As rainfall exhibits high spatiotemporal variability, accurate and real-time rainfall monitoring is vitally important in fields such as hydrometeorological research, agriculture and disaster prevention and control. Nevertheless, the current dedicated rain sensors cannot fulfill the requirement for comprehensive precipitation observation, owing to their respective limitations. Within the last two decades, the utilization of commercial microwave links (CMLs) for rainfall estimation, as an opportunistic sensing method, has generated considerable attention. Relying on CML networks deployed and maintained by mobile network operators can provide near-surface precipitation information over large areas at a low cost. Although scholars have developed several algorithms for obtaining rainfall estimates from CML data, the rainfall estimation technique based on CMLs remains challenging due to the complex effect in the microwave radiation transmission process. In this paper, we provide a comprehensive review of the technical principles, developments and workflows for this technology, alongside its application in environmental monitoring and hydrological modeling. Furthermore, this paper outlines the current challenges and future research directions, which will hopefully draw the attention of researchers and provide valuable guidance.

Recently, commercial microwave links (CMLs) have become one of the alternative sources of precipitation information. In this technique, a core issue is the separation of data into dry and wet periods. However, most of the existing classification methods require rainfall data for calibration, furthermore, they face the challenge of unbalanced datasets due to the unevenness of the spatiotemporal distribution of rainfall. To solve these problems, we propose a one-class classification machine learning method based on autoencoders (AE), which can achieve classification of dry and wet periods only using the dry period data without statistical modeling of rainfall intensity distribution. Based on the new method, four CMLs from Jiangyin were used to carry out classification and rainfall inversion experiments, and the rolling standard deviation (RSD) method was used for comparison. The results show that the AE method has slightly better classification performance compared to the calibrated RSD method.

The significance of rainfall detection is generally acknowledged and the linked opportunistic approach to giving it new momentum has also been extensively demonstrated. This study conducted a rainfall monitoring experiment using two stationary smartphones in a light indoor setting, receiving downlink signals from an LTE (Long Term Evolution) base station for two months. The analysis reveals that the received signals are not stable during dry periods, but the RSRP (Reference Signal Receiving Power) and RSSI (Reference Signal Strength Indicator) parameters can produce a certain degree of degradation during rainfall. To address the concern about frequent switching of connection to the base station resulting in different fluctuation levels, the standardized standard deviations of the four signalling parameters for different time windows were extracted as features to build a dry-rainy classification model. Further, a rain rate class identification model is also established based on the extraction of the specific rain-induced attenuation and standardized standard deviation. The experiment mentioned has shown promising results in rainfall monitoring based on widely available opportunistic signal sources from wireless terminals.

Meteorological operations rely heavily on quantitative precipitation estimation (QPE) derived from weather radars, calibrated against ground-based rain gauges. However, the limitations of rain gauge networks, namely their low spatial representativeness and often sparse and inhomogeneous distribution, pose challenges. Against this backdrop, commercial microwave links (CMLs), which opportunistically offer denser rainfall observations, emerge as viable alternatives for fusion with radar data, with particular potential in economically underdeveloped developing countries. This paper introduces an adaptive data fusion method that dynamically calibrates the relationship between radar reflectivity factor (Z) and rain rate (R) to adapt the variations in weather systems, leveraging attenuation data from CMLs and radar reflectivity factor data. The experimental study was conducted based on data from 47 CMLs and one S-band weather radar in Jiangyin City, alongside 8 rain gauges used for validation purposes. The results of the proposed method agree well with the reference measurements and reach a Pearson correlation coefficient of 0.65. Furthermore, it improves the coefficient of variation and root-mean-square error by 17% (24%) and 15% (27%), respectively, over the results from the empirical Z-R relationship (CMLs-based radar mean-field bias adjustment method). This study not only validates the method's efficacy in delivering precise quantitative precipitation estimates but also delves into the influence of weather system instability and observation time on its performance.

Microwave links as a novel method of rainfall monitoring have been extensively studied over the last two decades. Still, the applicability of γ (rain-induced attenuation rate) - R (rain rate) relationship in quantitative rainfall inversion with microwave link under tilted conditions has not been systematically investigated to date. With the establishment of rain-induced attenuation fields based on multiple sets of measured raindrop spectral profile data from micro-rain radar, microwave links under various tilted conditions are simulated and the performance of γ - R relationship in inversion of rain rate is theoretically analyzed. Afterwards, the effects of link height, tilt angle and rainfall reference point variations on the rainfall inversion performance are discussed in depth from the theoretical and data analysis perspectives, respectively. Furthermore, the connection between the study and the satellite-ground link-based rainfall measurement technique is mapped. Finally, the applicability analysis to different regions and the introduction of errors is also investigated.