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Today, rain remains one key and well-known natural phenomenon that offsets and attenuates the propagated radio, microwave, and millimeter-wave signals at different transmission frequencies and wavelengths over propagation paths. Specialised rain attenuation studies can be utilized to analyze their stochastic behavior on propagated radio signals and also come up with appropriate rain attenuation model for network application planning and optimisations. In this contribution, empirical rainfall depths data has been acquired, effectively categorized, and employed to examine the implicative intensity level trends over a ten years period, starting from 2011 to 2020. More importantly, the Recommendation ITU-R P.1511 power-based model combined with the acquired categorized rainfall depths data has been explored to prognostically estimate and quantity the amount of specific attenuation loss due over 3.5G transmission frequency. The results reveal that the level of attenuation attained versus 0.01% percentage of time depends on the type of rain intensity levels (heavy rain, very heavy rain, extremely heavy rain), which in turn is dependent upon rain depth or rate drop sizes. As a case in point, 0.001 percent of the time due to heavy rain, the amount of specific attenuation attained stood at 2dB, while for very heavy and extremely heavy rain, the specific attenuation levels amount to 2.3dB and 4dB respectively. These different amounts of specific attenuation simplify imply that the heavier the rain, the more scattering, and absorption the propagated electromagnetic signals undergo, thus leading to degraded and higher attenuation levels. The empirical based-rain attenuation quantification and impact analysis method explored in this paper will significantly provide radio network engineers with the best way to monitor and evaluate the radio attenuation effect over a propagation channel.

Understanding the spatiotemporal environment of the ocean after a heavy rain disaster is critical for satellite remote sensing research and disaster prevention. We attempted to reproduce changes in marine debris distributions using multidate data of Landsat-8 spectral reflectance acquired immediately after a heavy rain disaster in western Japan in July 2018. Data from cleaning ships were used for screening the marine debris area. As most of the target marine debris consisted of plant fragments, a method based on the corrected floating algae index (cFAI) was applied to Landsat-8 data. Data from cleaning ships clarify that most of the marine debris accumulated in the waters in the northern part of Aki Nada, a part of the Seto Inland Sea. The spectral characteristics of the corresponding marine debris spectral reflectance obtained from the Landsat-8 data were explained by the FAI with band 5 (central wavelength: 865 nm) as the maximum value. Unlike traditional FAI, cFAI eliminated the effect of background water turbidity. The Otsu method was effective for the automatic threshold determination for cFAI. Although Landsat-8 data have limited spatial resolution and observation frequency, these data were useful for understanding marine debris distribution after a heavy rain disaster.

Korea’s early warning system for flood disaster management is starting preparations for flood response by the heavy rain advisory (HRA) of the Korea Meteorological Administration (KMA). However, the HRA criterion has a critical limitation in that it considers only consistent rainfall patterns (e.g. 60 mm/3 hrs or 110 mm/12 hrs) without considering the characteristics of heavy rain damage in the region. To address this problem, the present study proposes a heavy rain damage prediction technique based on optimization and ensemble method. To predict damage as accurately as possible, fifteen rainfall variables according to the duration and magnitude are considered. The dataset is divided into a training dataset (70%) and a test dataset (30%) by random extraction. An optimal threshold that the damage can be occurred is derived for each region via optimization method of the training dataset. The area under the receiver operating characteristic (AUROC) curve, F1 score, and F2 score are each considered as objective function, and the F2 score was selected because it is more effective in terms of disaster management. In addition, the method is designed to predict damage probabilistically by applying the ensemble concept. This novel method is defined as a heavy rain damage prediction technique (HDPT). Finally, the HDPT is evaluated using the test dataset and by comparison with the results from the HRA data, and the F2 score of the HDPT is shown to be about 10% higher than that of the HRA. Thus, the proposed methodology is expected to be more effective than the current HRA method for the early warning system and for disaster management.

This study evaluated the performance of the Weather Research and Forecasting (WRF) model version 3.7 for simulating a series of rainfall events in August 2014 over Japan and investigated the impact of uncertainty in sea surface temperature (SST) on simulated rainfall in the record-high precipitation period. WRF simulations for the heavy rainfall were conducted for six different cases. The heavy rainfall events caused by typhoons and rain fronts were similarly accurately reproduced by three cases: the TQW_5km case with grid nudging for air temperature, humidity, and wind and with a horizontal resolution of 5 km; W_5km with wind nudging and 5-km resolution; and W_2.5km with wind nudging and 2.5-km resolution. Because the nudging for air temperature and humidity in TQW_5km suppresses the influence of SST change, and because W_2.5km requires larger computational load, W_5km was selected as the baseline case for a sensitivity analysis of SST. In the sensitivity analysis, SST around Japan was homogeneously changed by 1 K from the original SST data. The analysis showed that the SST increase led to a larger amount of precipitation in the study period in Japan, with the mean increase rate of precipitation being 13 ± 8% K−1. In addition, 99 percentile precipitation (100 mm d−1 in the baseline case) increased by 13% K−1 of SST warming. These results also indicate that an uncertainty of approximately 13% in the simulated heavy rainfall corresponds to an uncertainty of 1 K in SST data around Japan in the study period.

This study focused on producing flash flood hazard susceptibility maps (FFHSM) using frequency ratio (FR) and statistical index (SI) models in the Xiqu Gully (XQG) of Beijing, China. First, a total of 85 flash flood hazard locations (n = 85) were surveyed in the field and plotted using geographic information system (GIS) software. Based on the flash flood hazard locations, a flood hazard inventory map was built. Seventy percent (n = 60) of the flooding hazard locations were randomly selected for building the models. The remaining 30% (n = 25) of the flooded hazard locations were used for validation. Considering that the XQG used to be a coal mining area, coalmine caves and subsidence caused by coal mining exist in this catchment, as well as many ground fissures. Thus, this study took the subsidence risk level into consideration for FFHSM. The ten conditioning parameters were elevation, slope, curvature, land use, geology, soil texture, subsidence risk area, stream power index (SPI), topographic wetness index (TWI), and short-term heavy rain. This study also tested different classification schemes for the values for each conditional parameter and checked their impacts on the results. The accuracy of the FFHSM was validated using area under the curve (AUC) analysis. Classification accuracies were 86.61%, 83.35%, and 78.52% using frequency ratio (FR)-natural breaks, statistical index (SI)-natural breaks and FR-manual classification schemes, respectively. Associated prediction accuracies were 83.69%, 81.22%, and 74.23%, respectively. It was found that FR modeling using a natural breaks classification method was more appropriate for generating FFHSM for the Xiqu Gully.

Crustal response to the 2024 September heavy rain episode in the northern Noto Peninsula, Central Japan, was studied using a dense network of global navigation satellite system receiving stations. Over the region in and around the Noto Peninsula, the regionally integrated subsidence was proportional to the daily rain, i.e., ~ 0.1 km 3 volumetric subsidence occurred in response to 1 Gt daily rain. The subsidence lasted for only a day or so. These findings are consistent with past cases of elastic response of the Japanese Islands lithosphere to rain loading. We also found that a small island, to the north of the peninsula, subsided by a few centimeters on heavy rain days. This cannot be explained by terrestrial water storage loads within the island. Rainwater may have remained partly in the ocean surrounding the island and depressed the ocean floor as a surface load. Graphical Abstract

This study compares the physical processes responsible for the heavy and light rain variations from the perspective of spring precipitation over Southern China. The results indicate that, heavy rain variation has a closer connection with the western North Pacific anticyclone. Intensified western North Pacific anticyclone and associated warm and humid air transport, coupled with intensified East Asian subtropical jet, favor significant moisture convergence and enhanced convective feedback over the key region, which causes increased heavy rain rather than light rain over there. In comparison, light rain variation shows a close relationship with the anomalous low over Lake Balkhash, which causes lower-tropospheric cyclone over the key region. On the one hand, anomalous cyclone favors lower-tropospheric cooling; concurred with cyclone-induced increased moisture, the lower-tropospheric relative humidity increases over there. On the other hand, the lower-tropospheric cooling center shifts northward with height and causes enhanced atmospheric baroclinity over there. Such atmospheric conditions are conducive to the occurrence of low cloud and more light rain. In addition, intensified East Asian subtropical jet associated with Lake Balkhash anomalous low also provides favorable dynamic lifting condition. Moreover, heavy and light rain variations are more related to El Niño-Southern Oscillation and North Atlantic horseshoe sea surface temperature in preceding winter, respectively. In addition, heavy rain shows a closer relationship with total precipitation amount variation, whereas light rain is more related to extreme consecutive dry days variation.

Based on daily precipitation data from the Chinese Meteorological Administration and reanalysis data from the National Centers for Environmental Prediction-Department of Energy, the character of low-frequency precipitation variability during the first rainy season (April–June) over South China and its corresponding atmospheric circulations in the mid-high latitudes are investigated. The results show that the precipitation anomalies during this period exhibit obvious quasi-biweekly oscillation (QBWO) features, with a period of 8–24 days. The influence of wave trains in the mid-high latitudes to low-frequency persistent heavy rain event (PHR-LF event, the 8–24-day filtered precipitation larger than one standard deviation of filtered time series and persisting at least three days over South China) is further discussed. During the first rainy season over South China, there are two low-frequency wave trains in the mid-high latitudes associated with the PHR-LF event—the wave train crossing the Eurasian continent and the wave train along the subtropical westerly jet. Analysis of wave activity flux indicates that the wave energy disperses toward eastern China along these two low-frequency wave trains from north to south and from west to east, and then propagates downward over South China. Accordingly, the disturbance of the relative vorticity of the cyclonic anomalies over eastern China is strengthened, which enhances the meridional gradient of relative vorticity. Owing to the transport of low-frequency relative vorticity and geostrophic vorticity by meridional wind, the ascending motion over South China intensifies and lasts for a long time, triggering a PHR-LF event. In addition, the tropical system is also a key factor to PHR-LF event. The QBWO of the convection over the South China Sea provide moisture for PHR-LF events, maintaining persistent rainfall and vertical ascending motion over South China.

This paper examines the impact of rain on traffic sign recognition system, addressing one of the challenges posed by harsh environmental conditions like low lighting, extreme weather (rain,fog, snow) and reduced sign visibility. A novel system is proposed in this work, which is capable of handling three different rain types (drizzle, torrential, and heavy). This work explores how different rain types affect training and testing of three customized convolutional neural networks for traffic sign recognition. Results show that the system’s performance is dependent on the rain type during training and testing. To address this variability, a multistage classifier is proposed: level 1 classifies rain type, and level 2 selects an appropriate traffic sign classifier based on output of level1. This work also analyzes the effect of augmenting three different types of rain for developing noise robust traffic sign recognition system. Experiments were conducted using publicly available German Traffic Sign Recognition Benchmark dataset. The proposed system attains overall classification accuracy of 95.10% measured through the accuracy score metric, which has not been claimed till now in any previous work in addressing the challenges of recognizing traffic signs in presence of different types of rain.