Search Results (58)

Recent studies have reported a rapid increase in short-duration, high-intensity rainfall over the Seoul Metropolitan Area (SMA), primarily associated with mesoscale convective systems (MCSs), highlighting the need for high-resolution and multi-platform observations for accurate forecasting. To address this challenge, the Korea Meteorological Administration (KMA) established the Korean Precipitation Observation Program (KPOP), an intensive observation network integrating radar, wind lidar, wind profiler, and storm tracker measurements. This study introduces the design and implementation of the KPOP network and evaluates its observational and forecasting value through a heavy rainfall event that occurred on 17 July 2024. Wind lidar data and weather charts reveal that a strong low-level southwesterly jet and enhanced moisture transport from the Yellow Sea played a key role in sustaining a quasi-stationary, line-shaped rainband over the metropolitan region, leading to extreme short-duration rainfall exceeding 100 mm h⁻¹. To investigate the impact of KPOP observations on numerical prediction, preliminary data assimilation experiments were conducted using the Korean Integrated Model-Regional Data Assimilation and Prediction System (KIM-RDAPS) with WRF-3DVAR. The results demonstrate that assimilating wind lidar observations most effectively improved the representation of low-level moisture convergence and spatial structure of the rainband, leading to more accurate simulation of rainfall intensity and timing compared to experiments assimilating storm tracker data alone. These findings confirm that intensive, high-resolution wind observations are critical for improving initial analyses and enhancing the predictability of extreme rainfall events in densely urbanized regions such as the SMA. Full article

In numerical weather prediction (NWP) models, radar data are commonly utilized to retrieve relative humidity fields, thereby mitigating forecast errors arising from uncertainties in the initial moisture field. This study addresses the degradation in convective structure retrieval capability caused by temporal biases in NWP when using spatial neighborhood sampling methods with radar data for relative humidity field retrieval. We developed a time-extended Bayesian retrieval method and constructed a dual-polarization radar data assimilation framework compatible with the China Meteorological Administration Mesoscale Model (CMA-MESO). The core of this approach lies in expanding the Bayesian retrieval sample library by integrating forecast fields from two hours before and after the assimilation time, thereby improving the inadequate performance of traditional spatial sampling under model temporal bias. When applied to a heavy rainfall event in North China in July 2023, this method effectively reduced retrieval errors. The subsequent assimilation of retrieved humidity fields enhanced the Threat Scores for 0–6 h precipitation forecasts and demonstrated improvement in overprediction bias. This confirms that the time-extended strategy can effectively enhance moisture field analysis and nowcasting accuracy by overcoming the inherent limitations of spatial-only sampling. Full article

Every year, thunderstorms initiating in the eastern Pyrenees cause a wide range of adverse phenomena, not only in the mountainous areas but also in the surrounding regions. Events such as heavy rainfall leading to flash floods, large or giant hail, and strong winds are common in this area. These phenomena cause significant damage and have major impacts on the population. We used remote sensing data, specifically weather radar, to identify areas that are more prone to convection initiation. This initial analysis covers the period from 2022 to 2024 and is intended to serve as the foundation for a more extensive study. The aim of this study is to characterize the diurnal convection cycle over the Pyrenees. Additionally, we plan to develop a technique that can be applied to other mountainous regions where similar data are available. The steps are as follows: (1) identifying events with precipitation over the area; (2) selecting cases associated with diurnal convection; (3) applying algorithms to determine the tracks of convective cells; and finally, (4) selecting the initial points of these trajectories. The result is a map highlighting these “hotspot” areas, which will allow us to incorporate other variables in the future, both meteorological and non-meteorological, to identify the main factors influencing the characteristics of each event. Full article

Extreme rainfall events are frequently associated with regions of complex topography, where terrain-induced convergence and uplift enhance storm development. Understanding the interaction between surface relief and atmospheric dynamics is essential for improving severe weather forecasting and hazard mitigation. Storm “Daniel”, which affected Greece from 4–7 September 2023, produced extreme rainfall and widespread flooding in the Thessaly region—a landscape characterized by significant elevation gradients. This study investigates the spatial relationship between lightning activity and terrain elevation, aiming to assess whether deep convection was preferentially triggered over mountainous regions or followed specific orographic patterns. High-resolution elevation data (SRTM 1 Arc-Second Global DEM) were used to calculate the mean elevation around each lightning strike across four spatial scales (2 km, 5 km, 10 km, and 20 km). Statistical analysis, including correlation coefficients and third-degree polynomial regression, revealed a non-linear relationship, with a distinct peak in lightning frequency at mid-elevations (~200–400 m). These findings suggest that topographic features at local scales can significantly modulate convective initiation, likely due to a combination of mechanical uplift and favorable thermodynamic conditions. The study integrates geospatial techniques and statistical modeling to provide quantitative insights into how terrain influences the formation, location, and intensity of thunderstorms during high-impact weather events. Full article

The Kingdom of Saudi Arabia launched a Regional Cloud Seeding Program in 2022 to enhance rainfall in central and southwestern regions. This study highlights a cloud seeding case on 1 November 2023, using convective development products derived from the Nowcasting Satellite Application Facility (NWCSAF), part of the SAF Network coordinated by the European Organization for the Exploitation of Meteorological Satellites. NWCSAF provided real-time satellite data for assessing cloud dynamics and precipitation. Analysis focused on Convection Initiation (CI) products issued 30–90 min before cloud seeding activities. Results showed the CI+30, +60, and +90 min outputs had high predictive accuracy, aligning with observed convection and demonstrating the value of satellite-based nowcasting in potential adaptation during cloud seeding operations. Full article

High-resolution numerical weather prediction requires accurate cloud microphysical initial conditions to enhance forecasting capabilities for high-impact severe weather events such as convective storms. This study integrated Fengyun-2 (FY-2) geostationary satellite data (equivalent blackbody temperature and total cloud cover) and next-generation 3D weather radar reflectivity from the China Meteorological Administration (CMA) to construct cloud microphysical initial fields and evaluate their impact on the CMA-MESO 3 km regional model. An analysis of the catastrophic rainfall event in Henan on 20 July 2021, and a 92-day continuous experiment (May–July 2024) revealed that assimilating cloud microphysical variables significantly improved precipitation forecasting: the equitable threat scores (ETSs) for 1 h forecasts of light, moderate, and heavy rain increased from 0.083, 0.043, and 0.007 to 0.41, 0.36, and 0.217, respectively, with average hourly ETS improvements of 21–71% for 2–6 h forecasts and increases in ETSs for light, moderate, and heavy rain of 7.5%, 9.8%, and 24.9% at 7–12 h, with limited improvement beyond 12 h. Furthermore, the root mean square error (RMSE) of the 2 m temperature forecasts decreased across all 1–72 h lead times, with a 4.2% reduction during the 1–9 h period, while the geopotential height RMSE reductions reached 5.8%, 3.3%, and 2.0% at 24, 48, and 72 h, respectively. Additionally, synchronized enhancements were observed in 10 m wind prediction accuracy. These findings underscore the critical role of cloud microphysical initialization in advancing mesoscale numerical weather prediction systems. Full article

Weather forecasting is essential for agriculture, yet current methods often lack the localized accuracy required to manage extreme weather events and optimize irrigation. The MAGDA Horizon Europe/EUSPA project addresses this gap by developing a modular system that integrates novel European space-based, airborne, and ground-based technologies. Unlike conventional forecasting systems, MAGDA enables precise, field-level predictions through the integration of cutting-edge technologies: Meteodrones provide vertical atmospheric profiles where traditional data are sparse; GNSS-reflectometry offers real-time soil moisture insights; and all observations feed into convection-permitting models for accurate nowcasting of extreme events. By combining satellite data, GNSS, Meteodrones, and high-resolution meteorological models, MAGDA enhances agricultural and water management with precise, tailored forecasts. Climate change is intensifying extreme weather events such as heavy rainfall, hail, and droughts, threatening both crop yields and water resources. Improving forecast reliability requires better observational data to refine initial atmospheric conditions. Recent advancements in assimilating reflectivity and in situ observations into high-resolution NWMs show promise, particularly for convective weather. Experiments using Sentinel and GNSS-derived data have further improved severe weather prediction. MAGDA employs a high-resolution cloud-resolving model and integrates GNSS, radar, weather stations, and Meteodrones to provide comprehensive atmospheric insights. These enhanced forecasts support both irrigation management and extreme weather warnings, delivered through a Farm Management System to assist farmers. As climate change increases the frequency of floods and droughts, MAGDA’s integration of high-resolution, multi-source observational technologies, including GNSS-reflectometry and drone-based atmospheric profiling, is crucial for ensuring sustainable agriculture and efficient water resource management. Full article

On 27 February 2023, the municipality of Itajubá in southeastern Brazil experienced a short-duration yet high-intensity rainfall event, causing significant socio-economic impacts. Hence, this study evaluates the performance of the Weather Research and Forecasting (WRF) model in simulating this extreme event through a set of sensitivity numerical experiments. The control simulation followed the operational configuration used daily by the Center for Weather and Climate Forecasting Studies of Minas Gerais (CEPreMG). Additional experiments tested the use of different microphysics schemes (WSM3, WSM6, WDM6), initial and boundary conditions (GFS, GDAS, ERA5), and surface datasets (sea surface temperature and soil moisture from ERA5 and GDAS). The model’s performance was evaluated by comparing the simulated variables with those from various datasets. We primarily focused on the representation of the spatial precipitation pattern, statistical metrics (bias, Pearson correlation, and Kling–Gupta Efficiency), and atmospheric instability indices (CAPE, K, and TT). The results showed that none of the simulations accurately captured the amount and spatial distribution of precipitation over the region, likely due to the complex topography and convective nature of the studied event. However, the WSM3 microphysics scheme and the use of ERA5 SST data provided slightly better representation of instability indices, although these configurations still underperformed in simulating the rainfall intensity. All simulations overestimated the instability indices compared to ERA5, although ERA5 itself may underestimate the convective environments. Despite some performance limitations, the sensitivity experiments provided valuable insights into the model’s behavior under different configurations for southeastern Brazil—particularly in a convective environment within mountainous terrain. However, further evaluation across multiple events is recommended. Full article

Between 11 and 14 August 2017, the southern belt of the central Himalaya experienced extreme precipitation, with some stations recording more than 500 mm of accumulated rainfall, which resulted in widespread, devastating flooding. Precipitation was concentrated over the sub-Himalaya, and the established forecasting systems failed to predict the event. In this study, we evaluate the performance of six cloud microphysics schemes in the Weather Research and Forecasting (WRF) model forced with the advanced ERA5 dataset. We also examine the importance of the cumulus scheme in WRF at 3 km horizontal grid spacing in highly convective events like this. Six WRF simulations, each with one of the six different microphysics schemes with the Kain–Fritsch cumulus scheme turned off, all fail to reproduce the spatial variability of accumulated precipitation during this devastating flood-producing precipitation event. In contrast, the simulations exhibit greatly improved performance with the cumulus scheme turned on. In this study, the cumulus scheme helps to initiate convection, after which grid-scale precipitation becomes dominant. Amongst the different simulations, the WRF simulation using the Morrison microphysics scheme with the cumulus turned on displayed the best performance, with the smallest normalized root mean square error (NRMSE) of 0.25 and percentage bias (PBIAS) of −6.99%. The analysis of cloud microphysics using the two best-performing simulations reveals that the event is strongly convective, and it is essential to keep the cumulus scheme on for such convective events and capture all the precipitation characteristics showing that in regions of extreme topography, the cumulus scheme is still necessary even down to the grid spacing of at least 3 km. Full article

Mesoscale convective systems (MCSs) are major contributors to extreme precipitation in urban agglomerations, exhibiting complex characteristics influenced by large-scale climate variability and local urban processes. This study utilizes a high-resolution MCS database spanning from 2001 to 2020 to investigate the spatiotemporal variations of MCSs in the Yangtze River Delta (YRD) urban agglomeration and assess their response to urbanization. Our analysis reveals significant spatial and temporal differences in MCS activities during the warm season (April to September), including initiation, movement, and lifespan, with notable trends observed over the study period. MCSs are found to contribute substantially to hourly extreme precipitation, accounting for approximately 60%, which exceeds their contribution to total precipitation. Furthermore, the role of MCSs in extreme precipitation has also increased, driven by the intensification of MCS-induced extreme rainfall. Additionally, MCS characteristics exhibit significant regional differences. Urban areas experience more pronounced changes in MCS activity and precipitation compared to the surrounding rural regions. Specifically, urbanization contributes approximately 16% to MCS-related precipitation and 19% to MCS initiation, highlighting its substantial role in enhancing these processes. Moreover, mountainous areas and water bodies surrounding cities show stronger trends in certain MCS characteristics than urban and rural plains. This may be attributed to climatological conditions that favor MCS activity in these regions, as well as the complex interactions between urbanization, topography, and land–sea contrasts. These complicated dynamics warrant further investigation to better understand their implications. Full article

A simulation of an extreme precipitation event in southern Xinjiang, which is the driest area in China, seizes the whole initiation process of the intense convective cell responsible for the high hourly rainfall amount. Considering the inner connection between convection and vertical motions, the characteristics and mechanisms of the vertical accelerations during this initial development of the deep convection are studied. It is shown that three key accelerations are responsible for the development from the nascent cumuli to a precipitating deep cumulonimbus, including sub-cloud boundary-layer acceleration, in-cloud deceleration, and cloud-top acceleration. By analyzing the right-hand terms of the vertical velocity equation in the framework of the WRF model, together with a diagnosed relation of perturbation pressure to perturbation potential temperature, perturbation-specific volume (or density), and moisture, the physical processes associated with the corresponding accelerations are revealed. It is found that sub-cloud acceleration is associated with three-dimensional divergence, indicating that the amount of upward transported air must be larger than that of horizontally convergent air. This is favorable for the persistent accumulation of water vapor into the accelerated area. In-cloud deceleration is caused by the intrusion or entrainment of mid-level cold air, which cools down the developing cloud and delays the deep convection formation. Cloud-top acceleration is responsible for the rapid upward extension of the cloud top, which is highly correlated with the convergence and upward transport of moisture. Full article

Air–sea exchanges play a crucial role in intense weather events over Sri Lanka, particularly by providing the heat and moisture that fuel heavy rainfall. We present a year-round dataset of meteorological observations from the southern shoreline of Sri Lanka in the equatorial Indian Ocean for 2017, aiming to investigate its seasonal characteristics and evaluate the performance of reanalysis data in this region. The observations reveal distinct diurnal and seasonal patterns. During the winter and spring, higher shortwave (646.2 W/m²) and longwave radiation (−86.9 W/m²) are coupled with higher temperatures (30.6 °C) and lower humidity (67.4% at noon). In contrast, the Indian summer monsoon period features reduced shortwave (579.8 W/m²) and longwave radiation (−58.6 W/m²), lower temperatures (29.2 °C), higher humidity (over 79.7%), and stronger winds (6.25 m/s). The observations were compared with the ERA5 reanalysis dataset to evaluate the regional performance. The reanalysis data correlated well with the observed data for the radiation, temperature, and sensible heat flux, although notable deviations occurred in terms of the wind speed and latent heat flux. During the impact of Tropical Cyclone Ockhi, the reanalysis data tended to underestimate both the wind speed and precipitation. This dataset will provide vital support for studies on monsoons and coastal atmospheric convection, as well as for model initialization and synergistic applications. Full article

The effects of global warming, coupled with the continuing expansion of urbanization, have significantly increased vulnerability to urban flooding, widespread erosion risks, and related phenomena such as shallow landslides and mudflows. These challenges are particularly evident in both lowland and hill/foothill environments of urbanized regions. Improving resilience to urban flooding has emerged as a top priority at various levels of governance. This paper aims to perform an initial analysis with the goal of developing an early warning system to efficiently manage intense convective rainfall events in urban areas. To address this need, the paper emphasizes the importance of analyzing different hazard scenarios. This involves examining different hydro-meteorological conditions and exploring management alternatives, as a fundamental step in designing and evaluating interventions to improve urban flood resilience. The Turin Metropolitan Area (TMA), located in north-western Italy, represents a unique case due to its complex orography, with a mountainous sector in the west and a flat or hilly part in the east. During the warm season, this urban area is exposed to strong atmospheric convection, resulting in frequent hailstorms and high-intensity rainfall. These weather conditions pose a threat to urban infrastructure, such as drainage systems and road networks, and require effective management strategies to mitigate risks and losses. The TMA’s urban areas are monitored by polarimetric Doppler weather radars and a dense network of rain gauges. By examining various summer precipitation events leading to urban flooding between 2007 and 2021, this study assesses the practicability of deploying a weather-radar early-warning system. The focus is on identifying rainfall thresholds that distinguish urban flooding in lowland areas and runoff erosion phenomena in urbanized hills and foothills. Full article

A ground-based microwave radiometer (MWR) provides continuous atmospheric profiles under various weather conditions. The change in total precipitable water vapor (PWV) and liquid water path (LWP) before rainfall events is particularly important for the improvement in the rainfall forecast. However, the analysis of the PWV and LWP before rainfall event on the plateau is especially worth exploring. In this study, the MWR installed at Xining, a city located over the northeastern Tibetan Plateau, was employed during September 2021 to August 2022. The results reveal that the MWR-retrieved temperature and vapor density demonstrate reliable accuracy, when compared with radiosonde observations; PWV and LWP values during the summer account for over 70% of the annual totals in the Xining area; both PWV and LWP at the initiating time of rainfall events are higher during summer, especially after sunset (during 20-00 local solar time); and notably, PWV and LWP anomalies are enhanced abruptly 8 and 28 min prior to the initiating time, respectively. Furthermore, the mean of LWP anomaly rises after the turning time (the moment rises abruptly) to the initiating time; as the intensity of rainfall events increases, the occurrence of the turning time is delayed, especially for PWV anomalies; while the occurrence of the turning time is similar for both convective cloud and stratiform cloud rainfall events, the PWV and LWP anomalies jump more the initiating time; as the intensity of rainfall events increases, the occurrence of the turning time is delayed, especially for PWV anomalies; while the occurrence of the turning time is similar for both convective cloud and stratiform cloud rainfall events, the PWV and LWP anomalies jump more dramatically after the turning time in convective cloud events. This study aims are to analyze the temporal characteristics of PWV and LWP, and assess their potential in predicting rainfall event. Full article

This study on the warm-sector heavy rainfall event in Guangzhou on 7 May 2017, examined the effects and mechanisms of incorporating 30 m high-resolution land surface data into its numerical simulation. The updated 1km numerical model, integrating 30 m high-resolution land surface data, successfully captured the initiation, back-building, and organized development of warm-sector convections in Huadu and Zengcheng District. The analysis revealed that the high spatial resolution of the surface data led to a reduced urban area footprint (urban −6.31%), increased vegetation cover (forest 11.63%, croplands 1%), and enhanced surface runoff (water 2.77%) compared with a model’s default land cover (900 m). These changes mitigated the urban heat island (UHI) effect within the metropolitan area and decreased the surface sensible heat flux. This reduction contributed to a pronounced temperature gradient between Huadu Mountain and the urban area. Additionally, a stronger high-pressure recirculation and sea–land breezes facilitated the transport of warm and moist air from the sea inland, creating a humid corridor along the sea–land interface. The consistent influx of warm and moist air near the mountain front, where strong temperature gradients were present, forcibly triggered warm-sector convection, intensifying its organization. This study highlighted the critical role of high-resolution land surface data in the accurate numerical simulation of warm-sector heavy rainfall. Full article