Rain Detection Research Articles

Multifeatured Electronic Helmet to Enhance Road Safety and Rider’s Comfort

This paper presents a multi-featured electronic helmet designed to tackle critical road safety issues, including accidents, drunk driving, and over-speeding. The design incorporates a global system for mobile communications (GSM) and global positioning system (GPS) modules to accurately capture driver’s current location and send messages to predefined contacts. When encountering an accident, the helmet promptly notifies designated contacts and authorities, ensuring swift assistance. By integrating features for detecting over-speeding, accidents, and drunk driving, the helmet renders real-time alerts to both the driver’s family and traffic police, enhancing accountability and safety measures. Additionally, the helmet includes solar charging functionality for mobile devices receive 100% charged in 3.37 hours, thereby optimizing usability and emergency communication. A rain detection system protects mobile devices, while an internal warming mechanism caters to adverse weather conditions, and proffers enhanced comfort by maintaining internal form temperature between 26-27 ℃ and safety, especially in cold regions or for military personnel.

Private sensors and crowdsourced rainfall data: Accuracy and potential for modelling pluvial flooding in urban areas of Oslo, Norway

Cloudbursts and extreme rainstorms pose a growing threat to urban areas. Accurate rainfall data is essential for predicting inundations and urban flooding. Private weather stations are becoming increasingly common, and their spatial distribution roughly follows population density. This makes them a valuable source of crowdsourced data for high-resolution rainfall fields in urban areas. We evaluated the performance of private rain gauges in two recent pluvial flood events in Oslo. We also explored the potential use of private rain gauge data in inundation models. Our results indicate that private sensors have excellent rain detection capabilities, but they tend to underestimate the reference value on average by approximately 25%. However, bias-corrected crowdsourced rainfall data can produce significantly more accurate inundation maps than those generated from official rain gauges, if compared with maps resulting from bias-corrected weather radar. Overall, our study highlights the potential of utilizing crowdsourced rainfall data from private sensors for accurately representing pluvial flooding in urban areas. These findings have significant implications for improving flood prediction and mitigation strategies in vulnerable urban settings.

Performance assessment of satellite rainfall estimates in rain detection capabilities at different thresholds over Nigeria

ABSTRACT In Nigeria, where rainfall plays a pivotal role in agriculture and disaster management, assessing the accuracy of satellite rainfall estimates at various thresholds is imperative. This study assesses the areal averages of nine satellite precipitation estimates (SPEs) against 48 ground-based raingauge observations in Nigeria. Employing categorical statistical metrics and compromise programming, the research assesses the performance of SPEs at seven rainfall thresholds. Results reveal that 82–94% of rainfall estimates align with ground truth, while 5–14% of rainy days are incorrectly detected by raingauges. Notably, SPEs tend to overestimate low rain between 1 mm and 5 mm d−1 and underestimate low heavy rain rates (10 mm ≤ rain < 20 mm), impacting flood monitoring and disaster preparedness. Global Precipitation Climatology Centre (CPCC), Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT) African Rainfall Climatology And Time series (TARCAT), and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) emerge as more reliable SPEs, particularly in highland regions. This research underscores the importance of choosing appropriate satellite precipitation estimates for enhanced disaster preparedness and resource management across different Nigerian regions.

A robust antagonistically assembled triboelectric nanogenerator based on dead‐end closed pore through a one‐step improved phase separation

AbstractThe ongoing global energy crisis presents a significant challenge all over the world. In this study, triboelectric nanogenerator (TENG) made of recyclable thermoplastic polycarbonate has been proposed as a promising environmentally friendly solution due to its potential capability to convert low‐frequency mechanical energy sources such as human motion and ocean waves into electricity with potential applications including powering wearable electronic devices and self‐powered sensors. We introduce an antagonistically structured TENG (A‐TENG) composed of honeycomb porous polycarbonate (hc‐PC) and convex‐patterned dimethylsiloxane, employing a novel one‐stage improved phase separation method. This A‐TENG overcomes the demerits of others thanks to its high surface contact area, simplified fabrication process, enhanced energy conversion efficiency, and cost‐effectiveness, requiring minimal investment. More importantly, this work utilized hc‐PC as a framework for slippery liquid‐infused porous surface (SLIPS) based TENG owing to the special honeycomb porous structure to impregnate silicon oil within pore arrays and mechanically support the whole system. The practical application of SLIPS‐TENG was demonstrated while deployed on the house rooftop to harvest raindrop energy and act as a rain detection sensor.

PENGEMBANGAN PERANGKAT MULTI SENSOR UNTUK PRECISION FARMING MONITORING MEDIA TANAM

The development of a multi-sensor device and the implementation of a data transmission system to a database for predicting plant suitability parameters are the focus of this paper. This technology enables accurate and continuous data collection for agricultural condition monitoring. The study utilizes a DHT22 sensor for air temperature and humidity, a pH sensor for soil pH, a soil moisture sensor for soil moisture, a rain gauge sensor for rainfall, and a raindrop sensor for rain detection. The data from these sensors are managed by an ESP32 microcontroller and transmitted to a Firebase database. The multi-sensor device was functionally tested and compared with valid reference sensor readings. The results show that all the multi-sensor devices and the recording system in the Firebase database functioned well. The average measurement error for the DHT22 temperature sensor was 1.15°C, with 2.9% for humidity, 0.72% for the pH sensor, and the soil moisture sensor showed 100% accuracy. This system can be applied as a parameter for predicting the suitability of plant types with soil and environmental conditions.

Validating IMERG data for diurnal rainfall analysis across the Indonesian maritime continent using gauge observations

The availability of surface rainfall data with high spatial-temporal resolution is needed to understand the diurnal rainfall characteristics in the Indonesian Maritime Continent (IMC) to improve the accuracy of weather and climate models in this region. This study validates the accuracy of the Final Run product of IMERG data version 06B (V06B) and version 07A (V07A), which have a resolution of 0.1°–30 min for diurnal rainfall analysis over IMC. Validation was conducted for precipitation amount (PA), precipitation frequency (PF), and precipitation intensity (PI), by recording 302 automatic rain gauges (RG) every 10 min from January 2014 to September 2021. IMERG V06B and V07A perform well in observing diurnal PA and PF but struggle in observing diurnal PI compared to RG observations. This has been determined by the correlation coefficient (CC) values of IMERG V06B (V07) data, which are 0.76 (0.72) for PA, 0.77 (0.76) for PF, and 0.21 (0.13) for PI. The IMERG data align to an extent with RG observations for the PA value, having a small relative bias (RB). The results also display a systematic PF and PI values error. The high false alarm ratio (FAR) of IMERG data suggests rain detection errors, leading to overestimated PF values. Additionally, the underestimation of PI values is due to the limitation of IMERG data in observing extreme and small-scale rain events. About 86.10% (78.14%) and 81.45% (81.78) of IMERG V06B (IMERG V07A) data show a peak time difference of <3h for PA and PF when compared with RG observations. Overall, IMERG V06B performs better than V07, possibly due to inaccurate orbits of GPROF data, removal of SAPHIR satellite observations, and inter-calibration issues with CORRA and GPCP data. Continuous monitoring and data improvements are necessary to improve the accuracy and reliability of IMERG data in detecting diurnal rainfall patterns in the IMC region.

Bias Correction of IMERG Data in the Mountainous Areas of Sumatra Based on A Single Gauge Observation

The performance of surface precipitation data from satellite precipitation products (SPPs) in mountainous areas has greater error and bias than in plain areas. In this study, linear scaling (LS), local intensity (LOCI), power transformation (PT), and cumulative distribution function (CDF) methods are used to correct the bias of Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) data in the mountainous region of Sumatra based on long-term and high-resolution optical rain gauge (ORG) observations. The ORG is installed at Equatorial Atmospheric Observatory (EAO) in Kototabang, West Sumatra, Indonesia (100.32 °E, 0.20 °S, 865 m above sea level (ASL) with an observation period from 2002 to 2016. The impact of the bias correction method is tested based on accuracy and capability detection tests. The bias correction method is more effective at the daily resolution than the hourly resolution of the IMERG data in the mountainous region of Sumatra. The LS method exhibited the best improvement in accuracy with reduced root-mean-square error (RMSE) and relative bias (RB), although there was no significant increase in coefficient correlation (CC) values. However, the accuracy improvement was not observed in the bias correction for hourly data. The lack of improvement in the accuracy of the hourly IMERG data is due to the high local variability of rainfall in the mountainous area of Sumatra. The high data variability causes large differences in the mean and variance of the IMERG calibration and evaluation data periods. On the other hand, the LOCI, PT, and CDF methods were successfully improved the rain detection capability of IMERG, as indicated by the better critical succession index (CSI) values compared to the original hourly and daily IMERG data. It increased the CSI value by reducing false alarms for rain with intensity below 2 mm/h. Furthermore, the CDF method can improve the analysis of extreme rainfall in the mountainous region of Sumatra by improving the estimation of the extreme rainfall index. Therefore, these methods can be applied to improve the accuracy and detectability of IMERG data in the mountainous region of Sumatra. However, the scale factor and transfer function constructed in this study need to be further evaluated on other rain gauge observation data in Sumatra’s mountainous region to improve performance. HIGHLIGHTS The LS method shows the best improvement in the accuracy of IMERG data in the mountainous area of Sumatera compared to the LOCI, PT, and CDF methods, as indicated by the largest decrease in RMSE and RB values CSI values prove that LOCI, PT, and CDF methods successfully improve the detection capability of IMERG hourly and daily data in the mountainous region of Sumatra The CDF method shows the best quality in improving extreme rainfall observations in the mountainous region of Sumatra GRAPHICAL ABSTRACT

Do satellite-based products suffice for rainfall observations over data-sparse complex terrains? Evidence from the North-Western Himalayas

Remotely sensed observations are crucial for conducting meteorological studies, particularly over complex mountainous terrains such as, the North-Western Himalayas (NWH), which are characterized by a very sparse and uneven distribution of ground observatories. These complex terrains are subject to huge inter-basin climatic and topographic heterogeneity, which often induces large inconsistencies in the form of varying rainfall estimations and observations among the satellite-based gridded precipitation products (GPPs). This underpins the need for a detailed sub-regional validation of GPPs and attribution of the observed discrepancies over such complex terrains. This study provides a comprehensive validation analysis of four major high-resolution GPPs (TMPA-3B42V7, CHIRPS-V2.0, CMORPH-CDR, and PERSIANN-CDR) against the in-situ gridded observations provided by IMD, across the six sub-basins of NWH. Utilizing various descriptive, statistical, and categorical metrics, we evaluated the daily, seasonal, and annual rainfall climatology for each dataset and also extracted their longitudinal and latitudinal error profiles and detection capabilities as a function of elevation and local relief. High inconsistencies were observed among the datasets, with each dataset performing differently over different sub-basins. All the datasets underperformed IMD over higher elevations, while TMPA-3B42V7 performed better even over the Upper Indus sub-basin. CHIRPS-V2.0 performed well over Jhelum, and Chenab sub-basins, whereas the CDR version of CMORPH, performed well over the flatter terrains of Ravi, Beas, and Satluj sub-basins, particularly during the summer monsoons. The IR-based PERSIANN-CDR, on the other hand, failed to capture the daily rainfall estimates and underperformed IMD throughout NWH. The findings further highlight low detection capability of datasets at higher rainfall thresholds, indicating underestimation of extreme rainfall. With respect to the relationship between GPPs performance and topographic components, the results showed that the occurrence frequency of rain detection in GPPs decreases, while the occurrence frequency of over-detection increases with increase in elevation and local relief. Although, the performance of GPPs co-varies with both topographic components, local relief governed by the multi-step topographic gradients comprising valleys, orographic fronts, and orographic interiors was observed to have a more profound effect on error profiles. Lastly, the study identifies and discusses the probable regional (e.g., rainfall structure, orographic influence, biophysical, data scarcity, spatial resolution), and product-specific (e.g., source of generation, remote sensing instrumentation, satellite algorithms, data assimilation techniques) factors attributing to the inconsistencies observed among the datasets. The findings from the present research offer insights to the algorithm developers for improving product accuracy and hydro-meteorologist for utilizing appropriate GPP over respective sub-basin.

ARMing the Edge: Designing Edge Computing–Capable Machine Learning Algorithms to Target ARM Doppler Lidar Processing

Abstract There is a need for long-term observations of cloud and precipitation fall speeds in validating and improving rainfall forecasts from climate models. To this end, the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) site at Lamont, Oklahoma, hosts five ARM Doppler lidars that can measure cloud and aerosol properties. In particular, the ARM Doppler lidars record Doppler spectra that contain information about the fall speeds of cloud and precipitation particles. However, due to bandwidth and storage constraints, the Doppler spectra are not routinely stored. This calls for the automation of cloud and rain detection in ARM Doppler lidar data so that the spectral data in clouds can be selectively saved and further analyzed. During the ARMing the Edge field experiment, a Waggle node capable of performing machine learning applications in situ was deployed at the ARM SGP site for this purpose. In this paper, we develop and test four algorithms for the Waggle node to automatically classify ARM Doppler lidar data. We demonstrate that supervised learning using a ResNet50-based classifier will classify 97.6% of the clear-air images and 94.7% of cloudy images correctly, outperforming traditional peak detection methods. We also show that a convolutional autoencoder paired with k-means clustering identifies 10 clusters in the ARM Doppler lidar data. Three clusters correspond to mostly clear conditions with scattered high clouds, and seven others correspond to cloudy conditions with varying cloud-base heights.

COMPARISON ANALYSIS OF HIMAWARI 8, CHIRPS AND GSMaP DATA TO DETECT RAIN IN INDONESIA

The need for rainfall data, especially for areas where the number of observation stations is not very close, is very important for local climate analysis activities. This data need can be met, one of which is from remote sensing data, such as Himawari 8. The Himawari 8 rainfall data are data derived using the INSAT Multi-Spectral Rainfall Algorithm (IMSRA) method based on the infrared channel on the Himawari 8 satellite. However, research on the IMSRA method was carried out using a case study of a region in India. Thus, validation is needed to determine the ability of Himawari 8 rainfall data to detect rain in Indonesia. The data used for comparison are CHIRPS and GSMaP rainfall data. In addition, BMKG rainfall data are used as benchmark data. The technique used for validation is using the Contingency Table method. The results of the validation show that the rain detection ability for Himawari 8 rainfall data is relatively good, namely 66% for 2019 and 85% for 2020. In addition, the ability to detect rain using Himawari 8 rainfall data is quite good compared to the ability to detect rain using CHIRPS rainfall data and GSMaP rainfall data.

Detection of Rain in Tropical Cyclones by Underwater Ambient Sound

Abstract Rain in tropical cyclones is studied using eight time series of underwater ambient sound at 40–50 kHz with wind speeds up to 45 m s−1 beneath three tropical cyclones. At tropical cyclone wind speeds, rain- and wind-generated sound levels are comparable, and therefore rain cannot be detected by sound level alone. A rain detection algorithm that is based on the variations of 5–30-kHz sound levels with periods longer than 20 s and shorter than 30 min is proposed. Faster fluctuations (&lt;20 s) are primarily due to wave breaking, and slower ones (&gt;30 min) are due to overall wind variations. Higher-frequency sound (&gt;30 kHz) is strongly attenuated by bubble clouds. This approach is supported by observations that, for wind speeds &lt; 40 m s−1, the variation in sound level is much larger than that expected from observed wind variations and is roughly comparable to that expected from rain variations. The hydrophone results are consistent with rain estimates by the Tropical Rainfall Measuring Mission (TRMM) satellite and with Stepped-Frequency Microwave Radiometer (SFMR) and radar estimates by surveillance flights. The observations indicate that the rain-generated sound fluctuations have broadband acoustic spectra centered around 10 kHz. Acoustically detected rain events usually last for a few minutes. The data used in this study are insufficient to produce useful estimation of rain rate from ambient sound because of limited quantity and accuracy of the validation data. The frequency dependence of sound variations suggests that quantitative rainfall algorithms from ambient sound may be developed using multiple sound frequencies. Significance Statement Rain is an indispensable process in forecasting the intensity and path of tropical cyclones. However, its role in the air–sea interaction is still poorly understood, and its parameterization in numerical models is still in development. In this work, we analyzed sound measurements made by hydrophones on board Lagrangian floats beneath tropical cyclones. We find that wind, rain, and breaking waves each have distinctive signatures in underwater ambient sound. We suggest that the air–sea dynamic processes in tropical cyclones can be explored by listening to ambient sound using hydrophones beneath the sea surface.

Heavy rain detection through swarm IoT sensors in small and medium scale catchments with AI-enhanced cell movement prognosis as an addition to precipitation radar systems

Heavy rain detection through swarm IoT sensors in small and medium scale catchments with AI-enhanced cell movement prognosis as an addition to precipitation radar systems

Joint Collaboration on Comparing NOAA’s Ground-Based Weather Radar and NASA–JAXA’s Spaceborne Radar

Abstract The National Aeronautics and Space Administration (NASA) and National Oceanic and Atmospheric Administration (NOAA) have a long and successful history of weather radar research. The NOAA ground-based radars—WSR-88D network—provide nationwide precipitation observations and estimates with advanced polarimetric capability. As a counterpart, the NASA–JAXA spaceborne radar—the Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM DPR)—has global coverage and higher vertical resolution than ground-based radars. While significant advances from both NOAA’s WSR-88D network and NASA–JAXA’s spaceborne radar DPR have been made, no systematic comparisons between the WSR-88D network and the DPR have been done. This study for the first time generates nationwide comprehensive comparisons at 136 WSR-88D radar sites from 2014 to 2020. Systematic differences in reflectivity are found, with ground radar reflectivity on average 2.4 dB smaller than that of the DPR (DPR version 6). This research found the discrepancies between WSR-88D and DPR arise from different calibration standards, signal attenuation correction, and differences in the ground and spaceborne scattering volumes. The recently updated DPR version 7 product improves rain detection and attenuation corrections, effectively reducing the overall average WSR-88D and DPR reflectivity differences to 1.0 dB. The goal of this study is to examine the systematic differences of radar reflectivity between the NOAA WSR-88D network and the NASA–JAXA DPR and to draw attention to radar-application users in recognizing their differences. Further investigation into understanding and alleviating the systematic bias between the two platforms is needed.

Listening for rain: Principal component analysis and linear discriminant analysis for broadband acoustic rainfall detection.

Rain falling on the ocean creates acoustic signals. Ma and Nystuen [(2005). J. Atmos. Oceanic Technol. 22, 1225-1248] described an algorithm that compares three narrowband "discriminant" frequencies to detect rain. In 2022, Trucco, Bozzano, Fava, Pensieri, Verri, and Barla [(2022). IEEE J. Oceanic Eng. 47(1), 213-225] investigated rain detection algorithms that use broadband spectral data averaged over 1 h. This paper implements a rainfall detector that uses broadband acoustic data at 3-min time resolution. Principal Component Analysis (PCA) reduces the dimensionality of the broadband data. Rainfall is then detected via a Linear Discriminant Analysis (LDA) on the data's principal component projections. This PCA/LDA algorithm was trained and tested on 5 months of data recorded by hydrophones in a shallow noisy cove, where it was not feasible to average spectral data over 1 h. The PCA/LDA algorithm successfully detected 78 ± 5% of all rain events over 1 mm/h, and 73 ± 5% of all rain events over 0.1 mm/h, for a false alarm rate of ≈ 1% in both cases. By contrast, the Ma and Nystuen algorithm detected 32 ± 5% of the rain events over 1.0 mm/h when run on the same data, for a comparable false alarm rate.

The classification of atmospheric hydrometeors and aerosols from the EarthCARE radar and lidar: the A-TC, C-TC and AC-TC products

Abstract. The EarthCARE mission aims to probe the Earth's atmosphere by measuring cloud and aerosol profiles using its active instruments, the Cloud Profiling Radar (CPR) and ATmospheric LIDar (ATLID). The correct identification of hydrometeors and aerosols from atmospheric profiles is an important step in retrieving the properties of clouds, aerosols and precipitation. Ambiguities in the nature of atmospheric targets can be removed using the synergy of collocated radar and lidar measurements, which is based on the complementary spectral response of radar and lidar relative to atmospheric targets present in the profiles. The instruments are sensitive to different parts of the particle size distribution and provide independent but overlapping information in optical and microwave wavelengths. ATLID is sensitive to aerosols and small cloud particles, and CPR is sensitive to large ice particles, snowflakes and raindrops. It is therefore possible to better classify atmospheric targets when collocated radar and lidar measurements exist compared to using a single instrument. The cloud phase, precipitation and aerosol type within the column sampled by the two instruments can then be identified. ATLID-CPR target classification (AC-TC) is the product created for this purpose by combining the ATLID target classification (A-TC) and CPR target classification (C-TC). AC-TC is crucial for the subsequent synergistic retrieval of cloud, aerosol and precipitation properties. AC-TC builds upon previous target classifications using CloudSat and CALIPSO synergy while providing richer target classification using the enhanced capabilities of EarthCARE's instruments, specifically CPR's Doppler velocity measurements to distinguish snow and rimed snow from ice clouds and ATLID's lidar ratio measurements to objectively discriminate between different aerosol species and optically thin ice clouds. In this paper, we first describe how the single-instrument A-TC and C-TC products are derived from ATLID and CPR measurements. Then the AC-TC product, which combines the A-TC and C-TC classifications using a synergistic decision matrix, is presented. Simulated EarthCARE observations based on combined cloud-resolving and aerosol model data are used to test the processors generating the target classifications. Finally, the target classifications are evaluated by quantifying the fractions of ice and snow, liquid clouds, rain, and aerosols in the atmosphere that can be successfully identified by each instrument and their synergy. We show that radar–lidar synergy helps better detect ice and snow, with ATLID detecting radiatively important optically thin cirrus and cloud tops, while CPR penetrates most deep and highly concentrated ice clouds. The detection of rain and drizzle is entirely due to C-TC, while that of liquid clouds and aerosols is due to A-TC. The evaluation also shows that simple assumptions can be made to compensate for when the instruments are obscured by extinction (ATLID) or surface clutter and multiple scattering (CPR); this allows for the recovery of the majority of liquid cloud not detected by the active instruments.

Bayesian Algorithm for Rain Detection in Ku-Band Scatterometer Data

Ku-band scatterometers are sensitive to rain effects due to their cm-scale radar wavelength. The NSCAT-4DS geophysical model function (GMF) corrects for sea surface temperature (SST), whereas it doesn’t consider rain. Rain causes biases in the retrieved wind fields and to prevent these, quality control (QC) flags play an important role in rain identification. Since horizontal polarization and vertical polarization radar beams have a particular sensitivity to rain clouds, a noticeable difference between the rain-dominated backscatter distribution and the wind-dominated backscatter distribution is observed. Employing a Bayesian approach and exploiting these particular wind and rain backscatter characteristics, the authors propose an algorithm to provide the posterior rain probability for each measurement in a Wind Vector Cell and test the method for the Haiyang-2C scatterometer. In a comprehensive comparison between posterior rain probability, KNMI QC flag and Joss flag, for posterior rain probabilities higher than 0.5, the rejection rate is approximately a quarter of that of the KNMI QC flag with better rain detection behavior. While the Joss flag, the difference between the retrieved wind speed and the two-dimensional variational ambiguity removal analysis wind speed, has the best performance in identifying rain in the sweet swath, it comes at the cost of a higher missing rate. The comparison with ASCAT winds also proves the method’s effectiveness. Posterior rain probability has the best rain identification ability in the nadir swath. A combination of different QC flags should be beneficial and applied in wind retrieval.

Rain detection and removal from slowly varying background videos

Rain in bad weather becomes very challenging issue for automated driver assistance systems, security applications and many other computer vision algorithms. Number of rain removal algorithms have been proposed in these years but none of them has been able to increase the accuracy to large extent. In this paper a novel algorithm has been proposed where the slowly varying back ground scenes are taken for analysis. One part of the algorithm is based on the detection of rain pixels and the other part is removing them such that the original image must not lose its originality. Initially one background is modelled by taking the intensities of neighbourhoods of pixels. Then rain is detected by comparing each pixel with the modelled back ground followed by updating the background. The detected rain pixels are repaired with the help of some temporal neighbours. The results are promising to prove the work.

IoT-based Sun and Rain Detection System

The field of IoT has made significant advancements by using software and sensors to collect and share data about device usage and the surrounding environment. This data analysis can be used to identify potential problems before they occur and provide solutions. This technology is applicable to various industries, including healthcare, automation, and wearable technology. Our research shows that there is a correlation between atmospheric pressure, humidity, and rain. To address the issue of people getting caught in unexpected rain, we have used the Bosch BMP280 environment monitor to predict rain and high temperatures. By measuring temperature, atmospheric pressure, and altitude and using an IoT interface with a Magnetic switch sensor, we record and transfer the data to Firebase. We notify the user with a beep if they need to carry an umbrella.

Development and Performance Evaluation of a Infrared Rain Sensor

We design a rain sensor to automatically control the car wiper action. In the circuit and optical path design, innovative design of four rain detection channel, that increase the rain sensing area. In the design of optical lens, the main compensating lens and airfoil compensating lens are designed to enhance the signal strength and improve the sensitivity of detection. In the software design, we use self-calibration technology, anti-interference technology, so that wiper control accurate and reliable. We also design a device to test the rain sensing area of the sensor, and evaluated the sensor performance by compared the rain sensing area. The results show that the sensing area of the designed rain sensor is far beyond the standard.

Rain Removal from Light Field Images with 4D Convolution and Multi-scale Gaussian Process.

Existing deraining methods mainly focus on a single input image. However, with just a single input image, it is extremely difficult to accurately detect and remove rain streaks, in order to restore a rain-free image. In contrast, a light field image (LFI) embeds abundant 3D structure and texture information of the target scene by recording the direction and position of each incident ray via a plenoptic camera, which has emerged as a popular device in the computer vision and graphics research communities. However, making full use of the abundant information available from LFIs, such as 2D array of sub-views and the disparity map of each sub-view, for effective rain removal is still a challenging problem. In this paper, we propose a novel network, 4D-MGP-SRRNet, for rain streak removal from LFIs. Our method takes as input all sub-views of a rainy LFI. In order to make full use of the LFI, we adopt 4D convolutional layers to build the proposed rain steak removal network to simultaneously process all sub-views of the LFI. In the proposed network, the rain detection model, MGPDNet, with a novel Multi-scale Self-guided Gaussian Process (MSGP) module is proposed to detect high-resolution rain streaks from all sub-views of the input LFI at multi-scales. Semi-supervised learning is introduced for MSGP to accurately detect rain streaks by training on both virtual-world rainy LFIs and real-world rainy LFIs at multi-scales via calculating pseudo ground truths for real-world rain streaks. We then feed all sub-views subtracting the predicted rain streaks into a 4D convolution-based Depth Estimation Residual Network (DERNet) to estimate the depth maps, which are later converted into fog maps. Finally, all sub-views concatenated with the corresponding rain streaks and fog maps are fed into a powerful rainy LFI restoring model based on the adversarial recurrent neural network to progressively eliminate rain streaks and recover the rain-free LFI. Extensive quantitative and qualitative evaluations conducted on both synthetic LFIs and real-world LFIs demonstrate the effectiveness of our proposed method.