Multiscale structure and turbulent dynamics of Mediterranean tropical-like cyclone (Medicane) Ianos: A modal decomposition approach

Assessment of wind resources in two parts of Northeast Brazil with the use of numerical models

Mediterranean tropical-like cyclones (MTLCs), commonly referred to as Medicanes, are high-impact weather systems characterized by complex interactions between baroclinic forcing and tropical-like processes. Despite growing interest, their vertical structures and dynamical regimes remain incompletely understood. In this study, high-resolution Weather Research and Forecasting (WRF) simulations at 1 km resolution are used to investigate the structure and evolution of two dynamically contrasting MTLCs: Ianos (2020) and Qendresa (2014). The analysis focuses on the temporal evolution of kinetic energy and turbulent dissipation as well as on the three-dimensional organization of wind and temperature fields during representative phases of the cyclone life cycle. The results reveal pronounced differences between the two events, with Ianos exhibiting a compact, vertically coherent, convection-dominated structure and Qendresa showing a wider, more asymmetric, and less stationary organization influenced by baroclinic processes. A comparative framework with the ERA5 reanalysis is employed to contextualize cyclone intensity, with ERA5 used as a dynamically consistent large-scale reference rather than as an observational benchmark. Overall, the study highlights the importance of vertical structure and boundary-layer processes in shaping Mediterranean tropical-like cyclones and demonstrates the added value of high-resolution numerical simulations for distinguishing between different dynamical regimes.

In this study, we conduct a 15‐year long (2008–2022) convection‐permitting simulation over the Tibetan Plateau (TP) using the Weather Research and Forecasting (WRF) model to evaluate its added value compared to ERA5 in characterizing subdaily precipitation and its extremes over the TP. Our findings indicate that the overestimation of precipitation amounts caused by overestimated precipitation frequency in ERA5 is mitigated in WRF mainly through a reduction in light precipitation occurrences. This improvement is likely linked to increased temperatures and decreased relative humidity in the lower troposphere. Moreover, the much earlier precipitation peak in ERA5 is corrected in WRF, a result attributed to differences in related net water vapor transport onto the TP. Additionally, WRF provides a more accurate estimation of the percentage of short‐ and long‐duration precipitation events with only slight underestimations and overestimations, respectively, whereas ERA5 exhibits significantly larger errors. The relatively larger temperature changes in WRF, indicating increased solar heating, are likely responsible for the higher occurrence of short‐duration precipitation events. In contrast, the anticyclonic anomalies and northeastern wind anomalies in WRF reduce the persistence of long‐duration precipitation. Importantly, WRF also produces more realistic timings of precipitation with different durations compared to ERA5. For extreme precipitation, short‐duration extreme events dominate accounting for 50% of extreme precipitation. These events are better reproduced by WRF than by ERA5. Although WRF outperforms ERA5 in estimating the proportion and contribution of extreme events to total precipitation and in capturing their triggering timing, a slight underestimation persists in the simulated proportion and contribution.

The Weather Research and Forecasting (WRF) community model is widely used to explore cross‐scale atmospheric features. Although WRF uncertainty studies exist, these usually involve ensembles where different physics options are selected (e.g., the boundary layer scheme) or adjusting individual parameters. Uncertainty from perturbing initial conditions, which generates internal model variability (IMV), has rarely been considered. Moreover, many off‐line WRF research studies generate conclusions based on a single model run without addressing any form of uncertainty. To demonstrate the importance of IMV, or noise, we present a 4‐month case study of summer 2018 over London, UK, using a 244‐member initial condition ensemble. Simply by changing the model start time, a median 2‐m temperature range or IMV of 1.2 °C was found (occasionally exceeding 8 °C). During our analysis, episodes of high and low IMV were found for all variables explored, explained by a relationship with the boundary condition data. Periods of slower wind speed input contained increased IMV, and vice versa, which we hypothesis is related to how strongly the boundary conditions influence the nested region. We also show the importance of IMV effects for the uncertainty of derived variables like the urban heat island, whose median variation in magnitude is 1 °C. Finally, a realistic ensemble size to capture the majority of WRF IMV is also estimated, essential considering the high computational overheads (244 members equaled 140,000 CPU hours). We envisage that highlighting considerable IMV in this repeatable manner will help advance best practices for the WRF and wider regional climate modeling community.

The use of nudging in the Weather Research and Forecasting (WRF) Model to constrain regional climate downscaling simulations is gaining in popularity because it can reduce error and improve consistency with the driving data. While some attention has been paid to whether nudging is beneficial for downscaling, very little research has been performed to determine best practices. In fact, many published papers use the default nudging configuration (which was designed for numerical weather prediction), follow practices used by colleagues, or adapt methods developed for other regional climate models. Here, a suite of 45 three-year simulations is conducted with WRF over the continental United States to systematically and comprehensively examine a variety of nudging strategies. The simulations here use a longer test period than did previously published works to better evaluate the robustness of each strategy through all four seasons, through multiple years, and across nine regions of the United States. The analysis focuses on the evaluation of 2-m temperature and precipitation, which are two of the most commonly required downscaled output fields for air quality, health, and ecosystems applications. Several specific recommendations are provided to effectively use nudging in WRF for regional climate applications. In particular, spectral nudging is preferred over analysis nudging. Spectral nudging performs best in WRF when it is used toward wind above the planetary boundary layer (through the stratosphere) and temperature and moisture only within the free troposphere. Furthermore, the nudging toward moisture is very sensitive to the nudging coefficient, and the default nudging coefficient in WRF is too high to be used effectively for moisture.

Abstract. Wind farm power production is known to be significantly affected by turbine wakes. When mesoscale numerical models are used to predict power production, the turbine wakes cannot be resolved directly because they are sub-grid features, and therefore their effects need to be parameterized. Here we propose a new wind farm parameterization that is based on the Jensen model, a well-known analytical wake model that predicts the expansion and wind speed of an ideal wake. The Jensen parameterization is implemented and inserted into two commonly used atmospheric numerical models: the Weather Research and Forecasting (WRF) model (herein referred to as just “WRF”) and the Model for Prediction Across Scales (MPAS). In addition, the internal variability in wind speed and direction within a wind farm, the wind direction uncertainty, and the superposition of multiple wakes are taken into account with an innovative approach. The proposed approach and parameterization are tested against observational data at two offshore wind farms: Lillgrund (small in size and tightly spaced) and Anholt (large and widely spaced). Results indicate that power production is predicted more accurately with the Jensen wind farm parameterization than with the Fitch wind farm parameterization, which is the only one available in WRF. Power predictions with the Jensen parameterization are similar in WRF and MPAS. The sensitivity to grid resolution is small, and the bias is generally low and negative. In conclusion, we recommend that the Jensen wind farm parameterization be used in WRF and MPAS, especially for coarse resolution, high turbine density, and wind directions aligned with the turbine columns.

<p>The Mediterranean Sea’s distinctive and complicated terrain and the surrounding high mountain systems create a favorable environment for cyclogenesis. Despite Mediterranean cyclones being relatively weaker in intensities, smaller sizes, shorter lifetimes, and rarer than mid-latitude cyclones that develop over open oceans, they strongly influence the Mediterranean climate, including extremes. Despite notable improvements in the fine-scale processes, regional models still have considerable uncertainty. They are related to various constraints such as physical parameterization (convection schemes and microphysics), boundary conditions, and horizontal/vertical resolution. This study is focused on the sensitivity to microphysics schemes and convective parametrization using the non-hydrostatic Weather Research and Forecasting (WRF) numerical weather prediction model for a recent intense Medicane, ‘IANOS’, which was formed over the central Mediterranean Sea during 15-21 September. IANOS attained its mature stage on days 17–18 September and landed over the coast of Greece on 18 September. The horizontal grid spacing of 3 km × 3 km and 39 sigma levels up to 25 hPa were taken on one single domain covering most of the Mediterranean Sea, part of Europe, northern Africa, and part of the eastern North Atlantic Ocean. The simulation of IANOS shows strong sensitivity to the initial conditions, and the simulations initialized on 15 September or later show reasonable skill. All the microphysics schemes, ‘with and without’ enabling the convection scheme, reproduce the IANOS characteristics reasonably well with notable inter-simulation differences in magnitude and location. The large discrepancy and inter-simulation difference is noticed in the track and intensity on 18 September before hitting the coast.</p> <p>Keywords: IANOS, WRF, convection parametrization, microphysics</p>

Comparative assessment of RAMS and WRF short-term forecasts over Eastern Iberian Peninsula using various in-situ observations, remote sensing products and uncoupled land surface model datasets

This study implements a variable resolution version of Community Earth Systems Model (VR‐CESM; regionally refined at 1/8° resolution) to assess the improvement in simulating the seasonal climate across the Tibetan Plateau (TP) at higher horizontal resolutions compared to its uniform (UN) 1° counterpart (UN‐CESM). The Weather Research and Forecasting (WRF) model, run at 12‐km horizontal grid spacing, is compared to both CESM simulations. VR‐CESM and WRF are more comparable to satellite and surface‐based observations than UN‐CESM in simulating summertime (June to August) temperature across the TP and TP foothills, respectively. WRF and VR‐CESM also more accurately simulate precipitation than UN‐CESM across the region due to more accurate terrain treatment. The number of days in which effective (>2.5 mm/day) and heavy (>25 mm/day) precipitation events are generally better captured in VR‐CESM compared to UN‐CESM and WRF. Finally, snow cover fraction in VR‐CESM is better simulated for all months compared to UN‐CESM, with the largest improvements simulated from June to August, while WRF performs even better than VR‐CESM in simulating snow cover fraction. The improvements in simulated temperature, precipitation, and snow cover are due to WRF and VR‐CESM's ability to resolve more complex topographic features rather than time step differences between the UN‐CESM and VR‐CESM experiments. While it is fairly intuitive that improved topography accuracy should bring forth more accurate simulated meteorology, this evaluation suggests that VR‐CESM is competitive or may even be preferred over regional climate models (such as WRF) when examining internal and external climate variability, as it bypasses several drawbacks associated with regional climate models.

Abstract. Complex air–sea interactions play a major role in both the variability and the extremes of the Mediterranean climate. This study investigates the differences between an atmosphere-only and an ocean-coupled model in reproducing Mediterranean cyclones and their associated atmospheric fields. To this end, two climate simulations are performed over the Mediterranean basin, both driven by the ECMWF ERA5 reanalysis, for a common 33-year period (1982–2014). The atmosphere standalone simulation uses the Weather Research and Forecasting (WRF) model with prescribed ERA5 sea surface temperature (SST), while in the second experiment WRF is coupled to the Massachusetts Institute of Technology General Circulation Model (MITgcm). A cyclone-tracking algorithm, based on sea level pressure, is applied to both simulations and to the ERA5 reanalysis to assess the model capability to reproduce the climatology of intense, potentially greatly impactful cyclones. Results show that the seasonal and spatial distribution of the 500 most intense cyclones is similarly reproduced between WRF and ERA5, regardless of the use of coupling. The two simulations are then compared in terms of sub-daily fields at the cyclones' maximum intensity. Differences in SST distribution between the models primarily control variations in atmospheric variables, not only at the surface but also throughout the planetary boundary layer, due to the mixing by the turbulent processes, enhanced during intense cyclones. Additionally, this research investigates cyclone effects on ocean properties in the coupled simulation, revealing that strong winds enhance surface heat fluxes and upper-ocean mixing while lowering SST. The analysis shows the ability of the coupled model to coherently represent the dynamic and thermodynamic processes associated with extreme cyclones across both the atmosphere and the ocean.

Wind farm power production is known to be significantly affected by turbine wakes. When mesoscale numerical models are used to predict power production, the turbine wakes cannot be resolved directly because they are sub-grid features and therefore their effects need to be parameterized. Here we propose a new wind farm parameterization that is based on the Jensen model, a well-known analytical wake model that predicts the expansion and wind speed of an ideal wake. The Jensen parameterization is implemented and inserted into two commonly-used atmospheric numerical models: the Weather Research and Forecasting (WRF) Model and the Model for Prediction Across Scales (MPAS). In addition, the internal variability in wind speed and direction within a wind farm, the wind direction uncertainty, and the superposition of multiple wakes are taken into account with an innovative approach. The proposed approach and parameterization are tested against observational data at two offshore wind farms: Lillgrund (small in size and tightly spaced) and Anholt (large and widely spaced). Results indicate that power production is predicted more accurately with the Jensen than with the Fitch wind farm parameterization, which is the only one available in WRF. Power predictions with the Jensen parameterization are similar in WRF and MPAS. The sensitivity to grid resolution is small and the bias is generally low and negative. In conclusion, we recommend that the Jensen wind farm parameterization be used in the WRF and MPAS models, especially for coarse resolution, high turbine density, and wind directions aligned with the turbine columns.

Wind farm power production is known to be significantly affected by turbine wakes. When mesoscale numerical models are used to predict power production, the turbine wakes cannot be resolved directly because they are sub-grid features and therefore their effects need to be parameterized. Here we propose a new wind farm parameterization that is based on the Jensen model, a well-known analytical wake model that predicts the expansion and wind speed of an ideal wake. The Jensen parameterization is implemented and inserted into two commonly-used atmospheric numerical models: the Weather Research and Forecasting (WRF) Model and the Model for Prediction Across Scales (MPAS). In addition, the internal variability in wind speed and direction within a wind farm, the wind direction uncertainty, and the superposition of multiple wakes are taken into account with an innovative approach. The proposed approach and parameterization are tested against observational data at two offshore wind farms: Lillgrund (small in size and tightly spaced) and Anholt (large and widely spaced). Results indicate that power production is predicted more accurately with the Jensen than with the Fitch wind farm parameterization, which is the only one available in WRF. Power predictions with the Jensen parameterization are similar in WRF and MPAS. The sensitivity to grid resolution is small and the bias is generally low and negative. In conclusion, we recommend that the Jensen wind farm parameterization be used in the WRF and MPAS models, especially for coarse resolution, high turbine density, and wind directions aligned with the turbine columns.

Wind farm power production is known to be significantly affected by turbine wakes. When mesoscale numerical models are used to predict power production, the turbine wakes cannot be resolved directly because they are sub-grid features and therefore their effects need to be parameterized. Here we propose a new wind farm parameterization that is based on the Jensen model, a well-known analytical wake model that predicts the expansion and wind speed of an ideal wake. The Jensen parameterization is implemented and inserted into two commonly-used atmospheric numerical models: the Weather Research and Forecasting (WRF) Model and the Model for Prediction Across Scales (MPAS). In addition, the internal variability in wind speed and direction within a wind farm, the wind direction uncertainty, and the superposition of multiple wakes are taken into account with an innovative approach. The proposed approach and parameterization are tested against observational data at two offshore wind farms: Lillgrund (small in size and tightly spaced) and Anholt (large and widely spaced). Results indicate that power production is predicted more accurately with the Jensen than with the Fitch wind farm parameterization, which is the only one available in WRF. Power predictions with the Jensen parameterization are similar in WRF and MPAS. The sensitivity to grid resolution is small and the bias is generally low and negative. In conclusion, we recommend that the Jensen wind farm parameterization be used in the WRF and MPAS models, especially for coarse resolution, high turbine density, and wind directions aligned with the turbine columns.

Wind farm power production is known to be significantly affected by turbine wakes. When mesoscale numerical models are used to predict power production, the turbine wakes cannot be resolved directly because they are sub-grid features and therefore their effects need to be parameterized. Here we propose a new wind farm parameterization that is based on the Jensen model, a well-known analytical wake model that predicts the expansion and wind speed of an ideal wake. The Jensen parameterization is implemented and inserted into two commonly-used atmospheric numerical models: the Weather Research and Forecasting (WRF) Model and the Model for Prediction Across Scales (MPAS). In addition, the internal variability in wind speed and direction within a wind farm, the wind direction uncertainty, and the superposition of multiple wakes are taken into account with an innovative approach. The proposed approach and parameterization are tested against observational data at two offshore wind farms: Lillgrund (small in size and tightly spaced) and Anholt (large and widely spaced). Results indicate that power production is predicted more accurately with the Jensen than with the Fitch wind farm parameterization, which is the only one available in WRF. Power predictions with the Jensen parameterization are similar in WRF and MPAS. The sensitivity to grid resolution is small and the bias is generally low and negative. In conclusion, we recommend that the Jensen wind farm parameterization be used in the WRF and MPAS models, especially for coarse resolution, high turbine density, and wind directions aligned with the turbine columns.

The diurnal variations of precipitation over East Asia simulated by the National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM) and the Weather Research and Forecasting (WRF) model are evaluated during the integration period of June–July–August (JJA) 2006. The models reproduce the observed seasonal mean of large‐scale features and precipitation satisfactorily, although the bias patterns differ in both models. The lower tropospheric circulation features are better reproduced by the WRF, while the upper‐level circulations closely follow the RSM analysis. Furthermore, the RSM simulated seasonal precipitation is distinctly overestimated over land, whereas the oceanic precipitation is exaggerated by the WRF. However, the characteristics of the diurnal cycle of precipitation simulated by the two models are very similar in many aspects. Both models reproduce an afternoon peak over land and a daybreak peak over oceans. The simulated diurnal and semidiurnal cycles of precipitation amount are also comparable to the corresponding observations. However, the peaks are shifted approximately 2 h ahead. The diurnal variation of the frequency is fairly well simulated, although the semidiurnal variations are poorly resolved. The diurnal and semidiurnal variations of the intensity are not captured by either model. The ensemble mean of the model results does not provide a distinct advantage in appraising the diurnal variation of precipitation. Further physics sensitivity experiments reveal that the cumulus parameterization process influences the modulation of the simulated phase at maximum precipitation over land, whereas the amplitude is more highly controlled by the boundary layer processes.