Search Results (75)

Hurricane Florence (2018) proved to be a damaging tropical cyclone that formed off the coast of the Cabo Verde Islands. On 12 UTC 14 September 2018, Florence made landfall as a weakened category 1 Hurricane in Wrightsville Beach, NC. In the midst of landfall, Florence’s ground speed stalled considerably to near zero. Because of this stall, Florence continued to accumulate feet of rain along the coastline, and the inundation of seawater became extreme. Due to the impacts of Florence, the Weather Research and Forecasting Model (WRF-ARW) was used to simulate the tropical cyclone and provide insight into the thermodynamics and dynamics that played a significant role at the time of landfall. After the control case, several sensitivity experiments were conducted. The historical sensitivity experiments utilize the thermodynamic and moisture fields of ERA5 reanalysis data from 1968 and 1998, respectively, to modify the thermodynamic and moisture fields in the initial conditions of the WRF–ARW control case. In addition, to study the potential future climate impacts of Florence, the NCAR CESM Global Bias-Corrected CMIP5 Output to Support WRF/MPAS Research dataset was utilized. The same approach was taken as the historical versions of Florence for sensitivity experiments for future climate, i.e., thermodynamic and moisture fields for both 2038 and 2068 under the RCP6.0 and RCP8.5 climate scenarios, respectively. Results suggest a corresponding intensity shift with minor track deflections. Based on these modifications, synoptic and mesoscale dynamics will be studied to provide insight into how Florence-like hurricanes may change based on certain climate scenarios. Full article

Large-eddy simulation (LES) within a weather research and forecasting (WRF) model coupled with an active scalar transport equation was used to simulate Atmospheric Boundary Layer conditions during the Mosquito fire, the largest wildland fire in California during September 2022. The simulations were conducted with realistic boundary conditions derived from the National Oceanic and Atmospheric Administration (NOAA) High Resolution Rapid Refresh (HRRR) model, with the aim of better understanding the two-way coupling between the ABL and plume dynamics. The terrain was extremely inhomogeneous, and the topography varied significantly within the numerical domain. Initially, LES of the smoke-free ABL was conducted on nested domains, and detailed ABL data were gathered from 8 to 9 September 2022. LES simulations were validated using four Automated Surface Observing System (ASOS) stations and NOAA meteorological (MET) observations, as well as NOAA met Twin Otter measurements, and the desired accuracy was established. The smoke plume was then released into the ABL at noon on 9 September 2022, and the plume simulations were conducted for a period of one hour following the release. During this period, the ABL transitioned from convective to buoyancy-shear-driven regimes. Late-night and early-morning conditions are influenced by the complex topography and low-level jet, whereas buoyancy and shear control the ABL dynamics during the morning and afternoon hours. The plume vertical transport is influenced by the ABL depth and the size of the vertical turbulence structures during that time, whereas the wind conditions and turbulent kinetic energy within the ABL dictate the horizontal transport scales of the plume. In addition, the results demonstrate that the plume modifies the microclimate along its path. Full article

The Weather Research and Forecasting Model (WRF-ARW) version 4.5 was used to simulate the synoptic to mesoscale evolving atmosphere of Tropical Cyclone (TC) Hilary’s (2023) remnant passage over the southwestern United States. The atmospheric dynamic processes conducive to the precursor rain events were extensively studied to determine the effects of mid-level jetogenesis. Concurrently, the dynamics of mesoscale processes related to the interaction of TC Hilary over the complex topography of the western United States were studied with several sensitivity simulations on a nested 2 km × 2 km grid. The differential surface heating between the cloudy California coast and clear/elevated Great Basin plateau had a profound impact on the lower-mid-tropospheric mass field resulting in mid-level jetogenesis. Diagnostic analyses of the ageostrophic flow support the importance of both isallobaric and inertial advective forcing of the mid-level jetogenesis in response to differential surface sensible heating. This ageostrophic mesoscale jet ultimately transported tropical moisture in multiple plumes more than 1000 km poleward beyond the location of the extratropical transition of the storm, resulting in anomalous flooding precipitation within a massive arid western plateau. Full article

On September 7, 2024, a deep convection event was observed in Northern and Central Greece, and based on radar data analysis, three supercells were identified. One of these, the most intense with maximum radar reflectivity of 68 dBZ, had a lifetime of almost 7 h and covered a distance of more than 200 km, producing damaging winds and large hail along its track. The goal of this study was to analyze this case using radar data and to evaluate the predictability of such a high-impact event using a numerical weather prediction model. The Weather Research and Forecasting (ARW-WRF) model was used to perform an array of simulations, and using multiple initialization times, the influence of lead time was examined. Furthermore, the dependence of the results on the choice of parameterization scheme used in the model is assessed below. The model performed satisfactorily in predicting intense storm activity, without reaching the extreme values observed by the radar. Full article

Anthropogenic alterations of the land surface through activities such as agriculture, forestry and urban development represent important human-induced forcings on the Earth’s climate system. This study, conducted in the framework of the UpClim project, employs the non-hydrostatic WRF-ARW v4.5.1 model forced by ERA5 reanalysis data to assess the impact of land use changes (LUCs) on the European climate. The study aims to quantify the effects of LUCs over the EURO-CORDEX domain at 0.11° resolution during 1980–1985 by comparing simulations with transient land use forcing against a control run with static land use. Full article

Seasonal climate forecasts are an essential tool for providing early insight into weather-related impacts and supporting decision-making in sectors such as agriculture, energy, and disaster management. Accurate representation of atmospheric circulation at the seasonal scale is essential, especially in regions such as the Eastern Mediterranean, where complex synoptic patterns drive significant climate variability. The aim of this study is to perform a comparison of weather type classifications between ERA5 reanalysis and seasonal forecasts in order to assess the ability of seasonal data to capture the synoptic patterns over the Eastern Mediterranean. For this purpose, we introduce a regional seasonal forecasting framework based on the state-of-the-art Advanced Research WRF (WRF-ARW) model. A series of sensitivity experiments were also conducted to evaluate the robustness of the model’s performance under different configurations. Moreover, the ability of seasonal data to reproduce observed trends in weather types over the historical period is also examined. The classification results from both ERA5 and seasonal forecasts reveal a consistent dominance of anticyclonic weather types throughout most of the year, with a particularly strong signal during the summer months. Model evaluation indicates that seasonal forecasts achieve an accuracy of approximately 80% in predicting the daily synoptic condition (cyclonic or anticyclonic) up to three months in advance. These findings highlight the promising skill of seasonal datasets in capturing large-scale circulation features and their associated trends in the region. Full article

To improve the prediction of Typhoon Doksuri (2023), this paper explores how variations in horizontal scale factors used in assimilating radar-derived wind velocities influence the performance of numerical simulations and forecasts. Using the WRF-ARW model in conjunction with the WRF-3DVAR data assimilation system, two assimilation configurations were tested with horizontal length scale factors of 1.0 and 0.25. Results show that a reduced length scale facilitates a more detailed reconstruction of mesoscale features, including the typhoon’s eye and inner-core circulation, leading to improved accuracy in short-term intensity and structure forecasts. The experiment utilizing the 0.25 length scale exhibited a tighter warm core, stronger cyclonic wind bands, and a better representation of the vortex’s three-dimensional structure. However, this configuration also led to growing forecast deviations in the latter stages, likely due to imbalances introduced by excessive localization. In contrast, the 1.0-scale experiment produced smoother but less accurate structures and demonstrated larger track deviations. These findings highlight a key trade-off between localized observational influence and long-term forecast stability. The study underscores the importance of optimizing horizontal scale parameterization in variational assimilation to enhance the forecasting accuracy of high-impact tropical cyclones and offers practical insights for operational forecasting systems in regions frequently affected by typhoon activity. Full article

Numerical models were employed to simulate the effects of urban infrastructure on the city-scale precipitation distribution during multiple closely occurring convective squall line events over Chicago. Two high-resolution simulations were inter-compared, one using standard land use databases to initialize the WRF-ARW numerical model and the other using a high-resolution urban canopy formulation and detailed land use databases to initialize the WRF-UCM numerical model. Two squall lines organized and propagated over Chicago during an eight-hour period. The (1 km) spatio-temporal evolution of the first squall line was more accurately simulated by the WRF-UCM than that simulated by the WRF-ARW. The WRF-UCM captures more realistic urban heat island-induced buoyancy forcing when validated against multiple airport meteograms and Doppler radar-derived reflectivity and precipitation. The WRF-UCM increases surface heating, substantially strengthening the surface-based convective available potential energy (SBCAPE) and subsequent cold downdrafts. Additionally, the increased surface heating acts to strengthen and bifurcate the upper-level divergence and energize three low-level jets that converge upon the city and shape the convective organization. While the effect of this additional buoyancy on the first squall line was critical, the second line’s dissipation was not substantially different in the two simulations because of diminishing tropospheric forcing. Full article

This research evaluates the quality of high-impact rainfall forecasts across Vietnam and its sub-climate regions. The 3-day rainfall forecast products evaluated include the European Centre for Medium-Range Weather Forecasts (ECMWF) High-Resolution Integrated Forecasting System (IFS) and its downscaled outputs using the Weather Research and Forecasting (WRF) model with the Advanced Research WRF core (WRF-ARW): direct downscaling and downscaling with data assimilation. A full 5-year validation period from 2019 to 2025 was processed. The validation focused on basic rainfall thresholds and also considered the distribution of skill scores for intense events and extreme events. The validations revealed systematic errors (bias) in the models at low rainfall thresholds. The forecast skill was the lowest for northern regions, while the central regions exhibited the highest. For regions strongly affected by terrain, high-resolution downscaling with local observation data assimilation is necessary to improve the detectability of extreme events. Full article

This study evaluates the sensitivity of the Weather Research and Forecasting (WRF-ARW) model in its version 4.3.3 during different experiments on a torrential precipitation event associated with the 2017 El Niño Costero in Peru. The results are compared with two reference datasets: precipitation estimations from CHIRPS satellite data and SENAMHI meteorological station values. The event, which had significant economic and social impacts, is simulated using two nested domains with resolutions of 9 km (d01) and 3 km (d02). A total of 22 experiments are conducted, resulting from the combination of two planetary boundary layer (PBL) schemes: Yonsei University (YSU) and Mellor–Yamada–Janjic (MYJ), with five cumulus parameterization schemes: Betts–Miller–Janjic (BMJ), Grell–Devenyi (GD), Grell–Freitas (GF), Kain–Fritsch (KF), and New Tiedtke (NT). Additionally, the effect of turning off cumulus parameterization in the inner domain (d02) or in both (d01 and d02) is explored. The results show that the YSU scheme generally provides better results than the MYJ scheme in detecting the precipitation patterns observed during the event. Furthermore, it is concluded that turning off cumulus parameterization in both domains produces satisfactory results for certain regions when it is combined with the YSU PBL scheme. However, the KF cumulus parameterization is considered the most effective for intense precipitation events in this region, although it tends to overestimate precipitation in high mountain areas. In contrast, for lighter rains, combinations of the YSU PBL scheme with the GD or NT parameterization show a superior performance. It is worth nothing that for all experiments here used, there is a clear underestimation in terms of precipitation, except in high mountain regions, where the model tends to overestimate rainfall. Full article

On the evening of 31 March 2019, Parsa and Bara Districts in central Nepal were severely hit by a wind storm which was the first documented tornadic incidence in Nepal.In this paper, we investigate the background of the tornado formation via numerical simulations with the WRF-ARW model. The results show that: (1) a flow situation favorable to the generation of mesocyclones was formed by a combination of local plain-to-mountain winds consisting of warm and humid southwesterly wind in the lower atmosphere and synoptic northwesterly wind aloft over the southern foothills of the Himalayan Mountain range, leading to significant vertical wind shear and strong buoyancy; (2) the generated mesocyclone continuously shed rain-cooled outflow with 600∼800 m depth above the ground into the Chitwan valley while moving southeastward along the Mahabharat Range at the northeastern rim of the Chitwan valley; (3) the cold outflow propagated in the valley, forming a front; and (4) the tornado was generated when this cold outflow passed over the Siwalik Hills bordering the southern rim of the Chitwan valley. At this point, descending flow around a high mountain generated positive vertical vorticity near the ground; blocking by this high mountain and channeling through a mountain pass enhanced updrafts at the front by forming a hydraulic jump. These updrafts amplified the positive vertical vorticity via stretching, and this interaction of the cold outflow with the Siwalik Hills contributed to tornadogenesis. The simulated location and time of the disaster showed generally good agreement with the reported location and time. Full article

Super Typhoon Mujigae (2015) was simulated using the WRF-ARW model version 4.1 with the WSM3, WSM5, WSM6, and WSM7 microphysics schemes, which include 3, 5, 6, and 7 hydrometeor classes, respectively. This study investigated the species number of hydrometeors (SNHs) from simple to complex on the rapid intensification (RI) of a tropical cyclone (TC). SNHs significantly affected the distribution of hydrometeors, microphysical conversion processes (MCPs), latent heat budget, and the interaction between thermal and dynamic processes, thereby influencing the RI. Different SNHs resulted in varied MCPs and a latent heat budget. The WSM3 and WSM5 schemes share the same top three dominating MCPs: condensation of cloud water (COND), accretion of cloud water by rain (RACW), and evaporation of rain (REVP). COND, accretion of cloud water by graupel (GACR), and RACW contributed to the WSM6 scheme. The WSM7 scheme included hail, with contributions from the instantaneous melting of snow, graupel, and COND, respectively. The dominating latent cooling processes were identical, while in different orders, which were evaporation of rain (REVP), sublimation of snow (SSUB), and evaporation of cloud water (CEVP) in the WSM3 and WSM5 schemes; while CEVP, REVP, and SSUB were in the WSM6 and WSM7. The interaction between thermal and dynamic processes was ultimately responsible for the RI. The WSM6 scheme presented an excellent latent heating rate, warm-core structure, and secondary circulation, which enhanced convection and absolute angular momentum transportation, and further indicating RI. The results highlighted the importance of an adequate complexity microphysics scheme to better reproduce the RI. Full article

The Model for Prediction Across Scales-Atmosphere (MPAS) was used to simulate meteorological conditions for a two-week winter episode during 10–23 January 2013, and a two-week summer episode during 18–31 July 2016, using both as a global model and a regional model with a focus on California. The results of both global and regional applications of MPAS were compared against the surface and upper air rawinsonde observations while the variations of characteristic meteorological variables and modeling errors were evaluated in space, time, and statistical sense. The results of the Advanced Weather Research and Forecast (WRF-ARW, hereafter WRF) model simulations for the same episodes were also used to evaluate the results of both applications of MPAS. The temporal analyses performed at surface stations indicate that both global and regional applications of MPAS and WRF model predict the diurnal evolution of characteristic meteorological parameters reasonably well in both winter and summer episodes studied here. The average diurnal bias in predicting 2 m temperature by MPAS and WRF are about the same with a maximum of 2 °C in winter and 1 °C in summer while that of 2 m mixing ratio is within 1 g/kg for all three modeling applications. The rawinsonde profiles of temperature, dew point temperature, and wind direction agree reasonably well with observations while wind speed is underestimated by all three applications. The comparisons of the spatial distribution of anomaly correlation and mean bias errors calculated from each model results for 2 m temperature, 2 m water vapor mixing ratio, 10 m wind speed and wind direction indicate that all three models have similar magnitudes of agreement with observations as well as errors away from observations throughout California. Full article

Recent advancements in computational technologies have enhanced the importance of meteorological modeling, driven by an increased reliance on weather-dependent systems. This research implemented a lightning data assimilation technique to improve short-term weather forecasts in South America, potentially refining initialization methods used in meteorological operation centers. The main goal was to implement and enhance a data assimilation algorithm integrating lightning data into the WRF model, assessing its impact on forecast accuracy. Focusing on southern Brazil, a region with extensive observational infrastructure and frequent meteorological activity, this research utilized several data sources: precipitation data from the National Institute of Meteorology (INMET), lightning data from the Brazilian Lightning Detection Network (BrasilDAT), GOES-16 satellite images, synoptic weather charts from the National Institute for Space Research (INPE), and initial conditions from the GFS model. Employing the WRF-ARW model version 3.9.1.1 and WRFDA system version 3.9.1 with 3DVAR methodology, the study conducted three experimental setups during two meteorological events to evaluate the assimilation algorithm. These included a control (CTRL) without assimilation, a lightning data assimilation (LIGHT), and an adaptive humidity threshold assimilation (ALIGHT). Results showed that the lightning data assimilation system enhanced forecasts for large-scale systems, especially with humidity threshold adjustments. While it improved squall line timing and positioning, it had mixed effects when convection was thermally driven. The lightning data assimilation methodology represents a significant contribution to the field, indicating that using such alternative data can markedly improve short-term forecasts, benefiting various societal sectors. Full article

The Weather Research and Forecasting (WRF) model was used to simulate Typhoon Rumbia in this paper. The sensitivity experiments were conducted with 16 different parameterization combination schemes, including four microphysics (WSM6, WSM5, Lin, and Thompson), two boundary layers (YSU and MYJ), and two cumulus convection (Kain–Fritsch and Grell–Freitas) schemes. The impacts of 16 parameterization combination schemes and the data assimilation (DA) of Global Navigation Satellite System (GNSS) water vapor were evaluated by the simulation accuracy of typhoon track and intensity. The results show that the typhoon track and intensity are significantly influenced by parameterization schemes of cumulus and boundary layers rather than microphysics. The averaged track error of Lin_KF_Y is 104.73 km in the entire 72-h simulation period. The track errors of all the other combination schemes are higher than Lin_KF_Y. During the entire 72-h, the averaged intensity error of Thompson_GF_M is 1.36 hPa. It is the lowest among all the combination schemes. As for data assimilation, the simulation accuracy of typhoon tracks can be significantly improved by adding the GNSS water vapor. Thompson_GF_M-DA combination scheme has the lowest average track error of 45.05 km in the initial 24 h. The Lin_KF_Y-DA combination scheme exhibits an average track error of 32.17 km on the second day, 28.03 km on the third day, and 35.33 km during 72-h. The study shows that the combination of parameterization schemes and the GNSS water vapor data assimilation significantly improve the initial conditions and the accuracy of typhoon predictions. The study results contribute to the selection of appropriate combinations of physical parameterization schemes for the WRF-ARW model in the mid-latitude region of the western Pacific coast. Full article