Distribution Of Rain Intensity Research Articles

Distribution of Rain Intensity: Daily Maximum Rainfall Data in The Province of South Sumatera and West Sumatera

The high intensity of rain in May 2024 caused flooding in the Sumatra region, especially Padang City and Palembang City. The region of West Sumatra is also known as the ethalae of disasters with frequent earthquakes, floods, and landslides. In Indonesia, many frequency analyses are conducted using the Gumbel distribution without testing data and clear hydrological reasons. It is feared that this method is considered as a routine way, thus causing the risk of unwanted deviations, resulting in many disasters and errors in the planning of water buildings. This research aims to determine and know the type of distribution that is representative for the maximum daily rainfall frequency in West Sumatra and South Sumatra Provinces as a reference for development and disaster mitigation in the region. The research location is in West Sumatra and South Sumatra. The data used for this study were taken from daily maximum rainfall data from rain stations for 35 to 40 years from 24 rain stations in West Sumatra Province, and 16 rain stations for South Sumatra region. This type of research is development research with the 4D development model (Define, Design, Develop, Disseminate). The development method consists of four different phases: Initial Investigation, Design, Realisation and Development. The data of each station was then organised into two types of data series, namely the annual maximum data series and the annual minimum data series. The results of this data series test are expected to follow one or more types of distributions commonly used in Hydrology and Drainage. The distribution results obtained for the South Sumatra region are Gama-III and LP-III, while for the West Sumatra region the distribution types are Gama-III, LP-III Normal Log and Gembel. The conclusion obtained is that West Sumatra does not have only one type of distribution. For the type of test using the SPSS.22 application, the results obtained are goodness of fit test, parametric test, Chi-Square test, Kolmogorov Smirnov test and Anderson-Darling test and Histrogram (visual) method.

Wind turbine rotor speed design optimization considering rain erosion based on deep reinforcement learning

Rain erosion is one of the most detrimental factors contributing to wind turbine blade (WTB) coating fatigue damage especially for utility-scale wind turbines (WTs). To prevent rain erosion induced WTB coating fatigue damage, this paper proposes a deep reinforcement learning (DRL)-based optimization method for finding the optimal rotor speed under different rain intensities and wind speeds. First, an efficient physics-based model for predicting WTB coating fatigue damage considering the comprehensive blade coating fatigue mechanism, rain intensity distribution, and wind speed distribution is presented. Then, a WT rotor speed design optimization problem is constructed to search for the optimal rotor speed under different rain intensity and wind speed conditions. To address the challenge of optimizing the efficiency, the original design optimization problem is converted into a DRL-based design optimization model. A hybrid reward is proposed to enhance the DRL agent trained by a deep deterministic policy gradient algorithm. Finally, the proposed DRL-based design optimization method is utilized to guide the optimal rotor speed scheduling of a 5-MW WT under given wind speed and rain intensity conditions. The results show that the proposed method could extend the predicted WTB blade coating fatigue life by 2.55 times with a minor reduction in the energy yield (0.027%) compared to the original rotor speed schedule that only considers maximum power capture. The computational time of the proposed method is reduced significantly compared to that of the traditional gradient and evolutional design optimization methods.

Steeper temporal distribution of rain intensity at higher temperatures within Australian storms

The response of rain and storm dynamics to climate warming is unclear. An analysis of high-resolution rainfall records from 79 Australian stations suggests that rain intensity rises and falls more steeply within a storm at warmer temperatures.

장마와 볼라벤(태풍 15호)에 동반된 집중호우 레이더관측과 위성관측 자료로부터 도출한 강우강도의 비교연구

The heavy rainfalls caused large property damages and human casualties. For example, Changma caused 0.25 billion dollars in damages and 57 deaths and 112 missing by accompanying the torrentially convective heavy rainfall in Seoul, 2011. In addition, TY15 (Bolaven) caused a small damage by bringing a relatively small amount of rainfall and strong wind in Gwanju, 2012. The investigation and analyses of these mesoscale processes of rainfall events for different physical properties using KLAPS for weather environments of the above cases were performed. These typical and ideal meoscale systems by better and more favorable cloud systems were chosen to retrieve rain intensity from Radar and Chullian data. The quantitative rain intensities of Radar and Chullian differ greatly from the ground-based gauge values with underestimating over 50 mm/hr at the peak time of hourly maximum rain intensity about over than 85 mm/hr. However, the Radar rain intensity demonstrated approximately lower than 35 mm/hr, and the Chullian rain intensity less than 60 mm/hr for Changma in Seoul, 2011. For typhoon (TY15, Bolaven) in Gwangju, similarly, the quantitative rain intensities of Radar and Chullian differ from the ground-based gauge values. At the peak time, the hourly maximum rain intensity of ground-based gauge was more than 15 mm/hr. However, the Radar rain intensity showed lower than 5 mm/hr, and the Chullian rain intensity lower than 10 mm/hr. Regarding the above two cases of typhoon and Changma, even though Radar and Chullian rain intensities have been underestimated when compared to the ground-based rain intensity, the distributions of time scale features of both Radar and Chullian rain intensities still delineated a similar tendency of rain intensity distribution of the ground-based gauge data.

Application of Self-Organizing Map (SOM) in Missing Daily Rainfall Data in Malaysia

organizing map (SOM) is applied to deal with missing daily rainfall data with different rainfall patterns in Peninsular Malaysia. In this study, stations from Damansara and Kelantan are focused and aimed to evaluate the effectiveness of SOM in clustering and imputation of missing data. The missing data that are imputed by SOM are evaluated by computing the mean square error (MSE) and coefficient correlation(R). Besides, the effects of the imputed data to the mean and variance of the rainfall data is also been observed. The clustering analysis showed that all the stations in Damansara are grouped distinctively, and having a good and even distribution of rain intensity as compared to Kelantan. Meanwhile it is also found that SOM is an excellent tool in estimation of missing data. Keywords-organizing map (SOM); missing values Recently, the impact of climatic change causes the varying of rainfall patterns in Peninsular Malaysia. The high frequency of flood due to monsoon and non-monsoon seasons will have significant influence to the existence of missing rainfall data. Therefore, this study will use the SOM algorithm in treating this missing data problem. Besides, the clustering analysis by SOM on the two river basins namely Damansara and Kelantan, Malaysia also will be examined. The results will be discussed in Section 4.

Flash flood prediction using an uncalibrated hydrological model and radar rainfall data in a Mediterranean watershed under changing hydrological conditions

Summary Flash floods cause some of the most severe natural disasters in Europe but Mediterranean areas are especially vulnerable. They can cause devastating damage to property, infrastructures and loss of human life. The complexity of flash flood generation processes and their dependency on different factors related to watershed properties and rainfall characteristics make flash flood prediction a difficult task. In this study, as part of the EU-FLASH project, we used an uncalibrated hydrological model to simulate flow events in a 27 km2 Mediterranean watershed in Israel to analyze and better understand the various factors influencing flows. The model is based on the well-known SCS curve number method for rainfall–runoff calculations and on the kinematic wave method for flow routing. Existing data available from maps, GIS and field studies were used to define model parameters, and no further calibration was conducted to obtain a better fit between computed and observed flow data. The model rainfall input was obtained from the high temporal and spatial resolution radar data adjusted to rain gauges. Twenty flow events that occurred within the study area over a 15 year period were analyzed. The model shows a generally good capability in predicting flash flood peak discharge in terms of their general level, classified as low, medium or high (all high level events were correctly predicted). It was found that the model mainly well predicts flash floods generated by intense, short-lived convective storm events while model performances for low and moderate flows generated by more widespread winter storms were quite poor. The degree of urban development was found to have a large impact on runoff amount and peak discharge, with higher sensitivity of moderate and low flow events relative to high flows. Flash flood generation was also found to be very sensitive to the temporal distribution of rain intensity within a specific storm event.

Intensidade-duração-frequência de chuvas para o Estado de Mato Grosso do Sul

A ausência de equações de chuvas intensas para o Estado do Mato Grosso do Sul e a necessidade de maior segurança na elaboração de projetos e dimensionamento de obras hidráulicas, foram o alicerce para, com este estudo, se obtivessem e espacializassem as relações de intensidade, duração e frequência de precipitações para o Estado. Utilizou-se, então, da metodologia da desagregação da chuva de 24 h e se utilizaram dados pluviométricos consistidos de 109 estações disponíveis no banco de dados da Agência Nacional de Águas. As equações de chuvas intensas apresentaram bom ajuste, com coeficientes de determinação acima de 0,99 para todas as localidades estudadas. Os parâmetros ajustados apresentaram alta variabilidade resultando em grandes diferenças nos valores de precipitações intensas esperadas para as diferentes localidades. A espacialização permitiu boa visualização das diferenças evidenciando maiores intensidades esperadas na região centro-norte e as menores intensidades nas regiões sudeste e sudoeste do Estado. Os resultados demonstram a importância da obtenção da equação de intensidade-duração-frequência para cada localidade e sua utilização para a realização de estudos e projetos hidráulicos.

Rain intensity distribution in the splash region of atomized flow

Atomized flow is an unnatural two-phase flow produced while water discharges in water release structures. This flow might threaten the normal operation of hydraulic and hydroelectric installations owing to the unnatural and high-density rain as well as the unnatural and dirty mist. The splash region, the region with the highest rain intensity, hence should receive much attention during the design and operation of the hydraulic and hydroelectric installations. In this paper rain intensity distribution in the splash region of the atomized flow is investigated experimentally, and the method of random simulation is used to predict the rain intensity distribution in the splash region.

Effective concentration time for design storms in complex urban basins

Analytical solutions for runoff caused by a moving rain storm of time-varying rain intensity from a complex drainage basin are derived assuming constant concentration times for the different systems within the basin. It is found that the runoff caused by a peaked rain intensity distribution is higher than that from a uniform distribution of the same mean intensity. It is also shown that the storm movement influences the runoff peak, especially in elongated basins. Correction factors for the non-uniform rain intensity distribution and for the storm movement are introduced allowing the rational formula to be used in its traditional way for the first design of a storm drainage system. The concept of effective kinematic time of concentration is introduced to be used in the rational formula.

A radar-based stochastic model for the time-space arrivals of the rain fields onto a geographical region

Rainfall in time at any ground location can be described by the rain intensity distribution over the subtrajectories which are drawn by the ground location point on the radar-detected incremental rain fields (IRFs) that pass over this location. Based on this conceptualization, this study develops a stochastic description of the arrivals of IRF subtrajectories (SIRFs) onto any given set of ground locations over a geographical region. The arrivals of SIRFs is described by a multivariate doubly stochastic Poisson process, excited by the time-space arrival process of IRFs onto the designated geographical region. The arrival process of IRFs is described by a 3-level time-space nonhomogeneous Poisson cluster model. At the primary level of this model are the arrivals of synoptic cyclonic systems; at the secondary level are the arrivals of large mesoscale rain areas (LMSAs) and at the tertiary level are the arrivals of IRFs onto the designated geographical region, all detected by the weather radar. The models are substantiated both by the graphical analysis of rain fields, as detected on a radar scope, and by the statistical analysis of the arrivals data at all of the four levels at three different ground locations over a rectangular geographical region in Northern Kentucky. It is pointed out that a doubly stochastic Poisson process is basically a Poisson cluster process. Hence, the final process of SIRF arrivals onto any ground location may be interpreted as a 4-level Poisson cluster process. Finally, heavy tails in the sample covariance density functions of the IRF and SIRF arrivals are detected. This study shows that the appropriate incorporation of clustering at all observable distinct scales of the rain fields models the heavy tail behavior of the covariance density adequately.

An estimation method for rain attenuation distribution in microwave multirelay links–compound lognormal distribution model

AbstractEstimation of the rain attenuation distributions in microwave multirelay links at frequencies over 10 GHz involves estimation of the rain intensity distribution at various locations, the rain attenuation distribution in each relay path and the simultaneous probability in multirelay paths. In this paper, the yearly rain intensity distribution data at various locations are studied. It is found that the distribution can be approximated by a compound lognormal model. From the yearly rain data of the weather bureau, the rain intensity distribution parameters of compound lognormal distribution are estimated at 80 locations. It is assumed that the rain intensity is of compound lognormal distribution and the spatial correlation of rain intensity is e‐αd (α: parameter, d: distance between two points). The simultaneous probability of rain attenuation in multirelay paths is calculated. Also calculated is the correction factor Mp (the probability that at least one out of n paths/n times the probability that the attenuation exceeds the specified value at one path) of the added rain attenuation in a multirelay link with respect to the sum of probabilities of attenuation in each path. The measured Mp in the 20‐GHz multi‐relay link between Musashino and Yokosuka agrees well with the estimate Mp from the compound lognormal distribution model.

Prediction of the effects of rain on satellite communication systems

The major propagation effects for satellite communication systems operating above 4 GHz are caused by rain. With the possible exceptions of depolarization and multiple scattering at frequencies above 20 GHz, these effects may be calculated if the distribution of rain intensity is known in both time and space. The major effects-attenuation and interference-require information about path and volume averaged rain intensities. Current prediction models are not capable of adequately estimating the statistical distributions of path and volume averaged values. Radar observations could provide the required data. The best information currently available for modeling these distributions are statistical cell or storm models derived from radar observations.