Modern communication systems, such as 5G networks promise to meet the crescent demand of users for high data rates in order to support new technologies. However, the frequency range of operation of these networks deserves special attention, since the high frequencies are more susceptible to interferences caused by atmospheric phenomena. The recommendations ITU-R P.838-3 and ITU-R P.676-11 describe methodologies to estimate signal degradations caused by phenomena related to rain and atmospheric gases, respectively. In this work, the impact of such phenomena on speech signal quality is investigated. The perceptual speech quality is evaluated using the algorithm described in ITU-T Rec. P.862. The experimental results show that the higher the frequency of operation, the higher the level of signal degradation for the same atmospheric conditions. Based on these results a parametric speech quality assessment model is proposed that uses an artificial neural network. Performance evaluation results demonstrated a high correlation between the proposed model and subjective test results, reaching an PCC and an RMSE of 0.9869 and 0.4773, respectively. Hence, the proposed model intends to be useful for network planning tasks.