Impact of precipitating ice on the simulation of a heavy rainfall event with advanced research WRF using two bulk microphysical schemes

Currently, to include the effects of wind farms in the Weather Research and Forecasting (WRF) model, a common choice is to use the Fitch wind farm parametrisation (WFP). This WFP has long been implemented into the WRF's standard repository and has been the subject of several wind resource assessment studies. However, one of the disadvantages of its current (WRF version 4.4) set-up is that it is constrained to one Planetary Boundary Layer (PBL) parametrisation. The Fitch scheme is coupled to the Mellor-Yamada Nakanishi Niino (MYNN) PBL parametrisation because it can inject Turbulent Kinetic Energy (TKE) from the turbines into the atmosphere. More importantly, it is the only PBL where the TKE advection can be activated. This feature is essential since it stores the TKE from one time step to the next and prevents the high TKE concentration at the turbine's location.One way for the WFPs to become PBL-independent is to move away from focusing on the TKE source term and parametrise the turbulence in some other way. The Explicit Wake Parametrisation (EWP) is a WFP coupled to WRF that, as opposed to the Fitch scheme, does not include an explicit TKE source term and the turbulence is produced via enhanced vertical shear. The EWP is based on the assumption that the advection and diffusion terms in the RANS Navier-Stokes equations dominate the development of the wake. As a result, the drag equation is also related to the diffusion term from a 1.5 turbulence closure. The EWP then needs the turbine's information, wind speed and the turbulent diffusivity coefficient (Km) from the PBLs to calculate the wind deficit. Given the latter, the EWP can work if Km is present and comes from at least a 1.5-order turbulence closure PBL. However, studies have yet to attempt to prove this feature since it has only been used with the MYNN scheme.In this study, we demonstrate the use of the EWP in WRF when other PBL schemes are used and the implications of this approach. We demonstrate this implementation under ideal neutral conditions with similar setups and forcings (surface roughness length, Coriolis parameter and hub-height wind speed) for two local (1.5-order closure) PBL schemes. Similarly, we test the possibility of coupling EWP into two non-local PBL schemes (first-order closure). The study focuses on the wake recovery behaviour, the drag strength and the power produced by an idealized wind farm under the four PBL schemes. Early results show faster wake recovery from non-local PBls than local ones, which could be related to the diffusivity coefficient values and the PBL's mixing rates.

The Weather Research and Forecasting (WRF) is a next-generation mesoscale numerical weather prediction system. It is designed with a dual purpose, forecasting and research. The WRF software infrastructure consists of a number of components such as dynamic solvers and physical simulation modules. Dynamic solvers are intensive computational components of the WRF model. In this paper, the Horizontal Diffusion method, which is part of the ARW (Advanced Research WRF) dynamic solver, is accelerated using GPUs. The performance of the GPU-based method was compared to that one of a CPU-based single-threaded counterpart on a computational domain of 433x308 horizontal grid points with 35 vertical levels. Thus, the achieved speedup is 19x on a NVIDIA Tesla M2090, without considering data I/O.

The most severe large-scale flood on record occurred on the Amur River and its main tributaries (the Songhua, the Zeya, and the Bureya Rivers) in August-early September 2013. Prolonged heavy rainfalls over the vast territory of the Amur River basin produced the flood during the summer of 2013. During the flood monitoring, it was noted that observed precipitation data from the Amur River observational network had not represented areal precipitation over drainage basins of the Amur River and its tributaries well enough. Therefore, operational Weather Research and Forecasting (WRF)-Advanced Research WRF (WRF-ARW) model with grid distance of 15 km was applied for prediction of areal precipitation over that territory. The results of the simulation were compared with observed precipitation and water level data from the outlet points of partial drainage basins of the Amur River in June-September 2012 and 2013 to discuss the possibility of using numerically simulated precipitation in hydrological applications related to the Amur River basin. During the summer months of those years, an extreme flood occurred in 2013, while the hydrological situation was normal in 2012 on the Amur River. The results of the comparison show that the amount of precipitation simulated on grid points of partial basins of the Amur River and its tributaries are in better agreement with major flood peaks than precipitation data obtained from the observational network. Additionally, if the five-day total areal precipitation averaged over the territory of a partial drainage basin exceeds 20 mm, the water level on an outlet point of a partial drainage basin of the Amur River monotonically increases independent of any variations of precipitation at an amount above the 20 mm value.

This study examines how cloud microphysical parameterizations in the Weather Research and Forecasting (WRF) model influence the simulation of rainfall over Kuwait during November 2018, with particular focus on the extreme event of 14 November. The WRF model was configured at 4-km resolution and dynamically downscaled from the Community Climate System Model version 4 (CCSM4) to evaluate the performance of four bulk microphysics schemes: WSM6, Lin, Thompson, and Morrison. Model output was evaluated against observations from the Kuwait Automatic Weather Station (29.22°N, 47.96°E) using cumulative and distributional rainfall characteristics, event-scale analysis, and standard statistical metrics (RMSE, MAE, bias, and correlation). All schemes reproduced the timing of rainfall events, while notable differences were found in simulated rainfall intensity. WSM6 produced rainfall amounts closest to observations, with the lowest error values and a small positive bias. Lin showed moderate overestimation, Thompson produced larger overestimation during heavy-rain periods, and Morrison consistently underestimated rainfall totals. For the 14 November event, which recorded 79.0 mm at the station, simulated totals ranged from 55.6 to 91.6 mm across the schemes, indicating that inter-scheme differences were dominated by rainfall magnitude rather than timing. These results highlight the sensitivity of convection-permitting rainfall simulations in arid regions to microphysical formulation. Evaluation using additional events and seasons is needed to assess whether these results extend beyond the period examined.

Abstract. Regions with high ice water content (HIWC), composed of mainly small ice crystals, frequently occur over convective clouds in the tropics. Such regions can have median mass diameters (MMDs) <300 µm and equivalent radar reflectivities <20 dBZ. To explore formation mechanisms for these HIWCs, high-resolution simulations of tropical convective clouds observed on 26 May 2015 during the High Altitude Ice Crystals – High Ice Water Content (HAIC-HIWC) international field campaign based out of Cayenne, French Guiana, are conducted using the Weather Research and Forecasting (WRF) model with four different bulk microphysics schemes: the WRF single‐moment 6‐class microphysics scheme (WSM6), the Morrison scheme, and the Predicted Particle Properties (P3) scheme with one- and two-ice options. The simulations are evaluated against data from airborne radar and multiple cloud microphysics probes installed on the French Falcon 20 and Canadian National Research Council (NRC) Convair 580 sampling clouds at different heights. WRF simulations with different microphysics schemes generally reproduce the vertical profiles of temperature, dew-point temperature, and winds during this event compared with radiosonde data, and the coverage and evolution of this tropical convective system compared to satellite retrievals. All of the simulations overestimate the intensity and spatial extent of radar reflectivity by over 30 % above the melting layer compared to the airborne X-band radar reflectivity data. They also miss the peak of the observed ice number distribution function for 0.1<Dmax<1 mm. Even though the P3 scheme has a very different approach representing ice, it does not produce greatly different total condensed water content or better comparison to other observations in this tropical convective system. Mixed-phase microphysical processes at −10 ∘C are associated with the overprediction of liquid water content in the simulations with the Morrison and P3 schemes. The ice water content at −10 ∘C increases mainly due to the collection of liquid water by ice particles, which does not increase ice particle number but increases the mass/size of ice particles and contributes to greater simulated radar reflectivity.

Downbursts are very disruptive weather events that can produce large amounts of damage. The most studied downbursts are those occurring in the United States and continental Europe, but they can happen globally. This work is an observational and modelling analysis of a major downburst event that occurred on 14 October 2016 over eastern Liguria (Italy). This downburst affected an area 30 km long and 10 km wide, producing observed wind gusts of 40 m/s with major impacts to railways, trees, and houses, with more than 2.5 million euros of damage. First, the general environment influencing this downburst is identified and analyzed, then the event is reproduced with a small multi-physics high-resolution ensemble using the Weather Research and Forecasting (WRF)–advanced research WRF (ARW) model, with 1 km horizontal grid spacing. The event was poorly predicted beforehand, and the difficulty in forecasting this event is confirmed by the fact that so few ensemble members suggested the occurrence of damaging winds over eastern Liguria. However, one of the eight members performed well and its output helped to reveal the primary mechanisms for the downburst, suggesting that high-resolution ensembles using mixed physics may be a useful tool for improving the prediction of similar extreme events in the Mediterranean region in the future.

In this work, the impact of assimilation of conventional and satellite remote sensing observations (Oceansat-2 winds, MODIS temperature/humidity profiles) is studied on the simulation of two tropical cyclones in the Bay of Bengal region of the Indian Ocean using a three-dimensional variational data assimilation (3DVAR) technique. The Weather Research and Forecasting (WRF)-Advanced Research WRF (ARW) mesoscale model is used to simulate the severe cyclone JAL: 5–8 November 2010 and the very severe cyclone THANE: 27–30 December 2011 with a double nested domain configuration and with a horizontal resolution of 27 × 9 km. Five numerical experiments are conducted for each cyclone. In the control run (CTL) the National Centers for Environmental Prediction global forecast system analysis and forecasts available at 50 km resolution were used for the initial and boundary conditions. In the second (VARAWS), third (VARSCAT), fourth (VARMODIS) and fifth (VARALL) experiments, the conventional surface observations, Oceansat-2 ocean surface wind vectors, temperature and humidity profiles of MODIS, and all observations were respectively used for assimilation. Results indicate meager impact with surface observations, and relatively higher impact with scatterometer wind data in the case of the JAL cyclone, and with MODIS temperature and humidity profiles in the case of THANE for the simulation of intensity and track parameters. These relative impacts are related to the area coverage of scatterometer winds and MODIS profiles in the respective storms, and are confirmed by the overall better results obtained with assimilation of all observations in both the cases. The improvements in track prediction are mainly contributed by the assimilation of scatterometer wind vector data, which reduced errors in the initial position and size of the cyclone vortices. The errors are reduced by 25, 21, 38 % in vector track position, and by 57, 36, 39 % in intensity, at 24, 48, 72 h predictions, respectively, for the two cases using assimilation of all observations. Simulated rainfall estimates indicate that while the assimilation of scatterometer wind data improves the location of the rainfall, the assimilation of MODIS profiles produces a realistic pattern and amount of rainfall, close to the observational estimates.

On the morning of 21 June 1978, exceptional sea level oscillations with a trough‐to‐crest height of 6 m and a period of 10–20 min were observed in Vela Luka Bay. Slightly less pronounced variability was observed in a wider middle and south Adriatic east coastal area and, with some delay, along the west coast. In this paper, one of the original hypotheses put forward to interpret the event, relating it to a mesoscale air pressure disturbance, is reconsidered by using all the available data as well as state‐of‐the‐art meteorological and oceanographic models. A fresh look at the meteorological data confirms that the atmospheric disturbance propagated at about 22 m/s in a northeastward direction. Additionally, the data suggest that it had the shape of the boxcar function characterized by an air pressure offset of 3 mbar and duration of 10 min. The meteorological model employed (Weather Research and Forecasting (WRF)‐Advanced Research WRF) proves unable to reproduce the mesoscale disturbance coinciding with the surge, but it shows that the background atmospheric conditions were favorable for the development of such disturbances. The oceanographic model Advanced Circulation Model for Shelves, Coasts and Estuaries (two‐dimensional depth integrated), forced by the described air pressure disturbance, successfully reproduces sea level variability in Vela Luka Bay reaching a few meters and thus surpassing the inverted barometer response by two orders of magnitude. The enhancement appears to be due to a four‐phase process. The model also suggests that the scattering due to the variable bathymetry and the reflection from the east Adriatic coast resulted in waves that returned towards the west coast and generated considerable sea level activity there.

The Weather Research and Forecasting (WRF) model with the solver—Advanced Research WRF (ARW)—is commonly used in the meteorological community for forecasting tropical cyclones. However, it is well known that vegetation reduces the tangential wind velocity in a cyclonic storm. In this study, the reduction in wind during tropical cyclone (TC) Remal due to vegetation is calculated by using the difference between two WRF-ARW simulations of the event in the presence and absence, respectively, of the Sundarbans mangrove forest (SMF) within the simulated domain. It is found that, on average, the presence of SMF decreases the maximum surface wind of Remal by 5 ms⁻¹ and subsequently influences the spatial distribution of precipitation and upward moisture flux over the SMF area. In addition to that, it is shown that the reduction in drag force in the absence of this mangrove forest is connected with the increase in the small-scale ice water mixing ratio to a maximum value (0.010) of 77 nautical miles (NM) away from the coast through atmospheric teleconnection. Furthermore, the absence of the SMF is connected with the elimination of updraft after landfall, and the increase of wind flow from above 12.5 km height to 17.5 km height. Even though the absence of the SMF does not significantly change the track of the TC, TC centre speed, surface temperature, column-integrated water vapour mixing ratio and net radiation. The overall findings of this research suggest the importance of preserving and enhance the coverage of such forests in order to reduce coastal hazards’ risks, in agreement with previous studies.

A two‐moment bulk microphysical scheme has been implemented into the Weather Research and Forecasting (WRF) model to investigate the aerosol‐cloud interaction. The microphysical scheme calculates the mass mixing ratios and number concentrations of aerosols and five types of hydrometeors and accounts for various cloud processes including warm and mixed phase microphysics. The representation of the aerosol size distribution is evaluated, showing that the three‐moment modal method produces results better in agreement with the sectional approach than the two‐moment modal method for variable supersaturation conditions in clouds. The effects of aerosols on cloud processes are investigated using the two‐moment bulk microphysical scheme in a convective cumulus cloud event occurring on 24 August 2000 in Houston, Texas. The modeled evolution of the distribution of radar reflectivity in the y‐z section, the cell lifetime, and averaged accumulated precipitation with the aerosol concentration under the polluted urban condition are qualitatively consistent with the measurements. Sensitivity simulations are initialized using a set of aerosol profiles with the number concentrations ranging from 200 to 50,000 cm−3 and mass ranging from 1 to 10 μg m−3 at the surface level. The response of precipitation to the increase of aerosol concentrations is nonmonotonic, because of the complicated interaction between cloud microphysics and dynamics. The precipitation increases with aerosol concentrations from clean maritime to continental background conditions, but is considerably reduced and completely suppressed under highly polluted conditions, indicating that the aerosol concentration exhibits distinct effects on the precipitation efficiency under different aerosol conditions. The maximal cloud cover, core updraft, and maximal vertical velocity exhibit similar responses as precipitation. Comparison is made to evaluate the effects of different autoconversion parameterizations and bulk microphysical schemes on cloud properties. Because of its broad application in numerical weather prediction, implementation of the two‐moment microphysical scheme to the WRF model will greatly facilitate assessment of aerosol‐cloud interaction from individual cumulus to mesoscale convective systems.

This study uses the Weather Research and Forecasting (WRF) Model to investigate the performance of hail parameterizations of the WRF double-moment 7-class (WDM7), aerosol-aware Thompson (AAT), and National Taiwan University triple-moment (NTU3M) bulk microphysics schemes (BMSs) on a real case of a hailstorm initiated in Shandong Province, China. The maximum hail size is particularly evaluated because it is crucial to hail severity prediction, along with areal coverage and intensity of the 24-h solid precipitation during the simulations. Compared with the radar-derived maximum hail size, the objective analysis shows that the NTU3M scheme has the best score in the forecast skill of hail-fall coverage and size, while two BMSs with single-moment rimed ice species overestimate hail diameters aloft but underpredict the coverage at the surface. A deeper investigation suggests that the derived size tendencies from the three BMSs are comparable to the benchmark solutions from the detailed hailstone growth and melting models. The NTU3M scheme displays the most consistent size tendency of the maximum diameter with the benchmark solution in the growth processes. The behaviors of melted diameter by parameterizations are highly related to the treatments of number concentration, which are consistent with the predicted hail severity and coverage. Finally, the sensitivity study shows that increasing the model resolution does not improve the forecast of the maximum hail size, given the biases in the hail mass budget equations and the parameterization of particle size distribution, with single-moment rimed ice species of the AAT scheme. Significance Statement Improving hail-forecasting skill, including the size, severity, and the spatial and temporal coverage of hail fall, has become an important subject for numerical weather prediction models as the model resolution increases. The objective of this study is to investigate the fundamental differences in hail parameterizations of three bulk microphysics schemes that lead to differences in the prediction of severe hail events and the spatial coverage of hail fall, hopefully providing insights into hail prediction with a regional numerical weather prediction model in the future.

In the present study using the Weather Research and Forecasting (WRF) and Eta models, recent heavy rainfall events that occurred (i) over parts of Maharastra during 26 to 27 July, 2005, (ii) over coastal Tamilnadu and south coastal Andhra Pradesh during 24 to 28 October, 2005, and (iii) the tropical cyclone of 30 September to 3 October, 2004/Monsoon Depression of 2 to 5 October 2004, that developed during the withdrawal phase of the southwest monsoon season of 2004 have been investigated. Also sensitivity experiments have been conducted with the WRF model to test the impact of microphysical and cumulus parameterization schemes in capturing the extreme weather events. The results show that the WRF model with the microphysical process and cumulus parameterization schemes of Ferrier et al. and Betts-Miller-Janjic was able to capture the heavy rainfall events better than the other schemes. It is also observed that the WRF model was able to predict mesoscale rainfall more realistically in comparison to the Eta model of the same resolution.

The influence of double-moment representation of warm-rain and ice hydrometeors on the numerical simulations of a mesoscale convective system (MCS) over the US Southern Great Plains has been evaluated. The Weather Research and Forecasting (WRF) model is used to simulate the MCS with three different microphysical schemes, including the WRF single-moment 6-class (WSM6), WRF double-moment 6-class (WDM6), and Morrison double-moment (MORR) schemes. It is found that the double-moment schemes outperform the single-moment schemes in terms of the simulated structure, life cycle, cloud coverage, precipitation, and microphysical properties of the MCS. However, compared with UND-Citation observations, collected during the Midlatitude Continental Convective Clouds Experiment (MC3E), the WRF simulated ice hydrometeors with all three schemes do not agree well with the observations. Overall results from this study suggest that uncertainty in microphysical schemes could still be a productive area of future research from perspective of both model improvements and observations.

POWERS is a polarimetric weather radar signal simulator developed by the RaSS (Radar and Surveillance System) Laboratory. This software allows to produce realistic weather radar measurements from a known meteorological scenario, generated by the WRF (Weather Research and Forecast) model. One of the problems encountered when developing complex simulators like POWERS, is to identify a number of methods through which one can assess their ability to generate realistic signals. In previous works we identified some of them. Here we discuss the general issue of the consistency of the simulator's outputs with respect to the original atmospheric scenario. Such analysis is based on a couple of real weather events: the first occurred in August 2015 in the Florence area and the second one in October 2012 over the Strait of Sicily. The analysis involves the precipitating liquid water provided by WRF and the reflectivity outputs gathered by POWERS when operating in the meteorological scenario defined by WRF itself. The objective of the analysis is to define the physical consistency between such scenario and the POWERS' reflectivity outputs by evaluating the errors made to retrieve the water content through the radar observables in both ideal and non-ideal conditions, and correlating the error spread to the variability of the microphysical parameters in the WRF scene.

Rainfall dynamics play a vital role in tropical peatland by providing sufficient water to keep peat moist throughout the year. Therefore, information of rainfall data either historical or forecasting data has risen in recent decades especially for an alert system of fire. Here the Weather and Research Forecasting (WRF) model may act as a tool to provide forecasting weather data. This study aims to do parameterization on WRF parameters for peatland in Sumatra, and to perform bias correction on the WRF’s rainfall output with observed data. We performed stepwise calibration to choose the best five physical schemes of WRF for use in the study area. The output WRF’s rainfall was bias corrected by spatially observed rainfall data for 2019 at day resolution. Our results showed the following schemes namely (i) Eta scheme for cloud microphysical parameters; (ii) GD scheme for cumulus cloud parameters, (iii) MYJ scheme for planetary boundary layer parameters; (iv) RRTM for longwave radiation; and (v) New Goddard schemes for shortwave radiation are best combination for being used to predict rainfall in maritime continent. The spatially interpolated observed rainfall with the Inverse Distance Weighting (IDW) was outperformed for calibration process of WRF’s rainfall as shown by statistical indicators used in this study. Further, the findings have contributed to advance knowledge of rainfall forecasting in maritime continent, particularly in providing data to support the development of fire danger rating system for Indonesian peatland.