Rain Rate Data Research Articles

Climatology and convective mode of severe hail in the United Kingdom

Severe or large hail, with diameter ≥20 mm, is a hazard associated with severe convective storms that can cause significant damage. In the UK, the rarity and small footprint of severe hail events makes obtaining well-documented hail reports difficult, and the reports are spread across multiple databases. In this study, three databases of UK severe hail reports are merged for the first time. The combined event set (1979–2022), comprising >800 reports, is used to investigate interannual variability and the seasonal, spatial and size distributions of severe hail. The seasonal cycle peaks in early–mid summer, and the peak month has shifted from June to July since around 2005. The distribution of reported hail size is exponential, with a slower decay (larger hail) during summer. The time of day, basic convective mode (isolated, clustered or linear), and presence or absence of supercellular characteristics are assessed for 274 of the reports since 2006, using composite radar rainrate data. The diurnal cycle is strong year-round, peaking during the late afternoon (1500–1800 UTC). 53% of severe hail events are associated with isolated cells, 33% with clusters, and 14% with linear storms. Around 35% of severe hail-producing storms are probable supercells, increasing to 70% for storms producing ≥40 mm hail. This demonstrates that the prevalence of supercells producing very large hail extends to temperate maritime climates. These results may be of relevance in other regions with a relatively low incidence of severe hail in the present climate. This comprehensive analysis of severe and potentially impactful hail in the UK provides novel insight into its characteristics, enabling improved assessment of climate risk from this hazard.

Assessing Nowcast Models in the Central Mexico Region Using Radar and GOES-16 Satellite Data

In this study, the nowcast models provided by the Python pySTEPS library were evaluated using radar derived rain rate data and the satellite product Split-Window Difference (SWD) based on GOES-16 data, focusing on central Mexico. Initially, we obtained a characterization of the rainfall that occurred in the region using the radar rain rate and the SWD. Subsequently the nowcasts were evaluated using both variables. Two nowcast models were employed from pySTEPS: Extrapolation and S-PROG. The results indicate that average SWD is below 2.5 K, 90 min before the onset of rainfall events, and, on average, the SWD is 2 K during rainfall events. The results from both nowcast models were accurate and produced similar results. The nowcasts performed better when SWD data were used as input, having an average Probability of Detection (PoD) above 70% and a False Alarm Rate (FAR) reaching 40% for the 15-min prediction. The nowcasts were less accurate using the radar rain rate as input for the 15-min forecast, where the PoD was maximum 70% and FAR reaching 40%. However, these nowcasts were more reliable during well-organized precipitation events. In this work, it was determined that the nowcast models provided by pySTEPS can provide valuable rain forecasts using GOES-16 satellite and radar data for the central Mexico region.

Implementation of Rainfall Forecasting using ANN

Prediction of rainfall is essential for many sectors, including flood mitigation, disaster prevention, agriculture production, and water resource management. Rain forecasts are used to warn of natural disasters such as floods or to plan planting activities to improve the quality of crop yields. An accurate rainfall prediction remains a challenging task due to the uncertainty of natural phenomena considering that rainfall is a non-linear and dynamic process. Various climatic variables such as temperature, relative humidity, wind speed and direction, are among those that affect the dynamic process of rainfall. This study suggests a machine learning technique based on Artificial Neural Network (ANN) model to forecast rainfall by using two-years historical local rain rate and others meteorological data. Evaluation metrics such as mean absolute error, root mean square error, and correlation coefficient, are used to evaluate model’s performance. The results show good performance and sensible prediction accuracy. The comparison with existing model clearly depicted that ANN model predicts rainfall better than statistical models particularly at lower and moderate rain rate.

Influence of wind speed on rain-based attenuation at Ku-band for earth-satellite links in Nigeria

The peculiarity of the tropical climate shows that when the quality of service (QoS) on Satellite-Earth propagation links is to be determined, the statistics of rain-based attenuation (RbA), storm speed, and their impacts are the important parameters to be considered, especially at frequencies above 10 GHz. This paper assesses the influence of storm speed in estimating RbA at Ku-band, based on 2-year rain rate data obtained using automatic weather station (AWS) and RbA beacon measurements in Nigeria. The rain rates based on the time series were employed to deduce the time series RbA based on the synthetic storm technique (SST) algorithm. The results show a seasonal pattern of rain rate that correlates with the SST-based RbA. The RbA generated closely follows suit with the beacon measurement, especially under low wind speed, and outperforms the international telecommunications union–radiocommunication sector (ITU-R) model based on the lowest metric measures. However, at a higher storm speed, the RbA generated deviated widely from the measured RbA values by about 16%. These results are crucial for figuring out what needs to be done to protect QoS in a tropical area where wind and rain are common.

Comparative Study of Worst Month Rain Rate and Attenuation Statistics for Satellite Links in Nigeria

AbstractThis study aims to characterize the dynamic trend of the worst case scenario of rain rate and attenuation for satellite links applications in Nigeria. Rain rate data for four locations (Akure, Jos, Ikeja, and Port Harcourt) were obtained from the Tropospheric Data Acquisition Network at the Centre for Atmospheric Research, NASRDA at an integration time of 5 min from January 2013 to December 2014. The statistics of time series of worst month rain induced attenuation were computed using the method of Synthetic Storm Technique. The International Telecommunication Union model was found to overestimate the Q‐factor in Akure, Port Harcourt, and Ikeja by 34%, 115%, and 50%, respectively while a 25% underestimation was observed in Jos. The results obtained in this study will be useful for location based link design in tropical Nigeria to reduce the uncertainty of specification of design for communication links.

On highly skewed fractional log‐stable noise sequences and their application

Considering log‐LFSN (log‐linear fractional stable noise) sequences , driven by non‐Gaussian one‐sided LFSN with constant skewness intensity , for any and , we show that the auto‐covariance function (ACVF) exists if and only if is persistent, with stability index , Hurst exponent and extreme skewness (if ) or (if ). Within that range of existence, and in short, we calculate explicitly and establish persistence of too, by showing asymptotic proportionality of , as . We discuss explicit links of to a generalized co‐difference function of the driving one‐sided LFSN , and to the ACVF's of fractional Gaussian noise (FGN) and log‐FGN. The results are numerically demonstrated via ensemble simulation of synthetic time series generated by the considered log‐LFSN model fitted to time series of spatio‐temporal accumulations of rain rate data.

Tropospheric attenuation prediction for future millimeter wave terrestrial systems: Estimating statistics and extremes.

SummaryTropospheric attenuations can be significant in the millimeter wave (mmWave) frequency bands; hence, accurate prediction modeling of tropospheric attenuation is important for reliable mmWave communication. Several models have been established by the International Telecommunication Union (ITU), yet estimation accuracy is limited due to the large spatial scales used for model input parameters. In this paper, we address this and apply local precipitation data to analyze tropospheric attenuation statistics and compare to results when using ITU regional input rain data. Specifically, tropospheric attenuation is predicted via simulations using the ITU method at 30, 60, and 90 GHz in four distinct geographic locations with different climate types. From our simulations, we gather statistics for annual average rain attenuation, worst month rain attenuation, and rain attenuation per decade. Our results indicate that when using local measured rain data, for 1 km link distance, mean rain event attenuation increases from 0.5 to 2 dB. Local rain data yield larger attenuations at essentially all percentages of time not exceeded (essentially corresponding to all probability values): for example, for 0.1% of time not exceeded, in Columbia, SC, rain attenuation for 30 GHz frequency increases to 9 dB with local rain data, compared to 5 dB with ITU's regional data, corresponding to rain rates of 38.2 and 17.5 mm/h, respectively; at the same probability and location, the 90 GHz attenuation increases by 10 dB, from 10 to 20 dB when local rain data are used. Fog attenuations are also appreciable, reaching 8 dB for the 90 GHz frequency. Moreover, for the example locations, peak rain attenuations have increased at a rate of approximately 2 dB/decade over the past 50 years. Our results indicate that actual tropospheric attenuations may be substantially larger than that predicted by the ITU model when using regional rain rate data.

Numerical Experiments Applying Simple Kriging to Intermittent and Log-Normal Data

This study evaluates the effect of considering data intermittency and log-normality in applications of simple Kriging. Several sets of synthetic data, both intermittent and log-normal, were prepared for this purpose, and then four different Kriging applications were repeated with these synthetic data under different assumptions of data intermittency and log-normality. The effects of these assumptions on the simple Kriging applications were evaluated and compared with each other. As a result, it was found that the derived correlation length of a variogram becomes longer when considering both data intermittency and log-normality, and the sill height becomes smaller when data intermittency is high. The data field generated by simple Kriging was also closer to the original data when considering both data intermittency and log-normality. In the application to rain rate data, the effect of considering data intermittency was confirmed. However, the effect of considering data log-normality was found to be vague. The general assumption of log-normality in relation to the rain rate data seems not to be so valid, at least not for the rain rate data considered in this study.

Performance evaluation of metric measures for converting 30-min GPM rain data to 1-min for microwave applications in Tropical region of Nigeria: A multivariate approach

The quest for a one-minute rain rate is high because it is an important input for generating high accuracy of rain-induced attenuation and will in turn earn good use by radio engineers in designing good and high-quality terrestrial and satellite links. This study utilizes eleven (11) years (2008–2018) rain rate data obtained from a satellite-based program, the Global Precipitation Measurement (GPM) over five different locations in the South West, Nigeria, and 2 years (2017 and 2018) gauge measured rain rate data in one of the locations Akure, Nigeria for validation. The GPM platform was an improved satellite-based program over the Tropical Rainfall Measuring Mission (TRMM) for measuring tropical rainfall with spatial and temporal resolution. Hence, a good application of measured GPM rain rate data can be a good tool for estimating rain-induced attenuation if an adequate conversion is made for a one-minute integration regime as recommended by the \\International Telecommunication Union (ITU). The 30 min GPM rain rate data was converted to one-minute integration time based on a multivariate approach. The coefficient of determination of GPM rain rate data closely matches with the gauge and was found to be 0.9194. Analyses were also performed on seasonal bases for rain rate and rain-induced attenuation ranging from frequencies 10 GHz, to 30 GHz, to assess the seasonal trend between the two sources of data. The seasonal analyses were scaled as pre-monsoon, intensive, cessation, and dry months. A seasonal comparison shows that the GPM data presented the highest rain rate and rain attenuation during the intense rainy regime. Attenuations are considerably high at 0.01, 0.005, and 0.001 percentages of time. Overall, a deduction was made that the GPM rain rate measurements offered reliability in the estimation of rain-induced attenuation analyses in the tropical region of Nigeria due to the spatial–temporal coverage when compared with the rain gauge coverage which is sparse and costly to implement. This research has presented a reliable 30 min to one-minute integration time conversion model suitable for tropical locations, as well as information on seasonal rain attenuation statistics which would be utilized for effective satellite communication systems by the radio engineers.

Enhanced adaptive code modulation for rainfall fade mitigation in Ethiopia

Rain attenuation is considerably noticed in a frequency spectrum above 7-GHz for tropical equatorial regions and in a frequency spectrum higher than 10-GHz for temperate climates. The attenuation prediction method provided by the International Telecommunication Union-Recommendation (ITU-R), through Recommendation P.530-16 and P.618-13 utilize data collected from temperate regions. Since the average raindrop size is bigger and the rainfall rate is high in magnitude in tropical regions than that of non-tropical areas, this prediction model is not suitable for the measured rain data. Unfortunately, a rain fade mitigation technique based on local rain data has not been adequately studied in tropical regions. This paper presents an enhanced adaptive code modulation (ACM) for rainfall fade mitigation in Ethiopia. In this research work, locally collected one-minute rain rate data is used to determine the rain attenuation. Then based on this result, the neuro-fuzzy inference system is employed to enhance the mitigation technique. Furthermore, a comparison of the performance of this proposed scheme is with the non-adaptive technique, and fuzzy-based adaptive modulation and coding technique is carried out. MATLAB simulation result showed that lower-order quadrature amplitude modulation (QAM) scheme with a lower convolutional coding rate is better in maintaining link availability in bad weather conditions. However, spectral efficiency is improved by utilizing a larger constellation size of quadrature amplitude modulation (QAM) scheme with a higher convolutional coding rate when the channel is not affected by rain.

Evaluation Of Signal Strength And Quality Of A Ku-Band Satellite Downlink During Raining Season In Guinea Savanna Region Of Nigeria

This study presents the evaluation of signal strength and quality of a ku-band satellite downlink signals during raining season in Guinea Savanna region of Nigeria. Rain rate data and ku-band signal over Jos were retrieved from the Nigeria Meteorological Agency (NIMET). Rain rate data was measured using Davis Vantage Vue weather station while microwave signal were obtained using spectrum analyzer. The measured data were analyzed using Microsoft office excel. Rainfall is broadly classified into two types: stratiform (drizzle: ≤5mm/hr and widespread: >5≤25mm/hr) and convection (shower: >25≤50mm/hr and thunderstorm: >50mm/hr). Results were calculated based on ITU-Recommendations P.618-12 and P.838. The results obtained revealed that the effect of rain on signal strength and quality on a ku-band link depend on the rain type and duration of rainfall. The effect of drizzle (≤5mm/hr) on the ku-band link is not significant regardless the duration of rainfall. The results also shows that severe signal losses above 60dB were obtained under shower (>25≤50mm/hr) and thunderstorm (>50mm/hr). Also, widespread (>5≤25mm/hr) that prevailed for a longer period causes severe signal losses. This is to say that lower rain rate of about 25mm/hr that prevailed for a very long time have significant effect on Kuband link. The results of this study will serve as crucial design parameters for communication systems engineers for providing fade margins and developing accurate fade mitigation techniques (FMTs) for the Guinea Savanna region of Nigeria. It was observed from this study that the experimental studies within the Ku-band frequency are getting over exhausted. Therefore, experimental studied in the region of higher band like K and Ka are highly recommended.

Evaluation of Time Diversity Technique for Satellite Mitigation in Tropical Climate

Time diversity is one of the mitigation techniques for satellite communications (SatCom) systems that aims to combat deep fades cause by propagation impairments. This study presents the performance of time diversity technique for rain attenuation at Ku- and Ka-band SatCom systems in Johor Bahru, Malaysia. The Synthetic-Storm Technique (SST) has been utilized to obtain rain attenuation time series based on rain rate data measured by local rain gauge. The time diversity has been quantified through diversity gain as a function of retransmission time delay, in which shows the effectiveness of time diversity technique to improve the Quality of Service (QoS) and link availability. The results demonstrated it is feasible to use time diversity as mitigation technique at higher frequencies in a mission to support future SatCom operation in tropical region.

Validation of Synthetic Storm Technique for Rain Attenuation Prediction Over High-Rainfall Tropical Region

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.

Evaluation and Mitigation of Rain Effect on Wave Direction and Period Estimation From X-Band Marine Radar Images

In this article, the accuracy of wave direction and period estimation from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i> -band marine radar images under different rain rates is analyzed, and a simple subimage selection scheme is proposed to mitigate the rain effect. First, each radar image is divided into multiple subimages, and the subimages with relatively clear wave signatures are identified based on the random-forest-based classification model. Then, wave direction is estimated by performing the Radon transform on each valid subimage. As for wave period estimation, a new method is proposed. Texture features are first extracted from each pixel of the selected subimage using the gray-level co-occurrence matrix and combined as a feature vector. Those feature vectors extracted from both rain-free and rain-contaminated training samples are then used to train a random-forest-based wave period regression model. The shore-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i> -band marine radar images, simultaneous rain rate data, as well as buoy-measured wave data collected on the West Coast of the United States are used to analyze the rain effect on wave parameter estimation accuracy and validate the proposed method. Experimental results show that the proposed subimage selection scheme improves the estimation accuracy of both wave direction and wave period under different rain rates, with reductions of root-mean-square errors (RMSEs) by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{6.9}^\circ$</tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{6.0}^\circ$</tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{4.9}^\circ$</tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{1.0}^\circ$</tex-math></inline-formula> for wave direction under rainless, light rain, moderate rain, and heavy rain conditions, respectively. As for wave period estimation, the RMSEs decrease by 0.13, 0.20, 0.30, and 0.20 s under those four rainfall intensity levels, respectively.

Preliminary study on validation of HIMAWARI-8 data with ground based rainfall data at South Sumatera, Indonesia

An accurate rainfall data has an important role in the Indonesia Fire Danger Rating System (Ina-FDRS) which has been developed by PTPSW – BPPT since 2017. Continuing the previous study, during wet and dry season, an accurate rainfall data becomes one of the important inputs and has big impact on the Ina-FDRS. Unfortunately, surface rainfall data from AWS and ARG have limited availability especially on the spatial resolution data, therefore Ina-FDRS will use HIMAWARI-8 rain rate data as an input. On the other hand, satellite rainfall data also has limitation on the accuracy, HIMAWARI-8 detects top cloud temperature, then by using IMSRA method to calculate rainfall values. This preliminary study aims to validate rain rate from HIMAWARI-8 satellite image data with ground-based rainfall data from AWS/ARG during fire and non-fire period, from 1 – 31 August 2015 and 15 April – 15 May 2018. Afterward, we will use the validated rainfall data as an input for Ina-FDRS.

Application of synthetic storm technique for rain attenuation prediction at Ka and Q band for a temperate Location, Vigo, Spain

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.

Assimilation of GPM Rain Rate Products With GSI Data Assimilation System for Heavy and Light Precipitation Events

AbstractThe 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.

Rain Detection From X-Band Marine Radar Images: A Support Vector Machine-Based Approach

Since rain alters the histogram pattern of radar images, rain-contaminated radar data can be identified. In this article, a support vector machine (SVM)-based method for rain detection using X-band marine radar images is presented. First, the normalized histogram bin values for each image are extracted and combined into feature vector. Then, SVMs are employed to classify between rain-free and rain-contaminated images. Radar images and simultaneous rain rate data collected from a sea trial in North Atlantic Ocean are utilized for model training and testing. Comparison with the zero pixel percentage (ZPP) threshold method shows that the SVM-based method obtains higher detection accuracy, with 98.4% for the Decca radar data and 99.7% for the Furuno radar. It is also found that as the total number of bins does not significantly affect detection accuracy, the proposed method can be applied to different radar systems directly with a suitable number of bins. In addition, compared to the ZPP threshold method, the SVM-based method proves to be more robust even with limited training samples.

Fog and rain attenuation characterization and performance of terrestrial free space optical communication in Akure, Nigeria

Reliable broadband communication requires secure high data rate and bandwidth links. With the observedincrease in broadband users, known communication systems such as RF and microwave links cannot promise suchrequirements due to link interference and low bandwidth. A current communication system that promises suchrequirements and more is Free Space Optical (FSO) communication. This system basically involves the transmissionof signal-modulated optical radiation from a transmitter to a receiver through the atmosphere or outer space. However,location-variant atmospheric channel degrades the performance of an FSO system under severe atmosphericconditions, thus necessitating local atmospheric attenuation studies.This paper presents the characterization of both fog- and rain-induced attenuation and the performance ofan FSO system in a terrestrial terrain at Akure, Nigeria. One-year archived visibility data and in-situ measured 1-minute integration time rain rate data obtained from Nigerian Meteorological Agency (NIMET) and the Departmentof Physics, Federal University of Technology, Akure were used to compute the fog- and rain-induced specificattenuations using Kruse model and Carboneur model respectively. The performance of the FSO system is analyzedthrough link margin by using the parameters of a commercial optical transceiver, Terescope 5000.

Assessment of fog and rain induced-attenuation on terrestrial FSO links

Launching into the next generation of wireless communication network (5G network) requires secure highdata rate, high speed and huge bandwidth links. With the tremendous increase in broadband users, the existingcommunication systems such as radio frequency (RF) and microwave links cannot meet up with the challenges due totheir link interference and low bandwidth. A current technology that promises such requirements and more is FreeSpace Optical (FSO) communication. The FSO basically involves the transmission of signal-modulated opticalradiation from a transmitter to a receiver through the atmosphere or outer space. It is designed to complement thetraditional fibre optical communication links. However, location-variant atmospheric channel degrades the qualityand performance of an FSO system under severe atmospheric conditions. This paper attempts to assess both fog- andrain-induced attenuation on the performance of FSO link in a terrestrial terrain using measured visibility and rainrate data at Akure, Nigeria. 5-year (2012-2016) archived visibility data and measured rain rate data of 1-minuteintegration time obtained from Nigerian Meteorological Agency (NIMET) and the Department of Physics, FederalUniversity of Technology, Akure respectively, were used to compute the fog- and rain-induced specific attenuationsusing Kruse and Carboneur models. The performance of the FSO system was analyzed through link margin by usingthe parameters of a commercial optical transceiver, Terescope 5000. Findings from this work will be useful for FSOsystem design in the area.