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Millimeter-wave (30–300 GHz) frequency is a promising candidate for 5G and beyond wireless networks, but atmospheric elements limit radio links at this frequency band. Rainfall is the significant atmospheric element that causes attenuation in the propagated wave, which needs to estimate for the proper operation of fade mitigation technique (FMT). Many models have been proposed in the literature to estimate rain attenuation. Various models have a distinct set of input parameters along with separate estimation mechanisms. This survey has garnered multiple techniques that can generate input dataset for the rain attenuation models. This study extensively investigates the existing terrestrial rain attenuation models. There is no survey of terrestrial rain mitigation models to the best of our knowledge. In this article, the requirements of this survey are first discussed, with various dataset developing techniques. The terrestrial links models are classified, and subsequently, qualitative and quantitative analyses among these terrestrial rain attenuation models are tabulated. Also, a set of error performance evaluation techniques is introduced. Moreover, there is a discussion of open research problems and challenges, especially the exigency for developing a rain attenuation model for the short-ranged link in the E-band for 5G and beyond networks.

The values of ratio a of the linear relationship between specific attenuation and specific differential phase vary significantly in convective storms as a result of resonance scattering. The best-linear-fit ratio a at X band is determined using the modified attenuation correction algorithm based on differential phase and attenuation, as well as the premise that reflectivity is unattenuated in S band radar detection. Meanwhile, the systemic reflectivity bias between the X band radar and S band radar and water layer attenuation (ZW) on the wet antenna cover of the X band radar are also considered. The good performance of the modified correction algorithm is demonstrated in a moderate rainfall event. The data were collected by four X band dual-polarization (X-POL) radar sites, namely, BJXCP, BJXFS, BJXSY, and BJXTZ, and a China’s New Generation Weather Radar (CINRAD/SA radar) site, BJSDX, in Beijing on 20 July 2016. Ratio a is calculated for each volume scan of the X band radar, with a mean value of 0.26 dB deg−1 varying from 0.20 to 0.31 dB deg−1. The average values of systemic reflectivity bias between the X band radar (at BJXCP, BJXFS, BJXSY, and BJXTZ) and S band radar (at BJSDX) are 0, −3, 2, and 0 dB, respectively. The experimentally determined ZW is in substantial agreement with the theoretically calculated ones, and their values are an order of magnitude smaller than rain attenuation. The comparison of the modified attenuation correction algorithm and the empirical-fixed-ratio correction algorithm is further evaluated at the X-POL radar. It is shown that the modified attenuation correction algorithm in the present paper provides higher correction accuracy for rain attenuation than the empirical-fixed-ratio correction algorithm.

A new physical-mathematical model for predicting first-order rain attenuation statistics for slant-path links is presented in this study. It is assumed that point rain rate and slant-path rain attenuation follow the inverse Gaussian (IG) distribution. The statistical parameters of the IG distribution of the induced rain attenuation on a slant path are calculated in terms of the rain rate statistical parameters by adopting a spatial inhomogeneity model for specific rain attenuation. The method is validated using experimental data taken from Database of Study Group 3 (radiowave propagation) of International Telecommunication Union-Radio (ITU-R), hereafter named DBSG3 and with simulated rain attenuation data using the Synthetic Storm Technique on recent rain rate measurements in Athens, Greece. Its performance is also compared to the ITU-R P. 618-10 model. The proposed model shows better performance comparing to the ITU-R model and the results are very encouraging.

High‐resolution rainfall field reconstruction is a critical task in hydrological applications, with significant implications for flood forecasting, water resource management, and urban resilience. Conventional remote sensing instruments such as rain gauges and weather radars suffer from sparse coverage or high operational costs, while commercial microwave links (CMLs) provide a dense, low‐cost, and opportunistic ground‐based remote sensing network. However, deriving gridded rainfall fields from path‐averaged CML attenuation constitutes an ill‐posed inversion problem. To overcome this challenge, we propose CML‐RainCDM, a physics‐informed conditional diffusion framework that integrates link encoding, implicit spatial structure prior injection, and a forward rain attenuation constraint. By jointly leveraging spatial priors and physical consistency, the model achieves more accurate rainfall intensity estimation and improved preservation of fine‐scale spatial structures compared with interpolation, statistical, and deep learning baselines. Sensitivity analyses further demonstrate its robustness in reconstructing heavy rainfall events, although extreme intensities remain difficult to capture. This study highlights the potential of opportunistic remote sensing via CMLs combined with generative diffusion models for practical, high‐resolution precipitation monitoring and environmental remote sensing applications.

Photonic radar systems offer a promising solution for high-precision sensing in various applications, particularly in autonomous vehicles, where reliable detection of obstacles in real-time is critical for safety. However, environmental conditions such as atmospheric turbulence and rain attenuation significantly impact radar performance, potentially compromising detection accuracy. This study aims to assess the performance of a photonic radar system under different environmental scenarios, including free-space, Gamma-Gamma atmospheric turbulence, and light and heavy rain conditions, with a focus on detecting three distinct targets positioned at various distances. Our simulations demonstrate that Gamma-Gamma atmospheric turbulence introduces variability in the received signal, with fluctuations becoming more pronounced at greater distances. Additionally, rain attenuation was found to substantially degrade performance, with heavy rain causing up to a 1 dBm reduction in received power at 50 meters and nearly a 1.5 dBm reduction at 100 meters, compared to light rain. For three targets located at 50m, 100m, and 150m, the combined effects of rain and turbulence were particularly noticeable at longer distances, with the received power under heavy rain dropping to −100.4 dBm at 150 meters. These findings indicate the importance of accounting for environmental conditions in the design of photonic radar systems, especially for autonomous vehicle applications. Future improvements could focus on developing adaptive radar techniques to compensate for adverse weather effects, ensuring robust and reliable performance under varying operational conditions. The novelty of this study lies in the integration of photonic radar technology with an advanced modeling framework that accounts for both free-space propagation and adverse weather conditions. Unlike conventional radar studies, our work incorporates Gamma-Gamma turbulence modeling and rain attenuation effects to provide a more comprehensive analysis of radar performance in real-world environments. This study also proposes an optimized detection strategy for multiple targets at varying distances, demonstrating the potential of photonic radar for autonomous vehicle applications.

We present the NEFOCAST project (named by the contraction of “Nefele”, which is the Italian spelling for the mythological cloud nymph Nephele, and “forecast”), funded by the Tuscany Region, about the feasibility of a system for the detection and monitoring of precipitation fields over the regional territory based on the use of a widespread network of new-generation Eutelsat “SmartLNB” (smart low-noise block converter) domestic terminals. Though primarily intended for interactive satellite services, these devices can also be used as weather sensors, as they have the capability of measuring the rain-induced attenuation incurred by the downlink signal and relaying it on an auxiliary return channel. We illustrate the NEFOCAST system architecture, consisting of the network of ground sensor terminals, the space segment, and the service center, which has the task of processing the information relayed by the terminals for generating rain field maps. We discuss a few methods that allow the conversion of a rain attenuation measurement into an instantaneous rainfall rate. Specifically, we discuss an exponential model relating the specific rain attenuation to the rainfall rate, whose coefficients were obtained from extensive experimental data. The above model permits the inferring of the rainfall rate from the total signal attenuation provided by the SmartLNB and from the link geometry knowledge. Some preliminary results obtained from a SmartLNB installed in Pisa are presented and compared with the output of a conventional tipping bucket rain gauge. It is shown that the NEFOCAST sensor is able to track the fast-varying rainfall rate accurately with no delay, as opposed to a conventional gauge.

The current ITU rain attenuation model that is used in the design of satellite-earth communication link was derived using data collected predominantly from temperate regions and has limitations when applied to tropical and equatorial regions that are characterized by heavy rainfall. Elevation and frequency scaling was applied to the rain attenuation data obtained from tropical and equatorial regions to validate the linear relationship for rain rates above 90 mm/h in the TPRA model. Analysis of the data from these regions also confirms the linear relationship between the rain attenuation and rain rates above 90 mm/h. The TPRA model can be considered as an alternative model for a reliable satellite-earth link for use in the tropical and equatorial regions.

Atmospheric impairment-induced attenuation is the prominent source of signal degradation in radio wave communication channels. The computation-based modeling of radio wave attenuation over the atmosphere is the stepwise application of relevant radio propagation models, data, and procedures to effectively and prognostically estimate the losses of the propagated radio signals that have been induced by atmospheric constituents. This contribution aims to perform a detailed prognostic evaluation of radio wave propagation attenuation due to rain, free space, gases, and cloud over the atmosphere at the ultra-high frequency band. This aim has been achieved by employing relevant empirical atmospheric data and suitable propagation models for robust prognostic modeling using experimental measurements. Additionally, the extrapolative attenuation estimation results and the performance analysis were accomplished by engaging different stepwise propagation models and computation parameters often utilized in Earth–satellite and terrestrial communications. Results indicate that steady attenuation loss levels rise with increasing signal carrier frequency where free space is more dominant. The attenuation levels attained due to rain, cloud, atmospheric gases, and free space are also dependent on droplet depths, sizes, composition, and statistical distribution. While moderate and heavy rain depths achieved 3 dB and 4 dB attenuations, the attenuation due to light rainfall attained a 2.5 dB level. The results also revealed that attenuation intensity levels induced by atmospheric gases and cloud effects are less than that of rain. The prognostic-based empirical attenuation modeling results can provide first-hand information to radio transmission engineers on link budgets concerning various atmospheric impairment effects during radio frequency network design, deployment, and management, essentially at the ultra-high frequency band.

Recent years have witnessed a growing interest in techniques and systems for rainfall surveillance on regional scale, with increasingly stringent requirements in terms of the following: (i) accuracy of rainfall rate measurements, (ii) adequate density of sensors over the territory, (iii) space-time continuity and completeness of data and (iv) capability to elaborate rainfall maps in near real time. The devices deployed to monitor the precipitation fields are traditionally networks of rain gauges distributed throughout the territory, along with weather radars and satellite remote sensors operating in the optical or infrared band, none of which, however, are suitable for full compliance to all of the requirements cited above. More recently, a different approach to rain rate estimation techniques has been proposed and investigated, based on the measurement of the attenuation induced by rain on signals of pre-existing radio networks either in terrestrial links, e.g., the backhaul connections in cellular networks, or in satellite-to-earth links and, among the latter, notably those between geostationary broadcast satellites and domestic subscriber terminals in the Ku and Ka bands. Knowledge of the above rain-induced attenuation permits the retrieval of the corresponding rain intensity provided that a number of meteorological and geometric parameters are known and ultimately permits estimating the rain rate locally at the receiver site. In this survey paper, we specifically focus on such a type of “opportunistic” systems for rain field monitoring, which appear very promising in view of the wide diffusion over the territory of low-cost domestic terminals for the reception of satellite signals, prospectively allowing for a considerable geographical capillarity in the distribution of sensors, at least in more densely populated areas. The purpose of the paper is to present a broad albeit synthetic overview of the numerous issues inherent in the above rain monitoring approach, along with a number of solutions and algorithms proposed in the literature in recent years, and ultimately to provide an exhaustive account of the current state of the art. Initially, the main relevant aspects of the satellite link are reviewed, including those related to satellite dynamics, frequency bands, signal formats, propagation channel and radio link geometry, all of which have a role in rainfall rate estimation algorithms. We discuss the impact of all these factors on rain estimation accuracy while also highlighting the substantial differences inherent in this approach in comparison with traditional rain monitoring techniques. We also review the basic formulas relating rain rate intensity to a variation of the received signal level or of the signal-to-noise ratio. Furthermore, we present a comprehensive literature survey of the main research issues for the aforementioned scenario and provide a brief outline of the algorithms proposed for their solution, highlighting their points of strength and weakness. The paper includes an extensive list of bibliographic references from which the material presented herein was taken.