Highly accurate weather classifiers have recently received a great deal of attention due to their promising applications. An alternative to conventional Weather radars consists of using the measured attenuation data in commercial microwave links (CML) as input to a weather classifier. The design of an accurate weather classifier is challenging due to diverse weather conditions, the absence of predefined features, and specific domain requirements in terms of execution time and detection sensitivity. In addition to this, the quality of the data given as input to the classifier plays a crucial role as it directly impacts the classification output. However, the quality of the measured attenuation data in the CMLs poses a serious concern for different reasons, e.g. the nature of the data itself, the location of each link, and the geographical distance between the links. This mandates the adoption of a data preprocessing step before classification with the purpose to validate the quality of the input data. In this paper, we propose a data preprocessing framework which employs a deep learning model to (i) detect anomalies in the raw data and (ii) validate the measured CML attenuation data by adding quality flags. Moreover, the feasibility and possible generalizations of the proposed framework are studied by conducting an empirical case study performed on real data collected from CMLs at Ericsson AB in Sweden. The empirical evaluation indicates that the average area under the receiver operating characteristic curve exceeding 0.72 using the proposed data preprocessing framework.