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Validation of synthetic storm technique (SST) has been done for prediction of attenuation due to rain over a hilly, high-rainfall, tropical location, Shillong (25°N, 91°E), India. In this letter, considering one year of rain attenuation and rain rate data shows that SST predicted attenuation overestimates while comparing the available experimental result. Storm speed has been found to have no significant effect on the attenuation prediction by SST. Second-order rain attenuation prediction, i.e., prediction of fade slope, has also been studied. Fade slope is found to have been overestimated by SST for all categories of rain events at 20.2- and 30.3-GHz frequency. Exceedance plot of fade duration by SST shows an overestimation for higher frequency and underestimation for lower frequency.

Heavy rainfall is one of the major aspects of tropical cyclones (TC) and can cause substantial damages. Here, we show, based on satellite observational rainfall data and numerical model results, that between 1999 and 2018, the rain rate in the outer region of TCs has been increasing, but it has decreased significantly in the inner-core. Globally, the TC rain rate has increased by 8 ± 4% during this period, which is mainly contributed by an increase in rain rate in the TC outer region due to increasing water vapor availability in the atmosphere with rising surface temperature. On the other hand, the rain rate in the inner-core of TCs has decreased by 24 ± 3% during the same period. The decreasing trend in the inner-core rain rate likely results mainly from an increase in atmospheric stability., How the rainfall intensity of tropical cyclones changes with climate change is not well known. Here, the authors show that while the rain rate in the outer region of TCs is clearly increasing between 1999 and 2018, it decreases significantly in the inner-core of TCs during 1999-2018.

Rain cells are the most elementary unit of precipitation system in nature. In this study, fundamental geometric and physical characteristics of rain cells over tropical land and ocean areas are investigated by using 15-yr measurements of the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR). The rain cells are identified with a minimum bounding rectangle (MBR) method. The results indicate that about 50% of rain cells occur at length of about 20 km and width of 15 km. The proportion of rain cells with length > 200 km and width > 100 km is less than 1%. There is a a log-linear relationship between the mean length and width of rain cells. Usually, for the same horizontal geometric parameters, rain cells tend to be square horizontally and lanky vertically over land, while vertically squatty over ocean. The rainfall intensity of rain cells varies from 0.4 to 10 mm h−1 over land to 0.4–8 mm h−1 over ocean. Statistical results indicate that the occurrence frequency of rain cells decreases as the areal fraction of convective precipitation in rain cells increases, while such frequency remains almost invariant when the areal fraction of stratiform precipitation varies from 10% to 80%. The relationship between physical and geometric parameters of rain cells shows that the mean rain rate of rain cells is more frequently associated with the increase of their area, with the increasing rate over land greater than that over ocean. The results also illustrate that heavy convective rain rate prefers to occur in larger rain cells over land while heavy stratiform rain rate tends to appear in larger rain cells over ocean. For the same size of rain cells, the areal fraction and the contribution of convective precipitation are about 10%–15% higher over land than over ocean.

This study presents the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Dynamic Infrared Rain Rate (PDIR-Now) near-real-time precipitation dataset. This dataset provides hourly, quasi-global, infrared-based precipitation estimates at 0.04° × 0.04° spatial resolution with a short latency (15–60 min). It is intended to supersede the PERSIANN–Cloud Classification System (PERSIANN-CCS) dataset previously produced as the near-real-time product of the PERSIANN family. We first provide a brief description of the algorithm’s fundamentals and the input data used for deriving precipitation estimates. Second, we provide an extensive evaluation of the PDIR-Now dataset over annual, monthly, daily, and subdaily scales. Last, the article presents information on the dissemination of the dataset through the Center for Hydrometeorology and Remote Sensing (CHRS) web-based interfaces. The evaluation, conducted over the period 2017–18, demonstrates the utility of PDIR-Now and its improvement over PERSIANN-CCS at all temporal scales. Specifically, PDIR-Now improves the estimation of rain/no-rain days as demonstrated by a critical success index (CSI) of 0.53 compared to 0.47 of PERSIANN-CCS. In addition, PDIR-Now improves the estimation of seasonal and diurnal cycles of precipitation as well as regional precipitation patterns erroneously estimated by PERSIANN-CCS. Finally, an evaluation is carried out to examine the performance of PDIR-Now in capturing two extreme events, Hurricane Harvey and a cluster of summer thunderstorms that occurred over the Netherlands, where it is shown that PDIR-Now adequately represents spatial precipitation patterns as well as subdaily precipitation rates with a correlation coefficient (CORR) of 0.64 for Hurricane Harvey and 0.76 for the Netherlands thunderstorms.

The results of the study revealed that the mismatch intensity rain rate of water absorption into the soil for-mation of a surface relief and soil erosion, uneven and shallow soaking imperfection open irrigation system at a su-perficial irrigation, the need for different irrigation methods in the growing and not growing periods, low coeffi-cient land utilization, high cost of irrigation and other features are, to a certain extent in conflict with the require-ments of watering cultivated with techniques for / of crops in an area at the deep groundwater.

The raindrop size distribution (RSD) is useful in understanding various precipitation-related processes. Here, we analyze disdrometer data collected in Seoul, South Korea from May 2018 to July 2019 to characterize the RSD according to rain and weather types. Rain types are categorized into stratiform, mixed, and convective rain, and weather types into the Changma front (type CF) and low-pressure system (type L). The slope parameter Λ decreases and the intercept parameter N0 fluctuates with rain rate. Among the rain types, the RSD of stratiform (convective) rain shows the steepest (mildest) slope and the smallest (largest) mean diameter. The logarithm of generalized intercept parameter log10Nw and Λ for stratiform rain have considerably dispersed distributions, which may be attributed to the diversity within the stratiform rain type in Seoul. Mixed-type rain has a larger mean value of log10Nw compared to stratiform and convective rain. Regarding the weather types, the RSD of type CF exhibits a milder slope, a larger mass-weighted mean diameter, and a larger radar reflectivity than type L. These differences between the weather types can be explained by the larger convective proportion in type CF (33%) compared to type L (9%). Possible causes for the differences between the RSD characteristics of the two weather types are examined using reanalysis and satellite data. Type CF has a larger convective available potential energy, a higher cloud top, and more active ice microphysical processes than type L, which can lead to different RSD characteristics.

In the present study, rainfall estimates from TRMM (Tropical Rainfall Measuring Mission) and GPM (Global Precipitation Mission) constellation of satellites are analyzed in the context of rainfall induced landslide occurrences over Western Ghats (WG) of India along with the daily gridded rainfall data developed by the India Meteorological Department (IMD) and ARW- Weather Research and Forecasting (WRF) numerical model simulations. This study aims to analyze the pattern of changes in rain rate and total rainfall triggering the large landslides over WG in TMPA (product of TRMM) and IMERG (GPM product) rainfall data sets. As a case study, performance of IMERG V5 is assessed during Malin landslide which occurred on 30 July 2014 (initial GPM era). Results indicate that IMERG shows significant increase in rain rate (> 60mm/h in half-hourly data) during Malin landslide. Near real-time IMERG V5, underestimates the rain rate but increasing pattern of rain-rate are observed which is similar to that of final version. Spatial pattern of ARW-WRF rainfall output is also close to the satellite and IMD rainfall patterns. We propose that IMERG V5 can be used as an indicator to reliably depict the higher rainfall scenario over the sites that are vulnerable to rainfall induced landslide occurrence over the WG region.

Time series prediction of rain attenuation from rain rate measurement during rain is made by using Synthetic Storm Technique (SST) for Ka and Q band signal for a temperate location, Vigo, Spain. Rain rate and rain attenuation data for the three year measurement period from 2016 to 2018 have been used to test the validity of the SST model at the present location. As we move from the Ka band to the Q band, larger value of predicted rain attenuation for the same rain rate is observed. For a single rain event, better prediction depends on the actual storm speed measured for the present location. But the long term statistics of predicted rain attenuation are insensitive to storm translation speed. Applicability of the SST model is tested for predicting diurnal, monthly and yearly statistics. Comparison between prediction and measurements are done for both first (rain attenuation occurrence) and second order (fade duration) statistics. Good matching has been observed between prediction and measurement for single rain event as well as for long term statistics. Prediction errors are found to be less than 0.5 dB in all the cases which proves the efficiency of the SST model for the present location.

This study proposes a new method for estimating the parameters of a radar rain rate estimator, particularly of the dual-pol radar. The proposed method is similar to the probability matching method (PMM), except for being based on truncated data pairs. A truncation value is introduced to the log-transformed data in order to remove those in the low rain rate zone as well as to introduce Gaussian distribution. The parameters are then estimated by comparing the first- and second-order moments. The proposed method is applied to a total of six rainfall events observed by the Beaslesan Radar in Korea from 2014 to 2017. Summarizing the results, first, the truncation value should be applied to the horizontal reflectivity (dBZh) data. In this case only, the other two data, the rain rate (dBR) and the differential reflectivity (dBZdr), follow the Gaussian distribution well. It is also important to consider rather severe rainfall events for the parameter estimation. The parameters for only the severe rainfall events are all estimated rather reasonably. On the other hand, for the other moderate and light rainfall events, the parameters are estimated as being rather far from their normal ranges. This is mainly due to the relatively small variance of dBR compared to that of dBZh. That is, the variance of dBR is found to be greatly dependent on the peak rain rate, but the variance of dBZh remains almost unchanged, regardless of the peak rain rate. As a result, the peak rain rate plays a dominant role in the reasonable parameter estimation. These findings are also consistent with many previous studies.

This letter presents the rainfall intensity and rain attenuation analysis in tropical region based on a one-year measurement using the 73.5 GHz E-band link of 1.8 km with three rain gauges installed along the path. The measured rain rate and rain attenuation were analysed and bench-marked with previous measurements and prediction models. The findings from this work showed that Malaysia agrees with the ITU-R rain prediction model of Zone P by 99.99% of time. The maximum measured rain attenuation exceeding 0.03% of the year is around 40.1 dB at the maximum rain rate of 108 mm/h.

Rainfall kinetic energy is an important parameter to estimate erosion potential in connection to soil erosion or in the recent years to the erosion of the leading edges of wind turbine blades. Little is known about the seasonal drop size distribution and fall velocity dependence of rainfall kinetic energy as well as its relationship with wind speed. Therefore, 6 years of Thies Laser Precipitation Monitor disdrometer and wind measurements from Voulund, a field site in western Denmark, were analyzed. It was found that the rainfall kinetic energy was highest in summer due to higher drop concentrations and in autumn due to more time with rain. The rainfall kinetic energy peaked for drop diameters between 0.875 and 2.25 mm independent of the season. Rainfall kinetic energy decreased significantly with increasing wind speed, if considering the vertical fall speed of the drops for the calculation of the rainfall kinetic energy. However, it should be noted that the measurement uncertainty increases with increasing wind speed. As disdrometer observations are rarer than rain rate observations, the performance of empirical equations describing the relationship between rainfall kinetic energy rate and rain rate was investigated. It was found that an equation trained with an alternative method fulfilled the statistical requirements for linear regression and had a similar error compared to equations in the literature. Based on the analyses, it can be concluded that the erosion potential due to rainfall kinetic energy is highest between June and November at low wind speeds and high rain rates.

Rain attenuation plays a significant role in satellite communications in the Ka band. The paper studied the rain attenuation using empirical formulations of the prominent models like ITU-R P.618-13, ITU-R P.838-3, Moupfouma, and Garcia Lopez. At a rain rate of 47.6 mm/hr, measured by Indian Meteorological Department for 2018 for Gadanki, near Tirupati, India, the attenuation values were calculated. The measured data, acquired by Ka band digital beacon receiver, is compared with the calculated data at the frequency of 20.2 GHz for both horizontal and vertical polarizations. The value of parameters viz. relative error, root-mean-square error, standard deviation error, and Chi-square error were calculated. Frequency scaling for the frequency of 30.5 GHz was calculated with the help of ITU-R P.618-13 method.

The Z–R relationship is a scaling-law formulation, Z = ARb, connecting the radar reflectivity Z to the rain rate R. However, more than 100 Z–R relationships, with different values of the parameters, have been reported in literature. This abundance of relationships is in itself a strong indication that no one “physical” relationship exists, a state of affairs that we find similar to that of the protagonist of Luigi Pirandello’s novel One, No One and One Hundred Thousand. Nevertheless the “elevation” of a simple linear fit in the (logR, logZ) space to the role of “scaling law” is such a widespread tenet in literature that it eclipses the simple realization that the abundance of different intercepts and slopes reflects the inhomogeneous nature of rain, and, in ultimate analysis, the statistical variability existing between the number of drops and drop size distribution. Here, we “eliminate” the contribution of the number of drops by rescaling both reflectivity and rainfall rate to per unit drop variables, (Z, R) → (z, r), so that the remaining variability is due only to the variability of the drop size distribution. We use a worldwide database of disdrometer data to show that for the rescaled variables (z, r) only “one,” albeit approximate, scaling law exists.

Precipitation measurements with high spatiotemporal resolution are a vital input for hydrometeorological and water resources studies; decision-making in disaster management; and weather, climate, and hydrological forecasting. Moreover, real-time precipitation estimation with high precision is pivotal for the monitoring and managing of catastrophic hydroclimate disasters such as flash floods, which frequently transpire after extreme rainfall. While algorithms that exclusively use satellite infrared data as input are attractive owing to their rich spatiotemporal resolution and near-instantaneous availability, their sole reliance on cloud-top brightness temperature (Tb) readings causes underestimates in wet regions and overestimates in dry regions—this is especially evident over the western contiguous United States (CONUS). We introduce an algorithm, the Precipitation Estimations from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain rate model (PDIR), which utilizes climatological data to construct a dynamic (i.e., laterally shifting) Tb–rain rate relationship that has several notable advantages over other quantitative precipitation-estimation algorithms and noteworthy skill over the western CONUS. Validation of PDIR over the western CONUS shows a promising degree of skill, notably at the annual scale, where it performs well in comparison to other satellite-based products. Analysis of two extreme landfalling atmospheric rivers show that solely IR-based PDIR performs reasonably well compared to other IR- and PMW-based satellite rainfall products, marking its potential to be effective in real-time monitoring of extreme storms. This research suggests that IR-based algorithms that contain the spatiotemporal richness and near-instantaneous availability needed for rapid natural hazards response may soon contain the skill needed for hydrologic and water resource applications.

Rainfall parameters can be utilized to investigate the effect of climate change through scientific methods. However, data on rainfall rate exceeded for a fraction of an average year is grossly unavailable over Nigeria’s climate, thereby diminishing the capability of existing models to adequately estimate the effect of degradation due to rain. Hence, more accurate estimation is required for better predictions. Rainfall volume data for six different locations in the south-western region of Nigeria were obtained for rain rate computation using Semire and Rosmiwati model. The curve-fitted Cumulative Distribution Functions were compared with the ITU-R rain rate model (Recommendation P.837-6) and compensation function was obtained using error analysis while the performance was evaluated with respect to existing models using Chi-square, and Percentage Error and Root Mean Square Error (RMSE) metrics. The outcome of this study can be adopted for better understanding of spatial rainfall intensity in this region and other climatic zones of similar rainfall characteristics.

Rain attenuation is the main practical problem that confronts wireless signals specifically when it uses millimeter-waves for fifth-generation (5G) communication systems. In addition, due to that the urban environments are characterized by many high buildings act as diffraction objects can block the signal path and produce non-line of sight (NLOS) situations. These diffraction materials can cause further considerable losses that disturb the received power at the 5G receiver. This paper proposes a new model can investigate the influence of both precipitation and diffraction phenomena on wireless point to point (PPT) communication systems. This new research work utilizes measured rainfall data and actual scenarios in an urban environment to simulate the wireless PPT system and examine the influence of rainfall and knife-edge diffraction (KED) on the performance of the PPT system and signal strength at the receiver. Several durations of exceedances of rain rates and various operation scenarios have been employed to study and analyze the status of 5G wireless system links. The results indicated that there is an exchange of the effect of rain and KED diffraction in the lower millimeter-waves compared to the higher millimeter-waves. This study declares that at higher frequency bands the rain attenuation is observed to be greater, the diffraction loss is higher, and the path loss is also larger compared to the effect of these three factors seen at lower frequency bands. Furthermore, specific carrier frequencies, as in the case of 60 GHz, undergo extra huge atmospheric absorption loss which can diminish the communication coverage of the PPT system link.

Satellite signal propagation at Ka-Band is more prone to weather effects than those with lower frequencies. The impact is more severe in the tropics because of its natural climate with heavy rainfall throughout the year. Rain attenuation prediction models developed in this region need to be evaluated to find the optimum model that suits the experimental parameter. Therefore, the main objective of this paper is to evaluate ITU-R’s variation prediction models, namely Folasade, Renuka and Xiang Yeo models, including ITU-R P.618-13 model with data collected from MEASAT Ka-Band beacon live monitoring system from 2014 to 2016 in West Peninsular Malaysia using large-size antenna. The measured attenuation for an average of 3 years was 18.2 dB and 25.0 dB at 0.01% of the time at Cyberjaya and Rawang with the rain rate of 76.7 mm/h and 86.0 mm/h at the same percentage of time, respectively. As a result, ITU-R and other models overpredicted the attenuation compared to the measured data. This is because these models were derived from smaller-size antenna, while the measured data were from large-size antenna and higher elevation angle of 68.8°, which contributes to low attenuation on both sites. Therefore, ITU-R model was probed further, using Study Group 3 (SG3) data as the anchor parameters, and from the experiment, it was suggested that the ITU-R model is inappropriate to predict attenuation at elevation angle of higher than 54°. All the resultant graph and analysis were presented to support the facts.

The performance of the 5G system in the heavy region is closely related to the distance between the base station of the network as the mm-Wave frequency employed suffers from deep signal fade due to rain. For this reason, the knowledge of spatial variability of small scale rain profiles is of key importance. This work investigates such spatial features of the rain profile based on 16 months of measured rain gauge networks data in UTM, Johor, Malaysia. The spatial pattern and trend were carefully processed and analyzed according to the rain events extracted from 4 rain gauges within the UTM campus. The investigation work only focuses on the high rain rate profile with a threshold of above 50 mm/h. The preliminary results indicate the Gaussian profile seems to represent the small scale rain cell; however, the validity of the results should be further investigated with respect to the complete rain cell profile in this particular region. Nevertheless, this preliminary work gave radio engineers at least an early insight into the spatial variability of rain and their impact on the design of the 5G system in heavy rain regions.

Heavy precipitation events (HPEs) can lead to deadly and costly natural disasters and are critical to the hydrological budget in regions where rainfall variability is high and water resources depend on individual storms. Thus, reliable projections of such events in the future are needed. To provide high‐resolution projections under the RCP8.5 scenario for HPEs at the end of the 21st century, and to understand the changes in sub‐hourly to daily rainfall patterns, weather research and forecasting (WRF) model simulations of 41 historic HPEs in the eastern Mediterranean are compared with “pseudo global warming” simulations of the same events. This paper presents the changes in rainfall patterns in future storms, decomposed into storms' mean conditional rain rate, duration, and area. A major decrease in rainfall accumulation (−30% averaged across events) is found throughout future HPEs. This decrease results from a substantial reduction of the rain area of storms (−40%) and occurs despite an increase in the mean conditional rain intensity (+15%). The duration of the HPEs decreases (−9%) in future simulations. Regionally maximal 10‐min rain rates increase (+22%), whereas over most of the region, long‐duration rain rates decrease. The consistency of results across events, driven by varying synoptic conditions, suggests that these changes have low sensitivity to the specific synoptic evolution during the events. Future HPEs in the eastern Mediterranean will therefore likely be drier and more spatiotemporally concentrated, with substantial implications on hydrological outcomes of storms.

Continuous observations from geostationary satellites can show the morphology of precipitation cloud systems in quasi-real-time, but there are still large deviations in the inversion of precipitation. We used binary-connected area recognition technology to identify meso-β-scale rain clusters over Hainan Island from 1 June 2000 to 31 December 2018, based on Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM data. We defined and statistically analyzed the parameters of rain clusters to reveal the typical morphological and precipitation characteristics of rain clusters, and to explore the relationship between the parameters and rainfall intensity of rain clusters. We found that the area and long axis of rain clusters over land were larger than those over the ocean, and that continental rain clusters were usually square in shape. Rain clusters with a larger area and longer axis were concentrated on the northern side of the mountains on Hainan Island and the intensity of rain was larger on the northern and eastern sides of the mountains. The variation of continental rain clusters over time was more dramatic than the variation of oceanic clusters. The area and long axis of rain clusters was larger between 14:00 and 21:00 from April to September and the long axis of the oceanic rain clusters increased in winter. There were clear positive correlations between the area, long axis and shape of the rain clusters and the maximum rain rate. The area and long axis of continental rain clusters had a higher correlation with the rain rate than those of oceanic clusters. The establishment of a relationship between the morphology of rain clusters and precipitation helps us to understand the laws of precipitation and improve the prediction of precipitation in this region.

Frequency spectrum for satellite communication systems is moving towards Ka- and V-bands, the performance of which is severely degraded by rain attenuation, especially in tropical regions. ITU-R 618-13 and few other researchers proposed a complete method to predict rain attenuation at any desired frequency. All prediction models require rain rate at 0.01% of time occurrence measured by rain gauge with 1-min integration time. Few measurements of rain attenuation in Malaysia reported that ITU-R 618-13 model underestimated the measurement at Ku-band. However, ITU-R recommends that rain intensity must be measured with one-minute integration time or less in order to predict rain effects on microwave propagation. All Recommendations and prediction methods are reported and presented based on rain rate measured with 1-minute integration time. This paper presents the statistical distributions of rain rate measured in Malaysia with 10-sec, 20-sec, 30-sec, 1-min and 2-min integration times. ITU-R P.618-13 prediction method is used to estimate earth-to-satellite link’s rain attenuation using measured rain rate with 10-sec, 20-sec, 30-sec, 1-min and 2-min integration times and compared with measurements.

Surface precipitation phase is a fundamental meteorological property with immense importance. Accurate classification of phase from satellite remotely sensed observations is difficult. This study demonstrates the ability of the Precipitation Imaging Package (PIP), a ground-based, in situ precipitation imager, to distinguish precipitation phase. The PIP precipitation phase identification capabilities are compared to observer records from the National Weather Service (NWS) office in Marquette, Michigan, as well as co-located observations from profiling and scanning radars, disdrometer data, and surface meteorological measurements. Examined are 13 events with at least one precipitation phase transition. The PIP-determined onsets and endings of the respective precipitation phase periods agree to within 15 min of NWS observer records for the vast majority of the events. Additionally, the PIP and NWS liquid water equivalent accumulations for 12 of the 13 events were within 10%. Co-located observations from scanning and profiling radars, as well as reanalysis-derived synoptic and thermodynamic conditions, support the accuracy of the precipitation phases identified by the PIP. PIP observations for the phase transition events are compared to output from a parameterization based on wet bulb and near-surface lapse rates to produce a probability of solid precipitation. The PIP phase identification and the parameterization output are consistent. This work highlights the ability of the PIP to properly characterize hydrometeor phase and provide dependable precipitation accumulations under complicated mixed-phase and rain and snow (or vice versa) transition events.

In Nanjing, China, a comparative measurement of rainfall by using a self-developed Precipitation Microphysical Characteristics Sensor (PMCS), a 2D Video Disdrometer (2DVD), a OTT PARSIVEL Disdrometer (OTT), and a tipping bucket rain gauge (Gauge) is presented to quantitatively evaluate their performances. The rain rate, rainfall duration, rainfall accumulation, raindrop size distribution (DSD), Z-R relationship, μ-λ relationship, fall velocity, axis ratio, and their features are compared and discussed in detail. The PMCS, 2DVD, OTT and Gauge can measure the rainfall accumulation accurately, but the Gauge tends to underestimate the rainfall duration and rain rate significantly. The optical-based PMCS, 2DVD, and OTT can monitor rainfall with a higher precision in a wide range of rain rate. The PMCS, 2DVD, and OTT agree well between 0.8 and 3.25 mm. The PMCS is able to measure raindrops of at least 0.1 mm, and both the 2DVD and OTT can measure more large raindrops (> 3.5 mm). For fall velocity and axis ratio, both the PMCS and 2DVD basically agree well with the Atlas relationship, and the PMCS can describe the fall velocity with a lower deviation. It is interesting that 2.5% raindrops larger than 3 mm are found to be spherical, it may be attributed to the periodic oscillation of raindrops. This study reveals that a combination of the PMCS and 2DVD can compensate for their shortcomings and promote the DSD measurement in the entire drop size spectrum, and it's highly suggested that the reliability of DSD measurements needs careful cross-assessment after quality control.

Seasonal variation of raindrop size distribution (DSD) is examined using long-term (2007–2015) observations made with Joss-Waldvogel disdrometer at Thumba (8.53°N, 76.87°E), a tropical coastal station. Data sets were classified based on the rain rate and studied the DSD variations for pre-monsoon (PRE) season, post-monsoon (POST) season and summer monsoon (MON) season. The result shows that the larger drop concentration is higher in PRE season compared to other two seasons for the same rain rate. The values of median volume diameter (Dm) are found to be lowest (highest) in MON (PRE) seasons. The gamma parameters (Λ, μ) and normalized DSD scaling parameter for concentration (Nw) are also observed to have a clear-cut seasonal variation. There is a clear trend in the slope (Λ) and Nw parameters with rain rates. Although shape parameter (μ) did not show a linear trend as such, there exist a pattern to its evolution with rain rates that is similar across seasons. The coefficients of A and b in Z-R relations also showed seasonal variations. The plausible causative mechanisms for the observed surface seasonal DSD variations are discussed using satellite and radiosonde observations. The differences in cloud structure and cloud microphysics are found to have a direct influence on the rain DSD spectra at the surface. The present results envisages that the change in monsoon circulation and the origin of cloud system (i.e. maritime or continental) play a vital role in characterizing the rain DSD, while the microphysical and dynamical processes occurring during the descent of raindrops will modify the same.

The migration of satellite services to higher frequency bands, namely Ka- and Q-bands, necessitates the use of accurate propagation modeling. To this end, there is currently an ongoing effort to improve and develop new models based on actual propagation measurement data around the globe. In this letter, the results for the first 2 years of the Ka-band propagation measurement campaign conducted using Alphasat's Ka-band beacon at two locations in Attica, Greece, are analyzed and presented. The letter begins with a brief description of the experimental setup, then rain rate statistics as well as attenuation first-order statistics (overall, seasonal, and diurnal) for the two locations are presented. The letter focuses on the investigation of a site-diversity scenario across the two locations comparing three switching techniques. Finally, some overall remarks are made, commenting on the plans for future work.

The variability of the raindrop size distribution (DSD) contributes to large parts of the uncertainty in radar-based quantitative rainfall estimates. The variety of microphysical processes acting on the formation of rainfall generally leads to significantly different relationships between radar reflectivity Z and rain rate R for stratiform and convective rainfall. High-resolution observation data from three Micro Rain Radars in northern Germany are analyzed to quantify the potential of dual Z–R relationships to improve radar rainfall estimates under idealized rainfall type identification and separation. Stratiform and convective rainfall are separated with two methods, establishing thresholds for the rain rate-dependent mean drop size and the α coefficient of the power-law Z–R relationship. The two types of dual Z–R relationships are tested against a standard Marshall–Palmer relationship and a globally adjusted single relationship. The comparison of DSD-based and reflectivity-derived rain rates shows that the use of stratiform and convective Z–R relationships reduces the estimation error of the 6-month accumulated rainfall between 30% and 50% relative to a single Z–R relationship. Consistent results for neighboring locations are obtained at different rainfall intensity classes. The range of estimation errors narrows by between 20% and 40% for 10-s-integrated rain rates, dependent on rainfall intensity and separation method. The presented technique also considerably reduces the occurrence of extreme underestimations of the true rain rate for heavy rainfall, which is particularly relevant for operational applications and flooding predictions.

The utilization of satellites for communication systems has expanded considerably in recent years. C and Ku-bands of frequencies are already congested because of high demand. Future directions of satellite communications are moving towards Ka and V-bands. Earth to satellite communications are moving towards higher frequency bands in future which are more sensitive to environment. Rain causes severe degradation in performances at higher frequency bands specially in tropical regions. Several mitigation techniques are proposed to design reliable system. Time diversity is one of the potential candidate for it. However, time diversity analysis requires measured rain attenuation data. For future high frequency link design those data are not available at most of the places. This thesis proposes a method to utilize 1-minute rain rate to analyze time diversity technique at any desired frequency. This paper proposes a method to utilize 1-minute rain rate to analyse time diversity rain rate gain. In proposed method, it is assumed that rain rate gain with delay can represent rain attenuation gain with delay for same period of time at same location. The characteristics of rain rate and rain attenuation almost same because the attenuation causes due to rain. One year measured rain rate in Malaysia is used to predict rain rate gain. The measured gain at 12.225 GHz signal is compared with that predicted by ITU-R based on rain rate measurement and is found good agreement. Hence it is recommended that the time diversity gain can be predicted using measured rain rate for any desired frequencies.

This paper studies the rain-induced propagation characteristics over a tropical location Kolkata, India, using the data collected over a 7 year period, i.e., 2004–2010. This location experiences substantial rainfall during the Indian Summer Monsoon (ISM) season which is preceded by premonsoon rainfall accompanied by strong convection. Accumulations, event duration, and fade duration statistics are presented. Long-term observations for three propagation effects, namely rain attenuation, scintillations, and depolarization, are analyzed in this paper. The seasonal patterns of these phenomena are examined to reveal the characteristic features of Kolkata. The interrelations among rain rate, rain attenuation, scintillations, and depolarization are also investigated. Propagation detriments for identical rain rates are more severe in premonsoon compared to monsoon period, mainly because of the different microphysical properties of precipitation. However, the percentage occurrences of various propagation effects are higher in monsoon than premonsoon.

Collocated CloudSat rain rates and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) 89-GHz brightness temperature Tb retrievals allow for the development of an algorithm to estimate light, warm rain statistics as a function of AMSR-E 89-GHz Tb for shallow marine clouds. Four statistics are calculated from CloudSat rainfall rate estimates within each 4 km × 6 km Tb pixel sampled by both sensors: the probability of rainfall, the mean rain rate, the mean rate when raining, and the maximum rain rate. Observations with overlying cold clouds are removed from the analysis. To account for confounding variables that modify Tb, curves are fit to the mean relationships between Tb and these four statistics within bins of constant column-integrated water vapor from AMSR-E, and sea surface temperature and wind speed from reanalysis grids. The coefficients that define these curves are then applied to all available AMSR-E Tb retrievals to estimate rain rate throughout the eastern subtropical oceans. A preliminary analysis shows strong agreement between AMSR-E rain rates and the CloudSat training dataset. Comparison with an existing microwave precipitation product shows that the new statistical product has an improved sensitivity to light rain. A climatology for the year 2007 shows that precipitation rates tend to be heavier where the sea surface is warmer and that rain is most frequent where stratocumulus transitions to trade cumulus in the subtropics.

The study focuses on the inter-comparison of rain drop size distributions (DSD) and rain integral parameters retrieved using continuous observations from Micro Rain Radar (MRR) and Optical Disdrometer (OD). The variations of the rain parameters during the southwest monsoon period of 2012 over a tropical coastal station are examined. The rain rate and radar reflectivity obtained from both MRR and disdrometer show a correlation of ~0.9. Observations from both the instruments do show that rain intensity of ~2 mm h−1 contributes maximum towards the accumulated rain water. Rain rates are classified into six rain categories to understand the variation with rain intensity. For each rain rate regime, DSDs from MRR and disdrometer compare well for ~1–3 mm diameter range. On the other hand, at larger drop diameters (above D ~ 3 mm) disdrometer overestimates number concentration and at smaller diameters (below D ~ 0.7 mm) disdrometer underestimates number concentration. RD (integrand of rain rate integral) follows a bell-shaped distribution in which the peak position shifts between 1-2 mm drop diameter and widens along with each rain class. Profiles of DSD in Bright Band (BB) and Non-Bright Band (NBB) cases demonstrate that the BB rain events are associated with higher number concentration of larger drops and NBB events are associated with higher concentration of smaller drops. The vertical profile of RD explains that in BB conditions D ~ 1.3 mm significantly contributes total rain water content and for NBB contribution arises from D ~ 1.1 mm at surface level. The profiles also show smaller drops (

To improve rain rate estimation accuracy of the networked phased-array weather radars (PAWRs) installed in Osaka and Kobe, Japan, we employ a rain rate estimation method using a sequentially varying radar reflectivity and rain rate ( ${Z}$ – ${R}$ ) relationship. To calculate the time variation in the ${Z}$ – ${R}$ relationship, the rain rate of the existing X-band radar network comprising four dual-polarization radars and the radar reflectivity factor of the PAWRs are used. The proposed method is compared with estimation methods using the Marshall–Palmer relationship and the attenuation coefficient (k). From the comparisons between the rain rate with the three different estimation methods and the rain rate of the rain gauges installed in an analyzed area, we discuss the estimation accuracy. From the comparisons, the proposed method shows significantly better performance than do the other two methods even when weather conditions include heavy or violent rain.

The variation of rain effects for terrestrial communication are examined at frequencies above 10 GHz in the present study. Propagation parameters like attenuation coefficient and phase-delay coefficient are assessed for the rain filled medium. It is examined how the pulse is distorted while passing through rain cell. Specific attenuation is computed at different frequencies from 10 to 70 GHz and rain rate from 5 to 245 mm/hr for horizontal and vertical polarization. The effects of rain rate, the operating frequency and the propagation path length on the rain attenuation are tested. The impact of polarization type on terrestrial rain attenuation is examined here.

Non-brightband (NBB) rain is a shallow, orographic precipitation that does not produce a radar brightband as a result of melting ice crystals. However, NBB rain is not the same as warm rain, which excludes ice from being involved in the microphysical growth of precipitation. Despite this difference, NBB rain is often treated as warm rain in the literature, and past studies have mostly ignored the role of ice. Here, we use two wet-seasons (2015-16 and 2016-17) at four precipitation observing sites in the Northern Coast Ranges of California to show the role of echo top height and ice in determining NBB rain intensity. It was found that NBB rain was only absent of brightbands 32-46% of the time depending on location of the site. Additionally, all NBB rain rates that exceeded 10 mm hr−1 exhibited observable brightbands during the hour period. We also define Growth Efficiency (GE) as the ability of shallow rain clouds to produce raindrops larger than drizzle size (D > 0.5 mm). High-GE rain drop size distributions were composed of fewer small drops and more large drops than low-GE rain, which was mostly drizzle. High-GE rain occurred with echo top heights above the freezing level where rapid growth of precipitation was observed by radar. Echo tops that only extended 1 km or less above the freezing level suggested hydrometeor growth from mixed-phase processes, indicating that ice may be present in coastal precipitation at warmer temperatures than previously considered.

In this study, measurements of vertical profiles of rain parameters have been made using vertically pointing micro rain radar (VPMRR) at Akure (7.30° N, 5.13° E). Rain parameter data collected over seven-month rainfall episodes during the intense rainy season (April to October) have been analyzed for a dynamical evolutionary trend over the site. Nearly all the episodes observed followed a similar pattern, hence, a single continuous rainfall episode occurring between 20:45:00 h and 21:14:00 h Greenwich Meridian Time (GMT) local time on 6th August 2018 is presented in this report. The results show no significant changes to the rain parameters (such as rain rate and liquid water content) nor contributed to the raindrop size distribution, based on average fall velocity of 6.55 m s−1 and rain rates within 1.3 and 2.6 mm h−1. This is to enable a stable fall for the dominant drops during the period. Further, the results revealed the transformation and collision of smaller drops to enhance a stable fall of larger drops during the rain event. The information from the study will be useful for radar meteorologists and microwave engineers in their designs., Raindrop size; Rain types; Vertical profile; VPMRR; Tropical location; Dynamical trend.

During the passage of Typhoon Nida, the raindrop size distribution parameters, the raindrop spectra, the shape and slope (μ–Λ) relationship, the radar reflectivity factor, and rain rate (Z–R) relationship were investigated based on a two-dimensional (2D) video disdrometer in Guangdong, China, from August 1 to 2, 2016. Due to the underlying surface difference between the ocean and land, this process was divided into two distinct periods (before landfall and after landfall). The characteristics of raindrop size distribution between the period before landfall and the period after landfall were quite distinct. The period after landfall exhibited higher concentrations of each size bin (particularly small drops) and wider raindrop spectral width than the period before landfall. Compared with the period before landfall, the period after landfall had a higher average mass-weighted mean diameter Dm that was smaller than those of other TCs from the same ocean (the Pacific). The μ–Λ relationship and Z–R relationship in this study were also compared with other TCs from the same ocean (the Pacific). This investigation of the microphysical characteristics of Typhoon Nida before landfall and after landfall may improve radar quantitative precipitation estimation (QPE) products and microphysical schemes by providing useful information.

Rain attenuation is an important factor affecting wireless communication systems throughout the world. Rain attenuation and rain rate data are collected using multichannel radiometer and laser precipitation monitor at a tropical location. The dataset obtained is used to initially propose an empirical model for prediction of rain attenuation from rain rate data. An alternative model using linear spline regression-based machine learning is also used to predict rain attenuation. The machine learning-based model is found to be more accurate by an appreciable degree compared to the empirical model proposed in the previous instance.

Forecasting model of monthly orographic rainfall, which is fundamentally characterized by a long occurrence period within a year not having much of maximum rain rate values, has been developed using regression approach. The analysis has been done on the basis of historical data of rainfall of two hill stations of different altitude, Majitar and Ghum. Performance of the developed model is evaluated through exhaustive error calculation. Goodness of fit value shows that the performance of the developed model is acceptable for the two stations having different altitude. F-test shows the statistical reliability of the prediction of rainfall. Lower root mean square error (RMSE) value indicates good prediction of stochastic-deterministic events like orographic rainfall.

Raindrop size distribution (DSD) characteristics of monsoon and pre-monsoon seasons over Kolkata are analyzed by using three years raindrop size data from Joss–Waldvogel disdrometer located at Kolkata. The probability of occurrence DSD for different rain rate cluster has been observed for monsoon and pre-monsoon season. The mean raindrop concentration at a particular drop diameter and rain rate has been observed for both the seasons. The variations of number concentration with drop diameter and time have also been shown for monsoon and pre-monsoon season. The diurnal variations of DSD have also been investigated.

Since sensor characteristics and retrieval algorithms are not identical, there are differences of rainfall estimates among passive microwave imagers/sounders (MWIs/MWSs) carried by the Global Precipitation Measurement (GPM) Core Observatory and constellation satellites. This study proposes a Method of Microwave rainfall Normalization (MMN) for the Global Satellite Mapping of Precipitation (GSMaP) to mitigate the discrepancy of GSMaP rainfall estimates among the MWIs/MWSs. A basic idea of MMN module is to calibrate all the MWIs/MWSs except for the reference sensor (TRM_TMI) using cumulative distribution function of rain rate. In order to validate the MMN module, cumulative distribution function and normalized table are investigated, and normalized monthly mean rainfall is compared to original one. The results reasonably show that rain amount of the MWI/MWSs increase (decrease) to mitigate the discrepancy.

The estimates of the probabilities of communication outage on near-surface links during the worst periods of the year in Kharkov (Ukraine) were made on the basis of 2018 and 2019 summer cycles of continuous meteorological observations of rain intensity. The estimates of meteorological situations that are characteristic for communication interruptions were determined for frequencies of 12 GHz, 28 GHz, 38 GHz, 60 GHz and 94 GHz.

Accurate rainfall detection and estimation are essential for many research and operational applications. Traditional rainfall detection and estimation techniques have achieved considerable success but with limitations. Thus, in this study, the relationships between the gauge (point measurement) and the microwave links (MWL) rainfall (line measurement), and the MWL to the satellite observations (area-wide measurement) are investigated for (area-wide) rainfall detection and rain rate retrieval. More precisely, we investigate if the combination of MWL with Meteosat Second Generation (MSG) satellite signals could improve rainfall detection and rainfall rate estimates. The investigated procedure includes an initial evaluation of the MWL rainfall estimates using gauge measurements, followed by a joint analysis of the rainfall estimates with the satellite signals by means of a conceptual model in which clouds with high cloud top optical thickness and large particle sizes have high rainfall probabilities and intensities. The analysis produced empirical thresholds that were used to test the capability of the MSG satellite data to detect rainfall on the MWL. The results from Kenya, during the &ldquo, long rains&rdquo, of 2013, 2014, and 2018 show convincing performance and reveal the potential of MWL and MSG data for area-wide rainfall detection.

This study assesses where tropical cyclone (TC) surface winds can be measured as a function of footprint sizes and wavelengths (Ka- Ku- and C-band). During TCs, most high-resolution surface observations are impeded by considerable &lsquo, rain contamination.&rsquo, Under these conditions, high-resolution surface observations typically come from operational aircraft. Other techniques that provide high-resolution surface observations through rain are also hindered somewhat by rain contamination and are very sparse in space and time. The impacts of rain are functions of the remotely sensed wavelength and rain&ndash, drop size. Therefore, relative long wavelengths have been used to observe the surface, but at the cost of a larger footprint. We examine how smaller footprint sizes could be used to observe through gaps between moderate to heavy rainbands that circulate around the main low-pressure center of a TC. Aircraft data from the National Oceanic and Atmospheric Administration&rsquo, s (NOAA&rsquo, s) WP-3D turboprop aircraft will be used to create realistic maps of rain. Our results provide information on the satellite instrument characteristics needed to see the surface through these gaps. This information is expected to aid in developing hurricane-related applications of new higher-resolution satellites.

The National Aeronautics and Space Administration‐Japan Aerospace Exploration Agency Global Precipitation Measurement (GPM) mission consists of a multisatellite constellation that provides real‐time or near‐real‐time global observations of rain and snow. In this study, GPM Level 3 Integrated Multi‐satellitE Retrievals for GPM (IMERG) and Level 2 GPM Microwave Imager Goddard Profiling rainfall products have been assimilated into the Weather Research and Forecasting model using the community Gridpoint Statistical Interpolation (GSI) data assimilation system. Experiments have been conducted and compared to demonstrate the impact of rain rate data assimilation on forecasts of heavy rainfall related to Hurricane Harvey (2017) and moderate to light rainfall observed during the GPM Integrated Precipitation and Hydrology Experiment field campaign. The results indicate that both GPM Microwave Imager Goddard Profiling and IMERG data could generate apparent increments in moisture, temperature, wind, and pressure fields for Hurricane Harvey, which led to significant improvement in the precipitation forecast. Frequent (every 3 hr) assimilation of IMERG data also positively impacted the short‐term precipitation forecast skill for the Integrated Precipitation and Hydrology Experiment moderate to light rain events. However, results also indicate that the impact of rain data assimilation was limited for a system that had a small horizontal dimension with low rain rates and within a relatively stable synoptic environment.

In this contribution the one year period of the rain rate measurement in the Czech Republic was analyzed. The measurement was located at the experimental site of the Institute of Atmospheric Physics in the town of Hradec Kralove. The measurement was performed by a tipping-bucket rain gauge of high resolution of 0.03 mm of rain amount per one tip and by 2D-videodistrometer from Joaneum Research. This study is focused on rain type classification from which the more precise rain attenuation on microwave link can be predicted. Beside description of simple methods using cheap rain amount and rain rate measurement three methods of sophisticated classification (Tokay-Short, Testud and Waldvogel methods) are described and compared using one year Czech distrometer data.

Rain rate and Rain Attenuation predictions are important in radio system operating at Ku and Ka bands as they affect telecommunication systems performance. To adequately estimate rain-induced attenuation and fading, the International Telecommunication Union (ITU) recommends use of rainfall data collected using 1-minute integration time. For Tanzania, no rainfall data with 1-minute integration time is available either through measurements or conversion from rainfall data with longer integration time. In this paper the rain attenuation is predicted for seven locations in the coastal area of Tanzania. The 1- minute rainfall rate is determined by Chebil’s model using long-term measurements from Tanzania Metrological Agency (TMA) collected for a period of forty years, results obtained are used to estimate rain attenuations. By using the International Telecommunication Union-Recommendation (ITU-R) model, rain attenuation is predicted at horizontal polarization at Ku and Ka band. The results show that Unguja has the highest average annual rainfall accumulation with rain attenuation as high as ~53.22dB for Ka band and ~15.14dB for Ku band making the difference of about 38.08 between the two frequency bands. On the other hand, Kibaha which has the lowest average annual rainfall accumulation along the coast has rain attenuation of ~47.27dB for Ka and ~13.41dB for Ku bands, making the difference of 33.86dB between the frequency bands. Results obtained from this study can be useful in designing earth-satellite microwave links in the coastal area of Tanzania.

The spatial and temporal characteristics of rainfall and raindrop size distribution for terrestrial or space to earth communication link affects the perfromace of radio and radar communication system operating above 10 GHz in the temperate climate and above 7 GHz in tropical and equatorial climate. The rain drop size distribution plays an important role for limiting the performance of communiation system due to different rain attenuation and polarization of radio signals at mm wave rfequuencies. The single point measurement of rainfall rate as well as rain drop distribution, and non-uniform rainfall rate characteristics of terrestrial radio link causes difference in the rain attenuation measured value at mm wave frequencies from the rain attenuation based on raindrop size distribution or rainfall rate. In this paper, model for cross-polarisation discrimination (XPD) due to rain rate at 20 GHz and 30 GHz have been developed based on rain attenuation measurement of LOS link for vertical and horizontal polarisation signals. The variation of XPD with rain rate and co-polar attenuation have been discussed for understanding the effect of rain drop size distribution at 20/ 30 GHz.

During a precipitation a transient increase of gamma activity is generated by 214Pb and 214Bi, daughters of atmospheric 222Rn, which are collected by rain droplets and brought to the ground. A continuous monitoring of this gamma radiation can be an efficient alternative to a 222Rn direct measurement in clouds and allows for estimating rain-induced variations in environmental gamma radiation. This work presents the results of a seven months proximal gamma-ray spectroscopy experiment, specifically tailored for gathering reliable and unbiased estimates of atmospheric 214Pb gamma activity related to rainfalls. We developed a reproducible model for reconstructing the temporal evolution of the 214Pb net count rate during rain episodes as function of the rain rate. The effectiveness of the model is proved by an excellent linear correlation (r2 = 0.91) between measured and estimated 214Pb count rates. We observed that the sudden increase of 214Pb count rates ( Δ C ) is clearly related to the rain rate (R) by a power law dependence Δ C ∝ R 0.50 ± 0.03 . We assessed that the radon daughter 214Pb content (G) of the rain water depends on the rain rate with G ∝ 1 / R 0.48 ± 0.03 and on the rain median volume diameter ( λ m ) with G ∝ 1 / λ m 2.2 . We proved that, for a fixed rainfall amount, lower is the rainfall intensity (i.e. the longer is the rain duration), higher is the radon daughters’ content of the rain water.