the atmospheric weighted mean temperature without the need for measured meteorological parameters. The study provides valuable references for applying the GM(2,1) gray model to GNSS water vapor inversion and weather forecasting.