Occurrence Of Heavy Rainfall Research Articles

Synoptic-Scale Meteorological Patterns Associated with Heavy Rainfall in the Minnesota Region

Abstract Situated in the Upper Midwest, Minnesota’s midcontinental location places it in a climate transition zone between eastern U.S. humid conditions and western semiarid conditions as well as between warm, moist air from the Gulf of Mexico to the south and drier, polar air to the north. Potential adverse impacts on ecosystems due to changing climate and precipitation patterns, together with ongoing flash flooding risks, indicate that heavy rainfall occurrence and distribution are important considerations for Minnesota. This research used ERA5 reanalysis data with 0.25° grid spacing during May–September 1959–2021 to investigate the synoptic-scale drivers of Minnesota heavy rainfall. The study utilized a neural network, self-organizing map (SOM) technique to identify sea level pressure patterns and precipitation patterns associated with heavy rainfall and used composite analysis to explore the relationships between synoptic-scale conditions and environmental parameters during heavy rain hours. Six sea level pressure patterns were identified, three of which represented advancing surface cyclones and accounted for >70% of the heavy rain hours. The spatial distribution of heavy rainfall was represented by six precipitation patterns. The greatest frequency of heavy rain hours was associated with the northwest precipitation pattern, followed by the southwest and southeast patterns. Analysis of the frequency of pressure and heavy rain precipitation pattern pairs revealed that the top five most frequent pairs were associated with advancing surface cyclones and >26% of the total heavy rain hours. Composite analysis of environmental parameters showed that favorable conditions related to moisture and lift were associated with heavy rainfall.

Geostationary Satellite-Based Overshooting Top Detections and Their Relationship to Severe Weather over Eastern China

Overshooting tops (OTs), prominent signatures within deep convective storms, are produced by intense updrafts and are closely linked to heavy rainfall, strong winds, and other severe weather conditions. Using an OT dataset derived from multiyear observations of precipitation radar on board the Global Precipitation Measurement core observatory as a reference, the performances of two commonly used OT detection algorithms are evaluated for the Himawari-8 and Fengyun-4A satellites. The results indicate that the infrared contour-based algorithm based on Himawari-8 is the most effective for objective OT detection in eastern China. It exhibits a probability of detection (POD) of 62.1% and a false-alarm ratio (FAR) of 36.6%, outperforming others by achieving a greater POD and a lower FAR. Furthermore, based on the severe weather records from surface meteorological stations and nearby OT detections, a strong relationship is revealed between GEO-detected OTs and the occurrence of short-term heavy rainfall (e.g., ≥20 mm h−1) and extreme wind speed (e.g., ≥17.2 m s−1) events. The OT matched percentages for these events are 61.8% and 54.0%, respectively. This suggests that GEO satellite-based OT data can serve as an important objective product for forecasters to increase their understanding of severe convective storms.

Long-Term Energy Yield Analysis of the Rooftop PV System in Climate Conditions of Poland

In the past four years, the number of prosumers utilizing photovoltaic (PV) installations in Poland has increased significantly, exceeding 1.3 million, reaching a total power capacity of 10.5 GW by the end of 2023. This paper presents a three-year energy yield analysis of the prosumer PV systems operating in Eastern Poland. The 9.6 kW system consists of high-efficiency monocrystalline photovoltaic modules in half-cut technology. Over the three years of operation, specific yields have been analyzed along with weather parameters, such as solar intensity, outdoor temperature, humidity, wind speed, rainfall, or snowfall. The average annual final yield was found to be relatively high, exceeding 1000 kWh·kW−1 in each of the analysed years. The highest monthly specific yields of the analysed period were noticed during the summer, reaching the maximum value of 164 kWh·kW−1 in 2022. The daily final yields varied from a minimum of 0.15 kWh·kW−1 in Winter 2021 to a maximum of 6.8 kWh·kW−1 in Spring 2022. Weather conditions increasing the energy yields, such as low average ambient temperatures together with high insolation periods, were noticed. Energy production in such favorable conditions reached a surprisingly high value of energy yield in April 2020, comparable to the summer months (151.0 kWh·kW−1). The occurrence of heavy rainfall in summer was also noted as a desirable effect that leads to the natural cleaning of the PV modules. The average performance ratio during the analyzed period was found to be 0.85. The energy production of the PV system allowed the reduction of about 21 tons of CO2 emission.

Exploring the Spatio-Temporal Trends of Geomorphological Incidents Induced by Precipitation on Chinese Highways

The spatiotemporal distribution of geomorphological incidents was examined in the present study, including the characteristics of obstruction distances and durations, by utilizing nationwide incident mitigation data and precipitation observation records from the period spanning 2019 to 2022. By comparing rainfall features over different temporal scales across various regions, the aim of the present study was to enhance the current comprehension of the patterns through which regional precipitation initiates incidents on highways by comparing rainfall characteristics over distinct temporal scales across diverse geographical areas. The findings indicate that: (1) The spatial distribution of highway incidents in China is significantly correlated with regional natural environments, predominantly concentrated in the southern parts of the country’s second and third topographical terraces. The temporal distribution closely aligns with annual and monthly precipitation patterns, with the majority of occurrences taking place from June to September. Further, notable disparities in the distribution of highway-related incidents were observed among counties across most provinces; (2) National highways experience a notably higher frequency of incidents than expressways and provincial roads, with most obstruction lengths concentrated within 1 km and durations predominantly under 3 days; (3) The probability of daily rainfall inducing highway incidents is distinctly higher than that of short-duration rainfall, with eastern and southern China experiencing significantly greater inducing precipitation volumes than other regions. The majority of areas are susceptible to incidents within a 3-day window following heavy rainfall or within 24 h after intense short-duration rainfall. Moreover, it is observed that incidents are more closely associated with extreme precipitation occurring within a single day; (4) There is a certain lag between the timing of incidents and the occurrence of extreme short-duration heavy rainfall, with the highest frequency of incidents coinciding with continuous rainfall periods of 3 to 6 days.

Treatment Wetland Plant Harvests as a Tool for Soil Phosphorus Reduction in North Central US Agricultural Watersheds

Agricultural watersheds in the North Central United States have been intensively farmed for decades with widespread application of fertilizer and extensive tilling practices. Soil phosphorus built up in sediments over time as a result of these practices may be released under anaerobic conditions, such as flood events. These floods are increasing in frequency and intensity due to climate change, leading to downstream water-quality concerns. Edge-of-field best management practices, including constructed treatment wetlands, provide a natural buffer for excess phosphorus runoff, but may only be a temporary solution if soil becomes oversaturated with phosphorus over extended periods of time. Preventing wetlands from becoming sources of phosphorus to water bodies may be essential for management in future years when considering impacts from climate change. This research assesses how wetland plant harvesting can reduce soil phosphorus accumulation (measured as Olsen phosphorus) in edge-of-field treatment wetlands, thereby preventing these systems from becoming phosphorus sources and ensuring the longevity of water-quality benefits from these systems. Using several 380 L controlled wetland mesocosm experiments in 2018–2019, we assessed above-ground plant material (S. tabernaemontani and B. fluviatilis) and soil Olsen P through the growing season and after harvest. We observed a reduction in soil phosphorus from wetland plant harvesting between 1–50% over one year, with a mean reduction of 7.9 mg/kg. B. fluviatilis initially contained higher P concentration early in the season (0.82% P content) compared to S. tabernaemontani (0.76% P), but S. tabernaemontani retained higher P later in the season (0.3% P content) compared to B. fluviatilis (0.25%). Time of season may significantly impact plant P removal potential, including accessibility of treatment wetland sites. While controlled mesocosm experiments may not always be applicable to real landscape-level management, this study highlights the potential for reductions in soil phosphorus and corresponding downstream phosphorus fluxes in edge-of-field treatment wetlands through plant harvest during the growing season. Plant harvesting can be used by land managers in edge-of-field treatment wetlands as an adaptation mechanism for shifting environmental conditions, such as increased heavy rainfall occurrences and flood events, that are exacerbated by climate change in this region.

Diagnostic Analysis of Diabatic Heating in an Extreme Rainfall Event in Shandong Province, China

This study utilizes data from national ground meteorological observation stations in Shandong province, Fengyun-4 satellite data, and ERA5 reanalysis data. Through the calculation of atmospheric heat source changes, the role of diabatic heating in the occurrence and development of heavy rainfall is revealed. The widespread heavy-to-torrential rainfall event in Shandong province on 25 June 2018 is analyzed as a case study. It was found that a deep and robust southwest jet stream was the key system for the formation of this rainfall event. Satellite cloud images during the peak rainfall period showed vigorous development in the rainfall cloud region. During the concentrated rainfall period and when the low-altitude jet stream strengthened, there was mostly cold advection overhead at the observation station. The low-altitude jet stream transported moisture, increasing the humidity gradient, thus enhancing frontogenesis. The warm advection in the low-altitude jet stream was not the main energy supplier during heavy rainfall, and local temperature variations were the primary contributors to the thermodynamic conditions during the peak rainfall period. The rate of warming caused by the condensation and release of heat from water vapor significantly increased during the concentrated rainfall period. This warming effect played a heating role in the middle and lower layers, and the positive feedback from the latent heat release of water vapor condensation intensified the weather system affecting the rainfall, providing strong thermodynamic and dynamic conditions for heavy rainfall.

Spatial and Temporal Variations of the Precipitation Structure in Jiangsu Province from 1960 to 2020 and Its Potential Climate-Driving Factors

This study investigated the temporal and spatial variations of precipitation duration and intensity in Jiangsu Province from 1960 to 2020 using the IDW spatial interpolation method and Kendall’s tau trend test, based on daily precipitation data collected from 22 meteorological stations. Additionally, a Pearson correlation analysis was conducted to examine the correlations between the occurrence rate and contribution rate of precipitation with different durations and grades, as well as five large-scale climate indices. The results indicated the following trends: (1) An increase in the precipitation duration corresponded to a decrease in the occurrence rates, while the contribution rates initially increased and then decreased. The province was predominantly characterized by 1–3 days of light rainfall, with a higher probability of short-duration heavy rainfall in northern Jiangsu. (2) From 1960 to 2020, most stations experienced decreasing trends in the precipitation duration occurrence and contribution rates, but heavy rainfall increased, suggesting a shift to short-duration heavy precipitation. (3) The Arctic Oscillation (AO) notably negatively correlates with the 9-day occurrence rate of precipitation (9dOR), while it positively correlates significantly with the occurrence rate of moderate rainfall (MROR). The North Atlantic Oscillation (NAO) exhibits a significant positive correlation with the 2-day occurrence rate of precipitation (2dOR) and a notable negative correlation with the 9-day occurrence rate of precipitation (9dOR). The PDO (Pacific Decadal Oscillation) has shown significant positive correlations with the 2-day precipitation occurrence rate (2dOR) and contribution rate (2dCR), a negative correlation with the light rainfall occurrence rate (LROR), and significant positive correlations with both the moderate and heavy rainfall occurrence rates (MROR and HROR, respectively). The AO, NAO, and PDO are potential climate factors that influence changes in the precipitation structure in Jiangsu Province. These research findings offer valuable insights for regional water resource management, flood risk assessment, and predicting future precipitation trends under climate change scenarios.

Climatic-Geomorphological Investigation of the World's Wettest Areas around Cherrapunji and Mawsynram, Meghalaya (India)

This research paper comprehensively examines the climate and geomorphological features of Cherrapunji and Mawsynram, aiming to understand the factors and environmental implications of their extreme precipitation. The study investigates climatic patterns, identifies geomorphological characteristics, and explores the factors influencing the occurrence of heavy rainfall in these areas, and displays unique rainfall patterns with high precipitation levels and notable spatio-temporal variation influenced by topographic interactions. Trend analysis reveals stable rainfall conditions over the past 122 years. The shift of the world's wettest place from Cherrapunji to Mawsynram in recent decades have been attributed to various factors such as geographical location, geomorphology-local topography, LULC-human influence, rain shadow effect, and orographic lifting effects. Cherrapunji recorded maximum rainfall of 24.55 thousand mm, while Mawsynram received 26 thousand mm of rainfall in the last century. The analysis of long-term rainfall data indicates distinct dry and wet seasons, with recent trends (2000-2020) suggesting a decline in rainfall for both locations. Furthermore, extreme value analysis techniques are employed to estimate maximum rainfall for different return periods, offering insights into extreme rainfall events. The return period of one day's highest rainfall of 1340.82 mm is about 100 years. The findings contribute to our understanding of climate change impacts, support sustainable development practices, and inform strategies for water resource management and erosion mitigation in similar geographic contexts. This research enhances our knowledge of these unique regions and their significance within the broader context of global climate systems.

Differences in the distribution, availability, and sorption-desorption isotherms of phosphorus fractions in soil aggregates from cut slopes with different restoration methods

The construction of highways and other transportation facilities resulted in a large number of exposed cut slopes, leading not only to the alteration of soil structure but also to phosphorus (P) losses from soils with low utilization rates. These negative effects can result in stunted plant growth and the occurrence of heavy rainfall and earthquakes-associated landslide and mudslide disasters, threatening people's lives. However, few studies have investigated soil aggregates P in cut slopes restored with different methods in the Alpine Mountainous areas of Southwest China. Therefore, three types of cut slopes (cut slopes restored by three-dimensional mesh (TCS), cut slopes restored by galvanized wire mesh (GCS), and cut slopes restored by nature (NCS)) were selected for the study, and the present study aims to assess the total phosphorus (TP), available phosphorus (AP), and P fractions, as well as the P sorption-desorption isotherms in cut slope soil aggregates. The obtained results are as follows: (1) The TP contents in the soil aggregates did not differ significantly between the three types of cut slopes (p > 0.05), In addition, the AP contents and P activation coefficient (PAC) showed significant differences between TCS, GCS, and NCS, while the H2O-Pi, NaHCO3-Po, NaOH-Pi, NaOH-Po, HHCl-Pi, and HHCl-Po contents were significantly different between the three types of cut slopes (p < 0.05); (2) The AP contents in the three types of cut slopes showed a decreasing trend with increasing soil aggregate size from < 0.25 mm to > 5 mm; (3) H2O-Pi, NaOH-Pi, and HHCl-Pi were the main P fractions affecting the AP contents in the study area, among which NaOH-Pi exhibited the greatest effect on the AP contents; (4) The lowest P adsorption of soil aggregates was observed in NCS. The maximum P adsorption (Qm) values of the < 0.25 mm aggregates in TCS and GCS were higher than those of the other soil aggregate sizes. In addition, the trend of P desorption to increase with P adsorption was more obvious in large aggregates than in small aggregates. The present study provides a theoretical basis for P management in cut slope soils restored by different methods in the Alpine Mountainous region of Southwest China. Moreover, it provides some references for improving cut slope restoration and ecological slope protection techniques.

Verification of multiresolution model forecasts of heavy rainfall events from 23 to 26 August 2017 over Nigeria

AbstractThe study uses numerical weather prediction models to predict the occurrence of heavy convective rainfall associated with the passage of the African Easterly Wave (AEW) during the period 23–26 August 2017 over Nigeria. Fraction skill score (FSS) and method for object‐based diagnostic evaluation (MODE) verification techniques were applied to verify how well the models predict the high‐impact event and to demonstrate how these tools can support operational forecasting. Ensemble model forecasts at a convective scale from UK Met Office Unified Model (MetUM) and a one‐way nested weather research and forecasting (WRF) model were compared with the integrated multisatellite retrievals for global precipitation measurement (IMERG GPM). The purpose is to examine skills of improved model resolution and ensemble in reproducing rainfall forecasts on useful scales and how the skill varies with spatial scale. WRF 2 and 6 km model forecasts show comparable skill at smaller grid scales. The skill of MetUM improves dramatically when the verification statistics are applied to the ensemble mean of the binary fields of the individual member forecast. The object‐based analysis reveals a similar structure as observed, although displaced eastwards. Most improvement occurred for heavier rainfall events associated with the passage of the AEW. WRF 6 km compares reasonably well with WRF 2 km in terms of shape and structure of rainfall underscoring the ability of the model to reasonably represent convection at 6 km horizontal resolution. The ensemble members in MetUM explicitly reproduce convection at 4 km resolution but are displaced at about 166 km behind observed rainfall.

Vertical divergence profiles as detected by two wind‐profiler mesonets over East China: Implications for nowcasting convective storms

AbstractLow‐level convergence is one of the most important dynamic variables in governing the convection initiation (CI) and development of storms. However, our ability to obtain high‐resolution horizontal divergence profiles remains limited due to the lack of high‐resolution vertical wind observations. In this study, horizontal divergence (and vertical motion) profiles are derived from horizontal winds measured by two radar wind‐profiler (RWP) mesonets in East China, and then used to examine their relationship with CI occurring within a mesonet and an intense squall line moving across another mesonet. Results show that the RWP mesonets together with automated surface observations could provide realistically the lower‐tropospheric profiles of horizontal divergence (and upward motion) associated with the CI in the presence of the urban heat island effects, and lake and sea breezes; and the evolution of the squall line respectively. High‐resolution surface observations resolve better divergence induced by localized CI than the RWP measurements, whereas the latter capture well significant convergence below 4‐km altitude that precedes the onset of squall precipitation. A statistical analysis of the RWP data during the summer months of 2018 indicates that the lower‐tropospheric convergence can be detected up to about 40 min in advance of the occurrence of heavy rainfall, and the correlation is higher when it is closer to the heavy rainfall time. Nearly 40% of heavy rainfall moments (at 6‐min intervals) are accompanied by strong convergence signals occurring mostly near the top of the boundary layer. This work indicates that the RWP and surface wind measurements should be integrated to aid in nowcasting the location and timing of CI and development of convective storms.

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

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

Performance of Different Parameterization Configurations of WRF-ARW Model during Heavy Rainfall over Mi Oya River Basin

Short-term intense precipitation is one of the hallmarks of climate change. Mi Oya River basin experiences severe seasonal floods annually, but the damage can be lessened by developing a numerical weather forecasting (NWF) model for the entire basin and incorporating it with effective reservoir management. Several areas in Sri Lanka have undergone NWF studies, however they are insufficient to determine the best physics schemes for the basin. This study investigates the Weather Research and Forecasting (WRF-ARW) model's predictability with varying three microphysics and two cumulus schemes to discover the optimal set of physics parameters for predicting heavy rainfall occurrences throughout the Southwest and Northeast monsoon seasons within a nested domain configuration. The WRF model's forecasting results at 3 km grid resolution were compared with four rainfall gauging stations in the basin for three rainfall events in May 2016, April 2018, and November 2015. Total Model Performance was derived for the evaluation utilizing bias, MAE, RMSE, Correlation Coefficient, and slope of each model's output data with observed rainfall data. After comparing the model output to data, WSM6 microphysics and Betts-Miller-Janjic cumulus with other default physics settings were determined to be the optimal physics combination to forecast weather across the region.

Influence of Green Roofs on the Design of a Public Stormwater Drainage System: A Case Study

In the face of excessive soil sealing and the occurrence of heavy rainfall in short time periods leading to flooding, it is becoming increasingly urgent to implement public resilient stormwater drainage systems. Green roofs have several advantages at different levels, of which this paper highlights the ability to retain rainwater, to reduce problems with flooding in peaks of rainfall, and to increase in urban green infrastructure with all the benefits associated. In this sense, green roofs’ impact on the design of a public stormwater drainage system and their implications for urban stormwater management was analyzed when compared with conventional roofs. If green roofs are used on the buildings in the study urban area, which has about 2.1 ha and is located in rainfall region B of Portugal, then the weighted average runoff coefficient (Cm) for the study area is 0.59. This scenario leads to a reduction in the maximum flow rate of 15.89% compared to the use of conventional roofs, with a Cm of 0.70 for the same area. Thus, the use of green roofs instead of conventional roofs can have positive impacts on the surface runoff in urban areas and contribute to more sustainable urban drainage.

Role of the subtropical westerly jet wave train in the eastward-moving heavy rainfall event over southern China in winter: A case study

Under the wintertime background of westerly flows, heavy rainfall often occurs first over southwestern China and then spreads eastward downstream to southeastern China, causing serious impact on a large scope of southern China. By using the synoptic diagnosis and dynamic analysis methods, we examined a typical eastward-moving heavy rainfall event that occurred over southwestern China on January 8–9, 2015 and then gradually spread eastward to southeastern China on January 10–13. The results show the important role of the Rossby wave train propagating along the subtropical westerly jet in “pushing” the eastward movement of heavy rainfall over southern China. The remote Rossby wave train influenced the variation of heavy rainfall by modifying the local circulation. The Rossby wave train can change the morphology of the East Asian subtropical jet via wave—jet interactions and modify the related secondary upward circulation in the entrance area of the jet, providing favorable dynamic conditions for the occurrence of heavy rainfall. The Rossby wave train can also influence the low-level south branch trough by modifying the associated baroclinic energy conversion process under a background of deep layer subsidence, which favors the transport of plentiful water vapor to the region of heavy rainfall over southern China. The eastward-propagating Rossby wave train pushed the upper level East Asian subtropical jet and the lower level south branch trough as a whole while simultaneously moving east, which caused the region of heavy rainfall to move from southwestern to southeastern China. These results will help to provide a scientific basis for forecasting winter rainfall over southern China.

The Quantitative Comparison of Grid Re-analysis Rainfall Products, Satellite Rainfall Products, and Hourly Rainfall Gauge Observation over Bali Province

Grid re-analysis and satellite-based rainfall products offers a means of estimating rainfall information at both regional and worldwide level. Numerous rainfall estimates are accessible, each varying in terms of the retrieval algorithm, sensor instrumentation, spatial-temporal resolution, and geographical coverage. The objective of this research was to assess the accuracy of grid re-analysis precipitation dataset (ERA-5) and the satellite-derived rainfall dataset (IMERG) when compared to hourly rain gauge observations in Bali Province during the period spanning 2017 to 2020. To assess grid-based datasets, conventional point-to-pixel comparison methods, along with statistical metrics in the forms of continuous, categorical, and volumetric measurements. The comparative findings illustrate that IMERG exhibits superior performance at sub-daily scales in accurately detecting volume, whereas ERA-5 demonstrates greater capability in identifying rainfall events. Both products display a tendency to overestimate the capture of low to moderate rainfall occurrences and for underestimating intense to exceptionally heavy rainfall occurrences. The IMERG product excels across various elevations.

Heavy rainfall forecasting for Dehradun capital city during monsoon season 2020

Dehradun city is Uttarakhand's capital and part of the doon valley, which lies between the Himalayas and the Shivaliks. India Meteorological Department has undertaken the Impact Based Forecasting (IBF) of heavy rainfall events for capital/major cities on an experimental basis from the monsoon season 2020. With the improvement in meteorological observation &amp; monitoring network of Uttarakhand and short &amp; medium range Numerical Weather Prediction (NWP) model outputs, it has become possible to provide impact-based forecast at a very small spatial scale with good accuracy. With the help of a dense network of 5 General Packet Radio Service (GPRS) based Automatic Weather Stations (AWS) &amp; 2 Automatic Rain Gauges (ARG) within a 20 Km radius of Dehradun city center, it has become possible to capture &amp; monitor these events. The spatial analysis of these events suggests that the local orographic features rather than large-scale synoptic features played a major role in heavy rainfalls over Dehradun city. The Heidke skill score of 0.63 and 100% probability of detection is found for the capital city forecast of Dehradun. The presence of monsoon trough to the north of its normal position, a cyclonic circulation south of Uttarakhand over Haryana or East-Rajasthan or West Uttar Pradesh and a Western disturbance around 70°E were found to be the synoptic systems favorable for the occurrence of heavy rainfall in Dehradun city. The occasional strong south-westerly wind flow from the Arabian Sea converging over the foothills of the Western Himalayas is also found to be responsible for the heavy downpour.

Localized urban canopy model and improved anthropogenic heat parameters in the weather research and forecasting model: Simulation of a warm-sector heavy rainfall event over the pearl river delta urban agglomeration

Warm-sector heavy rainfall in South China is a frequent type of precipitation in summer in the Pearl River Delta region. The complexity of the mechanisms involved in the triggering of convection, especially the effects of urbanization, has greatly increased the uncertainty of numerical simulations of warm-sector heavy rainfall. In this study, five new surface parameters with five new anthropogenic heat (AH) parameters were constructed and coupled with the urban canopy model (UCM) of the Weather Research and Forecasting model, version 4.1, based on the local climate zone system over the Pearl River Delta Urban Agglomeration (PRDUA). Taking a typical warm-sector heavy rainfall process that occurred in the PRDUA on 20 April 2019 as an example, five groups of experiments involving different schemes were compared and analyzed, revealing that the precipitation simulated using the localized UCM with the new AH parameters was the best (closest to observations). The localized UCM successfully simulated the increase in 2 m temperature and sensible heat flux and the resultant thermal forcing in urban areas, which promoted the convergence of low-level southerly winds with water vapor and the lifting of the lower-layer warm and humid water vapor to the upper layers in the urban center, leading to a significant increase of precipitation. The improved AH parameters enhanced the anthropogenic heat and its vertical conduction in urban areas, but contributed only marginally to the convergence of 10 m winds. Compared with observations from wind profile radar, it was found that the localized UCM enhanced the accuracy of the simulated horizontal wind field convergence at upper and lower levels, while the improved AH parameters enhanced the accuracy of the simulated low-level jet intensity and vertical movement, which are important drivers for the spatial variation in warm-sector heavy rainfall over the PRDUA. The current findings will be helpful for improving the model skill in simulating warm-sector heavy rainfall over high-density urban areas, as well as enhancing understanding of the impact mechanism of urbanization on the occurrence and development of warm-sector heavy rainfall.

IOT based Flood Monitoring and Alerting System

Abstract: Flooding is considered one of the most destructive natural disasters in the world. In countries like India with climatic conditions occurrence of heavy rain fall and subsequent discharge of water leads to Flood. Flooding creates major damages to life, their habitats and the economy By installing of flood alerting systems near major waterways vital information can be provide so that lives and property can be protected. Normal Weather monitoring and alerting systems are not quick and accurate enough to predict floods in time to prevent personal or environmental damages. The government has to spend tons of money in flood mitigation plans to help the victims and also to reduce the number in the long run damages that can occur after flooding. Since Most of the flood alerting systems involves high cost they are deployed on select locations based on priority. In this project we make use of a cost effective system using raspberry pi board and sensors, to measure rise of water level in rivers and water bodies and alert government authorities and people instantly by transmitting information using IOT. In the present work we have used thingspeak-IOT platform and GSM module. The data can be accessed from android smart phones using things View mobile application at anytime from anywhere in the world where GSM module is used to send the alert message to the people.

Reducing uncertainty in simulated increases in heavy rainfall occurrence

Reducing uncertainty in simulated increases in heavy rainfall occurrence