Search Results (76)

The Upper Blue Nile Basin (UBNB), which contributes about 60% to the annual Nile flow, plays a critical role in the Nile water management. However, its complex terrain and climate create significant challenges for accurate regional climate simulations, which are essential for climate impact assessments. This study aims to address the challenges of climate simulation over the UBNB by enhancing the Regional Climate Model system (RegCM5) with its new non-hydrostatic dynamical core (MOLOCH) to simulate precipitation and temperature. The model is driven by ERA5 reanalysis for the period (2000–2009), and two scenarios are simulated using two different schemes of the Planetary Boundary Layer (PBL): Holtslag (Hol) and University of Washington (UW). The two scenarios, noted as (MOLOCH-Hol and MOLOCH-UW), are compared to the previously best-performing hydrostatic configuration. The MOLOCH-UW scenario showed the best precipitation performance relative to observations, with an accepted dry Bias% up to 22%, and a high annual cycle correlation >0.85. However, MOLOCH-Hol showed a very good performance only in the wet season with a wet bias of 4% and moderate correlation of ≈0.6. For temperature, MOLOCH-UW also outperformed, achieving the lowest cold/warm bias range of −2% to +3%, and high correlations of ≈0.9 through the year and the wet season. This study concluded that the MOLOCH-UW is the most reliable configuration for reproducing the climate variability over the UBNB. This developed configuration is a promising tool for the basin’s hydroclimate applications, such as dynamical downscaling of the seasonal forecasts and future climate change scenarios produced by global circulation models. Future improvements could be achieved through convective-permitting simulation at ≤4 km resolution, especially in the application of assessing the land use change impact. Full article

This study investigates two extreme convective rainfall events that struck Poland in August 2024, affecting Warsaw (Okęcie) on 19 August and Zamość on 21 August. The aim is to evaluate the meteorological background, intensity, and spatial characteristics of these short-duration storms. We used high-resolution meteorological observations, radar imagery, and satellite data provided by the Institute of Meteorology and Water Management (IMGW-PIB). The storms were analyzed using temporal rainfall profiles, Chomicz α index classification, and comparison with World Meteorological Organization (WMO) thresholds for extreme precipitation. Both events exceeded national and international criteria for torrential rainfall. In Zamość, over 88.3 mm of rain fell within one hour, and 131.3 mm within three hours—ranking this episode among the most intense short-duration rainfall events in the region. Convective organization patterns, including multicellular clustering and convective training, were identified as key factors enhancing rainfall intensity. The results demonstrate the diagnostic value of combining national indices with global benchmarks in rainfall assessment. These findings support further integration of convection-permitting models and real-time nowcasting into urban hydrometeorological warning systems. Full article

The main goal of this study is to present future changes in various precipitation indices at a kilometer-scale resolution for Bulgaria on an annual and seasonal basis. Numerical simulations were conducted using the Non-Hydrostatic Regional Climate Model version 4 (RegCM4-NH) following the Coordinated Regional Climate Downscaling Experiment Flagship Pilot Study protocol for three 10-year periods (1995–2004, 2041–2050, and 2090–2099), with horizontal grid resolutions of 15 km and 3 km, on the petascale supercomputer HPC Discoverer at Sofia Tech Park. Data from the Hadley Centre Global Environment Model version 2 (HadGEM2-ES), based on the Representative Concentration Pathway 8.5 (RCP8.5) scenario, were used as boundary conditions for the regional climate model (RCM) simulations, which were subsequently downscaled to the kilometer-scale (3 km) simulations using a one-way nesting approach. High-resolution model data were compared with high-resolution observational datasets as well as lower-resolution (15 km) data. Future changes in precipitation indices were analyzed on both annual and seasonal scales, including mean daily and hourly precipitation, the frequency and intensity of wet days (>1 mm/day) and wet hours (>0.1 mm/hour), extreme daily precipitation (99th percentile, p99), and extreme hourly precipitation (99.9th percentile, p99.9) for both future periods. Additionally, changes in near-surface (2 m) temperature and surface snow amount were also presented. There is no substantial difference in projected temperature change between the resolutions. A positive trend in annual mean precipitation is expected in the near future. Extreme precipitation (p99 and p99.9) is projected to increase in spring and winter, accompanied by a rise in daily and hourly precipitation intensity across both future periods. An increase in surface snow amount is observed in the central Danubian Plain, Thracian Lowland, and parts of the Rila and Pirin mountains for the near-future period. However, surface snow amount is expected to decrease by the end of the century. Full article

This study investigates an extreme squall line event that occurred in northern Jiangxi Province, China on 30–31 March 2024. Based on the WRF model, convection-permitting ensemble forecast experiments were conducted using two distinct initial perturbation approaches, namely, the Local Breeding of Growing Modes (LBGM) and the Ensemble Transform Kalman Filter (ETKF), to compare their perturbation structures, spatiotemporal evolution, and precipitation forecasting capabilities. The experiments demonstrated the following: (1) The LBGM method significantly improved the root mean square error (RMSE) of mid-upper tropospheric variables, particularly demonstrating superior performance in low-level temperature field forecasts, but the overall ensemble spread of the system was consistently smaller than that of ETKF. (2) The evolution of dynamical spread within the squall line system confirmed that ETKF generated greater spread growth in low-level wind fields, while LBGM exhibited better spatiotemporal alignment between mid-upper tropospheric wind field spread and the synoptic system evolution. (3) Vertical profiles of total moist energy revealed that ETKF initially exhibited higher total moist energy than LBGM. Both methods showed increasing total moist energy with forecast lead time, displaying a bimodal structure dominated by kinetic energy in upper layers (300–100 hPa) and balanced kinetic energy and moist physics terms in lower layers (1000–700 hPa), with ETKF demonstrating larger growth rates. (4) Kinetic energy spectrum analysis indicated that ETKF exhibited significantly higher perturbation energy than LBGM in the 100–1000 km mesoscale range and superior small- to medium-scale perturbation characterization at the 6–60 km scales initially. Precipitation and radar echo verification showed that ETKF effectively corrected positional biases in precipitation forecasts, while LBGM more accurately reproduced the bow-shaped echo structure near Nanchang due to its precise simulation of leading-edge vertical updrafts and rear-sector low pseudo-equivalent potential temperature regions. Full article

Between 14 and 16 June 2023, an extratropical cyclone affected the south-southeastern coast of Brazil, causing significant damage and loss of life. In the state of Rio Grande do Sul, Civil Defense authorities reported at least 16 fatalities. Although numerical models can simulate the general characteristics of extratropical cyclones, they often struggle to accurately represent the intensity and timing of strong winds and heavy precipitation. One approach to improving such simulations is the use of convective-permitting models (CPMs), in which convection is explicitly resolved. In this context, the main objective of this study is to assess the performance of the Weather Research and Forecasting (WRF) model in CP mode, nested in the ERA5 reanalysis, in representing both the synoptic and mesoscale structures of the cyclone, as well as its associated strong winds and precipitation. The WRF-CP successfully simulated the cyclone’s track, though with some discrepancies in the cyclone location during the first 12 h. Comparisons with radar-based precipitation estimates indicated that the WRF-CP captured the location of the observed precipitation bands. During the cyclone’s occlusion phase—when precipitation was particularly intense—hourly simulated precipitation and 10 m wind (speed, zonal, and meridional components) were evaluated against observations from meteorological stations. WRF-CP demonstrated strong skill in simulating both the timing and intensity of precipitation, with correlation coefficients exceeding 0.4 and biases below 0.5 mm h⁻¹. Some limitations were observed in the simulation of 10 m wind speed, which tended to be overestimated. However, the model performed well in simulating the wind components, particularly the zonal component, as indicated by predominantly high correlation values (most above 0.4), suggesting a good representation of wind direction, which is a function of the zonal and meridional components. Overall, the simulation highlights the potential of WRF-CP for studying extreme weather events, including the small-scale structures embedded within synoptic-scale cyclones responsible for producing adverse weather. 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

This study is focused on assessing the impacts of different regional climate model targeted simulations performed at convection-permitting resolution (CPRCM) in the AgS crop model yield simulations, evaluating to what extent climate model uncertainty impacts the modeled yield—considering the spatial and temporal variability of crop yield simulations over central-south Brazil. The ensemble of CPRCMs has been produced as part of a Flagship Pilot Study (FPS-SESA) framework, endorsed by the Coordinated Regional Climate Downscaling Experiment (CORDEX). The AgS simulated crop yield exhibited significant differences, in both space and time, among the simulations driven by the different CPRCMs as well as when compared with the simulations driven by observations. Rainfall showed the highest uncertainty in CPRCM simulations, particularly in its spatial variability, whereas modeled temperature and solar radiation were generally more accurate and exhibited smaller spatial and temporal differences. The results evidenced the need for multi-model simulations to account for different uncertainty, from different climate models and climate models parameterizations, in crop yield estimations. Inter-institutional collaboration and coordinated science are key aspects to address these end-to-end studies in South America, since there is no single institution able to produce such CPRCM-CropModels ensembles. Full article

This study investigates the impacts of climate change on precipitation and snowpack in the interior western United States (IWUS) using two sets of convection-permitting Weather Research and Forecasting model simulations. One simulation represents the ~1990 climate, and another represents an ~2050 climate using a pseudo-global warming approach. Climate perturbations for the future climate are given by the CMIP5 ensemble-mean global climate models under the high-end emission scenario. The study analyzes the projected changes in spatial patterns of seasonal precipitation and snowpack, with particular emphasis on the effects of elevation on orographic precipitation and snowpack changes in four key mountain ranges: the Montana Rockies, Greater Yellowstone area, Wasatch Range, and Colorado Rockies. The IWUS simulations reveal an increase in annual precipitation across the majority of the IWUS in this warmer climate, driven by more frequent heavy to extreme precipitation events. Winter precipitation is projected to increase across the domain, while summer precipitation is expected to decrease, particularly in the High Plains. Snow-to-precipitation ratios and snow water equivalent are expected to decrease, especially at lower elevations, while snowpack melt is projected to occur earlier by up to 26 days in the ~2050 climate, highlighting significant impacts on regional water resources and hydrological management. Full article

One of the main differences between regional climate model and convection-permitting model simulations is not just how well topographic characteristics are represented, but also how deep convection is treated. The convection process frequently occurs within hours, thus a sub-daily scale becomes appropriate to evaluate these changes. To do this, a series of simulations has been carried out at different spatial resolutions (0.11$°$ and 0.025$°$) using the COSMO-CLM (CCLM) climate model forced by the ECMWF Reanalysis v5 (ERA5) between 2011 and 2020 over a domain covering northwestern Türkiye. Hourly precipitation and heavy precipitation simulated by both models were compared with the observations by Turkish State Meteorological Service (TSMS) stations and Integrated Multi-satellitE Retrievals for GPM (IMERG). Subsequently, we aimed to identify the reasons behind these differences by computing several atmospheric stability parameters and conducting event-scale analysis using atmospheric sounding data. CCLM12 displays notable discrepancies in the timing of the diurnal cycle, exhibiting a premature shift of several hours when compared to the TSMS. CCLM2.5 offers an accurate representation of the peak times, considering all hours and especially those occurring during the wet hours of the warm season. Despite this, there is a tendency for peak intensities to be overestimated. In both seasons, intensity and extreme precipitation are highly underestimated by CCLM12 compared to IMERG. In terms of statistical metrics, the CCLM2.5 model performs better than the CCLM12 model under extreme precipitation conditions. The comparison between CCLM12 and CCLM2.5 at 12:00 UTC reveals differences in atmospheric conditions, with CCLM12 being wetter and colder in the lower troposphere but warmer at higher altitudes, overestimating low-level clouds and producing lower TTI and KI values. These conditions can promote faster air saturation in CCLM12, resulting in lower LCL and CCL, which foster the development of low-level clouds and frequent low-intensity precipitation. In contrast, the simulation of higher TTI and KI values and a steeper lapse rate in CCLM2.5 enables air parcels to enhance instability, reach the LFC more rapidly, increase EL, and finally promote deeper convection, as evidenced by higher CAPE values and intense low-frequency precipitation. Full article

The continental United States is a global hotspot of severe thunderstorms and therefore is particularly vulnerable to social and economic damages from high-impact severe convective weather (SCW), such as tornadoes, thunderstorm winds, and large hail. However, our knowledge of the spatiotemporal climatology and variability of SCW occurrence is still lacking, and the potential change in SCW frequency and intensity in response to anthropogenic climate warming is highly uncertain due to deficient and sparse historical records and the global and regional climate model’s inability to resolve thunderstorms. This study investigates SCW in the Central and Eastern United States in spring and early summer for the current and future warmed climate using two multi-year continental-scale convection-permitting Weather Research and Forecasting (WRF) model simulations. The pair of simulations consist of a retrospective simulation, which downscales the ERA-Interim reanalysis during October 2000–September 2013, and a future climate sensitivity simulation based on the perturbed reanalysis-derived boundary conditions with the CMIP5 ensemble-mean high-end emission scenario climate change. A proxy based on composite reflectivity and updraft helicity threshold is applied to infer the simulated SCW occurrence. Results indicate that the retrospective simulation captures reasonably well the spatial distributions and seasonal variations of the observed SCW events, with an exception of an overestimate along the Atlantic and Gulf coast. In a warmer-moister future, most regions experience intensified SCW activity, most notably in the early-middle spring, with the largest percentage increase in the foothills and higher latitudes. In addition, a shift of simulated radar reflectivity toward higher values, in association with the significant thermodynamic environmental response to climatic warming, potentially increases the SCW severity and resultant damage. Full article

Convection-permitting models (CPMs) are receiving growing scientific interest for their capability to accurately simulate extreme weather events at a kilometer-scale spatial resolution, offering valuable information for local climate change adaptation. This study employs both qualitative and quantitative bibliometric analysis techniques to examine research trends in CPM, utilizing data from 3508 articles published between 2000 and 2023. The annual number of publications exhibits a linear increase, rising from fewer than 50 in 2000 to over 250 after 2020, with the majority of research originating from the US, China, the UK, and Germany. The most productive institutes include the National Oceanic Atmospheric Administration (NOAA) and the National Center for Atmospheric Research (NCAR) in the US, each contributing over 10% of total publications. Title and abstract terms in publications related to keywords such as “scenario”, “climate simulation”, etc., dominate publications from 2018 to 2023, coinciding with advances in computing power. Notably, terms associated with CPM physical processes received the highest citations from 2000 to 2023, underscoring the importance of such these research topics. Given the computational expense of running CPMs and the increasing demand for future predictions using CPMs, novel methods for generating long-term simulations are imperative. Full article

The aim of this technical note is to describe the Cycle 46 reference configuration of the HARMONIE-AROME convection-permitting numerical weather prediction model. HARMONIE-AROME is one of the canonical system configurations that is developed, maintained, and validated in the ACCORD consortium, a collaboration of 26 countries in Europe and northern Africa on short-range mesoscale numerical weather prediction. This technical note describes updates to the physical parametrizations, both upper-air and surface, configuration choices such as lateral boundary conditions, model levels, horizontal resolution, model time step, and databases associated with the model, such as for physiography and aerosols. Much of the physics developments are related to improving the representation of clouds in the model, including developments in the turbulence, shallow convection, and statistical cloud scheme, as well as changes in radiation and cloud microphysics concerning cloud droplet number concentration and longwave cloud liquid optical properties. Near real-time aerosols and the ICE-T microphysics scheme, which improves the representation of supercooled liquid, and a wind farm parametrization have been added as options. Surface-wise, one of the main advances is the implementation of the lake model FLake. An outlook on upcoming developments is also included. Full article

The model simulation focuses on an extreme rainfall event that triggered a flood hazard in the Lake Victoria basin region of East Africa from June 24th to 26th, 2022. This study investigates the impacts of its urban canopy on the extreme rainfall events over the Lake Victoria basin in East Africa, employing the Weather Research and Forecasting (WRF) model at a convective-permitting resolution. The rapid urbanization of the region has given rise to an urban canopy, which has notable effects on local weather patterns, including the intensity and distribution of rainfall. The model incorporates high-resolution land use and urban canopy parameters to accurately capture the influences of urbanization on local weather patterns. This research comprises three sets of experiments, two with urban areas and one without, using the WRF model; the experiments focus on three days of an extreme rainfall event in the Lake Victoria basin. Satellite-based precipitation products and reanalysis datasets are employed for a synoptic analysis and model evaluation. The results demonstrate the model’s effectiveness in capturing meteorological variables during an extreme event compared to observed data. The synoptic patterns reveal that, during the extreme event, the Mascarene and St. Helena influenced rainfall conditions over the Lake Victoria Basin by directing moist air toward the northwest. This led to increased moisture convergence from the urban–rural interface toward urban areas, enhancing convection and processes that result in extreme rainfall. Moreover, this study indicates that the urban canopy, specifically the building effect parameterization, significantly amplifies the intensity and duration of rainfall in the urban areas of the region. This research also indicates a general increase in air temperature, relative humidity, latent heat flux, and surface sensible heat flux due to the urban canopy. These findings highlight the substantial influence of urbanization on rainfall patterns in the urban environment. Full article

In recent decades, climate change has become a critical global issue with far-reaching consequences for regional climates and ecosystems. While regional climate models provide valuable information, there is a growing need for high-resolution simulations to assess local impacts. This paper addresses this gap by presenting the first simulation of a 3 km convection-permitting (CP) scenario simulation for Bulgaria. The main aim of this study is to assess different precipitation indices and their future changes for Bulgaria under the Representative Concentration Pathway 8.5 (RCP8.5) scenario following the Coordinated Regional Climate Downscaling Experiment Flagship Pilot Study protocol. The simulations are evaluated against high-resolution observations. We downscale Coupled Model Intercomparison Project 5 Global Climate Model (CMIP5 GCM) data for historical (1995–2004) and future (2089–2098) periods using a regional climate model (RCM) at 15 km grid spacing and parametrized convection. We use these fields as initial and boundary conditions for convection-permitting kilometer-scale simulations. The 15 km grid spacing driving model is used as a reference to assess the added value of the kilometer-scale simulation. Additionally, the 3 km seasonal mean and projected 2 m temperature and the winter snow water equivalent are presented. The results show that the kilometer-scale simulation shows better performance of wet-hour intensity in all seasons, wet-hour frequency in the spring, fall, and winter, and extreme precipitation (99.9th percentile of all precipitation events, p99.9) in the winter and fall. The kilometer-scale simulation improves the projected precipitation distribution and modifies the signal of the precipitation frequency, intensity, and heavy precipitation change over some areas. A positive projected change in the wet-hour intensity is expected in all seasons (13.86% in spring, MAM, 17.48% in summer, JJA, 1.97% in fall, SON, and 17.43% in winter, DJF) and in the heavy precipitation in the spring (13.14%) and winter (31.19%) in the kilometer-scale experiment. The projected increase in mean winter precipitation is accompanied by a significant decrease in mean winter snowfall over lowlands (50−70%). The convection-permitting Regional Climate Model, version 4.7.1 (RegCM4.7.1) suggests an increase in winter snowfall over the highest parts of the country, but a significant increase in the 2 m temperatures there. The results of this study are encouraging and may be of interest to the community of climate scientists and users of climate data for making reliable estimates of the local impacts of future climate change. Full article

Compound heat–precipitation events exert significant impacts on severe weather occurrences. Intense vertical air movement, driving vigorous convection, primarily contributes to the formation of extreme precipitation. Nevertheless, such compound events’ temporal and spatial variation patterns at convection-permitting resolutions remain inadequately explored. This study assesses the performance of the Convection-Permitting Model (CPM) against a model of convection parameterization while investigating the spatial dynamics of compound heat–precipitation events in Guangdong, China. Our findings indicate that the CPM exhibits heightened reliability and precision in simulating temperature and precipitation patterns, especially in extreme precipitation simulation, which would be highly underestimated without a convection-permitting process. Projections from the CPM reveal that, across historical and future periods, the occurrence frequency and fraction of T-P events (instances of extreme heat followed by extreme precipitation) surpass those of P-T events (occurrences of extreme precipitation followed by extreme heat). For T-P events, the CPM exhibits better capability in capturing high-frequency occurrence areas, whereas the results of the relatively low-resolution model show less distinct spatial variations. Both types of events exhibit noticeable upward trends yearly within each period. By the close of this century, the provincial average frequency of P-T events is anticipated to decrease from 20.32 times to 14.55 times. In contrast, the frequency of T-P events is projected to increase from 87.7 times to 101.38 times. These projected changes underscore the shifting dynamics of compound heat–precipitation events in the study region. Full article