Rainfall Phenomena Research Articles

PEMANFAATAN NUMERICAL WEATHER PREDICTION DAN CITRA SATELIT HIMAWARI-9 DALAM ANALISIS KONDISI ATMOSFER SAAT HUJAN LEBAT

Heavy rainfall occurred in the Special Region of Yogyakarta on March 14, 2024. This rainfall event was categorized as extreme weather, as data from the Regional Disaster Management Agency (BPBD) reported damage in 496 affected locations. Heavy rainfall can occur due to atmospheric instability caused by the growth of convective clouds (cumulonimbus). The phenomenon of heavy rainfall was monitored using remote sensing systems in the form of satellites to observe and analyze the event. Yogyakarta's topography explains the use of ECMWF ERA-5 model data to identify wind distribution patterns (streamlines) influenced by westerly winds. The Convective Cloud Overlay (CCO), red-green-blue (RGB), and High-resolution Cloud Analysis Information (HCAI) methods were applied to interpret cumulonimbus cloud development, observed from the formation phase (08:00 UTC) to the dissipation phase (18:00 UTC). Observations indicated a decrease in cloud-top temperature to -80°C at 09:00 UTC, followed by dissipation with a temperature of -20°C at 18:00 UTC. Atmospheric instability indices were analyzed using numerical weather prediction (NWP) methods to obtain quantitative values for indices contributing to heavy rainfall, such as SSI, LI, KI, TT, SWEAT, and CAPE. This study concluded that a "moderate" increase in instability index values explained why convective cloud development occurred.

Characterization of the Physicochemical Properties of Salix psammophila Sand Barriers Degradation Under Ultraviolet Irradiation and Synergistic Water Environment

The degradation of Salix psammophila mechanical sand barriers in desert environments can lead to a reduction in their windbreak and sand-fixing benefits, thereby becoming a significant factor limiting the operational lifespan of these structures. Targeting the typical damage types of S. psammophila sand barriers in the desert, we conducted simulations of desert sunlight and rainfall phenomena and investigated the changing characteristics of the physical, mechanical, and chemical properties of sand barriers in the degradation process. The results clearly indicate that (1) accelerated aging for 288 h represents a critical time point for assessing changes in physical, mechanical, and chemical properties during the interaction between ultraviolet irradiation and water. Following 576 h of accelerated aging, compared with a fresh S. psammophila branch sample (CK), the mass loss percentage was 24.33%, while the basic density decreased by 35.87%, and the modulus of rupture and elasticity decreases by 24.93% and 23.03%, respectively. (2) After accelerated aging for 576 h, the contents of lignin, hemicellulose, and cellulose decreased by 35.93%, 33.84%, and 22.38%, respectively. The interaction between simulated ultraviolet irradiation and water under sunlight intensifies the vigorous physical and chemical reactions occurring within the S. psammophila sand barrier, alters its internal structure, diminishes its mechanical properties, and expedites the degradation of its protective capabilities.

Comparison of Different Quantitative Precipitation Estimation Methods Based on a Severe Rainfall Event in Tuscany, Italy, November 2023

Accurate quantitative precipitation estimation (QPE) is fundamental for a large number of hydrometeorological applications, especially when addressing extreme rainfall phenomena. This paper presents a comprehensive comparison of various rainfall estimation methods, specifically those relying on weather radar data, rain gauge data, and their fusion. The study evaluates the accuracy and reliability of each method in estimating rainfall for a severe event that occurred in Tuscany, Italy. The results obtained confirm that merging radar and rain gauge data outperforms both individual approaches by reducing errors and improving the overall reliability of precipitation estimates. This study highlights the importance of data fusion in enhancing the accuracy of QPE and also supports its application in operational contexts, providing further evidence for the greater reliability of merging methods.

Chemical characteristics of Salix psammophila sand barriers are accelerated degradation by ultraviolet irradiation and water.

The implementation of Salix psammophila sand barriers measures constitutes a crucial element in desertification control, providing a solid theoretical foundation for the future application and pretreatment of sand barriers in production practices. To address the specific damage types predominant in desert environments, we executed simulations of ultraviolet irradiation and rainfall phenomena on mechanical sand barriers in sandy areas and also inspected the variations in chemical properties during accelerated aging processes. The findings unequivocally demonstrate that: (1) The synergistic impact of ultraviolet irradiation and water accelerated the deformation and fracturing of the S.psammophila sand barriers, thereby causing a partial degradation of its chemical properties and conspicuous lignin oxidation; (2) The fissure of the sand barrier deepened, resulting in structural alterations. The existence of water expedites the degradation process of S.psammophila sand barriers under ultraviolet irradiation. (3)With respect to the binding form of C atoms, the carbon atoms at S.psammophila sand barriers were highly oxidized after 576 hours of accelerated aging. The components of C2 (C-O) and C3 (C=O) rising to 40.16% and 12.24% respectively, while the components of C1 (C-C) declined to 47.60%. The amount of hydroxyl (O-C-O), carbonyl (C=O), and carboxyl (O-C=O) groups increases in line with the expansion of the contact area between the sand barrier structure and ultraviolet irradiation as well as water. More free radical substances are generated, thereby causing the chemical binding properties to tend to be more stable. In summary, Ultraviolet irradiation and water change are the primary factors influencing the degradation of S.psammophila sand barriers material structure and properties. In future desertification control, it is imperative to focus on enhancing the longevity of sand barriers by ensuring their waterproofing capabilities and resistance to ultraviolet irradiation.

Raining-inspired method for construction of porous film material

The low-temperature environment caused by solvent evaporation leads to the condensation of water vapor into water droplets that remain on the surface of the film to form breath figure patterns. The conventional approach to regulate the pore morphology in the breath figure process is to optimize the ambient temperature, humidity, and solution concentration. However, realizing a wide adjustable window of pore size and uniform distribution of the pore are still challenges. Here, inspired by the rainfall phenomenon, we proposed a simple and efficient method called the “raining boxing method” (RBM) for preparing porous films based on exogenously given water droplets as templates. The RBM broadened the adjustable window of pore size (0.6–225 µm in this work) and solved the inherent problem of radial reduction of pore size from the film center to the edge caused by the significant difference in low-temperature duration at different locations accompanying the solvent evaporation process. Furthermore, this method could realize multi-types porous films, including surface porous films, spongy porous films, and honeycomb porous films, and could be universally applied in the casting process of various polymer solutions.

Northern Pacific sea-level pressure controls rain-on-snow in North America

Rain-on-snow (ROS) events, a phenomenon of liquid rainfall falling over accumulated snowpack, cause quick melting of snow, often leading to rapid and catastrophic flooding. Here we explore the causal drivers of ROS events across North America. A ROS identification method is proposed, which builds on the existing methods but adds more realism in terms of rain and snow conditions for ROS occurrence. We consider a wide range of observed hydrometeorological variables along with climatic oscillations over the period of 1951 to 2022. Causal linkages between the potential drivers and ROS frequency are explored by implementing Convergent Cross Mapping (CCM). Results suggest a strong causal link between the North-Pacific (NP) Index, a measure of sea-level pressure in the Northern Pacific Ocean, and ROS frequencies in North America, specifically in the eastern and western parts. We show the association of the NP index with the hydroclimatic variables and explain how this association might have contributed to this causal link. Thus, our findings provide valuable insights into the potential mechanisms of ROS events in different regions in North America.

Droplet Injection for Multiphase Rainfall Simulations on Dynamically Refined Mesh for Effective Interface Capturing

This paper presents a volume of fluid (VOF) model for multiphase rainfall simulation that injects rain droplets as a continuous phase alongside the adaptive mesh refinement (AMR) method to resolve air-water interfaces. An approach based on multi-level domains and a pseudo-random algorithm is proposed to determine injection locations in a random and non-intrusive manner with the least bias. Face zones instead of faces are adopted as constituent elements for droplet injection to prevent droplet fragmentation due to a change of face IDs caused by AMR. Correspondingly, a new multi-level address system is introduced to establish the correspondence among subdomains at different levels. The model is applied to multiphase flow VOF simulation for parametric 2-D and 3-D rainfall analysis. The results are verified by the data reported in the literature. It demonstrates that the model can efficiently simulate realistic rainfall phenomena, capture the droplet interface, and preserve droplet injection uniformity.

Fine‐Scale Characteristics and Dominant Synoptic Factors of Spring Precipitation Over Complex Terrain of the Southeastern Tibetan Plateau

AbstractSpring precipitation over the southeastern Tibetan Plateau (SETP) produces more than 34% of annual precipitation, which is comparable to summer precipitation. This pre‐monsoon rainfall phenomenon, influenced synthetically by atmospheric circulations and topography, makes the SETP an exception to its surroundings. Here, fine‐scale characteristics and typical synoptic backgrounds of this unique phenomenon have been investigated. The spring precipitation over the SETP is characterized by high frequency at hourly scale, with a single diurnal peak at night or early morning. Event‐based analysis further demonstrates that the spring precipitation is dominated by long‐lasting nocturnal rainfall events. From early to late spring, the dominant synoptic factor evolves from terrain‐perpendicular low‐level winds to atmospheric moisture, influencing the spatial heterogeneity and fine characteristics of the spring precipitation. The westerly dominated type, featured by lower geopotential height over the TP and enhanced westerlies along the Himalayas, produces limited‐area precipitation at those stations located at topography perpendicular to low‐level winds. In contrast, the moisture‐dominated type is featured by an anomalous cyclone over the Bay of Bengal and induces widespread precipitation around the SETP, which is the leading contributor to the spring precipitation there. Accumulated precipitation amount of long‐lasting nocturnal events is the largest under moisture‐dominated type, which has a large portion of weak precipitation due to weak thermal instability. Findings revealed in this study complete the picture of spring precipitation influenced by different dominant synoptic factors over the SETP, which deepen the current understanding of the joint influence of circulation and topography on the hydrological cycle of complex terrains.

The ‘Deluge’ of 25 October 1822 in Genoa, Italy

The ‘Deluge’ of 25 October 1822 in Genoa, Italy

Physical responses of Baiu extreme precipitation to future warming: Examples of the 2018 and 2020 western Japan events

Baiu (Meiyu) is a continuous and large-scale rainfall phenomenon in East Asia, whose extreme events have caused many disasters in recent years. Global warming will accelerate the global water cycle and tend to increase the risks of extreme precipitation events. However, to date, the response of Baiu extreme rainfall to future climate warming is still not well investigated. In this study, by conducting the ensemble experiments with a very high resolution (2 km) regional climate model and pseudo warming technique, we reproduced two Baiu extreme rainfall events in 2018 and 2020 that caused severe losses in western Japan, and investigated its response to a +4K warmer climate. We found that climate warming will apparently increase the intensity of the peak Baiu precipitation, and the increasing rates can be much larger than the well-known Clausius-Clapeyron (C–C) scaling (∼7% K−1) with the maximum rate reaching 16.7% K−1 for Shikoku in the 2018 event. This increase in peak precipitation is jointly caused by the increased atmospheric moisture and the accelerated vertical air motion. However, both super and under C–C scaling of changes are expected for total precipitation, with the range from 1.5% K−1 to 17.4% K−1 for different regions and events. The rainfall change rates are jointly influenced by moisture transport direction and elevation, topography, and vertical air motion. The moisture inflow with lower elevation tends to cause a larger rainfall increase. It should also be noted that our regional simulations underestimate the extreme precipitation when reproducing the two events, which is caused by the bias in the GCM data. Implementing bias correction methods or future improvement of GCM simulations could be the solutions for future study. Overall, this study points out the risk of extreme Baiu rainfall under future global warming, and highlights the different increasing rates of extreme Baiu rainfall due to specific climate conditions.

Vegetation responses to variations in climate: A combined ordinary differential equation and sequential Monte Carlo estimation approach

Vegetation responses to variation in climate are a current research priority in the context of accelerated shifts generated by climate change. However, the interactions between environmental and biological factors still represent one of the largest uncertainties in projections of future scenarios, since the relationship between drivers and ecosystem responses has a complex and nonlinear nature. We aimed to develop a model to study the vegetation's primary productivity dynamic response to temporal variations in climatic conditions as measured by rainfall, temperature and radiation. Thus, we propose a new way to estimate the vegetation response to climate via a non-autonomous version of a classical growth curve, with a time-varying growth rate and carrying capacity parameters according to climate variables. With a Sequential Monte Carlo Estimation to account for complexities in the climate-vegetation relationship to minimize the number of parameters. The model was applied to six key sites identified in a previous study, consisting of different arid and semiarid rangelands from North Patagonia, Argentina. For each site, we selected the time series of MODIS NDVI, and climate data from ERA5 Copernicus hourly reanalysis from 2000 to 2021. After calculating the time series of the a posteriori distribution of parameters, we analyzed the explained capacity of the model in terms of the linear coefficient of determination and the parameters distribution variation. Results showed that most rangelands recorded changes in their sensitivity over time to climatic factors, but vegetation responses were heterogeneous and influenced by different drivers. Differences in this climate-vegetation relationship were recorded among different cases: (1) a marginal and decreasing sensitivity to temperature and radiation, respectively, but a high sensitivity to water availability; (2) high and increasing sensitivity to temperature and water availability, respectively; and (3) a case with an abrupt shift in vegetation dynamics driven by a progressively decreasing sensitivity to water availability, without any changes in the sensitivity either to temperature or radiation. Finally, we also found that the time scale, in which the ecosystem integrated the rainfall phenomenon in terms of the width of the window function used to convolve the rainfall series into a water availability variable, was also variable in time. This approach allows us to estimate the connection degree between ecosystem productivity and climatic variables. The capacity of the model to identify changes over time in the vegetation-climate relationship might inform decision-makers about ecological transitions and the differential impact of climatic drivers on ecosystems.

Assessment of agricultural drought using multi-temporal synthetic aperture radar (SAR) and multispectral data – A case study on part of Odisha State, India

Drought is primarily considered as a natural hazard which is a resultant phenomenon of less rainfall. When an agricultural area constantly received low rainfall during the sowing and growing season of crop, the soil moisture of the cropland goes down and the crop began to wilt, the situation is known as agricultural drought. Present study, used the multi-temporal Sentinel 1 SAR and Landsat optical data to assess the agricultural drought in the part of Odisha state. Initially, to establish the methodology, correlation between NDVI and SAR backscattering has been carried out for the demarcated agricultural landscape of MODIS images derived landcover map of 2019. The drought assessment has been carried out for the month of Jun-July of 2020 and 2021 which is the kharif crop growing season. Thereafter, the backscattering vale range has been identified as a threshold value to delineate the agricultural drought affected areas from other SAR images of various vintages. The study area was more affected by the drought in 2020 compared to 2021. In this study, it is observed that although the area is drought affected, received less rainfall, but due to location in coastal region this area is mostly under cloud cover, therefore, it is very difficult assess the agricultural drought using optical remote sensing data. SAR data is also sensitive to soil moisture condition; therefore, it can be potentially to be used for drought assessment for the coastal agricultural area.

A MESO-scale Rain gauge NETwork-MESONET over Mumbai: Preliminary results and applications

Mumbai, the compact megacity, the economic capital of India, is highly vulnerable to severe weather conditions, particularly related to the rainfall phenomenon. A MESO-scale rain gauge NETwork (MESONET) comprises 131 rain gauges scattered over the Mumbai metropolitan region is established to have instantaneous rainfall information. A real-time web-based rainfall data portal is set up as a public informative/awareness system that would help the public to cope with unanticipated situations caused by massive rainfall events. In this work, we provide the rationale of Mumbai MESONET and preliminary results from the observations during July–October 2019.High-resolution MESONET rainfall data are analyzed to examine the mean and associated small-scale spatiotemporal variability of rainfall over Mumbai. The results depict that the maximum contribution of rainfall is from low to moderate and heavy rainfall events. The prevalent occurrence of prolonged rainfall events of moderate to high intensity during mid-night and early morning hours is attributed to the diurnal variability of events duration. It is observed that heavy rainfall events (64–124 mm/day) occur at a sub-daily scale. Correlogram analysis of rainfall further reveals the good spatial coherence in the spatial variability of rainfall even at sub-daily scales.

Modelagem chuva-vazão via redes neurais artificiais para simulação de vazões de uma bacia hidrográfica da Amazônia

The techniques of artificial neural networks (ANNs) have been used in the prediction of hydrological variables due to the ability to generalize information, which makes the implementation of models less costly and more agile. In this study, the phenomenon of converting rainfall into streamflows of a Guama River Hydrographic Sub-basin (GRHS) in the State of Para, Amazon, was simulated. The models are based on MLP (Multilayer Perceptron) and NARX (Nonlinear Autoregressive with Exogenous Inputs) ANNs, with Hyperbolic Tangent activation function in hidden layer neurons, being trained by the supervised training algorithm Levenberg-Marquardt. Comparing the proposed ANNs, the NARX-ANN models presented better performances compared to the MLP-ANN model. The best of the NARX-ANN models presented, on average, for the training, validation and test phases, R² equal to 0.9901, RMSE equal to 11.73 m3s-1 and MAPE equal to 5.94%. These results show the possibility of simulating the streamflow of small and medium hydrographic basins in the Amazon through the combination of NARX-ANNs, mainly those basins with no or limited rainfall-flow data.

Rainfall Model Using Principal Component Regression Analysis with R Software in Sulawesi

Indonesia is a tropical country that has two seasons, rainy and dry. Nowadays, the earth is experiencing the climate change phenomenon which causes erratic rainfall. The rainfall is influenced by several factors, one of which is the local scale factor. This research was aimed to build a rainfall model in Sulawesi to find out how the rainfall relationship with local scale factor in Sulawesi. In this research, the data used were secondary data which consisted of 15 samples with 6 variables from Badan Pusat Statistik (BPS). The limitation of the sample size in this study was due to the limited secondary data available in the field. The data was processed using Principal Component Regression Analysis. The first step was reducing local scale factor variables so that the principal component variable could be obtained that can explain variability from the original data which then that variable was analyzed using principal regression analysis. The data were analyzed by utilizing R Studio software. The results show that two principal component variables can explain 75.2% of the variability of original data and only one principal component variable that was significant to the rainfall variable. The regression model explained that the relationship between rainfall, humidity, air temperature, air pressure, and solar radiation was in the same direction while the relationship between rainfall and wind velocity was not in the same direction. Overall, the results of the study provided an overview of the application of the Principal Component Regression analysis to model the rainfall phenomenon in the Sulawesi region using the R program.

Simulation on Water Behavior in a Retention Pond at UNITEN

Retention pond have been used widely to manage stormwater runoff to avoid flooding and downstream erosion and even improve water quality. It is an artificial pond that have vegetation around the perimeter and a permanent pool of water. This paper presents the study on water flow behaviours in UNITEN’s retention pond by using ANSYS Fluent. Findings shows that the velocity of water in wet condition is higher than the dry condition because of an additional inlet in wet condition to imitate the heavy rainfall phenomenon. It is also found that the water pressure in wet condition is higher compare to dry condition. Therefore, the conditions that have a higher movement of water can be determined through this simulation. Based on the findings, it can be concluded that the wet condition have a high movement of water behaviour compare to dry condition.

Preprocessing and Assessment of Rain Drop Size Distributions Measured With a K-Band Doppler Radar and an Optical Disdrometer

Rain attenuation in millimeter-wave links depends on the drop size distributions (DSDs) of the raindrops. Empirical models disregard this dependence and estimate the specific attenuation using only the integrated rainfall rate [ $R$ (mm/h)]. This approach is valid for lower frequencies but it progressively losses accuracy as the frequency of interest becomes higher within the millimeter-wave range. Both the characterization of rainfall phenomena and the prediction of rain attenuation can be improved with the knowledge of DSD, which, in turn, depend on the type of rain event (stratiform or convective) and the $R$ . In this article, long-term DSD measurements from a vertical Doppler radar [Micro Rain Radar (MRR-2)] and a laser optical disdrometer (Thies laser disdrometer) are used to obtain, classify, and compare the statistics of DSD in Madrid in a period of more than ten years. The process to obtain the DSD from these advanced instruments is analyzed in detail, providing recommendations about the calibration of the radar data and the most appropriate particle filtering to apply on the laser disdrometer data.

Análise das chuvas na Região Metropolitana do Cariri e a influência do fenômeno climático El Niño - Oscilação do Sul

O estudo do clima é essencial para compreender e monitorar as atividades atmosféricas, como exemplo as mudanças climáticas. O clima de uma região sofre variadas modificações devido a fatores naturais ou antrópicos, destaca-se o El Niño - Oscilação Sul, anomalia esta que podem causar prejuízos financeiros na produção agrícolas, enchentes e secas severas. Destaca-se a Climatologia e a Meteorologia no estudo das precipitações históricas e os fenômenos de ordem climática, que corroboram no controle e compreensão das atividades atmosféricas que influenciam nas chuvas do Nordeste. Nesse sentido, o presente trabalho objetiva analisar o comportamento das precipitações pluviométricas na Região Metropolitana do Cariri e relacionar o comportamento das chuvas com os anos de influência do fenômeno climático El Niño-Oscilação do Sul, no período de 1980 a 2019, enfocando na identificação dos períodos mais secos do regime pluviométrico e os anos de chuvas intensas. Para a realização desse estudo, os dados foram disponibilizados pela Fundação Cearense de Meteorologia e Recursos Hídricos por meio das séries históricas. A tabulação e organização dos dados, como também os cálculos estatísticos, utilizou-se o software Excel. Foi possível identificar a variabilidade e irregularidades nas chuvas da Região e a influência do El Niño, intervindo nas chuvas da Região estudada.

UTILIZAÇÃO DE ESTATÍSTICA DESCRITIVA E DE MODELO SARIMA NO ESTUDO DE PRECIPITAÇÃO NA REGIÃO SUDESTE DE MATO GROSSO

Os modelos da classe ARIMA mostram ter grande potencial nos estudos de séries temporais de dados de chuva. Presente no Sudeste do Estado de Mato Grosso, o município de Rondonópolis apresenta grande destaque no cenário agropecuário e é um dos principais polos econômicos mato-grossenses. Diante da importância da aplicação de modelos de bons desempenhos, e da escassez de trabalhos utilizando esse método nessa região, este trabalho teve como objetivo a obtenção de um modelo do tipo SARIMA para o fenômeno de precipitação pluviométrica entre os anos de 2004 e 2015 no município de Rondonópolis-MT. Analisaram-se também parâmetros estatísticos descritivos, referentes à população de dados, para identificação das estações climáticas nesse período. Para isso, utilizaram-se dados referentes ao período de 2004 a 2015, pertencentes ao INMET (Instituto Nacional de Meteorologia). Para aplicação do modelo SARIMA, foram adotados a metodologia e os indicadores de desempenho propostos na literatura. A média da precipitação acumulada anual nesse período (2004 a 2015) foi de 1330 mm com desvio padrão de 145,6 mm, sendo as estações de chuva e seca definidas pelas faixas de meses outubro-março e abril-setembro, respectivamente. Obteve-se o modelo SARIMA (2,0,0) x (0,1,2) como o mais adequado, apresentando melhor performance em relação aos dados observados.

Climate variability in space-time variogram models of annual rainfall in arid regions

Global climate change affects weather patterns around the world. Rainfall variability, especially in arid regions, can increase or decrease drastically in the space and time domains. To determine the spatial and temporal continuity of rainfall phenomena, geostatistical models and variograms are key tools for estimating and modeling the space and time dependence in a data set. The basic steps in any regional modeling method involve obtaining the experimental variogram and fitting a model to the experimental variogram. Theoretical matching is performed for the experimental variogram considering the sill, nugget, and other features, which reflect the regional characteristics of the phenomena concerned. In this study, a product-sum space-time-standardized variogram procedure is proposed and used to identify the spatial and temporal structures of annual rainfall in the western part of Saudi Arabia. The standardized variogram is, in fact, the reduced scale of the original form without the loss of the characteristics of the underlying phenomena. The results of the space-time-standardized experimental and variogram models indicate that the average annual spatial variograms have a continuous structure based on an exponential model with a small nugget. The average annual temporal variograms have a continuous structure based on an exponential model with a large nugget. These results confirm the high variation in the rainfall pattern in the time domain and low variation in the spatial domain.