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Rain streaks are one of the main factors that degrade the performance of computer vision algorithms. Therefore, a preprocessing method is needed to remove rain streaks from rainy images. The main issue of the rain removal task is to prevent over (or under) de-raining. Over de-raining means that the background details are removed along with rain streaks in light rain, and under de-raining means that the rain streaks are not completely removed in heavy rain. These occur as the density of rain and intensity of rain streaks vary. In order to solve this, this paper proposes a two-step rain removal method. The proposed system first estimates the rain streaks image redefined with a simple operation from an input rainy image. The proposed rain streaks image contains rain density and rain streak intensity for the rainy image. By using this, the proposed system can adaptively remove rain streaks from images captured in various rain conditions. In addition, we propose a novel architectural unit, the elementwise attentive gating block, which is an optimized block used to deal with high frequency rain streaks. The proposed block selectively passes the desired components from the input feature maps by applying different weights to each element. It helps to clearly extract the rain streaks, and as a result, there are no traces of rain streaks on the restored image. The proposed method outperforms previous rain removal algorithms for both synthetic and real-world images.

As an important indicator of climate change, precipitation variability is critical to the ecological environment in arid and semi-arid mountains. This study examined the variability and trends of precipitation and rain days in the Qilian Mountains based on meteorological station data during the rainy seasons of 1960–2017 using a non-parametric method. The results revealed the following. (1) Many major precipitation events occurred during the rainy season (precipitation amount and number of rain days accounted for > 80% and > 70% of the annual total, respectively) and the proportion of precipitation increased significantly with altitude. (2) The distribution of different intensity classes of precipitation was complex in different months and at different altitudes. The distribution of rain days of different intensity classes was similar to that of precipitation. Variation of precipitation and rain days was significant (not significant) in central and eastern parts (western parts and in the Hexi Corridor on the northern slopes) of the Qilian Mountains. Regions with significant (non-significant) increasing trends for both light and moderate rain were distributed above the upper portion, i.e., 2600–3200 m (in the Hexi Corridor on the northern slopes) of the Qilian Mountains. (3) Mutations of light rain and rain days occurred before the mid-1980s above the upper portion but late in the first decade of the twenty-first century in the lower portion (2000–2600 m). Mutations of moderate rain and moderate rain days occurred earlier than for light rain and light rain days; the abrupt change points occurred later with decreasing altitude.

Atmospheric aerosols are of great climatic and environmental importance due to their effects on the Earth’s radiative energy balance and air quality. Aerosol concentrations are strongly influenced by rainfall via wet removal. Global climate models have been used to quantify their climate and health effects. However, they commonly suffer from a well-known problem of ‘too much light rain and too little heavy rain’. The impact of simulated rainfall intensities on aerosol burden at the global scale is still unclear. Here we show that rainfall intensity has profound impacts on aerosol burden, and light rain has a disproportionate control on it. By improving the representation of convection, the light-rain (1–20 mm d−1) frequency in two state-of-the-art global climate models is reduced. As a result, the aerosol burden is increased globally, especially over the tropics and subtropics, by as much as 0.3 in aerosol optical depth in tropical rain belts. It is attributed to the dominant contribution of light rain to the accumulated wet removal by its frequent occurrence despite its weak intensity. The implication of these findings is that understanding the nature of aerosol scavenging by rainfall is critical to aerosol–climate interaction and its impact on climate. Light rain plays a disproportionate role in aerosol wet removal, according to improved simulations on rain intensity and frequency in global climate models.

Unpredictable and heterogeneous weather conditions and road incidents are common factors that impact highway traffic speeds. A better understanding of the interplay of different factors that affect roadway traffic speeds is essential for policymakers to mitigate congestion and improve road safety. This study investigates the effect of precipitation and incidents on the speed of traffic in the eastbound direction of I-64 in Virginia. To the best of our knowledge, this is the first study that studies the relationship between precipitation and incidents as factors that would have a combined effect on traffic stream speeds. Furthermore, using a mixture model of two linear regressions, we were able to model the two different regimes that the traffic speed could be classified into, namely, free-flow and congested. Using INRIX traffic data from 2013 through 2016 along a 25.6-mi section of Interstate 64 in Virginia, results show that the reduction of traffic speed only due to incidents ranges from 41% to 75% if the road is already congested. In this case, precipitation was found to be statistically insignificant. However, regardless of the incident impact, the effect of light rain in free-flow conditions ranges from insignificant to a 4% speed reduction while the effect of heavy rain ranges from a 0.6% to a 6.5% speed reduction when the incident severity is low but has a roughly double effect when the incident severity is high. Published version

In this paper, hourly observations of precipitation, wind, and PM2.5 and PM10 concentrations in Qinhuangdao from 2016 to 2018 were used to study the effects of precipitation and wind on PM2.5 and PM10 concentrations. The results show that precipitation has a certain wet scavenging effect on PM2.5 and PM10, and the scavenging effect on PM10 is greater than that on PM2.5. Precipitation above moderate rainfall is concentrated from May to September, and light rain in winter increases the concentration of pollutants. The changes of PM2.5 before and after precipitation are related to the initial concentration of PM2.5 before precipitation, precipitation intensity, and precipitation duration. The scavenging effect of precipitation on PM10 is closely related to the initial concentration of PM10 before precipitation. The higher the initial concentration of PM10 is, the greater the removal amount of precipitation will be. Moderate or above pollution weather mainly occurs in the northeast, southwest, and west wind meteorological conditions; the more westerly the wind, the more the pollution; north wind and northwest wind have the most obvious scavenging effect on PM2.5 and Pm10; when the wind speed increases to 2 m/s, the concentration of PM2.5 and PM10 can be reduced; when the wind speed is more than 4 m/s, the concentration of PM10 increases under the south wind, southeast wind, east wind, and northeast wind.

Based on the PM2.5 and PM10 mass concentration data obtained from 51 national air quality monitoring stations and the corresponding rainfall intensity data in automatic meteorological stations in Hubei Province from 1 January 2015 to 31 December 2017, the impact of rainfall intensity on PM mass concentrations under relatively different humidity conditions was analyzed. The results showed that light rain occurred most frequently in the pollution process, with Xiangyang being affected for up to 587 h. PM concentration would not change drastically under the effect of precipitation. Mean rainfall intensity responsible for wet growth of PM10 and PM2.5 was mainly 2.5 was significantly higher (>1.4 mm/h) than that of PM10 (>1.0 mm/h). Precipitation was more likely to produce a wet removal effect for a greater initial value of PM mass concentration, and on the contrary, a wet growth effect was more likely, with the threshold of PM10 mass concentration being 150 μg/m3 and that of PM2.5 mass concentration being 95 μg/m3. Wet removal played a leading role in lower humidity (∼60%) and greater rainfall intensity, but wet growth played a leading role in higher humidity (∼90%) and lower rainfall intensity. As the precipitation level increased (rainfall ≥1.5 mm·h−1), the wet removal to PM10 mass concentration was enhanced more obviously. The variations of PM2.5 had similar distributions to those of PM10 under the effect of precipitation, but the wet removal effect of precipitation was weakened and the wet growth effect was enhanced.

Based on various statistical indices, the abilities of multi-generation reanalyses, namely the NCEP / NCAR Reanalysis 1 (R1), the NCEP-DOE Reanalysis 2 (R2) and the NCEP Climate Forecast System Reanalysis (CFSR), to reproduce the spatiotemporal characteristics of precipitation over Zhejiang Province are comprehensively compared. The mean absolute bias percentages for three reanalyses are 20% (R1), 10% (R2) and 37% (CFSR). R2 (R1) gives the best (worst) general depiction of the spatial characteristics of the observed precipitation climatology, whereas a significant wet bias is noticed in the CFSR. All reanalyses reasonably reproduce the interannual variability with the correlation coefficients of 0.72 (R1), 0.72 (R2) and 0.84 (CFSR). All reanalyses well represent the first two modes of the observed precipitation through Empirical Orthogonal Function analysis, with CFSR giving the best capture of the principal components. The root-mean-square error (RMSE) is the largest (smallest) in the CFSR (R2). The large RMSE of CFSR in summer (especially in June) contributes mostly to its systematic wet bias. After 2001, the wet bias of CFSR substantially weakens, probably attributed to increasing observations assimilated in the CFSR. On a monthly basis, the percentage of neutral bias cases are similar for all reanalyses, while the ratio of positive (negative) bias cases for CFSR is distinctly larger (smaller) than that of R1 and R2. The proportions of negative bias cases for R1 and R2 begin to increase after 2001 while keeping stable for CFSR. On a daily basis, all reanalyses give good performances of reproducing light rain; however, the reflection of moderate rain and heavier rain by the CFSR is better than R1 and R2. Overall, despite being a third-generation reanalysis product, the CRSR does not exhibit comprehensive superiorities over R1 and R2 in all aspects on a regional scale.

This paper presents high-capacity WDM (Wavelength Division Multiplexing)-based FSO system which utilizes two EDFAs (Erbium-Doped Fiber Amplifiers).It is used for long-distance transmission as a booster amplifier and SOA (Semiconductor Optical Amplifier) as a pre-amplifier. The proposed system supports 15Gbps transmission over optimized link length on various weather conditions, i.e., haze, light rain, medium rain, and heavy rain. This novel system provides Q-factor and BER of 17.55, 2.5e-69, respectively, for heavy rain conditions, whereas the conventional system provides Q-factor and BER of 9.14, 1.55e-20, respectively, for same weather condition. This paper increases Q-factor and BER, respectively, for heavy rain condition.

A water-logging disaster occurred in Eastern China on 1 June 2016 was utilized to investigate the practical performance of six radar rainfall estimators based on horizontal reflectivity ( $Z_{\mathrm{H}}$ ), specific attenuation ( $A_{\mathrm{H}}$ ), specific differential phase ( $K_{\text{DP}}$ ), and multi-variable estimators that integrate differential reflectivity ( $Z_{\text{DR}}$ ), namely $R(Z_{\mathrm{H}},\, Z_{\text{DR}}), R(K_{\text{DP}}, Z_{\text{DR}})$ and $R(A_{\mathrm{H}}, Z_{\text{DR}})$ . Detailed evaluation with a local rain gauge network and drop size distribution (DSD) measurements shows that (i) $R(A_{\mathrm{H}},\, Z_{\text{DR}})$ underestimates the light rain pattern, but it achieves the best scores and performs the best among the various estimators; (ii) each radar rainfall estimator can outperform other estimators at a certain period of time determined by the rainfall patterns; (iii) both the optimal radar rainfall relationship and high calibration accuracy are required to obtain accurate rainfall estimates; (iv) the contamination of melting solid hydrometeors on $A_{\mathrm{H}} \text{and}/\text{or}K_{\text{DP}}$ may make them less effective than $Z_{\mathrm{H}}$ . In addition, with appropriate adjustment of $\alpha$ coefficient, $R(A_{\mathrm{H}})$ can perform better than $R(K_{\text{DP}})$ .

Although light rain events have a small intensity and amount, light rain also exerts a significant influence on the environment and climate, and observations have revealed a significant reduction in light rain in many regions around the globe. The specific reasons for light rain reduction have not been clearly revealed. Some studies attribute the reduction in light rain to an increase in aerosol, while other studies tend to associate it with global warming. Therefore, further studies should be conducted to increase knowledge regarding the reasons for the decrease in light rain. Light rain events were analyzed using daily precipitation data collected from 67 gauge stations across Southwest China from 1960 to 2010. The results show that both the amount of light rain and the number of light rain days have decreased during the last 50 years at different magnitudes (0–2 mm, 2–5 mm, and 5–10 mm), in which the light rain amount and number of light rain days for 0–2 mm of rainfall present the largest regionally averaged decreasing rates of –0.20 mm/a and –0.73 days/a from 1960 to 2010, respectively. Furthermore, the maximum (minimum) light rain amount and number of light rain days appear in summer (winter). In addition, an increasing trend in the light rain amount in discrete events and a decreasing trend in the light rain amount in consecutive events are simultaneously revealed, in which the reduction in the frequency of consecutive light rain events is more significant than that of discrete light rain events. The reduction in light rain in the eastern part of Southwest China from 1979 to 2010 is mainly caused by a combination of water vapor reduction and warming. Moreover, the influence of aerosols on the observed reduction in light rain is not statistically significant at the inter-annual scale. The results provide some main correlation factors for predicting the long-term changes of light rain in China and even the global region.

Based on the Backward Four-Dimensional Variational Data Assimilation (Backward-4DVar) system with the Advanced Regional Eta-coordinate Model (AREM), which is capable of assimilating radio occultation data, a heavy rainfall case study is performed using GPS radio occultation (GPS RO) data and routine GTS data on July 5, 2007. The case study results indicate that the use of radio occultation data after quality control can improve the quality of the analysis to be similar to that of the observations and, thus, have a positive effect when improving 24-hour rainfall forecasts. Batch tests for 119 days from May to August during the flood season in 2009 show that only the use of GPS RO data can make positive improvements in both 24-hour and 48-hour regional rainfall forecasts and obtain a better B score for 24-hour forecasts and better TS score for 48-hour forecasts. When using radio occultation refractivity data and conventional radiosonde data, the results indicate that radio occultation refractivity data can achieve a better performance for 48-hour forecasts of light rain and heavy rain.

Ocean ambient noise during the passage of a very severe cyclonic storm, Vardah, in the Bay of Bengal has been studied and presented in this paper. A cyclone named Vardah formed near the Andaman and Nicobar Islands on December 6, 2016. The intensified cyclonic storm crossed the south-eastern coast of India in the afternoon hours (local time) of December 12, 2016. During the passage of this cyclone, ocean underwater ambient noise time series data were acquired from the shallow waters of the south-eastern coast of India. In this paper, the noises pertaining to heavy rain and light rain (drizzling) during the cyclonic event have been analysed along with the high wind noise, and the individual power spectra for both the noises have been estimated.

Satellite rainfall estimates (SREs) have become alternative sources of rainfall data for several applications. However, the accuracy of the SREs is likely to vary from region to region and must be evaluated on a local basis. This study evaluated the accuracy of three SREs for the Lake Tana basin in northwestern Ethiopia. This basin is characterized by complex topography comprising both lowlands and highlands. The three SREs were the Tropical Applications of Meteorology using SATellite data and ground-based observations (TAMSAT), the Climate Hazard Infrared Precipitation with Stations (CHIRPS), and the Africa Rainfall Climatology (ARC). The SREs were compared with gauge measurements in lowland and highland regions during the period 1995–2010 on a point-to-pixel basis at daily, dekadal (10 days), monthly, and seasonal periodicities. The results show that the three SREs underestimated rainy events, but TAMSAT captured rainfall occurrence relatively well in both regions. ARC better estimated light rain rates (1–5 mm d−1) than did TAMSAT and CHIRPS; however, all the SREs markedly underestimated moderate and heavier rain rates (≥10 mm d−1). TAMSAT and CHIRPS estimated the amount of rainfall reasonably well (high efficiency, low random errors, and bias 20%) at any time scale. On a seasonal scale, CHIRPS estimated the secondary rainy season (March–May) rainfall better than did ARC and TAMSAT, whereas TAMSAT outperformed both CHIRPS and ARC during the primary rainy season (June–September). Overall, the rainfall detection capabilities and rainfall amount estimates of the SREs were better over the lowlands, and the cumulative rainfall estimates tended to improve with increasing integration time (i.e., from daily to seasonal totals).

An hour precipitation dataset of 42 automatic weather stations is developed and applied to analyze the temporal and spatial characteristics of light precipitation in urban areas of Beijing City during 2007-2017. The stations are classified into three groups, including 18 sites in central urban area (4th RR), 10 sites in peri-urban area (4th-5th RR) and 14 sites in suburban area (5th-6th RR). Light precipitation is defined as hourly rainfall of 0.1-0.3mm. Analysis shows that light precipitation occurred in urban area in the whole day, with the peak value in 0600 LST and minimum value in 1600LST; monthly variation of light precipitation frequency (LPF) was characterized by the highest value in summer and the lowest value in winter; remarkable differences are found for the various urbanized areas, with the annual and seasonal mean LPF being generally small in central urban area and gradually increasing toward suburban area; the hourly mean LPF during morning and nighttime is higher in summer than those in other seasons in each of the urban areas; relative humidity, aerosol and wind speed might have been the major influential factors for the observed temporal and spatial pattern of light precipitation.

This research develops the raining detection using deep learning method. A training model is established through a neural network-like architecture to classify the images recorded by the vehicle camera into three classes: no rain, heavy rain, and light rain. The classification results are used to control the speed of the motor to implement an automatic wiper control system. The training model with ResNet is used to classify the image. For real-time application, the camera module on the Google Coral Dev board embedded development platform is used to test the video stream and to estimate the performance of this system. Results show that the recognition accuracy reaches 95%, and the processing speed can achieve 20 frames per second (fps).

The rapid development of cities puts forward higher requirements for the resilience of the road network. As one of the unfavorable weathers, rainfall has a great impact on the operation of road traffic. Using weather and vehicle speed data of a city in China, this paper compares and analyzes changes in traffic operation indicators such as the standard speed and the percentage reduction of the standard speed under different rainfall intensities. Results indicate that under the same rainfall conditions and time period, the traffic operation of the elevated road is least affected by rainfall, and the traffic operation of underpass tunnels and flat roads is more affected by rainfall. With the increase of rainfall intensity, the reduction percentage of the standard speed of the underpass tunnel shows an increasing trend; however, it is less affected in light rain and moderate rain, and more affected in heavy rain and rainstorm. With the increase of rainfall intensity, the standard speed of the elevated road has no obvious change trend, and it is less affected by light rain and moderate rain, but more by rainstorm. In each time period, the standard speed reduction percentage of flat roads shows an obvious increasing trend with the increase of rainfall intensity.

Intersection settings are important in traffic engineering. One of the variables considered in managing traffic at signalized intersections is saturation flow. So far many junction studies have been carried out in clear weather conditions, but are rarely explored during rainy weather. This research is intended to examine how saturation flow in sunny weather and compare it with saturation flow when it rains. Monjali Intersection in Yogyakarta Indonesia was chosen as the study location. Retrieval of traffic volume data using camcorders, while the amount of rainfall is obtained from simple rain intensity gauges. The recorded traffic volume data is played back to obtain the amount of saturation flow. From the calculation results, it can be concluded that saturation flow in light rain conditions is greater than the saturation flow when the weather is sunny.

The influence of topographical characteristics and rainfall intensity on the accuracy of satellite precipitation estimates is of importance to the adoption of satellite data for hydrological applications. This study evaluates the three GPM IMERG V05B products over the arid country of Saudi Arabia. Statistical indices quantifying the performance of IMERG products were calculated under three evaluation techniques: seasonal-based, topographical, and rainfall intensity-based. Results indicated that IMERG products have the capability to detect seasons with the highest precipitation values (spring) and seasons with the lowest precipitation (summer). Moreover, results showed that IMERG products performed well under various rainfall intensities, particularly under light rain, which is the most common rainfall in arid regions. Furthermore, IMERG products exhibited high detection accuracy over moderate elevations, whereas it had poor performance over coastal and mountainous regions. Overall, the results confirmed that the performance of the final-run product surpassed the near-real-time products in terms of consistency and errors. IMERG products can improve temporal resolution and play a significant role in filling data gaps in poorly gauged regions. However, due to the errors in IMERG products, it is recommended to use sub-daily rain gauge data in satellite calibration for better rainfall estimation over arid and semiarid regions.

Due to the discretized nature of rain, the measurement of a continuous precipitation rate by disdrometers is subject to statistical sampling errors. Here, Monte Carlo simulations are employed to obtain the precision of rain detection and rate as a function of disdrometer collection area and compared with World Meteorological Organization guidelines for a one-minute sample interval and 95 % probability. To meet these requirements, simulations suggest that measurements of light rain with rain rates R ≤ 0.50 mm h−1 require a collection area of at least 6 cm × 6 cm, and for R > 1 mm h−1, the minimum collection area is 10 cm × 10 cm. For R = 0.01 mm h−1, a collection area of 2 cm × 2 cm is sufficient to detect a single drop. Simulations are compared with field measurements using a new hotplate device, the Differential Emissivity Imaging Disdrometer. The field results suggest an even larger plate may be required to meet the stated accuracy, although for reasons that remain to be determined.

Using the daily precipitation data at 89 stations during 1975–2017 in the Qin-ba Mountain area, variation characteristics of different-grade precipitation in the north and south of the Qin-ba Mountain area are analyzed by the linear regression, Mann-Kendall test, and correlation analysis, based on the precipitation grade classification standard proposed by Dai Aiguo and the division of the geo-ecological boundary in mountain range presented by Kang Muyi. The results are as follows: (1) Light rain occurs most frequently, and the percentage of light rain in the north is twice that of the southern region. Rainstorms are the most rare. Precipitation above 800 mm mainly occurs south of the study area, and the result is consistent with the boundary between the north and south of the precipitation. (2) The beginning of precipitation mainly occurs in early April. The high precipitation concentration (PCD) in the north is an important reason for the debris flow disaster. The north-south precipitation concentration period (PCP) is about 1 month ahead of the central part. (3) In the past 43 years, the overall precipitation in the Qin-ba Mountain area has shown a decreasing trend, with the frequency of light rain and the percentage of heavy rain decreasing year by year. (4) The frequency of light rain is negatively correlated with other frequencies. In addition, the fewer days of precipitation, the later the precipitation concentration period (PCP) occurs, which means that the disaster occurs later.

Satellite-based precipitation products are becoming available at very high temporal and spatial resolutions, which has accelerated their use in various hydro-meteorological and hydro-climatological applications. Because the quantitative accuracy of such products is affected by numerous factors related to atmospheric and terrain properties, validating them over different regions and environments is needed. This study investigated the performance of two high-resolution global satellite-based precipitation products: the climate prediction center MORPHing technique (CMORPH) and the latest version of the Integrated Multi-SatellitE Retrievals for the Global Precipitation Mission (GPM) algorithm (IMERG), V06, over the United Arab Emirates from 2010 through 2018. The estimates of the products and that of 71 in situ rain gauges distributed across the country were compared by employing several common quantitative, categorical, and graphical statistical measures at daily, event-duration, and annual temporal scales, and at the station and study area spatial scales. Both products perform quite well in rainfall detection (above 70%), but report rainfall not observed by the rain gauges at an alarming rate (more than 30%), especially for light rain (lower quartile). However, for moderate and intense (upper quartiles) rainfall rates, performance is much better. Because both products are highly correlated with rain gauge observations (mostly above 0.7), the satellite rainfall estimates can probably be significantly improved by removing the bias. Overall, the CMORPH and IMERG estimates demonstrate great potential for filling spatial gaps in rainfall observations, in addition to improving the temporal resolution. However, further improvement is required, regarding the overestimation and underestimation of small and large rainfall amounts, respectively.

This chapter provides an overview of the CloudSat mission as it relates to precipitation remote sensing. We describe how the Cloud Profiling Radar (CPR) contributes to the global observing system through precise precipitation detection, quantification of snowfall rate, quantification of light rain fall, and stratiform/convective delineation. We provide an overview of the mission operations, which have changed at several points throughout the mission history, introducing sampling artifacts in the diurnal cycle that need to be accounted for when using the data. Finally, we describe the three operational precipitation products produced by the CloudSat data system.

To improve traffic operations and management under inclement weather, this work quantificationally investigated the influence of rainfall on the traffic flow characteristics of urban expressways in Beijing. Four basic traffic flow models (Greenshields model, Greenberg model, Underwood model, and Drake function model) were compared to determine the most suitable model for the detected data, which were collected by microwave detectors in the Beijing 2nd and 4thring expressway under normal or rainfall condition. Results showed that the Greenshields model was the best-fitted model. Under rainfall conditions, varying degrees of reductions were observed in the road capacity, free-flow speed, and speed at capacity. The findings also showed that the reductions of road capacity were 7.26%, 10.87%, and 17.09%, and the reductions of free-flow speed were 3.07%, 5.29%, and 6.64% under light rain, medium rain, and heavy rain, respectively. The reductions of speed at capacity revealed a similar tendency as that of the free-flow speed. Simultaneously, a new model that incorporated rainfall intensity is proposed on the basis of the Greenshields model and can achieve good performance. Overall, the results are beneficial for traffic managers in improving their decision s and predictions under rainfall weather.

Observations in September 2018 from 2-D video disdrometer (2DVD) and 1-D disdrometer (Parsivel2) are used to study the performance difference of the two instruments at the same location in the campus of CUIT (Chengdu University of Information Technology). The characteristics of rainfall intensity, raindrop size distribution (DSD) and particle number density from the two instruments respectively are analyzed and compared according to rainfall intensity classification. The results show that 1) In the process of light rain, moderate rain, heavy rain and rainstorm, the DSD of the two instruments shows a single peak characteristic, and the peak value appears at the raindrop diameter of about 0.6mm. 2) For the number of raindrops with diameter less than 2mm, 2DVD detection value is greater than Parsivel2 detection value, and the D-value increases with the increase of rainfall intensity. 3) With the increase of rainfall intensity, the proportion of raindrops with diameter less than 2mm to the total raindrops decreases. 4) Parsivel2 can hardly detect raindrops with a diameter less than 0.3mm, while 2DVD has a better detection effect on raindrops with a diameter of about 0.19mm, and the detection ability for raindrops with a diameter less than 1mm is far lower than 2DVD. 5) The rainfall in Chengdu is mainly composed of small raindrops with a diameter of about 1mm. 6) The peak value of rainfall intensity of 2DVD is prior to that of Parsivel2, and the two peaks are basically the same in light rain, but with the increase of rainfall, the peak value of 2DVD will be greater than that of Parsivel2.

General approaches to dual-channel consistency calibration of dual-polarized radar contain Solar Calibration Method, Traditional Offline Light Rain Method, Metal Ball Method. A couple of new calibration algorithms are coined in this paper, which are Online Light Rain Algorithm and Metal Cover Shielding Feed Source. The algorithms have been used to assess the dual-channel consistency of the new generation dual-polarized weather radar in Guangdong province. The key point of the Online Light Rain Algorithm is to extract the effective data of light rain in precipitation. Then throughout the localization study of Correlation Coefficient(CC), the Reflectivity Factor(Z), the Signal to Noise Ratio(SNR) and so forth, rational parameters are acquired. Different Reflectivity Factor(ZDR) of valid light rain is fitted by the way Least Square Method, which generates a Gaussian Distribution pattern of the frequency of ZDR. In the model, the peak point represents the measured zero point of ZDR.The measured Systematic Deviation and the Standard Deviation(SD) of ZDR emerge from the comparison between theoretical figures and statistics of long-term temporal data. After the assessment and correction of consistency in dual-channel, the measured ZDR becomes much more precise and credible.

In the present study, seven-year-long observations of rain microphysical properties are presented using a ground-based disdrometer located at Braemore; a site on the windward slope of the Western Ghats (WG) over the Indian Peninsula. The annual cycle of rainfall shows a bimodal distribution with a primary peak during summer monsoon and secondary peak during pre-monsoon. Pre-monsoon rain events are less in number but are with high intensity and characterize large raindrops and low number concentration. During summer monsoon, short and less intense rain events with small drops are noticed. Post-monsoon rain is having a long duration less intense events with lower concentration of large raindrops compared to the summer monsoon. In the seasonal variation of mean diameter (Dm) and raindrop concentration (NT) with Rain Intensity (RI), winter and pre-monsoon rains exhibit higher values of Dm and lower values of NT compared to the summer and post-monsoon seasons for all the RI ranges. The mean features of the rain microphysical parameters are also supported by the case studies of rain events. RI, Dm and NT are categorized into different range bins for all the seasons to identify their variation and relative rainfall contribution to the total seasonal rainfall. Heavy drizzle/Light rain has maximum rain duration, and the relative contribution to the rainfall is high from heavy rain type. Winter and pre-monsoon rains are mostly contributed from the larger raindrops (>Dm3), and during summer and post-monsoons it is from Dm2 onwards. The distribution of occurrence frequency of NT and rainfall are similar during all four seasons. NT2 recorded rainfall percentage nearly the same as NT1 in summer monsoon and this also supports large number of raindrops in this season. In RI-Duration analysis, all seasons showed similar distribution, and 90% of total duration is contributed from RI with less than 20 mm h−1.

In this study, we investigate the impacts of aerosols on tropical cyclone (TC) precipitation that occurred from 1980 to 2014 over China mainland. The TC-induced precipitation is objectively identified based on Western North Pacific (WNP) TC historical track data and daily precipitation data from meteorological stations. Aerosol optical depth (AOD) from the Second Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) is used to represent the amount of aerosol pollution. The long-term variations in AOD and TC precipitation from the past 35 years are analyzed. A significant increasing trend is found for both the relative percentage of TC rainstorm days and the amount of aerosol loading. Together with the decreasing trend in the annual number of TCs affecting China, this indicates that aerosols are invigorating TC precipitation. For the TC precipitation during the study period, the relative occurrence of light rain shows a decreasing trend, while the proportion of heavy rain and rainstorms shows an increasing trend. Meanwhile, the intensity of the TC daily precipitation increases with the elevated pollution level, particularly during the TC initial period and for heavy TC rainfall. These statistical results based on long-term observations suggest that aerosols have a substantial impact on TC precipitation through microphysical effects and have the potential to impact the track, intensity, size, and lifespan of TCs over Southeast Asia.

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.

Information on the spatiotemporal rainfall occurrence, its microphysical characteristics, and its reflectivity–rainfall (Z–R) relations required to provide rainfall mapping based on rain radar data is limited for tropical high mountains. Therefore, this study aims to analyze rainfall types in the Andes cordillera to derive different rain-type Z–R relations using disdrometer observations at three study sites representative for different geographic positions and elevations (2610, 3626, and 3773 m MSL). Rain categorization based on mean drop volume diameter (Dm) thresholds [0.1 < Dm (mm) ≤ 0.5; 0.5 < Dm (mm) ≤ 1.0; 1.0 < Dm (mm) ≤ 2.0] was performed using drop size distribution data at a 5-min time step over an approximate 2-yr period at each location. The findings are as follows: (i) Rain observations characterized by higher (lower) Dm and rain rates are more frequent at the lower (higher) site. (ii) Because of its geographic position, very light rain (drizzle) is more common at higher altitudes with longer-duration events, whereas rainfall is more convective at the lower range. (iii) The specific spatial exposition regarding cloud and rain formation seems to play an important role for derivation of the local Z–R relationship. (iv) Low A coefficients (≤60) for the first rain type resemble typical characteristics of orographic precipitation. (v) Greater values of A (lowest and highest stations for Dm > 1.0 mm) are attributed to transitional rainfall as found in other studies. (vi) Rain-type Z–R relations show a better adjustment in comparison with site-specific Z–R relationships. This study is the first contribution of Z–R relations for tropical rainfall in the high Andes.

Monitoring the spatiotemporal characteristics of light rain events is vital to water resources assessment and environmental protection. The Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) products provide new-generation satellite precipitation measurements with improved light rain estimates. However, the spatial distribution of the quality and error structure of IMERG light precipitation detections and the climatic dependence of the error sources have not been studied sufficiently. In this study, we evaluated the GPM IMERG daily light precipitation estimates' performance from 2015 to 2018 over mainland China. We found that: (1) IMERG captured the spatial patterns of light precipitation over mainland China but has limited ability to detect light rainfall events below 5 mm/d; (2) the hit bias is the dominant error of IMERG under 5 mm/d; (3) IMERG exhibits a worse performance and has relatively higher miss bias for light rainfall in areas with a complex winter precipitation phase; (4) the evaluation of IMERG for light rain is affected by the mismatch between gauge data and IMERG data caused by air pollution. These findings can improve the IMERG algorithms for light rainfall and provide a reference for applications based on these datasets.