Journal articles: 'Rain detection'

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Fu, Fangfa, Yao Wang, Fengchang Lai, Weizhe Xu, and Jinxiang Wang. "Efficient rain–fog model for rain detection and removal." Journal of Electronic Imaging 29, no. 02 (April 7, 2020): 1. http://dx.doi.org/10.1117/1.jei.29.2.023020.

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Akanni, J., A. O. Ojo, A. Abdulwahab, A. A. Isa, and O. Ogunbiyi. "Development and Implementation of a Prototype Automatic Rain-Sensor Car Wiper System." Journal of Applied Sciences and Environmental Management 26, no. 11 (November 30, 2022): 1821–26. http://dx.doi.org/10.4314/jasem.v26i11.13.

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Various studies have been conducted over the years on how to reduce driver distractions while driving, but with little effort on the distraction that could be caused by manually operated wipers while driving whenever it rains. Drivers frequently take their hands off the steering to turn ON/OFF and adjust the wiper speed when driving during rain, which causes a loss of concentration and increases the risk of a car accident. This paper presents an automatic car wiper prototype system that adjusts the speed of the wiper based on the intensity of the rain. The system also includes an audio alert that warns the driver to stop driving during heavy rain. The rain sensor/intensity and servo motor; which regulates the wiper's speed, were interfaced by an ATMega328 (Arduino Uno A000066). It performed satisfactorily, with average response times of 0.78 seconds, 1.95 seconds, and 6 seconds for rain water detection, increasing rain intensity, and no rain detection respectively. The wiper speed was 15 rpm at moderate rain intensity and 32 rpm at heavy rain intensity. The wiper average response time and speed shows that it is a system that eliminate delay as compare to manually operated car wiper system. The developed system will reduce driver distractions while driving thereby reduces the risk of a car accident. As a result, this system can be combined with new technologies seen in contemporary vehicles.

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Upadhyaya, S., and R. A. A. J. Ramsankaran. "Support Vector Machine (SVM) based Rain Area Detection from Kalpana-1 Satellite Data." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-8 (November 27, 2014): 21–27. http://dx.doi.org/10.5194/isprsannals-ii-8-21-2014.

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Rain is one of the major components of water cycle; extreme rain events can cause destruction and misery due to flash flood and droughts. Therefore, assessing rainfall at high temporal and spatial resolution is of fundamental importance which can be achieved only by satellite remote sensing. Though there are many algorithms developed for estimation of rainfall using satellite data, they suffer from various drawbacks. One such challenge in satellite rainfall estimation is to detect rain and no-rain areas properly. To address this problem, in the present study we have used the Support Vector Machines (SVM). It is significant to note that this is the first study to report the utility of SVM in detecting rain and no-rain areas. The developed SVM based index performance has been evaluated by comparing with two most popular rain detection methods used for Indian regions i.e. Simple <i>TIR</i> threshold used in Global Precipitation Index (GPI) technique and <i>Roca</i> method used in Insat Multi Spectral Rainfall Algorithm (IMSRA). Performance of the above considered indices has been analyzed by considering various categorical statistics like Probabil ity of Detection (POD), Probability of no-rain detection (POND), Accuracy, Bias, False Alarm Ratio (FAR) and Heidke Skill Score (HSS). The obtained results clearly show that the new SVM based index performs much better than the earlier indices.

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Hassim, Raima, Hamzah Asyrani Sulaiman, and Abdullah Bade. "An Efficient Rain Streaks Detection and Removal for Single Image Using Hybridization of Rain Detection and Rain Removal Technique (HyDRA)." Advanced Science Letters 24, no. 2 (February 1, 2018): 1027–31. http://dx.doi.org/10.1166/asl.2018.10680.

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Dong, Jianzhi, Wade T. Crow, and Rolf Reichle. "Improving Rain/No-Rain Detection Skill by Merging Precipitation Estimates from Different Sources." Journal of Hydrometeorology 21, no. 10 (October 1, 2020): 2419–29. http://dx.doi.org/10.1175/jhm-d-20-0097.1.

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AbstractRain/no-rain detection error is a key source of uncertainty in regional and global precipitation products that propagates into offline hydrological and land surface modeling simulations. Such detection error is difficult to evaluate and/or filter without access to high-quality reference precipitation datasets. For cases where such access is not available, this study proposes a novel approach for improved rain/no-rain detection. Based on categorical triple collocation (CTC) and a probabilistic framework, a weighted merging algorithm (CTC-M) is developed to combine noisy, but independent, precipitation products into an optimal binary rain/no-rain time series. Compared with commonly used approaches that directly apply the best parent product for rain/no-rain detection, the superiority of CTC-M is demonstrated analytically and numerically using spatially dense precipitation measurements over Europe. Our analysis also suggests that CTC-M is tolerant to a range of cross-correlated rain/no-rain detection errors and detection biases of the parent products. As a result, CTC-M will benefit global precipitation estimation by improving the representation of precipitation occurrence in gauge-based and multisource merged precipitation products.

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Kingsley, Kumah K., Ben H. P. Maathuis, Joost C. B. Hoedjes, Donald T. Rwasoka, Bas V. Retsios, and Bob Z. Su. "Rain Area Detection in South-Western Kenya by Using Multispectral Satellite Data from Meteosat Second Generation." Sensors 21, no. 10 (May 19, 2021): 3547. http://dx.doi.org/10.3390/s21103547.

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This study presents a rain area detection scheme that uses a gradient based adaptive technique for daytime and nighttime rain area detection and correction from reflectance and infrared (IR) brightness temperatures data of the Meteosat Second Generation (MSG) satellite. First, multiple parametric rain detection models developed from MSG’s reflectance and IR data were calibrated and validated with rainfall data from a dense network of rain gauge stations and investigated to determine the best model parameters. The models were based on a conceptual assumption that clouds characterised by the top properties, e.g., high optical thickness and effective radius, have high rain probabilities and intensities. Next, a gradient based adaptive correction technique that relies on rain area-specific parameters was developed to reduce the number and sizes of the detected rain areas. The daytime detection with optical (VIS0.6) and near IR (NIR1.6) reflectance data achieved the best detection skill. For nighttime, detection with thermal IR brightness temperature differences of IR3.9-IR10.8, IR3.9-WV73 and IR108-WV62 showed the best detection skill based on general categorical statistics. Compared to the Global Precipitation Measurement (GPM) Integrated Mult-isatellitE Retrievals for GPM (IMERG) and the gauge station data from the southwest of Kenya, the model showed good agreement in the spatial dynamics of the detected rain area and rain rate.

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Bauer, Peter, Dirk Burose, and Jörg Schulz. "Rain detection over land surfaces using passive microwave satellite data." Meteorologische Zeitschrift 11, no. 1 (March 5, 2002): 37–48. http://dx.doi.org/10.1127/0941-2948/2002/0011-0037.

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Zhao, Yuan, Nicolas Longépé, Alexis Mouche, and Romain Husson. "Automated Rain Detection by Dual-Polarization Sentinel-1 Data." Remote Sensing 13, no. 16 (August 10, 2021): 3155. http://dx.doi.org/10.3390/rs13163155.

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Rain Signatures on C-band Synthetic Aperture Radar (SAR) images acquired over ocean are common and can dominate the backscattered signal from the ocean surface. In many cases, the inability to decipher between ocean and rain signatures can disturb the analysis of SAR scenes for maritime applications. This study relies on Sentinel-1 SAR acquisitions in the Interferometric Wide swath mode and high-resolution measurements from ground-based weather radar to document the rain impact on the radar backscattered signal in both co- and cross-polarization channels. The dark and bright rain signatures are found in connection with the timeliness of the rain cells. In particular, the bright patches are demonstrated by the hydrometeors (graupels, hails) in the melting layer. In general, the radar backscatter under rain increases with rain rate for a given sea state and decreases when the sea state strengthens. The rain also has a stronger impact on the radar signal in both polarizations when the incidence angle increases. The complementary sensitivity of the SAR signal of rain in both channels is then used to derive a filter to locate the areas in SAR scenes where the signal is not dominated by rain. The filter optimized to match the rain observed by the ground-based weather radar is more efficient when both polarization channels are considered. Case studies are presented to discuss the advantages and limitations of such a filtering approach.

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Son, Chang-Hwan, and Xiao-Ping Zhang. "Rain Detection and Removal via Shrinkage-based Sparse Coding and Learned Rain Dictionary." Journal of Imaging Science and Technology 64, no. 3 (May 1, 2020): 30501–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2020.64.3.030501.

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Abstract Rain removal is essential for achieving autonomous driving because it preserves the details of objects that are useful for feature extraction and removes the rain structures that hinder feature extraction. Based on a linear superposition model in which the observed rain image is decomposed into two layers, a rain layer and a non-rain layer, conventional rain removal methods estimate these two layers alternatively from an observed single image based on prior modeling. However, the prior knowledge used for the rain structures is not always correct because various types of rain structures can be observed in the rain images, which results in inaccurate rain removal. Therefore, in this article, a novel rain removal method based on the use of a scribbled rain image set and a new shrinkage-based sparse coding model is proposed. The scribbled rain images have information about which pixels have rain structures. Thus, various types of rain structures can be modeled, owing to the abundance of rain structures in the rain image set. To detect the rain regions, two types of approaches, one based on reconstruction error comparison (REC) via a learned rain dictionary and the other based on a deep convolutional neural network (DCNN), are presented. With the rain regions, the proposed shrinkage-based sparse coding model determines how much to reduce the sparse codes of the rain dictionary and maintain the sparse codes of the non-rain dictionary for accurate rain removal. Experimental results verified that the proposed shrinkage-based sparse coding model could remove rain structures and preserve objects’ details due to the REC- or DCNN-based rain detection using the scribbled rain image set. Moreover, it was confirmed that the proposed method is more effective at removing rain structures from similar objects’ structures than conventional methods.

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Upadhyaya, Shruti, and R. Ramsankaran. "Multi-Index Rain Detection: A New Approach for Regional Rain Area Detection from Remotely Sensed Data." Journal of Hydrometeorology 15, no. 6 (December 1, 2014): 2314–30. http://dx.doi.org/10.1175/jhm-d-14-0006.1.

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Abstract In this article, a new approach called Multi-Index Rain Detection (MIRD) is suggested for regional rain area detection and was tested for India using Kalpana-1 satellite data. The approach was developed based on the following hypothesis: better results should be obtained for combined indices than an individual index. Different combinations (scenarios) were developed by combining six commonly used rain detection indices using AND and OR logical connectives. For the study region, an optimal rain area detection scenario and optimal threshold values of the indices were found through a statistical multi-decision-making technique called the Technique for Order Preference by Similarity Ideal Solution (TOPSIS). The TOPSIS analysis was carried out based on independent categorical statistics like probability of detection, probability of no detection, and Heidke skill score. It is noteworthy that for the first time in literature, an attempt has been made (through sensitivity analysis) to understand the influence of the proportion of rain/no-rain pixels in the calibration/validation dataset on a few commonly used statistics. Thus, the obtained results have been used to identify the above-mentioned independent categorical statistics. Based on the results obtained and the validation carried out with different independent datasets, scenario 8 (TIRt < 260 K and TIRt − WVt < 19 K, where TIRt and WVt are the brightness temperatures from thermal IR and water vapor, respectively) is found to be an optimal rain detection index. The obtained results also indicate that the texture-based indices [standard deviation and mean of 5 × 5 pixels at time t (mean5)] did not perform well, perhaps because of the coarse resolution of Kalpana-1 data. It is also to be noted that scenario 8 performs much better than the Roca method used in the Indian National Satellite (INSAT) Multispectral Rainfall Algorithm (IMSRA) developed for India.

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Heinselman, Pamela L., and Alexander V. Ryzhkov. "Validation of Polarimetric Hail Detection." Weather and Forecasting 21, no. 5 (October 1, 2006): 839–50. http://dx.doi.org/10.1175/waf956.1.

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Abstract This study describes, illustrates, and validates hail detection by a simplified version of the National Severe Storms Laboratory’s fuzzy logic polarimetric hydrometeor classification algorithm (HCA). The HCA uses four radar variables: reflectivity, differential reflectivity, cross-correlation coefficient, and “reflectivity texture” to classify echoes as rain mixed with hail, ground clutter–anomalous propagation, biological scatterers (insects, birds, and bats), big drops, light rain, moderate rain, and heavy rain. Diagnostic capabilities of HCA, such as detection of hail, are illustrated for a variety of storm environments using polarimetric radar data collected mostly during the Joint Polarimetric Experiment (JPOLE; 28 April–13 June 2003). Hail classification with the HCA is validated using 47 rain and hail reports collected by storm-intercept teams during JPOLE. For comparison purposes, probability of hail output from the Next-Generation Weather Radar Hail Detection Algorithm (HDA) is validated using the same ground truth. The anticipated polarimetric upgrade of the Weather Surveillance Radar-1988 Doppler network drives this direct comparison of performance. For the four examined cases, contingency table statistics show that the HCA outperforms the HDA. The superior performance of the HCA results primary from the algorithm’s lack of false alarms compared to the HDA. Statistical significance testing via bootstrapping indicates that differences in the probability of detection and critical success index between the algorithms are statistically significant at the 95% confidence level, whereas differences in the false alarm rate and Heidke skill score are statistically significant at the 90% confidence level.

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Kim, Min-Seong, and Byung Kwon. "Rainfall Detection and Rainfall Rate Estimation Using Microwave Attenuation." Atmosphere 9, no. 8 (July 24, 2018): 287. http://dx.doi.org/10.3390/atmos9080287.

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Eight microwave links operating at frequencies ranging from 6 to 8 GHz and with path lengths ranging from 5.7 to 37.4 km traversing the city of Seoul, Korea are used to detect rainfall and estimate path-averaged rainfall rates. Rainfall detection using rain-induced attenuation (dB) was validated by rain detectors installed at automatic weather stations, and the results confirmed that microwave links can be used to detect rainfall with an accuracy ≥80%. The power-law R-k relationships between rain-induced specific attenuation, k (dB km−1), and the rainfall rate, R (mm h−1), were established and cross-validated by estimating the path-averaged rainfall rate. The mean bias of the path-averaged rainfall rate, as compared to the rainfall rate from ground rain gauges, was between −3 and 1 mm h−1. The improved accuracy of rainfall detection led to the improved accuracy of the path-averaged rainfall rate. Hence, it was confirmed that microwave links, used for broadcasting and media communications, can identify rainy or dry periods (rain spells or dry spells) in a way comparable to rain detectors and provide high time-resolution rainfall rates in real time.

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Nugroho, Ginaldi Ari, Muhammad Miftahul Munir, and Khairurrijal. "A Computer-Based Marine Automatic Radar for Rain Detection." Applied Mechanics and Materials 771 (July 2015): 9–12. http://dx.doi.org/10.4028/www.scientific.net/amm.771.9.

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An improved rain detection system has been developed using a marine radar. The rain detection system is composed of a Furuno 1932 Mark II marine radar (radar scanner and display units), a radar control circuit, a signal conditioning circuit, an analog to digital converter (ADC), and a computer with a graphical user interface (GUI). The combination of the microcontroller and optocoupler in the radar control circuit was able to replace the omnipad and pushbutton control and it was also employed to activate the radar and the sector blanket mode. The signal conditioning circuit along with the ADC and the clutter removal made the video and navigation signals from the display unit become ADC-counted rain data. By comparing the ADC-counted rain data of the Furuno marine radar with the reflectivity obtained by GMR X Band weather radar, it was found that the Furuno rain detection sensitivity only spans from 30-55 dBZ.

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Srikanth T, Dhanalakshmi B, Amuktha D, Manikanta J, Ramalokeswar T, and Nagaphanindhra P. "Portable rain water detecting alarm using ic 555 timer." South Asian Journal of Engineering and Technology 12, no. 2 (May 31, 2022): 23–26. http://dx.doi.org/10.26524/sajet.2022.12.26.

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Everyone wants to stay educated and productive in a world when everything is automated. Water harvesting may also be simply accomplished with the help of automation. In this article, we'll talk about the Rain Alarm project, which is quite important for rainwater gathering. The circuit detects rain by sounding an alert while you are inside the house, allowing us to take appropriate action.This project is a portable rain water detecting alarm designed to protect goods from rain while also conserving water. It uses a rain sensor and a versatile multifunctional IC 555 Timer Chip to detect rain water in rainy conditions. LED, 9V battery, rain sensor, buzzer, 1N4007 PN junction diode, NPN transistor, and other components make up the rain water detection alarm. When the rain starts falling, the rain sensor detects it and the buzzer starts blaring and the LED lights up, and it will automatically reset when the rain stops The ultimate purpose of this project is to use a rain sensor to detect rain falling, and this concept can be used in the home, irrigation field, and cottage industries.

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Winkler, P. "Grundlagen für das automatische Erkennen von Regen." Meteorologische Zeitschrift 2, no. 1 (March 4, 1993): 27–34. http://dx.doi.org/10.1127/metz/2/1993/27.

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Portabella, M., and A. Stoffelen. "Rain Detection and Quality Control of SeaWinds." Journal of Atmospheric and Oceanic Technology 18, no. 7 (July 2001): 1171–83. http://dx.doi.org/10.1175/1520-0426(2001)018<1171:rdaqco>2.0.co;2.

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Yao, Chen, Ci Wang, Lijuan Hong, and Yunfei Cheng. "A Bayesian Probabilistic Framework for Rain Detection." Entropy 16, no. 6 (June 17, 2014): 3302–14. http://dx.doi.org/10.3390/e16063302.

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Zhao, Penghui, Xiaoyuan Yu, Zongren Chen, and Yangyan Liang. "A Real-Time Ship Detector via a Common Camera." Journal of Marine Science and Engineering 10, no. 8 (July 29, 2022): 1043. http://dx.doi.org/10.3390/jmse10081043.

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Advanced radars and satellites, suitable for remote monitoring, inappropriately reach the economical requirements of short-range detection. Compared with far-sightedness skills, common visible-light sensors offer more ample features conducive to distinguishing the classes. Therefore, ship detection based on visible-light cameras should cooperate with remote detection technologies. However, compared with detectors applied in inland transportation, the lack of fast ship detectors, detecting multiple ship classes, is non-negligible. To fill this gap, we propose a real-time ship detector based on fast U-Net and remapping attention (FRSD) via a common camera. The fast U-Net offered compresses features in the channel dimension to decrease the number of training parameters. The remapping attention introduced boosts the performance in various rain–fog weather conditions while maintaining the real-time speed. The ship dataset proposed contains more than 20,000 samples, alleviating the lack of ship datasets containing various classes. Data augmentation of the cross-background is especially proposed to further promote the diversity of the detecting background. In addition, the rain–fog dataset proposed, containing more than 500 rain–fog images, simulates various marine rain–fog scenarios and soaks the testing image to validate the robustness of ship detectors. Experiments demonstrate that FRSD performs relatively robustly and detects 9 classes with an mAP of more than 83%, reaching a state-of-the-art level.

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Puig-Centelles, Anna, Nicolau Sunyer, Oscar Ripolles, Miguel Chover, and Mateu Sbert. "Rain Simulation in Dynamic Scenes." International Journal of Creative Interfaces and Computer Graphics 2, no. 2 (July 2011): 23–36. http://dx.doi.org/10.4018/jcicg.2011070102.

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Rain is a complex phenomenon and its simulation is usually very costly. In this article, the authors propose a fully-GPU rain simulation based on the utilization of particle systems. The flexibility of CUDA allows the authors to include, aside from the rainfall simulation, a system for the detection and handling of the collisions of particles against the scenario. This detection system allows for the simulation of splashes at the same time. This system obtains a very high performance because of the hardware programming capabilities of CUDA.

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Liu, H., V. Chandrasekar, and E. Gorgucci. "Detection of rain/no rain condition on the ground based on radar observations." IEEE Transactions on Geoscience and Remote Sensing 39, no. 3 (March 2001): 696–99. http://dx.doi.org/10.1109/36.911127.

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Liu, Yuhang, Jianxiao Ma, Yuchen Wang, and Chenhong Zong. "A Novel Algorithm for Detecting Pedestrians on Rainy Image." Sensors 21, no. 1 (December 27, 2020): 112. http://dx.doi.org/10.3390/s21010112.

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Pedestrian detection is widely used in cooperative vehicle infrastructure systems. Traditional pedestrian detection methods perform sufficiently well under sunny scenarios and obtain trustworthy traffic data. However, the detection drastically decreases under rainy scenarios. This study proposes a pedestrian detection algorithm with a de-raining module that improves detection accuracy under various rainy scenarios. Specifically, this algorithm determines the density information of rain and effectively removes rain streaks through the de-raining module. Then the algorithm detects pedestrians as a pair of keypoints through the pedestrian detection module to solve the problem of occlusion. Furthermore, a new pedestrian dataset containing rain density labels is established and used to train the algorithm. For the scenarios of light, medium, and heavy rain, extensive experiments on synthetic datasets demonstrate that the proposed algorithm increases AP (average precision) of pedestrian detection by 21.1%, 48.1%, and 60.9%. Moreover, the proposed algorithm performs well on real datasets and achieves improvements over the state-of-the-art methods, which reveals that the proposed algorithm can significantly improve the accuracy of pedestrian detection in rainy scenarios.

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Xi, Yue, Wenjing Jia, Qiguang Miao, Junmei Feng, Xiangzeng Liu, and Fei Li. "CoDerainNet: Collaborative Deraining Network for Drone-View Object Detection in Rainy Weather Conditions." Remote Sensing 15, no. 6 (March 7, 2023): 1487. http://dx.doi.org/10.3390/rs15061487.

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Benefiting from the advances in object detection in remote sensing, detecting objects in images captured by drones has achieved promising performance in recent years. However, drone-view object detection in rainy weather conditions (Rainy DroneDet) remains a challenge, as small-sized objects blurred by rain streaks offer a little valuable information for robust detection. In this paper, we propose a Collaborative Deraining Network called “CoDerainNet”, which simultaneously and interactively trains a deraining subnetwork and a droneDet subnetwork to improve the accuracy of Rainy DroneDet. Furthermore, we propose a Collaborative Teaching paradigm called “ColTeaching”, which leverages rain-free features extracted by the Deraining Subnetwork and teaches the DroneDet Subnetwork such features, to remove rain-specific interference in features for DroneDet. Due to the lack of an existing dataset for Rainy DroneDet, we built three drone datasets, including two synthetic datasets, namely RainVisdrone and RainUAVDT, and one real drone dataset, called RainDrone. Extensive experiment results on the three rainy datasets show that CoDerainNet can significantly reduce the computational costs of state-of-the-art (SOTA) object detectors while maintaining detection performance comparable to these SOTA models.

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Xie, Hai Wei, and Yan Zhang. "The Research Status of Acid Rain." Advanced Materials Research 726-731 (August 2013): 4033–36. http://dx.doi.org/10.4028/www.scientific.net/amr.726-731.4033.

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This paper not only makes a detailed introduction about the research of acid rain including the mechanism of formation, the factor of formation, the harm of acid rain and the analysis, detection and forecast of acid rain, but also points out the problem in the research and the control measure of acid rain.

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Tran, N., E. Obligis, and F. Ferreira. "Comparison of Two Jason-1 Altimeter Precipitation Detection Algorithms with Rain Estimates from the TRMM Microwave Imager." Journal of Atmospheric and Oceanic Technology 22, no. 6 (June 1, 2005): 782–94. http://dx.doi.org/10.1175/jtech1742.1.

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Abstract This paper evaluates and compares the ability of two different Jason-1 dual-frequency altimeter algorithms (referred as Tournadre’s and Quartly’s rain flags, respectively) to detect rain events in order to flag rain-contaminated altimeter range measurements. They are based on departures from a defined relationship between the Ku- and C-band radar cross sections observed in no-rain conditions. The algorithms’ performances were assessed via collocations of these dual-frequency-based estimates with rain rates and a rain–no-rain flag from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The Jason-1–TMI analysis is built upon a yes–no discrimination, which is helpful in providing good insight into the altimeter rain detection flags’ efficiency through estimations of the percentages of hits, misses, false alarms, and correct negatives when compared with TMI measurements. Tournadre’s rain flag, based on a combination of altimeter and radiometer data, gives the best match with TMI estimates, compared to Quartly’s, and also has a higher sensitivity to low-intensity rainfall.

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Tran, Ngan, Jean Tournadre, and Pierre Femenias. "Validation of Envisat Rain Detection and Rain Rate Estimates by Comparing With TRMM Data." IEEE Geoscience and Remote Sensing Letters 5, no. 4 (October 2008): 658–62. http://dx.doi.org/10.1109/lgrs.2008.2002043.

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Choi, Dong Yoon, Seung Ji Seo, and Byung Cheol Song. "DSP Optimization for Rain Detection and Removal Algorithm." Journal of the Institute of Electronics and Information Engineers 52, no. 9 (September 25, 2015): 96–105. http://dx.doi.org/10.5573/ieie.2015.52.9.096.

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Luqman Hakim, Arif, and Prawito. "Rain detection in image using convolutional neural network." Journal of Physics: Conference Series 1528 (April 2020): 012010. http://dx.doi.org/10.1088/1742-6596/1528/1/012010.

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Kappatos, A. Vassilios, and S. Evangelos Dermatas. "Feature Extraction for Crack Detection in Rain Conditions." Journal of Nondestructive Evaluation 26, no. 2-4 (October 25, 2007): 57–70. http://dx.doi.org/10.1007/s10921-007-0020-2.

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Sauter, Gernot, Marcel Doring, and Rik Nuyttens. "High Performance Pavement Markings Enhancing Camera And LiDAR Detection." IOP Conference Series: Materials Science and Engineering 1202, no. 1 (November 1, 2021): 012033. http://dx.doi.org/10.1088/1757-899x/1202/1/012033.

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Abstract It is well known that camera and video sensors have limitations in detecting pavement markings under certain conditions e.g. glare from sunlight or other vehicles, rain, fog etc. First generations of lane keeping systems depend on visual light. Erroneous detection is also resulting from irregular road surfaces such as glossy bitumen sealing strips, rain puddles or simply worn asphalt. The role of higher performing markings and better visual camera detection has been studied with Vedecom France. LiDAR (light detection and ranging) technology could help to fill remaining gaps, as it actively sends out IR (infrared) light, that returns reliable images of the road scenario and pavement markings both day and nighttime. In order to evaluate the opportunities of LiDAR technology for the detection of road markings, 3M Company and the University of Applied Sciences in Dresden decided to work together in a joint research project. All-Weather Elements AWE, are the latest development of high-performance optics, using high index beads to provide reflectivity both in dry and wet condition. It could be determined that high performance markings help to increase the level of detection by both camera and LiDAR sensors. The AWE marking was detected from significantly longer distances, especially in wet and rainy conditions. In combination with common camera based LKA and LDW systems, the LiDAR sensors can increase the overall detection rate of pavement markings. This is especially important for vehicles with higher SAE levels of automated driving and can support the overall safety of vehicles. The research also evaluated existing test methods for wet and rain reflectivity in EN 1436 and ASTM E 2832 and how measured performance correlates with LiDAR detection.

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Jameson, A. R., M. L. Larsen, and A. B. Kostinski. "On the Detection of Statistical Heterogeneity in Rain Measurements." Journal of Atmospheric and Oceanic Technology 35, no. 7 (July 2018): 1399–413. http://dx.doi.org/10.1175/jtech-d-17-0161.1.

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AbstractThe application of the Wiener–Khintchine theorem for translating a readily measured correlation function into the variance spectrum, important for scale analyses and for scaling transformations of data, requires that the data be wide-sense homogeneous (stationary), that is, that the first and second moments of the probability distribution of the variable are the same at all times (stationarity) or at all locations (homogeneity) over the entire observed domain. This work provides a heuristic method independent of statistical models for evaluating whether a set of data in rain is wide-sense stationary (WSS). The alternative, statistical heterogeneity, requires 1) that there be no single global mean value and/or 2) that the variance of the variable changes in the domain. Here, the number of global mean values is estimated using a Bayesian inversion approach, while changes in the variance are determined using record counting techniques. An index of statistical heterogeneity (IXH) is proposed for rain such that as its value approaches zero, the more likely the data are wide-sense stationary and the more acceptable is the use of the Wiener–Khintchine theorem. Numerical experiments as well as several examples in real rain demonstrate the potential of IXH to identify statistical homogeneity, heterogeneity, and statistical mixtures. In particular, the examples demonstrate that visual inspections of data alone are insufficient for determining whether they are wide-sense stationary. Furthermore, in this small data collection, statistical heterogeneity was associated with convective rain, while statistical homogeneity appeared in more stratiform or mixed rain events. These tentative associations, however, need further substantiation.

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Ma, Barry B., and Jeffrey A. Nystuen. "Passive Acoustic Detection and Measurement of Rainfall at Sea." Journal of Atmospheric and Oceanic Technology 22, no. 8 (August 1, 2005): 1225–48. http://dx.doi.org/10.1175/jtech1773.1.

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Abstract Rainfall over the ocean is one of the most important climatic parameters for both oceanic and atmospheric science. Traditional accumulation-type rain gauges are difficult to operate at sea, and so an alternate technique using underwater sound has been developed. The technique of passive monitoring of the ocean rainfall using ambient sound depends on the accuracy of sound pressure level (SPL) detection. Consequently, absolute calibration of the hydrophone is desirable, but is difficult to achieve because typically the geometry of the laboratory calibration process does not fit the measurement geometry over the ocean. However, if one assumes that the sound signal that is generated by wind is universal then the wind signal can be used to provide an absolute calibration. Over 90 buoy months of ambient sound spectra have been collected on the Tropical Atmosphere Ocean (TAO) project array since 1998. By applying the Vagle et al. wind speed algorithm, the instrument noises and sensitivity bias for the absolute calibration of each acoustic rain gauge (ARG) are obtained. An acoustic discrimination process is developed to retrieve the pure geophysical signals. A new single-frequency rainfall-rate algorithm is proposed after comparing the ARG data with R.M. Young self-siphoning rain gauge data, collocated on the same moorings. The acoustic discrimination process and the rainfall algorithm are further tested at two other locations and are compared with R.M. Young rain gauges and the Tropical Rain Measuring Mission (TRMM) product 3B42. The acoustic rainfall accumulations show the comparable results in both long (year) and short (hours) time scales.

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Yuan, Feng, Yee Lee, Yu Meng, and Jin Ong. "Characterization of S-Band Dual-Polarized Radar Data for the Convective Rain Melting Layer Detection in A Tropical Region." Remote Sensing 10, no. 11 (November 5, 2018): 1740. http://dx.doi.org/10.3390/rs10111740.

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In the tropical region, convective rain is a dominant rain event. However, very little information is known about the convective rain melting layer. In this paper, S-band dual-polarized radar data is studied in order to identify both the stratiform and convective rain melting layers in the tropical region, with a focus on the convective events. By studying and analyzing the above-mentioned two types of rain events, amongst three radar measurements of reflectivity ( Z ), differential reflectivity ( Z DR ), and cross correlation coefficient ( ρ HV ), the latter one is the best indicator for convective rain melting layer detection. From two years (2014 and 2015) of radar and radiosonde observations, 13 convective rain melting layers are identified with available 0 °C isothermal heights which are derived from radiosonde vertical profiles. By comparing the melting layer top heights with the corresponding 0 °C isothermal heights, it is found that for convective rain events, the threshold to detect melting layer should be modified to ρ HV = 0.95 for the tropical region. The melting layer top and bottom heights are then estimated using the proposed threshold, and it is observed from this study that the thickness of convective rain melting layer is around 2 times that of stratiform rain melting layer which is detected by using the conventional ρ HV = 0.97 .

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VETROV, A. L., and S. V. KOSTAREV. "APPLICABILITY OF MULTIMODEL ENSEMBLE PREDICTION OF HEAVY PRECIPITATION FOR THE PERM REGION: A CASE STUDY FOR THE SUMMER OF 2019." Meteorologiya i Gidrologiya, no. 7 (2021): 35–49. http://dx.doi.org/10.52002/0130-2906-2021-7-35-49.

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The multimodel ensemble technique for predicting heavy rain ( 15 mm/12 hours) in the Perm region with a lead time of 15 and 27 hours is analyzed. The quality of precipitation forecasts from GFS, GEM, ICON global hydrodynamic models and from the WRF-ARW model with 3 and 9-km grid spacing is assessed. For the summer of 2019, the GEM was found to provide the highest quality of heavy rain forecasts. The averaging over the ensemble of global models causes a sharp decrease in the number of false alarms and in the probability of detection. The scheme of global models combination consisting in the filtration of the GEM heavy rain forecasts and in the averaging of GFS, GEM, and ICON forecasts at the remaining points is developed. Application of the proposed scheme increases the reliability of short-range forecasts of heavy frontal rains in summer in the Perm region as compared to any separate model.

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Khatab, Esraa, Ahmed Onsy, Martin Varley, and Ahmed Abouelfarag. "A Lightweight Network for Real-Time Rain Streaks and Rain Accumulation Removal from Single Images Captured by AVs." Applied Sciences 13, no. 1 (December 24, 2022): 219. http://dx.doi.org/10.3390/app13010219.

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In autonomous driving, object detection is considered a base step to many subsequent processes. However, object detection is challenged by loss in visibility caused by rain. Rainfall occurs in two main forms, which are streaks and streaks accumulations. Each degradation type imposes different effect on the captured videos; therefore, they cannot be mitigated in the same way. We propose a lightweight network which mitigates both types of rain degradation in real-time, without negatively affecting the object-detection task. The proposed network consists of two different modules which are used progressively. The first one is a progressive ResNet for rain streaks removal, while the second one is a transmission-guided lightweight network for rain streak accumulation removal. The network has been tested on synthetic and real rainy datasets and has been compared with state-of-the-art (SOTA) networks. Additionally, time performance evaluation has been performed to ensure real-time performance. Finally, the effect of the developed deraining network has been tested on YOLO object-detection network. The proposed network exceeded SOTA by 1.12 dB in PSNR on the average result of multiple synthetic datasets with 2.29× speedup. Finally, it can be observed that the inclusion of different lightweight stages works favorably for real-time applications and could be updated to mitigate different degradation factors such as snow and sun blare.

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Hamzeh, Yazan, and Samir A. Rawashdeh. "A Review of Detection and Removal of Raindrops in Automotive Vision Systems." Journal of Imaging 7, no. 3 (March 10, 2021): 52. http://dx.doi.org/10.3390/jimaging7030052.

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Research on the effect of adverse weather conditions on the performance of vision-based algorithms for automotive tasks has had significant interest. It is generally accepted that adverse weather conditions reduce the quality of captured images and have a detrimental effect on the performance of algorithms that rely on these images. Rain is a common and significant source of image quality degradation. Adherent rain on a vehicle’s windshield in the camera’s field of view causes distortion that affects a wide range of essential automotive perception tasks, such as object recognition, traffic sign recognition, localization, mapping, and other advanced driver assist systems (ADAS) and self-driving features. As rain is a common occurrence and as these systems are safety-critical, algorithm reliability in the presence of rain and potential countermeasures must be well understood. This survey paper describes the main techniques for detecting and removing adherent raindrops from images that accumulate on the protective cover of cameras.

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Hakim, Arif Luqman, and Ristiana Dewi. "Automatic Rain Detection System Based on Digital Images of CCTV Cameras Using Convolutional Neural Network Method." IOP Conference Series: Earth and Environmental Science 893, no. 1 (November 1, 2021): 012048. http://dx.doi.org/10.1088/1755-1315/893/1/012048.

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Abstract The Meteorology, Climatology and Geophysics Agency (BMKG) has a duty to provide weather information including rainfall. BMKG has several types of rainfall gauges, but these are not evenly distributed across regions. The solution to increase the density of rainfall observations is to use existing sources to obtain weather information. This research uses Closed Circuit Television (CCTV) that is spread across the Jakarta area to produce information on rainy conditions. The method used is the Convolutional Neural Network (CNN). The image from CCTV will be used for the training and testing process, so as to get the best accuracy model. The results of this model will be used for rain detection on CCTV digital images. The rain detection process is carried out automatically and in real time. The results of the rain detection process will be displayed on the map according to the location where the CCTV was installed. This research has succeeded in making a CNN model for rain detection with a training accuracy of 98.8% and a testing accuracy of 96.4%, as well as evaluating the BMKG observation data, so it has an evaluation accuracy of 96.7%.

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Nan, Linjiang, Mingxiang Yang, Hao Wang, Zhenglin Xiang, and Shaokui Hao. "Comprehensive Evaluation of Global Precipitation Measurement Mission (GPM) IMERG Precipitation Products over Mainland China." Water 13, no. 23 (December 1, 2021): 3381. http://dx.doi.org/10.3390/w13233381.

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Due to the difficulty involved in obtaining and processing a large amount of data, the spatial distribution of the quality and error structure of satellite precipitation products and the climatic dependence of the error sources have not been studied sufficiently. Eight statistical and detection indicators were used to compare and evaluate the accuracy of the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement Mission (GPM IMERG) precipitation products in China, including IMERG Early, Late, and Final Run. (1) Based on the correlation coefficient between GPM IMERG precipitation products and measured precipitation, the precipitation detection ability is good in eastern China, whereas the root-mean-square error increases from northwest to southeast. (2) Compared with the Early and Late Run, the accuracy of the detection of a light rain of the IMERG Final Run is higher, but the precipitation is overestimated. With the increase in the precipitation intensity, the detection ability weakens, and the precipitation is underestimated. (3) The Final Run has a higher estimation accuracy regarding light rain in western high-altitude areas, whereas the accuracy of the detection of moderate rain, heavy rain, and rainstorms is higher in eastern coastal low-altitude areas. This phenomenon is related to the performance and detection principles of satellites. The altitude and magnitude of the precipitation affect the detection accuracy of the satellite. This study provides guidance for the application of GPM IMERG precipitation products in hydrological research and water resource management in China.

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Kolios, Stavros, Nikos Hatzianastassiou, Christos J. Lolis, and Aristides Bartzokas. "Accuracy Assessment of a Satellite-Based Rain Estimation Algorithm Using a Network of Meteorological Stations over Epirus Region, Greece." Atmosphere 13, no. 8 (August 12, 2022): 1286. http://dx.doi.org/10.3390/atmos13081286.

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The study concerns the quantitative evaluation of a satellite-based rain rate (RR) estimation algorithm using measurements from a network of ground-based meteorological stations across the Epirus Region, Greece, an area that receives among the maximum precipitation amounts over the country. The utilized version of the rain estimation algorithm uses the Meteosat-11 Brightness Temperature in five spectral regions ranging from 6.0 to 12.0 μm (channels 5–7, 9 and 10) to estimate the rain intensity on a pixel basis, after discriminating the rain/non-rain pixels with a simple thresholding method. The rain recordings of the meteorological stations’ network were spatiotemporally correlated with the satellite-based rain estimations, leading to a dataset of 2586 pairs of matched values. A statistical analysis of these pairs of values was conducted, revealing a Mean Error (ME) of −0.13 mm/h and a correlation coefficient (CC) of 0.52. The optimal computed Probability of False Detection (POFD), Probability of Detection (POD), the False Alarm Ratio (FAR) and the bias score (BIAS) are equal to 0.32, 0.88, 0.12 and 0.94, respectively. The study of the extreme values of the RR (the highest 10%) also shows satisfactory results (i.e., ME of 1.92 mm/h and CC of 0.75). The evaluation statistics are promising for operationally using this algorithm for rain estimation on a real-time basis.

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Fu, Huiyuan, Yu Zhang, and Huadong Ma. "See clearly on rainy days: Hybrid multiscale loss guided multi-feature fusion network for single image rain removal." Computational Visual Media 7, no. 4 (March 23, 2021): 467–82. http://dx.doi.org/10.1007/s41095-021-0210-3.

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AbstractThe quality of photos is highly susceptible to severe weather such as heavy rain; it can also degrade the performance of various visual tasks like object detection. Rain removal is a challenging problem because rain streaks have different appearances even in one image. Regions where rain accumulates appear foggy or misty, while rain streaks can be clearly seen in areas where rain is less heavy. We propose removing various rain effects in pictures using a hybrid multiscale loss guided multiple feature fusion de-raining network (MSGMFFNet). Specially, to deal with rain streaks, our method generates a rain streak attention map, while preprocessing uses gamma correction and contrast enhancement to enhanced images to address the problem of rain accumulation. Using these tools, the model can restore a result with abundant details. Furthermore, a hybrid multiscale loss combining L1 loss and edge loss is used to guide the training process to pay attention to edge and content information. Comprehensive experiments conducted on both synthetic and real-world datasets demonstrate the effectiveness of our method.

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Doblas, J., A. Carneiro, Y. Shimabukuro, S. Sant’Anna, and L. Aragão. "ASSESSMENT OF RAINFALL INFLUENCE ON SENTINEL-1 TIME SERIES ON AMAZONIAN TROPICAL FORESTS AIMING DEFORESTATION DETECTION IMPROVEMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W12-2020 (November 6, 2020): 493–98. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w12-2020-493-2020.

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Abstract. This work aims to determinate the relationship between C-band SAR backscattering measurements over Amazonian tropical forests and hourly precipitation rates, and to study the feasibility of a SAR-anomaly masking method based on orbital rain measurements. To do so, a comprehensive dataset of ESA’s Sentinel-1 backscattering data and the concomitant GPM-IMERG precipitation data was collected and analysed. Backscattering anomalies were characterized in a statistically meaningful way. GAM models were then adjusted to the backscatter-rain data pairs. The computed models show a positive correlation between non-anomalous backscattering values and accumulated rain, of approximately 0,2 dB/mm·h−1 and 0,4 dB/mm·h−1 for VV and VH polarizations. Negative anomalies, which can easily mislead deforestation algorithms, have a strong negative correlation with rain rate observed at the time of the SAR acquisition. This is especially true for VV measurements. The subsequent anomaly masking procedure, based on computed accumulated and hourly rain thresholding, yielded unsatisfactory results. These poor results are probably due to the coarse resolution of the 0.1° GPM-IMERG data, which is insufficient to track anomaly-generating atmospheric events such as storm rain cells. Rain-related changes in SAR backscattering can compromise deforestation detection algorithms, and further research and sensor developing is needed to increase spatial resolution of precipitation measures, to reach an optimal backscattering anomaly screening.

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Padilla, H. G., R. Belmont, M. B. Torres, and A. P. Báez. "Hurricanes Pauline and Nora rainwater chemical composition." Canadian Journal of Earth Sciences 37, no. 4 (April 3, 2000): 569–78. http://dx.doi.org/10.1139/e99-114.

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Rainwater from hurricanes Pauline and Nora was sampled for chemical analysis at the Pacific Coast of Mexico. Rainwater sampling under extreme conditions presents a unique opportunity to study oceanic rain chemical composition. An excess sulphate ((SO24)xs) as low as 0% was measured near the centre of hurricane Pauline in Huatulco Bays. Another remarkable result was obtained in another rainwater sample of Pauline rain bands with a total SO24 concentration below the detection limit. Also, Na+ and Cl concentrations were extremely low (0.02 and 0.025 mg L1, respectively). The explanation of these results is also presented. Some light rains were also collected in Tapachula while Pauline was severely affecting Huatulco Bays. Only rainwater samples from hurricane Nora outer rain bands were sampled in Manzanillo, where it was interesting to evaluate the profound impact that a single power plant had on the chemical composition of hurricane Nora rains. Excess sulphate did not correlate with Mg2+ in Huatulco Bays and Manzanillo. However, it correlated with Mg2+ in Tapachula, even though this town is located 27 km from the coast. A further oxidation of organic sulphur containing compounds combined with a simultaneous transport of sea spray inland is proposed to explain this correlation.

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Nakakita, Eiichi, Hiroto Sato, Ryuta Nishiwaki, Hiroyuki Yamabe, and Kosei Yamaguchi. "Early Detection of Baby-Rain-Cell Aloft in a Severe Storm and Risk Projection for Urban Flash Flood." Advances in Meteorology 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/5962356.

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In July 2008, five people were killed by a tragic flash flood caused by a local torrential heavy rainfall in a short time in Toga River. From this tragic accident, we realized that a system which can detect hazardous rain-cells in the earlier stage is strongly needed and would provide an additional 5 to 10 min for evacuation. By analyzing this event, we verified that a first radar echo aloft, by volume scan observation, is a practical and important sign for early warning of flash flood, and we named a first echo as a “baby-rain-cell” of Guerrilla-heavy rainfall. Also, we found a vertical vorticity criterion for identifying hazardous rain-cells and developed a heavy rainfall prediction system that has the important feature of not missing any hazardous rain-cell. Being able to detect heavy rainfall by 23.6 min on average before it reaches the ground, this system is implemented in XRAIN in the Kinki area. Additionally, to resolve the relationship between baby-rain-cell growth and vorticity behavior, we carried out an analysis of vorticity inside baby-rain-cells and verified that a pair of positive and negative vertical vortex tubes as well as an updraft between them existed in a rain-cell in the early stage.

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Neate-Clegg, Montague H. C., Emily C. Morshuis, and Cristina Banks-Leite. "Edge effects in the avifaunal community of riparian rain-forest tracts in Tropical North Queensland." Journal of Tropical Ecology 32, no. 4 (June 3, 2016): 280–89. http://dx.doi.org/10.1017/s0266467416000249.

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AbstractMost evidence suggests anthropogenic edges negatively affect rain-forest bird communities but little has been done to test this in Australasia. In this study, avifaunal detection frequency, species richness and community composition were compared between the edge and interior and between flat and more complex-shaped edges of riparian rain-forest tracts in Tropical North Queensland. The detection frequency and richness of guilds based on diet, foraging strata and habitat specialism were also compared. This study detected 15.1% more birds at the rain-forest edge compared with the interior but no difference in species richness. Edge shape had no effect on detection frequency or richness. Many guilds (subcanopy, closed forest, frugivorous and insectivorous species) experienced increased detection frequency at the edge relative to the interior, but for some guilds this response was reduced (habitat generalists) or reversed (understorey and mixed-flock species) along complex edges. Overall community composition was affected by edge distance but not by edge shape. Edge habitat was shorter and had more open canopy than the interior, supporting habitat-based explanations for the observed avifaunal edge effects. These results suggest generally positive edge effects in Australian rain-forest bird communities, possibly reflecting local resource distributions or a disturbance-tolerant species pool.

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Birman, Camille, Fatima Karbou, and Jean-François Mahfouf. "Daily Rainfall Detection and Estimation over Land Using Microwave Surface Emissivities." Journal of Applied Meteorology and Climatology 54, no. 4 (April 2015): 880–95. http://dx.doi.org/10.1175/jamc-d-14-0192.1.

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AbstractSurface emissivities computed at 89 GHz from AMSU-A, AMSU-B, and SSMI/S instruments are used to detect rain events and to estimate a daily precipitation rate over land surfaces. This new retrieval algorithm, called the emissivity rainfall retrieval (EMIRR) algorithm, is evaluated over France and compared with several other precipitation products. The precipitation detection is performed using temporal changes in daily surface emissivities. A statistical fit, derived from a rainfall analysis product using rain gauge and radar data, is devised to estimate a daily precipitation rate from surface emissivities. Rain retrievals are evaluated over a 1-yr period in 2010 against other precipitation products, including rain gauge measurements. The EMIRR algorithm allows a reasonable detection of rainy events from daily surface emissivities. The number of rainy days and the daily rainfall rates compare well to estimates from other precipitation products. However, the algorithm tends to overestimate low precipitation amounts and to underestimate higher ones, with reduced performances in the presence of snow. Despite such limitations, this new method is very promising and provides a demonstration of the potential use of the 89-GHz surface emissivities to infer relevant information (occurrence and amounts) related to daily precipitation over land surfaces.

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Gianoglio, Christian, Ayham Alyosef, Matteo Colli, Sara Zani, and Daniele D. Caviglia. "Rain Discrimination with Machine Learning Classifiers for Opportunistic Rain Detection System Using Satellite Micro-Wave Links." Sensors 23, no. 3 (January 20, 2023): 1202. http://dx.doi.org/10.3390/s23031202.

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In the climate change scenario the world is facing, extreme weather events can lead to increasingly serious disasters. To improve managing the consequent risks, there is a pressing need to have real-time systems that provide accurate monitoring and possibly forecasting which could help to warn people in the affected areas ahead of time and save them from hazards. The oblique earth-space links (OELs) have been used recently as a method for real-time rainfall detection. This technique poses two main issues related to its indirect nature. The first one is the classification of rainy and non-rainy periods. The second one is the determination of the attenuation baseline, which is an essential reference for estimating rainfall intensity along the link. This work focuses mainly on the first issue. Data referring to eighteen rain events were used and have been collected by analyzing a satellite-to-earth link quality and employing a tipping bucket rain gauge (TBRG) properly positioned, used as reference. It reports a comparison among the results obtained by applying four different machine learning (ML) classifiers, namely the support vector machine (SVM), neural network (NN), random forest (RF), and decision tree (DT). Various data arrangements were explored, using a preprocessed version of the TBRG data, and extracting two different sets of characteristics from the microwave link data, containing 6 or 12 different features, respectively. The achieved results demonstrate that the NN classifier has outperformed the other classifiers.

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Yang Guoliang, 杨国亮, 喻丁玲 Yu Dingling, 王杨 Wang Yang, and 王艳芳 Wang Yanfang. "Moving Object Detection Under Rain and Snow Weather Conditions." Laser & Optoelectronics Progress 57, no. 24 (2020): 241507. http://dx.doi.org/10.3788/lop57.241507.

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Agha, Yusra, Ashwak Hazem Najim, Reem Ayad Talaat, and Shababa Abdulatife Bahjat. "Detection of Atypical Motile Staphylococcus aureus from Rain Floods." Open Access Macedonian Journal of Medical Sciences 10, A (July 22, 2022): 1373–77. http://dx.doi.org/10.3889/oamjms.2022.8686.

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Abstract: Heavy rain floods is one of the primary risk factors for human health, and it can significantly regulate microbial communities and enhance the transfer of infections within the affected areas. Recently, the flood crisis is becoming one of the severe natural events in Mosul / Iraq. It may continue for months during which samples of accumulated rainwater were collected. Twelve Staphylococcus aureus were isolated by using two selective media: Mannitol Salt agar and Vogel-Johnson media in addition to Blood agar. An unusual colony spreading which resembles. "Bacillus colonies in twelve Staphylococcus aureus isolates was observed on Mannitol Salt agar and semisolid nutrient agar. Actively motile cocci in single and cluster arrangements that is not characteristic of brownian movement was shown in wet mount microscopic observation Furthermore, biosurfactant detection by oil spreading method ( oil displacement activity) showed that all isolates demonstrated various degrees of surfactant production which has beeen reported. to be responsible for stimulating "colony spreading" phenomenon in S. aureux. Motility can play a crucial role for survival bacterial species by which they get nutrients, avoid toxins and predators, and genetic information exchange by mating. The present study highlights for the first time. Mosul city a motile opportunistic aureus obtained from harvested rainwater samples during high-rainfall periods. Utilization of untreated harvested rainwater could thus offer a significant health threat to consumers, especially children. and immunocompromised individuals.

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XUE, Xinwei, Xin JIN, Chenyuan ZHANG, and Satoshi GOTO. "Joint Feature Based Rain Detection and Removal from Videos." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E96.A, no. 6 (2013): 1195–203. http://dx.doi.org/10.1587/transfun.e96.a.1195.

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Chen, Tianyi, and Chengzhou Fu. "Single-image-based Rain Detection and Removal via CNN." Journal of Physics: Conference Series 1004 (April 2018): 012007. http://dx.doi.org/10.1088/1742-6596/1004/1/012007.

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Ozoner, Seyda Korkut, Elif Erhan, Faruk Yilmaz, Pinar Ergenekon, and Ismail Anil. "Electrochemical biosensor for detection of formaldehyde in rain water." Journal of Chemical Technology & Biotechnology 88, no. 4 (October 19, 2012): 727–32. http://dx.doi.org/10.1002/jctb.3896.

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Journal articles on the topic 'Rain detection'

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