基于径向基神经网络的地基微波辐射计反演算法研究

利用兰州大学半干旱气候与环境观测站(SACOL站)2009—2010年的地基微波辐射计亮温资料和榆中站探空资料,建立了应用于地基微波辐射计温度、相对湿度和水汽密度反演的径向基神经网络,并将反演结果与地基微波辐射计自带反演产品进行了对比,探究了径向基神经网络在地基微波辐射计气象要素反演算法本地化的应用效果。结果表明:径向基神经网络反演的温度、相对湿度和水汽密度的均方根误差最大值分别为2.72 K、22.32%和0.73 g·m^-3,在所有高度层上径向基神经网络的反演结果均优于微波辐射计,反演产品对2—10 km、1—7 km、0—3 km的大气温度、相对湿度和水汽密度廓线的反演均有明显改善,径向基神经网络能够应用于地基微波辐射计气象要素的反演算法的本地化。

Inversion of ground-based microwave radiometer measurements using radial basis function neural network

Using observational data from a ground-based microwave radiometer at the Semi-Arid Climate and Environment Observatory (SACOL) of Lanzhou University and radiosonde data from the Yuzhong station in 2009 and 2010, a radial basis function neural network (RBFNN) algorithm was established for the inversion of air temperature, relative humidity, and water vapor density, and the application of this algorithm was explored throughout comparing the inversion results with the original products of the microwave radiometer.The results show that the maximum mean square root error is 2.72 K for air temperature, 22.32% for relative humidity, and 0.73 g·m^-3 for water vapor density, respectively, derived using the RBFNN method.The RBFNN inversion performs better than the original products of microwave radiometer at all observational heights and significantly improves profiles of air temperature at 2-10 km, relative humidity at 0-3 km, and water vapor at 1-7 km, respectively.Therefore, the RBFNN algorithm is recommended for the local inversion of meteorological variables based on a ground-based microwave radiometer.