WRF-ARW Model Research Articles

Influence of modeling conditions on the estimation of the dry deposition velocity of aerosols on highly inhomogeneous surfaces

An approach to estimating the dry deposition velocity of aerosol particles on the surfaces of Arctic regions, where snow-covered surfaces, open water surface, tundra and coniferous forest predominate, is proposed and numerically investigated. Optimal modeling conditions are proposed, taking into account the characteristic sizes and densities of aerosol particles involved in transport in the planetary boundary layer, and the interaction of air flows with the surface through the parameter u*, calculated using the WRF-ARW model. The proposed approach is compared with other known models and experimental data. The dependence of the dry deposition velocity obtained by the proposed approach on the diameter, density of aerosol particles and dynamic velocity u* for the surfaces in the Far North is estimated.

Influence of weather research and forecasting model microphysics and cumulus schemes for forecasting monsoon rainfall over the Kelani River basin, Sri Lanka

ABSTRACT Creating precise quantitative precipitation forecasts is essential for reducing losses and damages. This study aimed to identify the best microphysics and cumulus schemes for forecasting monsoon rainfall over the Kelani River basin, Sri Lanka, using the WRF-ARW model. Four extreme rainfall events from the 2020 and 2021 monsoon seasons were simulated with various microphysics and cumulus parameterizations to find the optimal combinations. These combinations were then tested for their ability to forecast two monsoon events with a 24-h lead time. Simulated and forecasted rainfalls were compared with observations from 15 gauging stations. Results indicate that WSM3 and WSM6 microphysics schemes with the Betts–Miller–Janjic (BMJ) cumulus scheme are optimal for simulating rainfall, with WSM3_BMJ being the most suitable for forecasting. The findings of this study provide valuable initial data for research in regions with similar environmental conditions, offering insights into the suitability of various physics schemes for simulating and forecasting monsoon rainfall, particularly under extreme conditions. Furthermore, given the prevalence of monsoons in many tropical and subtropical climates, these results will be instrumental in enhancing the use of numerical weather prediction models for forecasting monsoon rainfall on a global scale.

Parameterization of a WRF Model Based on Microwave Measurements of Temperature Inversion Characteristics in PBL over Moscow City

In this work the WRF-ARW model was tested with several different combinations of physical parameterizations to assess the quality of temperature inversion parameter predictions over the Moscow city. The dynamic and statistical characteristics of temperature inversions have been calculated and analysed in selecting criteria for comparisons. The calculated of estimating of the dissipation conditions in dependence on the type of temperature inversions are presented. The data source was the results of temperature profiles measurements in a layer up to 1 km, obtained by the MTP-5 passive microwave profiler from 2018 to 2021. One MTP5 on the North of Moscow was used to tune the model parameters and another one on the East of Moscow for validation. The comparison results show that several parameterization options can be chosen to reproduce the main inversion parameters.

Role of PBL and air-sea flux parameterization schemes in the forecast of super cyclone Amphan and ESCS Phailin in the cloud-resolving scale using WRF-ARW model

Role of PBL and air-sea flux parameterization schemes in the forecast of super cyclone Amphan and ESCS Phailin in the cloud-resolving scale using WRF-ARW model

Прогноз сильной жары в Красноярске с использованием региональной модели WRF-ARW

The reasons for the occurrence of a severe heatwave in Krasnoyarsk have been studied, and the accuracy of the forecasts of the WRF-ARW regional model has been assessed. Four episodes of hot weather in 2020, 2021, and 2023 have been analyzed. The heatwave in June 2023 was especially intense and met the criteria for a severe weather event. All examined cases were associated with the intense advection of warm tropical air in the leading edge of the upper-air trough. The formation of the urban heat island has been investigated, and its intensity in the nighttime was found to be 6.5°C. During daytime hours, the heat island was weaker than at nighttime. It has been determined that on the second day of forecasting, the WRF-ARW model accurately reproduces the urban heat island and the impact of the Yenisei River, although it underestimates the predicted amplitude of daily temperature variations. The absolute error in air temperature predictions for the time moments close to the time of daily maximum temperatures was 2.6°C (33 hours of model time).

Ecohydrological investigation of cloud seeding effect on vegetation activity in the Boryeong Dam Basin, South Korea

Cloud seeding is well known to address water shortage problems caused by droughts by adding more precipitation and consequent runoff. Unlike previous studies, this study investigates another positive effect of cloud seeding on the activation of vegetation by integrating numerical cloud seeding simulations and processed-based modeling of various ecohydrological components. As the carbon cycle is closely related to the hydrological processes in ecosystems, we adopt the RHESSys ecohydrological modeling to synthetically simulate runoff and soil moisture along with primary productivity and vegetation respiration. Numerical simulations with and without cloud seeding are generated by the WRF-ARW model for the Boryeong Dam basin, one of the basins vulnerable to droughts, in 2021. The cloud seeding simulations of two cases are input into the RHESSys model to examine changes in hydrological and ecological components due to the added amount of precipitation. The results exhibit significant increases in annual precipitation (18 %) and runoff (22 %), and enhanced soil moisture stimulating the ecological components such as GPP and NPP, especially in spring. Cloud seeding can be considered to create optimal conditions for vegetation to absorb or sequester carbon from the atmosphere, thereby boosting vegetation growth. Additionally, the time-lagged correlations between cloud seeding and soil moisture, GPP, NPP, and respiration suggest that vegetation activity is highly dependent on antecedent 1–2 months occurrences of cloud seeding. This study implies that the cloud seeding effect on additional NPP can be considered as a countermeasure of the global average forest loss, which means that carbon emission rise in the global warming era can be partly alleviated by cloud seeding.

Modelling of the heat and the cold risks in Sofia and Varna – preliminary results

According to future climate projections, the expected thermal stress environmental conditions will get worse if the authorities do not apply appropriate measures for mitigation and adaptation to these changes. The health issues concerning air pollution and extreme temperatures have assumed great importance in recent years. The objective of this study is to estimate the thermal comfort in two of the biggest cities in Bulgaria - Sofia and Varna and their surroundings for the year 2017 by biometeorological indexes. We use computer simulations of the atmospheric parameters that define the thermal comfort indexes, by Advanced Research Weather Forecast Model WRF ARW version 3.9. We performed the simulations on four domains for 2017 with an output frequency of 1 hour. The outermost domain has a horizontal resolution of 9 km and encompasses the Balkan Peninsula. It uses initial and boundary conditions from the 0.25-degree NCEP Final Operational Model Global Tropospheric Analyses datasets with a time-frequency of 6 hours. The estimation of the thermal comfort conditions is performed with characteristics called indexes. The differences in the number of cases between the indexes are due to the specific definitions and the meteorological factors that each of them takes into account. Some of these characteristics have applications depending on the specific thermal conditions.

The WRF Simulation Influence of Assimilating GNSS Water Vapor and Parameterization Schemes on Typhoon Rumbia

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.

Exploring the Use of WRF-ARW Model for Analyzing Heatwaves in Bangladesh

This study attempts to simulate heatwave events over Bangladesh using the WRF-ARW Model that could be used to implement a best practice strategy for predicting heatwaves in Bangladesh. In this research, 17 heatwave events were identified from the period between 2007 and 2022. The thresholds for heatwave events were considered when the maximum temperature from a weather station was above the 95th percentile for three consecutive days. After carefully identifying the events, all the events were simulated with the WRF-ARW model. Simulated temperature, relative humidity, and wind vector (direction and speed) were analyzed for this research. The synoptic conditions were synthesized for all the heatwave events. It was found that the propagation of warm temperatures corresponded to the movement of wind and was aptly supported by the presence of humidity in the region of interest. This information could be used at the operational level for predicting heatwaves.
 The Dhaka University Journal of Earth and Environmental Sciences, Vol. 12(1), 2023, P 9-28

Evaluate WRF-based Lightning Potential Index (LPI) lightning parameterization over Sri Lanka during second inter-monsoon in 2018

It is important to develop accurate and reliable lightning prediction system that can be contributed towards the safety of life, both concerning forecasting for the public safety and safety of aviation and electrical power. This study aims to evaluate WRF-based Lightning Potential Index(LPI) lightning parameterization and its applicability for predicting lightning over Sri Lanka during the second inter-monsoon. The WRF-ARW model 3.9.1 was used to produce predictions for three lightning events with various physical parameterization schemes with two nested domains with a resolution of 12km and 4km respectively. The model simulated LPI values were evaluated using the Earth Networks Global Lightning (ENGLN) dataset. Results show corresponding lightning simulations were produced with spatial distribution aligned with ground-based lightning data. Results consistently show a high correlation of the LPI index with an hourly CG flash rate over the three cases. Moreover, the WRF model was able to capture the lightning using LPI in Sri Lanka, suggesting that it can be used operationally to predict lightning potential region.

Representation of a Post-Fire Flash-Flood Event Combining Meteorological Simulations, Remote Sensing, and Hydraulic Modeling

Wildfires are an escalating global threat, jeopardizing ecosystems and human activities. Among the repercussions in the ecosystem services of burnt areas, there are altered hydrological processes, which increase the risks of flash floods. There is limited research addressing this issue in a comprehensive way, considering pre- and post-fire conditions to accurately represent flood events. To address this gap, we present a novel approach combining multiple methods and tools for an accurate representation of post-fire floods. The 2019 post-fire flood in Kineta, Central Greece is used as a study example to present our framework. We simulated the meteorological conditions that caused this flood using the atmospheric model WRF-ARW. The burn extent and severity and the flood extent were assessed through remote sensing techniques. The 2D HEC-RAS hydraulic–hydrodynamic model was then applied to represent the flood event, using the rain-on-grid technique. The findings underscore the influence of wildfires on flooding dynamics, highlighting the need for proactive measures to address the increasing risks. The integrated multidisciplinary approach used offers an improved understanding on post-fire flood responses, and also establishes a robust framework, transferable to other similar cases, contributing thus to enhanced flood protection actions in the face of escalating fire-related disasters.

High Resolution Dynamical Downscaling of Air Temperature and Precipitation using WRF-ARW Model Over Iraq

The ability of the Weather Research and Forecasting (WRF-ARW) model to simulate precipitation variability throughout the Middle East, especially Iraq, is evaluated in this study. The Weather Research and Forecasting model (WRF v 4.2.1) was used. Using a two-way nesting technique and Mercator projection, the WRF model was set up with two nested grids with high regional resolution at 30 km (WRF30km) and 10 km (WRF10km) horizontal grid spacing. From the surface to 50 hPa, both domains have 41 vertical levels. The outer domain covers the Middle East region and the smaller one covers the Iraq region. The ARW supports horizontal nesting, allowing higher resolution over a region of interest, by introducing additional grids, which is available only for horizontal refinement. The nested grids are rectangular and are aligned with the parent (coarser) grid within which they are nested. To predict many atmospheric parameters WRF-ARW model was used based on the initial conditions taken from National Centre for Atmospheric Research (NCAR) records for the period from 23 to 25 of march 2019. The study area is essentially an area bounded by longitudes 35°-55° and latitudes 29°-38° typically including the area of Iraq. The WRF-ARW model was set up on a Linux platform with a 10 km grid size in the zonal and meridional directions. The aim of this study is to evaluate the two WRF tools dynamical downscaling and nesting) at high resolution (30 and 10 km), for simulating rainfall and temperature over Iraq.

Simulation of Track and Landfall Process of Severe Cyclonic Storm Mora over the Bay of Bengal using WRF-ARW Model

The simulations of Severe Cyclonic Storm (SCS) Mora (28-31 May 2017) generated over the Bay of Bengal (BoB) are performed in this study to analyze its features, landfall, and track using the Weather Research and Forecasting (WRF) model. WRF-ARW model has been used on a 10 km Horizontal Resolution (HR) domain for 96, 72, 48, and 24-hour lead time simulations. The model's performance is assessed by examining Mean Sea Level Pressure (MSLP), vertical distribution of velocity components, wind flow pattern, relative vorticity, vertical wind shear, relative humidity, latent heat flux at the surface, and track pattern. The simulated results are compared carefully to the observations from the Bangladesh Meteorological Department (BMD) and the India Meteorological Department (IMD). The findings are reasonably consistent with the observations. The simulated track is also reasonable even up to 72 hours in advance. Finally, the study's results suggest that the WRF model can be used as an effective tool in predicting TCs over the BoB.
 Dhaka Univ. J. Sci. 71(2): 142-152, 2023 (July)

Diagnosing Factors Leading to an Incorrect Supercell Thunderstorm Forecast

Abstract On 28 April 2019, hourly forecasts from the operational High-Resolution Rapid Refresh (HRRR) model consistently predicted an isolated supercell storm late in the day near Dodge City, Kansas, that subsequently was not observed. Two convection-allowing model (CAM) ensemble runs are created to explore the reasons for this forecast error and implications for severe weather forecasting. The 40-member CAM ensembles are run using the HRRR configuration of the WRF-ARW Model at 3-km horizontal grid spacing. The Gridpoint Statistical Interpolation (GSI)-based ensemble Kalman filter is used to assimilate observations every 15 min from 1500 to 1900 UTC, with resulting ensemble forecasts run out to 0000 UTC. One ensemble only assimilates conventional observations, and its forecasts strongly resemble the operational HRRR with all ensemble members predicting a supercell storm near Dodge City. In the second ensemble, conventional observations plus observations of WSR-88D radar clear-air radial velocities, WSR-88D diagnosed convective boundary layer height, and GOES-16 all-sky infrared brightness temperatures are assimilated to improve forecasts of the preconvective environment, and its forecasts have half of the members predicting supercells. Results further show that the magnitude of the low-level meridional water vapor flux in the moist tongue largely separates members with and without supercells, with water vapor flux differences of 12% leading to these different outcomes. Additional experiments that assimilate only radar or satellite observations show that both are important to predictions of the meridional water vapor flux. This analysis suggests that mesoscale environmental uncertainty remains a challenge that is difficult to overcome. Significance Statement Forecasts from operational numerical models are the foundation of weather forecasting. There are times when these models make forecasts that do not come true, such as 28 April 2019 when successive forecasts from the operational High-Resolution Rapid Refresh (HRRR) model predicted a supercell storm late in the day near Dodge City, Kansas, that subsequently was not observed. Reasons for this forecast error are explored using numerical experiments. Results suggest that relatively small changes to the prestorm environment led to large differences in the evolution of storms on this day. This result emphasizes the challenges to operational severe weather forecasting and the continued need for improved use of all available observations to better define the atmospheric state given to forecast models.

THE ASSESSMENTS OF WIND CHARACTERISTICS AT THIEN NGA - HAI AU FIELD AREA FOR OIL EXPLOITATION

The paper aims to identify wind characteristics at the Thien Nga - Hai Au field area using high resolution data generated by the WRF-ARW model. The results indicate that Northeast and Southwest winds are the most frequent, as compared to other directions. The highest wind speed values are observed during the winter monsoon. These findings exhibit reasonable consistency with those of climate survey in the Southern Bien Dong Sea. In addition, the study calculates the return period for extreme wind values, comparing between the cases of monsoon activity and storm activity. The extreme wind speed values are higher in winter (32.0 - 34.0 m/s) compared to summer (29.0 - 30.0 m/s), corresponding to return periods of 50 - 100 years. It is found that highest extreme wind speeds are associated with storm events (40.0 - 46.0 m/s).

Moisture Source Analysis of Two Case Studies of Major Extreme Precipitation Events in Summer in the Iberian Peninsula

Extreme summer precipitation events commonly affect the Iberian Peninsula (IP), and studying the moisture sources that generate intense precipitation is extremely important. Therefore, this study analyzed the moisture sources of two major extreme precipitation events in summer in the IP. The events occurred on 18 September 1999 and 7 September 1989, and the anomalies of the associated meteorological variables are shown with respect to a 30-year reference period (1985–2014). A Lagrangian approach is used for determining the moisture source pattern using only the precipitating particles that reach the target region. In this research a dynamic downscaling methodology is applied using the WRF-ARW model forced using the ERA5 reanalysis and then the WRF-ARW outputs used to force the Lagrangian dispersion model FLEXPART-WRF. Specifically, the first event was associated with an atmospheric river favoring strong moisture transport from remote sources and the second event was caused by local convergence of moisture under the influence of a cut-off low system. For the 18 September 1999 case study, the major contribution to moisture reaching the target region was associated with the central and eastern North Atlantic, with values of up to approximately 32%. In addition, the moisture source pattern exhibited a strong anomaly in the climatological pattern. However, the origin of the moisture sources associated with the case of 7 September 1989 was mainly the western Mediterranean Sea, with a contribution of up to 40% or higher. Finally, Northwest Africa and precipitation recycling processes over the IP contributed approximately 16% to the moisture supply for this event.

Assessments of Use of Blended Radar–Numerical Weather Prediction Product in Short-Range Warning of Intense Rainstorms in Localized Systems (SWIRLS) for Quantitative Precipitation Forecast of Tropical Cyclone Landfall on Vietnam’s Coast

This research presents a blended system implemented by the Vietnam National Center for Hydro-Meteorological Forecasting to enhance the nowcasting and forecasting services of quantitative precipitation forecasts (QPFs) of tropical cyclone (TC) landfalls on Vietnam’s coast. Firstly, the extrapolations of rain/convective systems from multiple radars in Vietnam in ranges up to 6 h were carried out using Short-Range Warning of Intense Rainstorms in Localized Systems (SWIRLS) developed by the Hong Kong Observatory. Secondly, the forecast from the numerical weather prediction (NWP) system, based on the WRF-ARW model running at 3 km horizontal resolution, was blended with radar-based quantitative precipitation estimates and nowcasts of SWIRLS. The analysis showed that the application of the nowcast system to TC-related cloud forms is complicated, which is related to the TC’s evolution and the different types and multiple layers of storm clouds that can affect the accuracy of the derived motion fields in nowcast systems. With hourly accumulated rainfall observation, skill score validation conducted for several TCs that landed in the center of Vietnam demonstrated that the blending of nowcasting and NWP improve the quality of the QPFs of TCs in forecast ranges up to 3 h compared to the pure NWP forecasts.

Wind Speed Analysis Method within WRF-ARW Tropical Cyclone Modeling

This paper presents an analysis of a new method for retrieving the parameters of the atmospheric boundary layer in hurricanes. This method is based on the approximation of the upper parabolic part of the wind speed profile and the retrieval of the lower logarithmic part. Based on the logarithmic part, the friction velocity, near-surface wind speed and the aerodynamic drag coefficient are obtained. The obtained data are used for the verification of the modeling data in the WRF-ARW model. The case of the Irma hurricane is studied. Different configurations of the model are tested, which differ in the use of physical parameterizations. The difference of wind profiles in various sectors of the hurricane is studied.

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.

Assessing the performance of WRF ARW model in simulating heavy rainfall events over the Pune region: in support of operational applications

Assessing the performance of WRF ARW model in simulating heavy rainfall events over the Pune region: in support of operational applications