The Lagrangian particle dispersion model FLEXPART-WRF version 3.1

Abstract. The Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) mission was a multi-aircraft project whose major objective was to investigate the factors driving changes in the Arctic's atmospheric composition and climate. It was conducted during April and June–July 2008. The summer ARCTAS deployment was preceded by a week of flights over and around California to address state issues of air quality and climate forcing. This paper focuses on meteorological conditions during the ARCTAS Spring and Summer campaigns. We examine mission averaged large-scale flow patterns at the surface, 500 hPa, and 300 hPa and determine their departures from climatology. Results from runs of the Weather Research and Forecasting (WRF) model are used to describe meteorological conditions on individual days. Our WRF configuration included a nested grid approach that provided horizontal spacing as small as 5 km. Trajectories calculated from the WRF output are used to determine transport pathways to the Arctic, including their origins and the altitudes at which they reach 70° N. We also present backward trajectories from selected legs of individual ARCTAS flights. Finally, the FLEXPART Lagrangian particle dispersion model, with the high resolution WRF data as input, is used to determine the paths of anthropogenic and biomass burning-derived CO. Results show that there was frequent and widespread transport to the Arctic during both phases of ARCTAS and that the three ARCTAS aircraft sampled air having a multitude of origins, following a myriad of paths, and experiencing many types of meteorological conditions.

A new method for driving a One-Dimensional Stratiform Cold (1DSC) cloud model with Weather Research and Forecasting (WRF) model outputs was developed by conducting numerical experiments for a typical large-scale stratiform rainfall event that took place on 4–5 July 2004 in Changchun, China. Sensitivity test results suggested that, with hydrometeor profiles extracted from the WRF outputs as the initial input, and with continuous updating of soundings and vertical velocities (including downdraft) derived from the WRF model, the new WRF-driven 1DSC modeling system (WRF-1DSC) was able to successfully reproduce both the generation and dissipation processes of the precipitation event. The simulated rainfall intensity showed a time-lag behind that observed, which could have been caused by simulation errors of soundings, vertical velocities and hydrometeor profiles in the WRF output. Taking into consideration the simulated and observed movement path of the precipitation system, a nearby grid point was found to possess more accurate environmental fields in terms of their similarity to those observed in Changchun Station. Using profiles from this nearby grid point, WRF-1DSC was able to reproduce a realistic precipitation pattern. This study demonstrates that 1D cloud-seeding models do indeed have the potential to predict realistic precipitation patterns when properly driven by accurate atmospheric profiles derived from a regional shortrange forecasting system. This opens a novel and important approach to developing an ensemble-based rain enhancement prediction and operation system under a probabilistic framework concept.

Kaiga, located on the west coast of Karnataka, India is a complex terrain region with dense forest cover often influenced by land-sea breeze flows during warm summer season. In this study, dispersion of tritium released from Kaiga Nuclear Power Plant (NPP) under the influence of land-sea breeze circulations is simulated using FLEXPART-WRF models. Sensitivity experiments with Weather Research and Forecasting (WRF) are conducted with two Land Surface Models (LSMs), Noah and Noah-Multi Physics (Noah-MP) to study their impact on the wind flow and meteorological characteristics. Mini Boundary Layer Masts (MBLMs) and Doppler SODAR observations are used for evaluating the model performance. WRF simulations showed large sensitivity of simulated sea breeze characteristics to the model land surface physics. It has been found that the Noah-MP better represented the onset time, inland extent, intensity and duration of the sea breeze circulation over the Kaiga complex terrain region with lesser error statistics compared to Noah. The improvements with Noah-MP over Noah are due to the better representation of vegetation and soil hydrological processes over the dense forest region, realistic simulation of surface energy and momentum fluxes and reduction in the land-sea temperature contrast by including a separate canopy physics. FLEXPART simulation of tritium releases from the Kaiga NPPs using WRF predicted meteorological fields showed large diurnal variability in the dispersion pattern and air concentrations around the Kaiga valley. Tritium plume is transported to the northwest sector during early morning and morning time by the land-breeze and to the east-southeast sector by the sea breeze during daytime. Predicted tritium concentrations are found to be higher during the morning under stable atmospheric conditions and lower during sea breeze hours due to stronger winds and deep mixed layers. FLEXPART predicted tritium activity concentrations at different measurement locations using WRF-Noah-MP meteorological data are in better agreement with measurements with higher correlation coefficient and index of agreement. • Tritium dispersion over a coastal complex terrain site is modeled by using the coupled WRF-FLEXPART model. • Dispersion of tritium released from a Nuclear Power Plant under mesoscale sea breeze and land breeze circulations is presented. • Sensitivity of Land Surface Models, Noah and Noah-MP in WRF on the simulation of sea breeze characteristics and tritium dispersion are assessed. • Explicit treatment of forest canopy in the Noah-MP LSM over a model domain with dense forest improved the model results.

The Southern Great Plains (SGP) region exhibits a relatively high frequency of periods with extremely high rainfall rates (RR) and hail. Seven months of 2017 are simulated using the Weather Research and Forecasting (WRF) Model applied at convection-permitting resolution with the Milbrandt–Yau microphysics scheme. Simulation fidelity is evaluated, particularly during intense convective events, using data from ASOS stations, dual-polarization radar, and gridded datasets and observations at the DOE Atmospheric Radiation Measurement site. The spatial gradients and temporal variability of precipitation and the cumulative density functions for both RR and wind speeds exhibit fidelity. Odds ratios > 1 indicate that WRF is also skillful in simulating high composite reflectivity (cREF, used as a measure of widespread convection) and RR > 5 mm h−1 over the domain. Detailed analyses of the 10 days with highest spatial coverage of cREF > 30 dBZ show spatially similar reflectivity fields and high RR in both radar data and WRF simulations. However, during periods of high reflectivity, WRF exhibits a positive bias in terms of very high RR (>25 mm h−1) and hail occurrence, and during the summer and transition months, maximum hail size is underestimated. For some renewable energy applications, fidelity is required with respect to the joint probabilities of wind speed and RR and/or hail. While partial fidelity is achieved for the marginal probabilities, performance during events of critical importance to these energy applications is currently not sufficient. Further research into optimal WRF configurations in support of potential damage quantification for these applications is warranted. Significance Statement Heavy rainfall and hail during convective events are challenging for numerical models to simulate in both space and time. For some applications, such as to estimate damage to wind turbine blades and solar panels, fidelity is also required with respect to hail size and joint probabilities of wind speed and hydrometeor type and rainfall rates (RR). This demands fidelity that is seldom evaluated. We show that, although this simulation exhibits fidelity for the marginal probabilities of wind speed, RR, and hail occurrence, the joint probabilities of these properties and the simulation of maximum size of hail are, as yet, not sufficient to characterize potential damage to these renewable energy industries.

[1] The capability of Weather Research and Forecasting (WRF) model in the simulation of cirrus clouds has been examined, with a focus on the effects of radiative processes and vertical model resolution. We incorporate in WRF a new radiation module, referred to as the Fu‐Liou‐Gu scheme, which is an improvement and refinement based on the Fu‐Liou scheme, particularly in reference to parameterization of the single‐scattering properties of ice crystal size and shape. We conducted a number of real‐time WRF simulations for cirrus cases that were observed in the coastal and western United States on 29–30 March 2007, and we compared these with available observations from Moderate Resolution Imaging Spectroradiometer (MODIS) and GOES visible and IR images over the same areas. Simulation results show that WRF is capable of generating reasonable cirrus cloud fields and their movement and dissipation processes, especially those associated with the large‐scale frontal system. Radiative processes are important in cirrus cloud simulations by affecting the vertical thermal structure and hence convection. The newly implemented radiation module, the Fu‐Liou‐Gu scheme, has been demonstrated to work well in WRF and can be effectively used for studies related to cirrus cloud formation and evolution and aerosol‐cloud‐radiation interactions. With the newly implemented radiation scheme, the simulations of cloud cover and cloud and ice water path (CWP and IWP) have been improved for cirrus clouds, with a more consistent comparison with the corresponding MODIS observations in terms of CWP and IWP means and CWP frequency distribution, especially for optically thin cirrus with an improvement of about 20% in simulated mean IWP. The model‐simulated ice crystal sizes have also been shown to be comparable to those determined from MODIS cloud products. Finally, we have demonstrated that model vertical resolution plays a significant role in cirrus cloud simulation in terms of altering vertical velocity field and the associated regional circulation.

<p>Floods, which are considered as one of the most destructive extreme weather events, are being more severe issues with changing climate, and they are threatening both human life and property. To address flood hazard issues, this study evaluates the application of a hydro-meteorological model system as an early warning system approach. The Weather Research and Forecasting Hydrological model system (WRF-Hydro), a fully-distributed, multi-physics, multi-scale hydrologic model, has the capability of accurately capturing the flood hydrographs in terms of shape and peak time corresponding to storm precipitation. WRF-Hydro model system is implemented with meteorological forcing data obtained from the Weather Research and Forecasting (WRF) atmospheric model. WRF/WRF-Hydro model system is operated in uncoupled mode. The study area is the Oymapinar Basin in Southern Turkey has complex topographic characteristics, and in the upstream basin area, the river network originates from mountainous region. Five heavy rainfall events occurred between January 2015 and May 2015 in the basin selected to assess the model performance of simulating flood hydrograph. The model calibration process is performed by covering three heavy rainfall events, while two of them are used for validation of the model system. This study provides an initial evaluation for possible coupled atmospheric-hydrological model simulations between WRF and WRF-Hydro model systems for future applications.</p>

Examining the effects of soil entrainment during nuclear cloud rise on fallout predictions using a multiscale atmospheric modeling framework

Atmospheric stratification involves differences in the air density caused by a positive (stable) or negative (unstable) vertical gradient of virtual potential temperature. The stability of the layer depends on the stratification and affects the atmospheric boundary layer depth and structure as well as velocity, temperature and turbulence properties. In the first phase of the work, artificially thickened stable and unstable boundary layers were simulated in the EnFlo wind tunnel over a very rough surface, by means of spires, roughness elements and heaters. The effect of different parameters was investigated (among them, inlet temperature profile, capping inversion and surface roughness). These boundary layers were then employed as approaching flow for two idealised urban model geometries. A regular array of rectangular blocks was considered as geometry while a pollutant tracer was released from a point source at ground level. Mean and fluctuating velocities, temperatures and concentrations were sampled, together with heat and pollutant fluxes. The analysis of the data revealed that even in case of weak stratification there are important modifications inside and above the canopy on both the urban boundary layer and the plume characteristics. Finally, the combined effects of a stable approaching flow and local surface heating were investigated in a bi-dimensional street canyon geometry. This was an entirely novel experimental design and the results highlighted how both local and incoming stratification can significantly affect the flow and dispersion at a microscale level in a complex way that depends on the particular case of study. This work sheds more light on the effects of stratification and encourages further work on the topic. The experimental database produced during the project is unique and of high quality. It can assist in developing, improving and validating numerical models, as well as developing parametrisations for simpler models.

A localized extreme precipitation event occurred over Munsiyari (Uttarakhand, India) on 2nd July 2018 causing flash floods, landslides and damage to the hydropower project. A preliminary study has been carried out by using Weather Research and Forecasting (WRF) model with three-dimensional variation data assimilation technique (3DVAR) to examine the feasibility of the model to predict the localized phenomena. Sensitivity experiments were carried out with two different microphysics in the model. Results show that P3 1-category plus double moment cloud water microphysics scheme with 3DVAR in WRF simulates the quantity of precipitation closer to the observed precipitation over Munsiyari. The vertical velocity and relative humidity were also simulated well during 3DVAR data assimilation as compared to without data assimilation over study region.

Evaluation of parameterization schemes in the Weather Research and Forecasting (WRF) model: A case study for the Kaiga nuclear power plant site

To examine the dynamical role of convection in African easterly wave (AEW) life cycles the Weather Research and Forecasting (WRF) model is used to simulate the evolution of a single AEW from September 2004. The model simulations are validated against corresponding numerical weather prediction analyses and the mean fields closely resemble composite structures from previous studies. A potential vorticity (PV) thinking approach is used to highlight the interactions between dynamics and convection. Organized deep convection embedded within the AEW has a large contribution to the synoptic-scale mean PV and energetics of the AEW. The PV tendency is maximized in the lower troposphere, consistent with the vertical gradient in diabatic heating rates in the areas of convection. By examining terms in the Lorenz energy cycle, it is shown that diabatic heating associated with convection is as important as adiabatic energy conversion in producing eddy available potential energy of the synoptic AEW, implying that AEWs are best described as hybrid adiabatic and diabatic structures. The net effect of convection is succinctly described using a simulation whereby the parameterizations associated with convection are switched off at the midpoint of the model run. This perturbation experiment shows that, although the AEW continues to propagate westward with a similar phase speed, the net PV value continually weakens with time. This result proves that convection is vital for the maintenance of the AEW as it propagates across West Africa and suggests that without active convection the synoptic AEW cannot persist for an extended length of time.

Abstract. Great efforts have been made to simulate atmospheric pollutants, but their spatial and temporal distributions are still highly uncertain. Observations can measure their concentrations with high accuracy but cannot estimate their spatial distributions due to the sporadic locations of sites. Here, we propose an ensemble method by applying a linear minimum variance estimation (LMVE) between multi-model ensemble (MME) simulations and measurements to derive a more realistic distribution of atmospheric pollutants. The LMVE is a classical and basic version of data assimilation, although the estimation itself is still useful for obtaining the best estimates by combining simulations and observations without a large amount of computer resources, even for high-resolution models. In this study, we adopt the proposed methodology for atmospheric radioactive caesium (Cs-137) in atmospheric particles emitted from the Fukushima Daiichi Nuclear Power Station (FDNPS) accident in March 2011. The uniqueness of this approach includes (1) the availability of observed Cs-137 concentrations near the surface at approximately 100 sites, thus providing dense coverage over eastern Japan; (2) the simplicity of identifying the emission source of Cs-137 due to the point source of FDNPS; (3) the novelty of MME with the high-resolution model (3 km horizontal grid) over complex terrain in eastern Japan; and (4) the strong need to better estimate the Cs-137 distribution due to its inhalation exposure among residents in Japan. The ensemble size is six, including two atmospheric transport models: the Weather Research and Forecasting – Community Multi-scale Air Quality (WRF-CMAQ) model and non-hydrostatic icosahedral atmospheric model (NICAM). The results showed that the MME that estimated Cs-137 concentrations using all available sites had the lowest geometric mean bias (GMB) against the observations (GMB =1.53), the lowest uncertainties based on the root mean square error (RMSE) against the observations (RMSE =9.12 Bq m−3), the highest Pearson correlation coefficient (PCC) with the observations (PCC =0.59) and the highest fraction of data within a factor of 2 (FAC2) with the observations (FAC2 =54 %) compared to the single-model members, which provided higher biases (GMB =1.83–4.29, except for 1.20 obtained from one member), higher uncertainties (RMSE =19.2–51.2 Bq m−3), lower correlation coefficients (PCC =0.29–0.45) and lower precision (FAC2 =10 %–29 %). At the model grid, excluding the measurements, the MME-estimated Cs-137 concentration was estimated by a spatial interpolation of the variance used in the LMVE equation using the inverse distance weights between the nearest two sites. To test this assumption, the available measurements were divided into two categories, i.e. learning and validation data; thus, the assumption for the spatial interpolation was found to guarantee a moderate PCC value (> 0.4) within an approximate distance of at least 70 km. Extra sensitivity tests for several parameters, i.e. the site number and the weighting coefficients in the spatial interpolation, the time window in the LMVE and the ensemble size, were performed. In conclusion, the important assumptions were the time window and the ensemble size; i.e. a shorter time window (the minimum in this study was 1 h, which is the observation interval) and a larger ensemble size (the maximum in this study was six, but five is also acceptable if the members are effectively selected) generated better results.

Predicting lightning activity in Greece with the Weather Research and Forecasting (WRF) model

GPU acceleration of MPAS microphysics WSM6 using OpenACC directives: Performance and verification

This study investigates the performance of two commonly used planetary boundary layer (PBL) schemes, the local turbulence closure (Mellor-Yamada-Janjić: MYJ) and the nonlocal turbulence closure (Yonsei University: YSU), over the tropical coastal region of Shenzhen, China, using the Weather Research and Forecasting (WRF) model. The simulation results are evaluated by comparing them to various measurements. Additionally, we also investigate the performance of the PBL schemes over different land cover types and their ability to reproduce the physical relationships between several pairs of interrelated near-surface atmospheric parameters. The results show that both PBL schemes underestimate temperature and PBL height and overestimate wind speed and turbulent fluxes throughout the whole day, but this can be improved by increasing horizontal resolution. We find that the overestimation of wind speed and turbulent heat fluxes is mainly caused by the strong mechanical mixing in the model. Some improvements are required such as more accurate surface morphology parameters and momentum transport coefficients. Overall, the YSU scheme outperforms the MYJ scheme at all horizontal resolutions over complex terrain in terms of near-surface variables, PBL vertical structures, and the internal relations between turbulent heat fluxes and atmospheric instability. However, the difference between the two PBL schemes, varying over different land covers and with time periods and altitudes within the PBL, is generally greater over the land than over the sea for thermal-related variables (especially during daytime), while for variables associated with water vapor is the opposite. This highlights the importance of the underlying surface for the performance of PBL schemes and the need to select appropriate PBL schemes for simulating the evolution of PBL processes over different land covers.