Limited Area Model Research Articles

Use of GNSS Tropospheric Delay Measurements for the Parameterization and Validation of WRF High-Resolution Re-Analysis over the Western Gulf of Corinth, Greece: The PaTrop Experiment

In the last thirty years, Synthetic Aperture Radar interferometry (InSAR) and the Global Navigation Satellite System (GNSS) have become fundamental space geodetic techniques for mapping surface deformations due to tectonic movements. One major limiting factor to those techniques is the effect of the troposphere, as surface velocities are of the order of a few mm yr−1, and high accuracy (to mm level) is required. The troposphere introduces a path delay in the microwave signal, which, in the case of GNSS Precise Point Positioning (PPP), can nowadays be partly removed with the use of specialized mapping functions. Moreover, tropospheric stratification and short wavelength spatial turbulences produce an additive noise to the low amplitude ground deformations calculated by the (multitemporal) InSAR methodology. InSAR atmospheric phase delay corrections are much more challenging, as opposed to GNSS PPP, due to the single pass geometry and the gridded nature of the acquired data. Thus, the precise knowledge of the tropospheric parameters along the propagation medium is extremely useful for the estimation and correction of the atmospheric phase delay. In this context, the PaTrop experiment aims to maximize the potential of using a high-resolution Limited-Area Model for the calculation and removal of the tropospheric noise from InSAR data, by following a synergistic approach and integrating all the latest advances in the fields of remote sensing meteorology (GNSS and InSAR) and Numerical Weather Forecasting (WRF). In the first phase of the experiment, presented in the current paper, we investigate the extent to which a high-resolution 1 km WRF weather re-analysis can produce detailed tropospheric delay maps of the required accuracy, by coupling its output (in terms of Zenith Total Delay or ZTD) with the vertical delay component in GNSS measurements. The model is initially operated with varying parameterization, with GNSS measurements providing a benchmark of real atmospheric conditions. Subsequently, the final WRF daily re-analysis run covers an extended period of one year, based on the optimum model parameterization scheme demonstrated by the parametric analysis. The two datasets (predicted and observed) are compared and statistically evaluated, in order to investigate the extent to which meteorological parameters that affect ZTD can be simulated accurately by the model under different weather conditions. Results demonstrate a strong correlation between predicted and observed ZTDs at the 19 GNSS stations throughout the year (R ranges from 0.91 to 0.93), with an average mean bias (MB) of –19.2 mm, indicating that the model tends to slightly underestimate the tropospheric ZTD as compared to the GNSS derived values. With respect to the seasonal component, model performance is better during the autumn period (October–December), followed by spring (April–June). Setting the acceptable bias range at ±23 mm (equal to the amplitude of one Sentinel-1 C-band phase cycle when projected to the zenithal distance), it is demonstrated that the model produces satisfactory results, with a percentage of ZTD values within the bias margin ranging from 57% in summer to 63% in autumn.

C-Band SAR Winds for Tropical Cyclone Monitoring and Forecast in the South-West Indian Ocean

Tropical cyclone (TC) monitoring and forecast in the South West Indian Ocean (SWIO) basin remain challenging, notably because of the lack of direct observations. During the 2018–2019 cyclone season, S-1 Sentinel SAR images were acquired, as part of the ReNovRisk-Cyclone research program, giving access to unprecedented detailed TC wind structure description without wind speed limitation. This paper assesses the quality of these data and the impact of their assimilation for TC forecasts. SAR observations are compared with analyses from a convection-permitting, limited area model AROME OI 3D-Var and with wind products used for operational TC monitoring. Their bias depends on the angle of incidence of the radar and the observation error is larger for extreme wind speed. The impact of SAR assimilation in AROME OI 3D-Var is assessed through two case studies. In the TC GELENA case, it leads to a better TC positioning and an improved representation of inner and outer vortex structures. The TC intensity reduction in the analysis propagates through subsequent analyses and it has an impact on forecasts for around 12 h. In the TC IDAI case, the 3D-Var does not manage to reproduce TC intensity captured by SAR. In both cases, the modification of the initial conditions has little influence on the intensification rate of the model forecasts. Sensitivity tests show that these results are robust to different observation errors and thinning.

Chinese lockdown as aerosol reduction experiment

The lockdown of large parts of Chinese economy beginning in late January 2020 lead to significant regional changes of aerosol loads, which suggests a reduction of backscatter and consequently a regional warming in the following months. Using local data and a numerical experiment with a limited area model, we have examined how strong this response may have been. The observed (local and reanalysis) observations point to a warming of less than 1.0 K, the simulations to a warming of the order of 0.5 K. These numbers are uncertain, because of large-scale natural variability and an ad-hoc choice of aerosol optical depth anomaly in the simulation. Thus, the result was, in short, that there was actually a weak warming of a few tenth of degrees, while noteworthy changes in circulation or in precipitation were not detected. More specifically, we found that at selected central China stations temperatures were found to be higher than in previous two years. This warming goes with a marked diurnal signal, with a maximum warming in the early afternoon (06 UTC), weakest at night (18 UTC). This may be related to a general warming of large swaths of Asia (including Siberia, which is not related to local aerosol forcing). Indeed, also the stations outside the immediate strong lockdown region are showing warming, albeit a weaker one. Thus, the difference 2020 minus 2019/2018 may overestimate the effect. The ad-hoc series of numerical experiments indicates that the simulated changes are robust and suffer little from internal dynamical variability. In particular, the overall reduction of the aerosol optical depth does not lead to phases of larger intermittent divergence among the model simulations, irrespective of the aerosol load. Instead, the simulations with reduced anthropogenic aerosol load show more a mere locally increased temperature. This may indicate that the aerosol effect is mostly thermodynamic in all local air columns in the region.

Crowd‐sourced observations for short‐range numerical weather prediction: Report from EWGLAM/SRNWP Meeting 2019

AbstractCrowd‐sourced observations (CSO) offer great potential for numerical weather prediction (NWP). This paper offers a synthesis of progress, challenges and opportunities in this area based on a special session of the EWGLAM Meeting in 2019, concentrating on high‐resolution limited‐area models (LAMs). Two main application areas of CSO are described: data assimilation and verification. One part of data assimilation developments concentrates on smartphone pressure observations, which represent a large volume of data. However, special care has to be taken about data protection and the quality of observations. In this paper, two examples are presented: the SMAPS experiment from Denmark and the uWx experiment from the United States. Another data assimilation topic is citizen observations with low‐cost weather sensors; here an example from Norway is presented using Netatmo stations. The other application area is the use of CSO for model verification. One novel method developed in the United Kingdom is applying social media data to detect severe weather events. This approach is especially important because one future application area of LAM NWP models is impact‐oriented warnings.

Improving Regional Model Skills During Typhoon Events: A Case Study for Super Typhoon Lingling Over the Northwest Pacific Ocean

The ability of forecasting systems to simulate tropical cyclones is still insufficient, and currently, there is an increased interest in improving model performance for intense tropical cyclones. In this study, the impact of reducing surface drag at high wind speeds on modeling wind and wave conditions during the super Typhoon Lingling event over the northwest Pacific Ocean in 2019 is investigated. The model response with respect to the parameterization for momentum exchange at the ocean surface is demonstrated using a fully coupled regional atmosphere model (the Consortium for Small-Scale Modeling-Climate Limited-area Modeling, CCLM) and a wind wave model (WAM). The active two-way coupling between the atmosphere and ocean waves model is enabled through the introduction of sea state-dependent surface drag into the CCLM and updated winds into the WAM. The momentum exchange with the sea surface is modeled via the dependency of the roughness length (Z0) on the surface stress itself and, when applicable, on the wind speed. Several high-resolution runs are performed using one-way or two-way fully coupled regional atmosphere-wave (CCLM-WAM) models. The model simulations are assessed against the best track data as well as against buoy and satellite observations. The results show that the spectral nudging technique can improve the model’s ability to capture the large-scale circulation, track and intensity of Typhoon Lingling at regional scales. Under the precondition of large-scale constraining, the two-way coupling simulation with the proposed new roughness parameterization performs much better than the simulations used in older studies in capturing the maximum wind speed of Typhoon Lingling due to the reduced drag at extreme wind conditions for the new Z0.

New consideration of lateral boundary treatment for meso- and micro-scale nested PBL simulations over complex terrain

A detailed planetary boundary layer (PBL) flow over complex terrain can be simulated by nesting mesoscale numerical weather prediction (NWP) models and microscale computational fluid dynamics (CFD). When fully prescribed lateral boundaries are encountered, the over-specified treatment of CFD or limited-area models may result in ill-posedness and numerical instabilities. Such behaviors are due to inconsistent governing equations, turbulent viscosity models, and underlying surfaces between meso- and micro-scale formulations. In this study, by applying the relaxation treatment in marginal zones of CFD, its wind components are forced toward the externally specified NWP values. The relaxation schemes, deduced from simple advective transport equations, are optimized using genetic algorithms. Their numerical abilities in absorbing wave reflections on shallow-water systems are more efficient than the published approach. Then, key questions concerning the selection of relaxation strength, turbulence model constants, and prognostic variables specified on NWP–CFD nested boundaries are systemically investigated in the neutrally stratified Askervein Hill case. The relaxation scheme from simpler systems also performs effectively in CFD and decreases the adaption distance of wind velocities near over-specified outlet boundaries. A full set approach to obtain well-posed PBL solutions from the microscale domains sustained with inflow boundaries from NWP is proposed and validated.

High-Resolution Simulation of Polar Lows over Norwegian and Barents Seas Using the COSMO-CLM and ICON Models for the 2019–2020 Cold Season

The lack of meteorological observations at high latitudes and the small size and relatively short lifetime of polar lows (PLs) constitute a problem in the simulation and prediction of these phenomena by numerical models. On the other hand, PLs, which are rapidly developing, can lead to such extreme weather events as stormy waves, strong winds, the icing of ships, and snowfalls with low visibility, which can influence communication along the Arctic seas. This article is devoted to studying the possibility of the numerical simulation and prediction of polar lows by different model configurations and resolutions. The results of the numerical experiments for the Norwegian and Barents seas with grid spacings of 6.5 and 2 km using the ICON-Ru configurations of the ICON (ICOsahedral Nonhydrostatic) model and with a grid spacing of 6.5 km using the COSMO-CLM (Climate Limited-area Modeling) configuration of the COSMO (COnsortium for Small-scale MOdelling) model are presented for the cold season of 2019–2020. All the used model configurations demonstrated the possibility of the realistic simulation of polar lows. The ICON model showed slightly more accurate results for the analyzed cases. The best results showed runs with lead times of less than a day.

Parametrizations of Liquid and Ice Clouds’ Optical Properties in Operational Numerical Weather Prediction Models

Parametrization of radiation transfer through clouds is an important factor in the ability of Numerical Weather Prediction models to correctly describe the weather evolution. Here we present a practical parameterization of both liquid droplets and ice optical properties in the longwave and shortwave radiation. An advanced spectral averaging method is used to calculate the extinction coefficient, single scattering albedo, forward scattered fraction and asymmetry factor (βext, ϖ, f, g), taking into account the nonlinear effects of light attenuation in the spectral averaging. An ensemble of particle size distributions was used for the ice optical properties calculations, which enables the effective size range to be extended up to 570 μm and thus be applicable for larger hydrometeor categories such as snow, graupel, and rain. The new parameterization was applied both in the COSMO limited-area model and in ICON global model and was evaluated by using the COSMO model to simulate stratiform ice and water clouds. Numerical weather prediction models usually determine the asymmetry factor as a function of effective size. For the first time in an operational numerical weather prediction (NWP) model, the asymmetry factor is parametrized as a function of aspect ratio. The method is generalized and is available on-line to be readily applied to any optical properties dataset and spectral intervals of a wide range of radiation transfer models and applications.

A post-processing method applied to simulated wind speeds in Romania

The quality of a numerical weather forecast is influenced by uncertainties that may arise from the initial conditions or the design of the model itself. Specifically, our evaluation of a limited area model wind speed forecasts over Romania shows that forecast errors are significant in areas with complex topography, or other local characteristics. The purpose of this paper is to reduce these errors obtained from a deterministic model forecast. A bias correction method that post-processes the model output was implemented in order to improve the accuracy of the wind speed prediction. The new forecast was evaluated using standard statistical scores, for winter and summer seasons of the year 2016, at six stations in Romania located in areas with different topography. The corrected forecast was also compared to that obtained from a simple linear regression model using both simulated and observed wind speed data. The results show that the application of such methods generally leads to an improvement of the wind speed forecast. Furthermore, the reduction in the forecast error depends both on the particularities of the area and the period under review.

Historical observation impact assessments for EUMETNET using the ALADIN/HU limited area model

Two historical Observing System Experiment (OSE) studies using the ALADIN limited area model and its assimilation system are described. The first study, using an OSE scenario that minimizes the impacts of observations through the lateral boundary conditions, demonstrated the importance of each assimilated terrestrial (radiosonde, aircraft, and wind profiler) observations on the analyses and short-range forecasts of the ALADIN/HU model and proved evidence, that the role of conventional observations cannot be even partly taken over by satellite measurements without degradation of the forecast quality. The second study demonstrated that the assimilation of radiosonde observations remains indispensable even with a progressively increasing amount of aircraft measurements.

Incoming data quality control in high-resolution urban climate simulations: a Hong Kong–Shenzhen area urban climate simulation as a case study using the WRF/Noah LSM/SLUCM model (Version 3.7.1)

Abstract. The growth of computational power unleashed the potential of high-resolution urban climate simulations using limited-area models in recent years. This trend empowered us to deepen our understanding of urban-scale climatology with much finer spatial–temporal details. However, these high-resolution models would also be particularly sensitive to model uncertainties, especially in urbanizing cities where natural surface texture is changed artificially into impervious surfaces with extreme rapidity. These artificial changes always lead to dramatic changes in the land surface process. While models capturing detailed meteorological processes are being refined continuously, the input data quality has been the primary source of biases in modeling results but has received inadequate attention. To address this issue, we first examine the quality of the incoming static data in two cities in China, i.e., Shenzhen and Hong Kong SAR, provided by the WRF ARW model, a widely applied state-of-the-art mesoscale numerical weather simulation model. Shenzhen has gone through an unprecedented urbanization process in the past 30 years, and Hong Kong SAR is another well-urbanized city. A significant proportion of the incoming data is outdated, highlighting the necessity of conducting incoming data quality control in the region of Shenzhen and Hong Kong SAR. Therefore, we proposed a sophisticated methodology to develop a high-resolution land surface dataset in this region. We conducted urban climate simulations in this region using both the developed land surface dataset and the original dataset utilizing the WRF ARW model coupled with Noah LSM/SLUCM and evaluated the performance of modeling results. The performance of modeling results using the developed high-resolution urban land surface datasets is significantly improved compared to modeling results using the original land surface dataset in this region. This result demonstrates the necessity and effectiveness of the proposed methodology. Our results provide evidence of the effects of incoming land surface data quality on the accuracy of high-resolution urban climate simulations and emphasize the importance of the incoming data quality control.

Effects of using different urban parametrization schemes and land-cover datasets on the accuracy of WRF model over the City of Ottawa

In the face of rapid urbanization and global warming, it is important to acquire a better understanding of urban climate and land-atmosphere interactions operating therein. The Weather Research and Forecasting (WRF) model is a limited area model that has been used to study urban microclimate in many cities across the globe. However, such a study is lacking for the Canada's capital city: Ottawa. In this article, the WRF model is set-up at 1 km spatial resolution over the Ottawa region and its sensitivity towards the use of different urban parametrization schemes and land-cover datasets is investigated from 01 June to 31 August 2018 which includes an extreme heat weather event spanning from June 30 to July 6. WRF-simulations are performed using the Noah land surface model with two urban parametrization schemes of different complexity, i.e., a simple bulk urban parametrization and a more advanced multilayer urban canopy model (UCM). Both WRF-simulations used the default land use-land cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) available in WRF. Between these two WRF-simulations, the simulation with the multilayer UCM is found to be more accurate than the bulk scheme simulation in terms of modeled near-surface wind-speed, relative humidity, and total precipitation compared to observations recorded at one urban weather gauging station located within the city limits. Finally, a third WRF-simulation is performed with the multilayer UCM but using a higher resolution 30 m land use-land cover data product for the urban domain. This third WRF-experiment improved further the near-surface wind speed, relative humidity, and total precipitation correspondence to observations within the city. It is worthy to mention that modeled total precipitation is found to be sensitive to both urban parametrization schemes and urban land use-land cover data sets.

Skill and Potential Economic Value of Forecasts of Ice Accretion on Wind Turbines

AbstractIn this paper, a verification study of the skill and potential economic value of forecasts of ice accretion on wind turbines is presented. The phase of active ice formation on turbine blades has been associated with the strongest wind power production losses in cold climates; however, skillful icing forecasts could permit taking protective measures using anti-icing systems. Coarse- and high-resolution forecasts for the range up to day 3 from global (IFS and GFS) and limited-area (WRF) models are coupled to the Makkonen icing model. Surface and upper-air observations and icing measurements at turbine hub height at two wind farms in central Europe are used for model verification over two winters. Two case studies contrasting a correct and an incorrect forecast highlight the difficulty of correctly predicting individual icing events. A meaningful assessment of model skill is possible only after bias correction of icing-related parameters and selection of model-dependent optimal thresholds for ice growth rate. The skill of bias-corrected forecasts of freezing and humid conditions is virtually identical for all models. Hourly forecasts of active ice accretion generally show limited skill; however, results strongly suggest the superiority of high-resolution WRF forecasts relative to other model variants. Predictions of the occurrence of icing within a period of 6 h are found to have substantially better accuracy. Probabilistic forecasts of icing that are based on gridpoint neighborhood ensembles show slightly higher potential economic value than forecasts that are based on individual gridpoint values, in particular at low cost-loss ratios, that is, when anti-icing measures are comparatively inexpensive.

An idealized model sensitivity study on Dead Sea desertification with a focus on the impact on convection

Abstract. The Dead Sea desertification-threatened region is affected by continual lake level decline and occasional but life-endangering flash floods. Climate change has aggravated such issues in the past decades. In this study, the impact on local conditions leading to heavy precipitation from the changing conditions of the Dead Sea is investigated. Idealized sensitivity simulations with the high-resolution COSMO-CLM (COnsortium for Small-scale MOdelling and Climate Limited-area Modelling) and several numerical weather prediction (NWP) runs on an event timescale are performed on the Dead Sea area. The simulations are idealized in the sense that the Dead Sea model representation does not accurately represent the real conditions but those given by an external dataset. A reference or Dead Sea simulation covering the 2003–2013 period and a twin sensitivity or bare soil simulation in which the Dead Sea is set to bare soil are compared. NWP simulations focus on heavy precipitation events exhibiting relevant differences between the Dead Sea and the bare soil decadal realization to assess the impact on the underlying convection-related processes. The change in the conditions of the Dead Sea is seen to affect the atmospheric conditions leading to convection in two ways. (a) The local decrease in evaporation reduces moisture availability in the lower boundary layer locally and in the neighbouring regions, directly affecting atmospheric stability. Weaker updraughts characterize the drier and more stable atmosphere of the simulations in which the Dead Sea has been dried out. (b) Thermally driven wind system circulations and resulting divergence/convergence fields are altered, preventing in many occasions the initiation of convection because of the omission of convergence lines. On a decadal scale, the difference between the simulations suggests a weak decrease in evaporation, higher air temperatures and less precipitation (less than 0.5 %).

Climate change impacts on water resources in the Upper Blue Nile (Abay) River Basin, Ethiopia

This study assesses the impact of climate change on water resources in the Upper Blue Nile (Abay) River Basin using a regional climate model (RCM), COSMO Climate Limited-area Model (CCLM), coupled with a hydrological model, Soil and Water Assessment Tool (SWAT). The SWAT model was calibrated and validated using measured streamflow data from four gauging stations. Climate change projections showed increases in mean annual temperature and decrease in precipitation in most parts of the Basin. Such changes are expected to affect the hydrologic regime of the Basin; these were assessed by running the SWAT model with the past (1981–2010) and future (2010–2039, 2040–2069 and 2070–2099) climate scenarios. The results show an increase of potential evapotranspiration (PET) by up to 27% by the end of the 21st century under RCP8.5 compared to the baseline period. Surface runoff is projected to increase by up to 14%. However, the increase in surface runoff could not increase the total water yield of the Basin. Instead, the total water yield of the Basin is estimated to decrease by −1.7 to −6.5% and −10.7 to −22.7%, for simulations forced by RCP4.5 and RCP8.5 scenarios, respectively. By the end of the 21st century, the contribution of baseflow to the total water yield of the Basin is also projected to decline to 11.4% from 41.3% during the baseline period. The decrease in baseflow partly explains the decline in the total water yield of the Basin. Such changes in the hydrologic balance will have significant implications for water management in the Basin.

A multi-meteorological comparison for episodes of PM10 concentrations in the Berlin agglomeration area in Germany with the LOTOS-EUROS CTM

Particulate matter (PM) remains as one of the most relevant air-quality concerns in urban environments. The Berlin agglomeration area is still affected by exceedances of the daily limit value of the PM concentration, especially during wintertime PM episodes. In this study, we present test-case studies with the LOTOS-EUROS CTM to improve the representation of PM episodes in the Berlin agglomeration area. A variety of simulations were compared for two winter episodes characterized by cold stagnant conditions, using different meteorological input data (from the European Centre for Medium Weather Forecast (ECMWF) and the Consortium for Small-Scale Modelling-Climate Limited-area Modelling (COSMO-CLM)) and horizontal and vertical resolutions of the LOTOS-EUROS CTM. The LOTOS-EUROS CTM indicates too high mixing from the planetary boundary layer (PBL) to higher layers, leading to an underestimation of the PM mass concentration in the Berlin agglomeration. As major impact factor the mixing layer height (MLH) can be identified. Through applying the COSMO-CLM model the meteorological representation of the PBL and MLH can significantly be improved, whereas sensitivity studies only exhibit a small variation of the PBL meteorology and did not further improve the MLH. As the MLHs of both models are underestimated compared to observations and their derivation is questionable, we advise not to use this quantity any longer in CTMs. By contrast, applying a multi-level approach excluding the MLH, provides a considerable increase in the total PM mass concentration amount. The redistribution and increased nitrate and ammonium concentration can be mentioned as the main culprit. However, the best-fit simulations were obtained for the multi-level configuration fed by COSMO-CLM input data, additionally representing a more realistic urban increment.

Verification of Solid Precipitation Forecasts from Numerical Weather Prediction Models in Norway

AbstractAssessing the quality of precipitation forecasts requires observations, but all precipitation observations have associated uncertainties making it difficult to quantify the true forecast quality. One of the largest uncertainties is due to the wind-induced undercatch of solid precipitation gauge measurements. This study discusses how this impacts the verification of precipitation forecasts for Norway for one global model [the high-resolution version of the ECMWF Integrated Forecasting System (IFS-HRES)], and one high-resolution, limited-area model [Applications of Research to Operations at Mesoscale (MEPS)]. First, the forecasts are compared with high-quality reference measurements (less undercatch) and with more simple measurement equipment commonly available (substantial undercatch) at the Haukeliseter observation site. Then the verification is extended to include all Norwegian observation sites: 1) stratified by wind speed, since calm (windy) conditions experience less (more) undercatch; and 2) by applying transfer functions, which convert measured precipitation to what would have been measured with high-quality equipment with less undercatch, before the forecast–observation comparison is performed. Results show that the wind-induced undercatch of solid precipitation has a substantial impact on verification results. Furthermore, applying transfer functions to adjust for wind-induced undercatch of solid precipitation gives a more realistic picture of true forecast capabilities. In particular, estimates of systematic forecast biases are improved, and to a lesser degree, verification scores like correlation, RMSE, ETS, and stable equitable error in probability space (SEEPS). However, uncertainties associated with applying transfer functions are substantial and need to be taken into account in the verification process. Precipitation forecast verification for liquid and solid precipitation should be done separately whenever possible.

Land Use and Land Cover Change Strongly Modulates Land‐Atmosphere Coupling and Warm‐Season Precipitation Over the Central United States in CESM2‐VR

AbstractPrior research indicates that land use and land cover change (LULCC) in the central United States has led to significant changes in surface climate. The spatial resolution of simulations is particularly relevant in this region due to its influence on model skill in capturing mesoscale convective systems (MCSs) and on representing the spatial heterogeneity. Recent advances in Earth system models (ESMs) make it feasible to use variable resolution (VR) meshes to study regional impacts of LULCC while avoiding inconsistencies introduced by lateral boundary conditions typically seen in limited area models. Here, we present numerical experiments using the Community Earth System Model version 2–VR to evaluate (1) the influence of resolution and land use on model skill and (2) impacts of LULCC over the central United States at different resolutions. These simulations are configured either on the 1° grid or a VR grid with grid refinement to 1/8° over the contiguous United States for the period of 1984–2010 with two alternative land use data sets corresponding to the preindustrial and present day states. Our results show that skill in simulating precipitation over the central United States is primarily dependent on resolution, whereas skill in simulating 2‐m temperature is more dependent on accurate land use. The VR experiments show stronger LULCC‐induced precipitation increases over the Midwest in May and June, corresponding to an increase in the number of MCS‐like features and a more conductive thermodynamic environment for convection. Our study demonstrates the potential of using VR ESMs for hydroclimatic simulations in regions with significant LULCC.

Wind farm: a new module for the BRAMS

A new module has been developed by the limited area model BRAMS to parameterize a wind farm power plant. BRAMS is developed to be a system for numerical weather prediction, and it is currently employed for operational environmental prediction by the CPTEC/INPE. The new module was able to simulate the impact from a set of wind turbines. The additional drag from turbines changes the turbulent kinetic energy (TKE). Some simulations were carried out considering wind power plant installed on the Brazilian territory.

Heavy Precipitation over Italy from the Mediterranean Storm “Vaia” in October 2018: Assessing the Role of an Atmospheric River

Abstract The Mediterranean storm “Vaia” developed within a typical autumn synoptic circulation, generally associated with heavy rain conditions over the western Mediterranean Sea basin. Intense precipitation was responsible for floods over Italy between 27 and 30 October 2018, and the storm was accompanied by explosive cyclogenesis, storm surge, and extremely intense wind gusts that caused casualties and extensive damage, especially to the Alpine forests. This study investigates the contribution of different moisture sources to the extreme precipitation by means of numerical model simulations using the Bologna Limited Area Model (BOLAM). In particular, the attention is focused on the significant amount of water vapor transported into the Mediterranean basin from the Atlantic Ocean tropical area and organized along a narrow corridor across the African continent. First, a newly developed detection algorithm is applied to identify this transport as an atmospheric river (AR). Then, the implementation of an atmospheric water budget diagnostic, supported by sensitivity experiments, allows us to assess the role of the AR in terms of water supply to the precipitation systems. Although the transport of moisture from remote regions is known to be an important ingredient for the onset of heavy precipitation in the Mediterranean, the role of ARs, already identified in correspondence with some of these events, has not been deeply investigated and quantified yet in this specific area. The results demonstrate that the AR was critical for determining the magnitude of this heavy precipitation episode over Italy, whereas evaporation from the sea played a secondary role, especially for precipitation over the Alps.