Evaluation of the WRF model for simulating deep convection and cold‐pool characteristics relevant to wind‐energy applications in Germany

Deep convection and cold-pool characteristics over Germany during July 2023 are investigated using DWD radar observations and a WRF model simulation. The analysis includes both instantaneous snapshots of convection and a Lagrangian approach tracking the life cycles of isolated convective cells. Evaluation against radar observations reveals that WRF captures the general distribution, morphology, and evolution of deep convection and the associated cold pools, though it tends to simulate smaller, more intense rain-producing cells.Simulated cold-pool characteristics, including median and extreme values of wind gusts and θv differences from the ambient background, align well with observations, indicating WRF’s skill in replicating the key features. Modeled θv drops (median of -2.95 K; extreme < -10 K) and wind gusts (median of 4.28 m/s; extreme > 10 m/s) highlight the potential for cold pools to impose significant impacts on wind turbines, although more observational statistics on extreme wind ramps due to convective cold pools are required for further model assessment.The temporal evolution of convective cell features reveals a downward-facing parabolic pattern in both WRF and observations, in terms of cell size, maximum rain rate, and mean radar reflectivity. However, WRF intensifies convective cells too quickly and overestimates rain rates throughout the life cycle, while cell shape remains in good agreement with observations.An analysis of wind energy-relevant metrics reveals that convective cold pools drive significant changes in wind speed, atmospheric stability, and vertical shear, with estimated power output associated with cold-pool passages increasing by 35-60% for long-lived cells and 33-50% for short-lived cells, peaking mid-to-late lifespan. These findings emphasize the importance of understanding and forecasting cold-pool dynamics for optimizing wind energy production.

The interaction of an urban heat island with a sea breeze front during moist convection over <scp>Tianjin, China</scp>

Convective cold pools are important modulators of the onset and evolution of deep convection in the tropics. This work leverages a dataset derived from the Advanced Scatterometer (ASCAT) satellite instrument to quantify seasonal variations in cold-pool activity and their relationship to deep convection across tropical ocean basins. The dataset identifies gradient features (GFs) in the surface wind field, which have been shown to serve as reliable proxies for the boundaries of atmospheric cold pools. We examine the relationship between GFs and climatologies of precipitation, column relative humidity (CRH), and bulk vertical wind shear. We also collocate GFs with precipitation and CRH. High GF frequency, precipitation, and CRH coincide in many regions of the tropics, consistent with our understanding of the physical connections between precipitation and cold-pool generation. On the other hand, climatological bulk wind shear is often low in convective regions, and there is a weak inverse correlation between GF frequency and bulk wind shear, while our prior expectation might have been that shear promotes cold-pool formation. Compared to GF frequency, GF size shows a weaker relationship with the convective environment, with some of the largest GF sizes occurring at lower CRH values for a given rainfall rate. In a few exceptional regions and seasons, such as the Indian Ocean in Northern Hemisphere summer, the region of greatest precipitation does not coincide with the region of greatest GF frequency. These cases also have very high seasonal mean CRH, suggesting that in these regions cold-pool formation is suppressed by reduced evaporation of precipitation.

On 2 July 2016 afternoon a heavy rainfall (52.8 mm) was observed over southwestern part of Armenia, at Talin station. High‐frequency radar observations and Weather Research and Forecasting (WRF) model output show that initiation of earliest convection occurred over Aragats mountain massif around noon due to low‐level convergence of thermally induced upslope winds. Further convective development was affected by generation of secondary convection as a result of interaction between cold pool outflows from developed convective cells and upslope winds. The high‐resolution WRF run (3 km) using NSSL two‐moment cloud microphysics parametrization and the European Centre for Medium‐Range Weather Forecasts operational model forcing data best reproduces the location, timing, magnitude, and microphysical structure of the observed convective rainfall among six WRF microphysical schemes tested in this study. Radar observations show that cold cloud process typical for continental‐type deep convection was observed in Armenia. The NSSL includes the double‐moment microphysics schemes for both warm and cold cloud processes, which might be a reason for improved simulation of observed heavy rainfall event in Armenia. Using the coarser resolution ERA5 analysis forcing data in the WRF model leads to simulation of earlier rainfall peaks at Talin station and spurious convective rainfall areas. The WRF model forced by the Global Data Assimilation System Final analysis from the National Centers for Environmental Prediction, even run at 3‐km resolution, is not able to reproduce the accurate location of convection and rainfall over the study area.

Regional convection‐permitting model simulations of cloud populations observed during the 2011 Atmospheric Radiation Measurement (ARM) Madden‐Julian Oscillation Investigation Experiment/Dynamics of the Madden‐Julian Oscillation Experiment (AMIE/DYNAMO) field campaign are evaluated against ground‐based radar and ship‐based observations. Sensitivity of model simulated reflectivity, surface rain rate, and cold pool statistics to variations of raindrop breakup/self‐collection parameters in four state‐of‐the‐art two‐moment bulk microphysics schemes in the Weather Research and Forecasting (WRF) model is examined. The model simulations generally overestimate reflectivity from large and deep convective cells, and underestimate stratiform rain and the frequency of cold pools. In the sensitivity experiments, introduction of more aggressive raindrop breakup or decreasing the self‐collection efficiency increases the cold pool occurrence frequency in all of the simulations, and slightly reduces the reflectivity and precipitation statistics bias in some schemes but has little effect on the overall mean surface precipitation. Both the radar observations and model simulations of cloud populations show an approximate power law relationship between convective echo‐top height and equivalent convective cell radius.

Accurately accounting for Low-level jets (LLJs) in wind resource assessment is increasingly important as the height of wind turbines continues to grow. During LLJ events, wind speeds increase, leading to a general increase in power output. However, the vertical wind shear and veer associated with LLJs also impact the performance and reliability of wind turbines. Atmospheric conditions, conductive of the LLJs may also modify the wake dissipation properties in large offshore wind farms, depending on the LLJ height relative to the height of the wind farm's rotors. This study aims to optimize the configuration of the Weather Research and Forecasting (WRF) model to represent LLJs around the North and Baltic Seas at heights relevant to wind energy production. Using the optimal WRF model configuration, we derive a detailed long-term LLJ climatology focusing on wind energy implications.We utilize wind measurements from LiDARs and a mast for five sites to assess the quality of the WRF model simulations for LLJ characterization. We also investigate the benefits of WRF simulations compared to the widely used ERA5 re-analysis. In the WRF model simulations, we vary the grid spacing, vertical resolution, and the planetary boundary layer scheme and land surface models, parameters we deemed most likely to have a substantial impact. The model&amp;#8217;s performance was evaluated based on its ability to replicate observed distributions of LLJs and relevant associated characteristics, such as the shear and veer across the rotor-plane of typical large offshore wind turbines (30-300 meters).&amp;#160;Our results show a strong dependency of the LLJ representation and the associated wind profiles on WRF model configuration and that relying on ERA5 for LLJ characterization is insufficient. For example, the LLJ rate-of-occurrence varied by up to a factor of 3 and more between some WRF model runs. The optimized model more accurately reflects the frequency, intensity, and vertical extension of LLJs, as confirmed by LiDAR data. Subsequent application of this configuration to a multi-year climatology provides new insights into the region's temporal patterns and potential wind energy impacts of LLJs.

The numerical weather forecast model Weather Research and Forecasting (WRF) has a range of applications because it offers multiple physical options, enabling the users to optimizing WRF for specific scales, geographical locations and applications. Summer rainfall cannot be predicted well in North West of Iran (NWI). Most of them are convective. Sometimes rainfall is heavy, so that it causes flash flood. In this research, some configurations of WRF were tested with four summer rainfall events in NWI to find the best configuration. Five cumulus, four planetary boundary layers (PBL) and two microphysical schemes were combined. Twenty-six different configurations (models) were implemented at two resolutions of 5 and 15 km for duration of 48 h. Four events, with over 20 mm convective daily rainfall total, were selected at NWI during summer season between 2010 and 2015. These events were tested by developing 26 unique models. Results were verified using several methods. The aim was to find the best results during the first 24 h. Although no single configuration can be introduced for all times, thresholds, and atmospheric system to provide reliable and accurate forecast, the best configuration for WRF can be identified. Kain-Fritsch (new Eta), Betts-Miller-Janjic, Modified Kain-Fritsch, Multi-scale Kain-Fritsch and newer Tiedtke cumulus schemes and Mellor-Yamada-Janjic, Shin-Hong ‘scale-aware’, Medium Range Forecast (MRF) and Yonsei University (YSU) Planetary Boundary Layer schemes and Kessler, WRF Single Moment 3 class simple ice (WSM3) microphysics schemes were selected. The result show that Cumulus schemes are the most sensitive and Microphysics schemes are the less sensitive. The comparison of 15 km and 5 km resolution simulations do not show obvious advantages in downscaling the results. Configuration with newer Tiedtke cumulus, Mellor-Yamada-Janjic PBL, WSM3 and Kessler microphysics schemes give the best results for the 5 and 15 km resolutions. The output image of models and statistical methods verification indexes show that WRF could not accurately simulate convective rainfall in the NWI in summer.

The Weather Research and Forecasting (WRF) model, driven laterally by ERA-Interim reanalyses, is used here to downscale rainfall, at relatively high resolution (~8 km) over Burgundy (eastern France), during the period 1989–2009. Regional simulations are compared to the Meteo-France Station Network (MFSN; 127 daily rain-gauge records), at various temporal scales, including interannual variability, the annual cycle, and weather types. Results show that the spatial distribution of WRF-simulated rainfall climatology is consistent with MFSN observation data, but WRF tends to overestimate annual rainfall by ~+15 %. At the interannual scale, WRF also performs very well (r ~ 0.8), despite almost constant, systematic overestimation. Only the average annual rainfall cycle is not accurately reproduced by WRF (r ~ 0.5), with rainfall overestimation in spring and summer, when convective rainfall prevails. During the winter season (October–March), when stratiform rainfall is prevalent, WRF performs better. Despite the biases for summertime convective events, these results suggest that high-resolution WRF simulations could successfully be used to document present and future climate variability at a regional scale. Nevertheless, because of overestimated convective rainfall, WRF-simulated rainfall should probably not be used directly to feed impact models, especially during the vegetative summer period.

The interplay between wind and clouds in the trades

The understanding of large‐scale rainfall microphysical characteristics plays a significant role in meteorology, hydrology and natural hazards managements. Traditional instruments for estimating raindrop size distribution (DSD), including disdrometers and ground dual‐polarization radars, are available only in limited areas. However, the development of space‐based radars and mesoscale numerical weather prediction models would allow for DSD estimation on a large scale. This study investigated the performance of the weather research and forecasting (WRF) model and the global precipitation measurement mission (GPM) dual‐frequency precipitation radar for DSD retrieval under different conditions. The DSD parameters (Dm and Nw), rain rate (R), rainfall kinetic energy (KE) and radar reflectivity (Z) were estimated in Chilbolton, United Kingdom, by using long‐term disdrometer observations for validation. The rainfall kinetic energy–rain rate (KE–R) and radar reflectivity–rain rate (Z–R) relationships were explored using a disdrometer, the WRF model and GPM. It was found that the DSD parameter distribution trends of the three approaches are similar although the WRF model has larger Dm and smaller Nw values. In terms of the rainfall microphysical relationship, GPM performs better when both Ku‐ and Ka‐band precipitation radars (KuPR and KaPR) observe precipitation simultaneously (R &gt; 0.5 mm h−1), while the WRF model shows high accuracy in light rain (R &lt; 0.5 mm h−1). The fusion of GPM and WRF model is recommended for the improved understanding of rainfall microphysical characteristics in ungauged areas.

Upscale convective growth remains a poorly understood aspect of convective evolution, and numerical weather prediction models struggle to accurately depict convective morphology. To better understand some physical mechanisms encouraging upscale growth, 30 warm-season convective events from 2016 over the United States Great Plains were simulated using the Weather Research and Forecasting (WRF) model to identify differences in upscale growth and non-upscale growth environments. Also, Bryan Cloud Model (CM1) sensitivity tests were completed using different thermodynamic environments and wind profiles to examine the impact on upscale growth. The WRF simulations indicated that cold pools are significantly stronger in cases that produce upscale convective growth within the first few hours following convective initiation compared to those without upscale growth. Conversely, vertical wind shear magnitude has no statistically significant relationship with either MCS or non-MCS events. This is further supported by the CM1 simulations, in which tests using the WRF MCS sounding developed a large convective system in all tests performed, including one which used the non-MCS kinematic profile. Likewise, the CM1 simulations of the non-upscale growth event did not produce an MCS, even when using the MCS kinematic profile. Overall, these results suggest that the near-storm and pre-convective thermodynamic environment may play a larger role than kinematics in determining upscale growth potential in the Great Plains.

Using the high-resolution numerical weather research and forecasting (WRF) model, study the squall line process that occurred on Hainan Island on 22 April 2020. The findings indicate that high terrain blocks the swift accumulation of water vapor carried by the sea breeze and aids in preserving the accumulated water vapor. According to the sensitivity experiment, terrain height has minimal impact on the macroscopic effects of mesoscale weather processes. However, it does influence where the sea breeze converges. During this process, the ocean-land thermal contrast not only takes the main responsibility for the sea breeze but also leads to uplift motion, which affects the formation, intensity, and duration of the squall line. Additionally, the unstable conditions suggest that a thermal and dynamic environment promote the scale of this squall line. Utilizing the Rotunno–Klemp–Weisman theory (RKW), this study analyzes the effects of the cold pool and vertical wind shear. The analysis reveals that significant vertical wind shear at lower levels and the ground-cold pool contribute to the sustenance and growth of the squall line system. This squall line process has had the greatest impact on the Haikou area due to the strong low-level vertical wind shear and prolonged interaction with the cold pool. When the interaction between the cold pool and the vertical wind shear weakens, the squall dissipates.

The relationship between the radar reflectivity factor (Z) and the rainfall rate (R) is recalculated based on radar observations from 10 Doppler radars and hourly rainfall measurements at 6529 automatic weather stations over the Yangtze–Huaihe River basin. The data were collected by the National 973 Project from June to July 2013 for severe convective weather events. The Z–R relationship is combined with an empirical qr–R relationship to obtain a new Z–qr relationship, which is then used to correct the observational operator for radar reflectivity in the three-dimensional variational (3DVar) data assimilation system of the Weather Research and Forecasting (WRF) model to improve the analysis and prediction of severe convective weather over the Yangtze–Huaihe River basin. The performance of the corrected reflectivity operator used in the WRF 3DVar data assimilation system is tested with a heavy rain event that occurred over Jiangsu and Anhui provinces and the surrounding regions on 23 June 2013. It is noted that the observations for this event are not included in the calculation of the Z–R relationship. Three experiments are conducted with the WRF model and its 3DVar system, including a control run without the assimilation of reflectivity data and two assimilation experiments with the original and corrected reflectivity operators. The experimental results show that the assimilation of radar reflectivity data has a positive impact on the rainfall forecast within a few hours with either the original or corrected reflectivity operators, but the corrected reflectivity operator achieves a better performance on the rainfall forecast than the original operator. The corrected reflectivity operator extends the effective time of radar data assimilation for the prediction of strong reflectivity. The physical variables analyzed with the corrected reflectivity operator present more reasonable mesoscale structures than those obtained with the original reflectivity operator. This suggests that the new statistical Z–R relationship is more suitable for predicting severe convective weather over the Yangtze–Huaihe River basin than the Z–R relationships currently in use.

The cold pool outflow has been previously shown to be generated by decaying Mesoscale Convective Complexes (MCCs) in the Maritime Continent. The cold pool also has a main role in the development processes of oceanic convective systems inducing heavy rainfall. This study investigated a cold pool event (January 1-2, 2021) related to a heavy rainfall system over the coastal region of Lampung, Southern Sumatra, within a high-resolution model simulation using a regional numerical weather prediction of the Weather Research and Forecasting (WRF) with convection permitting of 1 km spatial resolution, which was validated by satellite and radar data observations. It is important to note that the intensity, duration, timing, and structure of heavy rainfall simulated were in good agreement with satellite-observed rainfall. The results also showed that a cold pool (CP) plays an important role in inducing Mesoscale Convective Complex (MCC) and was responsible for the development of an offshore propagation of land-based convective systems due to the late afternoon rainfall over inland. This study also suggests that the propagation speed of the CP 8.8 m·s−1 occurring over the seaside of the coastal region, the so-called CP-coastal, is a plausible mechanism for the speed of the offshore-propagating convection, which is dependent on both the background prevailing wind and outflow. These conditions help to maintain the near-surface low temperatures and inhibit cold pool dissipation, which has implications for the development of consecutive convection.

With the projected expansion of arid/semi‐arid regions in a warming world, precipitation enhancement activities such as cloud seeding will become increasingly popular and relied upon. Due to the inherent costs, a successful planning is crucial, which involves accurate model predictions. In this study, the usefulness of the Weather Research and Forecasting (WRF) model forecasts for guidance into seeding operations in the United Arab Emirates, where seeding activities have been conducted for more than two decades, is assessed. The WRF predictions are compared with ground‐based, satellite‐derived and radar reflectivity data, and in‐situ observations onboard the airplanes used to perform the seeding operations. WRF is found to have higher skill in simulating the observed cloud top pressure/temperature than the cloud fraction, with the model vertical velocity predictions also more skillful than those of the radar reflectivity. A stronger Arabian Heat Low (AHL) in the model leads to drier conditions which, together with a surface cold bias, limits the spatial extent and vertical depth of the simulated convective clouds. Development of convective rolls in the boundary layer is reported in both observations and simulations and their interaction with cold pools from convective clouds promote the development of secondary convection. Sensitivity to the choice of the Planetary Boundary Layer (PBL) scheme is also noticed, with the Yonsei University PBL scheme giving the best performance. When considering the two factors needed for a successful seeding operation that is, the presence of an updraft and clouds, the model‐predicted seeding regions largely match the areas where precipitation was observed. As the proposed WRF set up can be used operationally, the model forecasts will bring added value to the seeding activities in the country.