利用商用微波链路衰减和温度改进的水汽密度估计

Itay Bragin, Y. Rubin, P. Alpert, J. Ostrometzky
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引用次数: 0

摘要

水汽测量对天气模式是有益的。已经提出了一种机器学习支持向量机模型,该模型使用来自商业微波链路的接收信号电平测量并使用来自参考气象站的数据进行训练,用于估计参考气象站位置的水蒸气密度。在本文中,我们提出了一种增强的机器学习模型,该模型利用给定区域内的三个商业微波链路以及额外的温度观测。该模型可以实现更高的水汽估算精度(与气象站作为地面真值相比)。具体来说,我们给出了初步结果,并表明尽管存在一定的不确定性,但通过所述方法获得的均方根误差比使用单个商用微波链路时使用的模型所获得的误差小两倍以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved Water Vapor Density Estimation With Commercial Microwave Links Attenuation And Temperature
Water vapor measurement is beneficial for weather models. A machine learning support vector machine model for estimating water vapor density at a reference weather station location using measurements of the received signal level from commercial microwave link and trained with data from the reference weather station has already been proposed. In this paper, we propose an enhanced machine learning model that utilizes three commercial microwave links inside a given area, as well as additional temperature observations. This model can achieve higher accuracy of water vapor estimation (when compared to the weather station as ground truth). Specifically, we present preliminary results, and show that although certain uncertainties exist, the root mean square error achieved by the presented approach was more than twice as small as the error achieved when utilizing a model using a single commercial microwave link.
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