Mesoscale Weather Forecasting Research Articles

Research on the refinement of atmospheric weighted average temperature model in Xi’an based on machine learning

One of the most important parameters for calculating the atmospheric precipitable water vapor (PWV) is the atmospheric weighted mean temperature (Tm). Meteorological parameters that are measured typically constrain the accuracy and regional applicability of commonly used empirical models. When precipitable water is inverted using the Global Navigation Satellite System (GNSS), it results in erroneous Tm being obtained. Thus, in this paper, a small-scale Tm model for Xi’an is proposed by least squares method based on data provided by the European Centre for Mesoscale Weather Forecasts (ECMWF) from 2019 to 2022, while improving the functional model’s capacity to fit nonlinear residuals through the Random Forest method and enabling it to predict peaks more accurately. It is based on a random forest to confirm the weighted average temperature model’s correctness (RF Tm). When comparing the RF Tm model’s projected Tm accuracy to the GPT3, UNB3, and regional Bevis models, using the 2023 ERA5 data as the experimental data, the improvements are 23.6 %, 18.6 %, and 11.4 %, respectively. With the sounding station data as the reference value, the BIAS of RF Tm is only 1.51 K. In determining the atmospheric weighted average temperatures, the two models’ accuracy was comparable to that of the Long Short-Term Memory neural network model (LSTM). However, the LSTM model requires short-term forward data for practical usage, the RF Tm model compensated for the shortfall. The RF Tm model is used to invert the atmospheric precipitable water vapor at the appropriate monitoring stations using data from GNSS monitoring stations in Xi’an. It was demonstrated that the RF Tm model’s PWV inversion accuracy outperformed the PWV inverted by the GPT3 model by at least 30 %. Moreover, Xi’an rainfall data integration shows that the PWV inverted by the RF Tm model reacts to brief rainfall episodes efficiently. To sum up, this study’s RF Tm model provides a dependable and effective approach to meteorological observation and climate research.

Impacts of urban expansion on air temperature and humidity during 2022 mega-heatwave over the Yangtze River Delta, China

The Yangtze River Delta (YRD) experienced record-breaking heat in the summer of 2022. However, the urban heat pattern and the role of urban expansion over the last two decades in this hot summer have not been explored. Using the advanced mesoscale Weather Research and Forecasting (WRF) model, we reproduced the fine spatial features and investigated the urban heat island (UHI) and dry island (UDI) effects during the two identified heatwaves in 2022. We further replace the current (2020) land use with the historical (2001) land use in WRF to evaluate the impacts of urban expansion from 2001 to 2020 on air temperature and moisture. Our finding revealed that the conversion of land use resulted in near-surface warming and drying, with pronounced diurnal variations, especially during the July heatwave. The analysis of surface energy balance demonstrated that the substantial decrease in evapotranspiration (ET) was the primary driver of daytime warming, elevating temperatures by 7 °C (July heatwave) and 2 °C (August heatwave). This ET reduction also led to the strong daytime coupling of warming and drying effects over new urban areas. At night, the release of stored heat resulted in the temperature increase of 2 °C (1 °C) during July (August) heatwave, highlighting the nighttime as a critical period for heightened thermal risk. Additionally, urban expansion at the periphery contributed modestly to the warming of urban cores, exacerbating conditions in an already hot environment. This study enhances understanding of the impacts of urban expansion on air temperature and humidity during extreme heatwaves, thereby supporting targeted adaptation and mitigation for extreme events within large cities.

Comparing annual extreme winds in Iran predicted by numerical weather forecasting and Gram-Charlier statistical model with meteorological observation data

Iran has been experiencing severe dust storms due to strong wind speeds that cause sand erosion. This erosion is particularly pronounced in the semi-arid and arid zones. Wind speed predictions using numerical weather forecasting (NWF) are indispensable; however, the applicability of NWF for predicting extreme annual wind speeds is not well known. To validate the NWF model, this study compared NWF-model-predicted 3-hour averaged wind speeds over one year and those observed at 390 weather stations. In addition, a statistical model was employed to estimate the probability densities of wind speeds for both the observational data and the NWF output. As an NWF model, the mesoscale Weather Research and Forecasting (WRF) model was utilized to simulate wind speeds. The results indicate that the observation data are consistent with the modeled datasets regarding the relationships among the mean, standard deviation, and skewness, whereas the WRF model tends to overestimate the mean wind speeds. In addition, the predictability of annual extreme wind speeds was determined using the peak factor. Moreover, skewness has emerged as an influential parameter for predicting extreme winds. Finally, the Gram-Charlier series model was utilized to estimate probability density functions of the wind speeds, demonstrating its effectiveness in capturing positively skewed distributions. The present analyses broaden the use of both NWF outputs and statistical methods to predict extreme wind speeds in Iran.

Challenges of constructing and selecting the “perfect” boundary conditions for the large-eddy simulation model PALM

Abstract. We present the process of and difficulties in acquiring the proper boundary conditions (BCs) for the state-of-the-art large-eddy simulation (LES)-based PALM model system. We use the mesoscale Weather Research and Forecasting (WRF) model as a source of inputs for the PALM preprocessor and investigate the influence of the mesoscale model on the performance of the PALM model. A total of 16 different WRF configurations were used as a proxy for a multi-model ensemble. We developed a technique for selecting suitable sets of BCs, performed PALM model simulations driven by these BCs, and investigated the consequences of selecting a sub-optimal WRF configuration. The procedure was tested for four episodes in different seasons of the year 2019, during which WRF and PALM outputs were evaluated against the atmospheric radiosounding observations. We show that the PALM model outputs are heavily dependent on the imposed BCs and have different responses at different times of the day and in different seasons. We demonstrate that the main driver of errors is the mesoscale model and that the PALM model is capable of attenuating but not fully correcting them. The PALM model attenuates the impact of errors in BCs in wind speed, while for the air temperature, PALM shows variable behavior with respect to driving conditions. This study stresses the importance of high-quality driving BCs and the complexity of the process of their construction and selection.

Influence of Assimilation of NEXRAD-Derived 2D Inner-Core Structure Data from Single Radar on Numerical Simulations of Hurricane Charley (2004) near Its Landfall

This study presents the first research that assimilates the ground-based NEXRAD observations-derived two-dimensional (2D), azimuthally averaged radar radial velocity and reflectivity within 60 km of radius from the hurricane center to examine their influence on the analysis and prediction of a hurricane near and after its landfall. The mesoscale community Weather Research and Forecasting (WRF) model and its four-dimensional variational (4D-VAR) data assimilation system are utilized to conduct data assimilation experiments for Hurricane Charley (2004). Results show that assimilation of the radar inner-core data leads to better forecasts of hurricane tracks, intensity, and precipitation. The improved forecast outcomes imply that the changes in dynamical, thermal, and moisture structures from data assimilations made more reasonable conditions for the hurricane development near and after its landfall. Overall results indicate that the assimilation of the radar-derived 2D inner-core structure could be a feasible way to utilize the radar data for improved hurricane prediction.

Research on Short-Term Prediction Methods for Small-Scale Three-Dimensional Wind Fields

The accurate prediction of small-scale three-dimensional wind fields is of great practical significance for aviation safety, wind power generation, and related fields. This study proposes a novel method for predicting small-scale three-dimensional wind fields by combining the mesoscale Weather Research and Forecasting (WRF) model with Computational Fluid Dynamics (CFD). The method consists of three components: the WRF module, the hybrid neural network prediction module, and the CFD module. First, mesoscale meteorological fields are simulated using the WRF module to establish a historical inflow boundary dataset for the CFD domain. Next, deep separable convolutions are incorporated, and convolutional long short-term memory (ConvLSTM) is combined with a deep separable convolution-gated recurrent unit (DSConvGRU) to construct a hybrid neural network prediction module named ConvLSTM-DSConvGRU. This module is employed for predicting inflow boundary data. Finally, the predicted inflow boundary conditions drive the CFD module to predict small-scale three-dimensional wind fields. The effectiveness of the WRF and CFD downscaling coupling method was validated using observed data from meteorological stations within the simulated domain, along with statistical indicators of errors. Additionally, a comparative evaluation was conducted between the proposed hybrid network model and the four commonly used spatiotemporal prediction models to assess its prediction performance. The results demonstrate that our proposed wind field prediction method achieves accurate simulation and short-term prediction of small-scale three-dimensional wind fields, and the hybrid network model exhibits comprehensive advantages in terms of model complexity and prediction accuracy.

Sensitivity analysis of different parameterization schemes of the Weather Research and Forecasting (WRF) model to simulate heavy rainfall events over the Mahi River Basin, India

A reliability-based assessment of heavy rainfall events can provide insights for developing appropriate measures and predictions to control flood risk. One of the contemporary challenges is to model heavy rainfall events, which depend on different synoptic conditions and model configurations. In this study, the regional mesoscale Weather Research and Forecasting (WRF) model was used to evaluate the applicability of different parameterization schemes for simulating rainfall events over the Mahi River basin between 22 and 26 August 2020 with the maximum rainfall occurring on 23 August 2020. A high-resolution triple-nested (27, 9, and 3 km) WRF model was configured, and data for initial boundary conditions were obtained from NCEP/NCAR FNL with a resolution of 1° × 1° (six-hourly). The model results were evaluated using gridded rainfall of ECMWF Reanalysis v5 (ERA5) and India Meteorological Department (IMD) at a spatial resolution of 0.25° × 0.25°. Further to verify the model output, GPM-IMERG was used to compute the skill score (bias, threat score, probability of detection, and accuracy) metrics at 24 hourly and 3 hourly scales. Eight sensitivity tests were conducted to identify the optimal sets of microphysics (WSM6 and Goddard), cumulus (Kain and BMJ), and planetary boundary layer (YSU and ACM2) parameterization schemes for rainfall simulation of the selected events. The Kain-Goddard-YSU and Kain-Goddard-ACM2 parameterization schemes demonstrated the optimal performance for simulating the rainfall event. Relative humidity and reflectivity were used for analyzing moisture and cloud brightness temperature, respectively. The model score revealed that the Kain-Goddard scheme outperformed other schemes in case of heavy precipitation, with a threshold of 60 mm/day over the Mahi basin. The findings of this study showed that the simulation of heavy rainfall events with a set of selected combinations of parameterization schemes can reproduce the structure of convective organization as well as prominent synoptic features associated with heavy rainfall events over the Mahi basin. Moreover, this information may help to better understand the future forecast trend of precipitation and explore solutions for sustainable water management to support decision-makers and operational water managers.

A new method for detecting urban morphology effects on urban-scale air temperature and building energy consumption under mesoscale meteorological conditions

Impact of urban morphology on urban-scale building energy consumption under mesoscale climate has not been elucidated. A new model was developed to examine the impact using the mesoscale Weather Research and Forecasting (WRF) model. For the first time, local climate zone and building categories (LCZBCs) are integrated into WRF, handling landuse/landcover and building configuration. The urban morphology parameters are clustered into three groups: urban structure, vegetation fraction, and impervious surface thermal properties. Our results show that urban structure induces a higher urban heat island (UHI) intensity at nighttime than does in daytime. The difference in urban-rural building/street structure is the key factor in compact and open high-rise areas, especially commercial-dominant ones, where the UHI intensity and AC load are increased by 40.33% and 34.52%, respectively. Besides, greenery is most effective to mitigate UHI problems, lessening AC demand in middle-rise and low-rise areas. Its benefit is most prominent for residential-dominant areas that could reduce the UHI intensity and AC load as much as 106.26% and 53.13%, respectively. In contrast to rural pavement, urban impervious surfaces induce negligible increases in temperature (4.75%) and AC load (2.33%). The outcome provides insights into the dominant parameters in LCZBCs and the strategy for urban (re)development.

Interaction among local flows, UHI, coastal winds, and complex terrain: Effect on urban-scale temperature and building energy consumption during heatwaves

Extreme heat aggravates thermal stress and electricity shortage in urban areas. This study investigates the (circulating) winds in Hong Kong during a heatwave. Unprecedentedly, the collective effect of coastal winds, complex terrain, and local flows on urban temperatures and air-conditioning load intensity (ACLI) is examined using the mesoscale Weather Research and Forecasting (WRF) model. Three representative wind patterns, including urban-accelerated channel wind, channel-wind-induced heat advection, and urban-mountain-stagnated sea-breeze, are analyzed. Our results show that the mountain blockage in foothill areas would increase 2-m temperatures (T2) and ACLI by 1 °C to 2 °C and 5 W/m2, respectively. ACLI in compact high-rise areas (LCZ 1) is most sensitive to extreme heat. Moreover, the urban heat island (UHI) downstream is crucial that would accelerate channel flows by 1.66 m/sec (50.26 %). On the other hand, terrain-induced channel winds augment heat advection, increasing downstream T2 (0.7 °C) and ACLI (2.62 W/m2). UHI-induced local flows interact with hilly slopes, stagnating the sea breeze on mountain leeward side. Subsequently, the winds would be slowed down by 0.81 m/sec while the temperature T2 would be increased by 0.9 °C in downstream urban areas. Eventually, the daytime ACLI could be raised as much as 6.41 W/m2.

Preparing the assimilation of the future MTG‐IRS sounder into the mesoscale numerical weather prediction AROME model

AbstractThe infrared sounder (IRS) instrument is an infrared Fourier‐transform spectrometer that will be on board the Meteosat Third Generation series of the future European Organization for the Exploitation of Meteorological Satellite's geostationary satellites. It will measure the radiance emitted by the Earth at the top of the atmosphere using 1,960 channels. The IRS will provide high spatial‐ and temporal‐frequency four‐dimensional information on atmospheric temperature and humidity, winds, clouds, and surfaces, as well as on the chemical composition of the atmosphere. The assimilation of these new observations represents a great challenge and opportunity for the improvement of numerical weather prediction (NWP) forecast skill, especially for mesoscale models such as the Applications de la Recherche à l'Opérationnel à Méso‐Echelle (AROME) at Météo‐France. The objectives of this study are to prepare for the assimilation of the IRS in this system and to evaluate its impact on the forecasts when added to the currently assimilated observations. By using an observing system simulation experiment constructed for a mesoscale NWP model. This observing system simulation experiment framework makes use of synthetic observations of both IRS and the currently assimilated observing systems in AROME, constructed from a known and realistic state of the atmosphere. The latter, called the “nature run”, is derived from a long and uninterrupted forecast of the mesoscale model. These observations were assimilated and evaluated using a 1 hr update cycle three‐dimensional variational data assimilation system over 2‐month periods, one in the summer and one in the winter. This study demonstrates the benefits that can be expected from the assimilation of IRS observations into the AROME NWP system. The assimilation of only 75 channels over oceans increases the total amount of observations used in the AROME three‐dimensional variational data assimilation system by about 50%. The IRS impact in terms of forecast scores was evaluated and compared for the summer and winter periods. The main findings are as follows: (a) over both periods the assimilation of these observations leads to statistically improved forecasts over the whole atmospheric column; (b) for the summer‐season experiment, the forecast ranges up to 48 hr are improved; (c) for the winter‐season experiment, the impact on the forecasts is globally positive but is smaller than the summer period and extends only to 24 hr. Based on these results, it is foreseen that the addition of future IRS observations in the AROME NWP systems will significantly improve mesoscale weather forecasts.

Comparison of the influences of different ventilation corridor forms on the thermal environment in Wuhan City in summer

The ventilation corridor is an essential element in urban planning and design to improve the climate and environment. In this paper, four forms of two ventilation corridors were set up in the southeast of Wuhan City based on its urban planning outline to quantitatively study the influences of different ventilation corridor forms on the urban thermal environment in summer. The urban micro-meteorological environment was simulated using the next-generation mesoscale Weather Research and Forecast (WRF) model coupled with an urban canopy model (UCM). Critical time-dependent meteorological values were extracted and plotted, including temperature and wind difference fields, average temperature at 2-m height, average wind speed at 10-m height, and surface energy flux. By conducting a comparative analysis of the quantitative results, the ventilation corridors in construction land are arranged at intervals in the upwind position of the city in summer, which can slightly adjust the thermal environment of the central area of the city, and have an effective regulation on the temperature of the corridors and surrounding areas during the daytime. Especially at 15:00 pm when the temperature is at its highest during the day, the temperature can be reduced by 0.8 °C. Compared to other corridors, the wind speed in and around the corridor is the strongest 11 h a day. Due to the internal arrangement of construction land, this corridor form is more advantageous in the urban land utilization. After considering comprehensively, the ventilation corridor form with construction land arranged at intervals is the preferred corridor form in the southeast of Wuhan. The experimental results can provide quantitative reference for the layout of ventilation corridors in hot inland cities located in central China.

Integration of atmospheric stability in wind resource assessment through multi-scale coupling method

The abundance level of wind resources is the first and most important factor to consider in the wind farm design. The accuracy of wind energy resource assessment directly determines the operating income of a wind farm after its construction is completed. Therefore, aiming to meet the requirements of accurate wind resource assessment in areas with a complex terrain or those lacking the observation information, this paper proposes a multi-scale coupling numerical simulation method based on atmospheric stability. First, the atmospheric flow field simulation results at the heights of 200 m, 300 m, and 400 m are extracted from the mesoscale Weather Research and Forecasting (WRF) model and used as inlet boundary conditions of a microscale Computational fluid dynamics (CFD) model. Then, to obtain more accurate coupling data at the height of a wind turbine hub, the atmospheric stability parameter is introduced into the multi-scale coupling process, data correction is conducted at the three heights, and a comparison with the observation data of a wind tower is made. Finally, a numerical analysis is conducted using real data of a wind farm in eastern Inner Mongolia and Jilin. The analysis results show that the proposed method can improve the assessing accuracy of wind resources of wind farms using mesoscale data, which is valuable to a certain extent in real-world applications.

A Numerical Study of Critical Variables on Artificial Cold Cloud Precipitation Enhancement in the Qilian Mountains, China

In this study, a mesoscale Weather Research and Forecast (WRF) model coupled with an AgI (silver iodide) cold cloud catalytic module were used to explore the potential impact of the catalytic position and rate in the catalytic module based on a ground rain enhancement operation in the Qilian Mountains, on 16 August 2020. Results show that the simulated precipitation, liquid water content (LWC), and water vapor content (PWV) are in good agreement with the observations, demonstrating that the WRF model using the coupled AgI cloud-seeding scheme is well-applicable to the precipitation simulation of the Qilian Mountains. It is also observed that there are some differences in the catalytic effect of catalysis at different cloud temperatures. The precipitation enhancement effect is the most favorable in the fifth layer of 15 km, followed by that in the fourth layer of 12 km and the sixth layer of 18 km. Considering the flight cost and catalytic efficiency, the fourth layer is highly recommended for seeding. Furthermore, the AgI seeding rate also plays a crucial impact on ground precipitation. In the case of a seeding rate of about 1.2 g·s−1, the precipitation enhancement effect tends to be stable, and the percentage of the precipitation increase reaches up to 10.4%. While in the case of a seeding rate of about 1.5 g·s−1, the percentage of ground precipitation increase is 10%, which is 0.4% lower than that of 1.2 g·s−1. In summary, the introduction of a AgI catalyst with a seeding rate of 1.2 g·s−1 can significantly increase the ground precipitation at a height of 12 km and a temperature of −3 °C in the Qilian Mountains.

A study of cloud microphysical processes and a mesoscale environment in a heavy rainfall case over Yan’an

A cloud-resolved numerical simulation was carried out for an extreme rainfall case in Yan’an, a city in arid and semi-arid regions in northwest China, on 3–4 September 2021, by using Mesoscale Weather Research and Forecasting Model. The Auto Weather Station and Doppler radar were applied to verify simulated results. The characteristics of mesoscale cloud environment and cloud microphysical processes were analyzed. Then, the quantitative rainwater mass budget and latent heat budget of microphysical conversions about water condensates calculated in Yan’an. The possible mechanism by which cloud microphysics affected the rainstorm was investigated and discussed. It was found that: (1) There was positive feedback between mesoscale cloud environment and cloud microphysical processes, especially diabatic heating processes due to ice phase particles conversions. (2) The process of snow conversions processes was the most important process of this heavy rainfall in Yan’an. It not only promoted the production of rain, but also contributed to the enhancement of updraft through latent heat release and produced positive feedback to other microphysical processes in cloud. (3) Heavy rainfall in arid regions of China is mostly cold-type precipitation, mainly manifested by snow-dominated cloud microphysical processes producing deep convection.

Satellite-Based Estimation of Roughness Length over Vegetated Surfaces and Its Utilization in WRF Simulations

Based on morphological methods, MODIS satellite remote sensing data were used to establish a dataset of the local roughness length (Z0) of vegetation-covered surfaces in Guangdong Province. The local Z0 was used to update the mesoscale Weather Research and Forecasting (WRF) model in order to quantitatively evaluate its impact on the thermodynamic environment of vegetation-covered surfaces. The specific results are as follows: evergreen broad-leaved forests showed the largest average Z0 values at 1.27 m (spring), 1.15 m (summer), 1.03 m (autumn), and 1.15 m (winter); the average Z0 values of mixed forests ranged from 0.90 to 1.20 m; and those for cropland-covered surfaces ranged from 0.17 to 0.20 m. The Z0 values of individual vegetation coverage types all exhibited relatively high values in spring and low values in autumn, and the default Z0 corresponding to specific vegetation-covered surfaces was significantly underestimated in the WRF model. Modifying the default Z0 of surfaces underlying evergreen broad-leaved forests, mixed forests, and croplands in the model induced only relatively small changes (<1%) in their 2 m temperature, relative humidity, skin surface temperature, and the planetary boundary layer height. However, the average daily wind speed of surfaces covered by evergreen broad-leaved forests, mixed forests, and croplands was reduced by 0.48 m/s, 0.43 m/s, and 0.26 m/s, respectively, accounting for changes of 12.0%, 11.1%, and 6.5%, respectively.

Simulation of Cooling Island Effect in Blue-Green Space Based on Multi-Scale Coupling Model

The mitigation of the urban heat island effect is increasingly imperative in light of climate change. Blue–green space, integrating water bodies and green spaces, has been demonstrated to be an effective strategy for reducing the urban heat island effect and enhancing the urban environment. However, there is a lack of coupled analysis on the cooling island effect of blue–green space at the meso-micro scale, with previous studies predominantly focusing on the heat island effect. This study coupled the single urban canopy model (UCM) with the mesoscale Weather Research and Forecasting (WRF) numerical model to simulate the cooling island effect of blue–green space in the Eastern Sea-River-Stream-Lake Linkage Zone (ESLZ) within the northern subtropical zone. In particular, we comparatively investigated the cooling island effect of micro-scale blue–green space via three mitigation strategies of increasing vegetation, water bodies, and coupling blue–green space, using the temperature data at the block scale within 100 m square of the urban center on the hottest day in summer. Results showed that the longitudinally distributed lakes and rivers in the city had a significant cooling effect on the ambient air temperature (Ta) at the mesoscale, with the largest cooling range occurring during the daytime and ranging from 1.01 to 2.15 °C. In contrast, a 5~20% increase in vegetation coverage or 5~15% increase in water coverage at the micro-scale was observed to reduce day and night Ta by 0.71 °C. Additionally, the most significant decrease in physiologically equivalent temperature (PET) was found in the mid-rise building environment, with a reduction of 2.65–3.26 °C between 11:00 and 13:00 h, and an average decrease of 1.25°C during the day. This study aims to guide the optimization of blue–green space planning at the meso-micro scale for the fast-development and expansion of new urban agglomerations.

A Deep Convolutional Neural Network Model for Improving WRF Simulations.

Advancements in numerical weather prediction (NWP) models have accelerated, fostering a more comprehensive understanding of physical phenomena pertaining to the dynamics of weather and related computing resources. Despite these advancements, these models contain inherent biases due to parameterization of the physical processes and discretization of the differential equations that reduce simulation accuracy. In this work, we investigate the use of a computationally efficient deep learning (DL) method, the convolutional neural network (CNN), as a postprocessing technique that improves mesoscale Weather Research and Forecasting (WRF) one-day simulation (with a 1-h temporal resolution) outputs. Using the CNN architecture, we bias-correct several meteorological parameters calculated by the WRF model for all of 2018. We train the CNN model with a four-year history (2014-2017) to investigate the patterns in WRF biases and then reduce these biases in simulations for surface wind speed and direction, precipitation, relative humidity, surface pressure, dewpoint temperature, and surface temperature. The WRF data, with a spatial resolution of 27 km, cover South Korea. We obtain ground observations from the Korean Meteorological Administration station network for 93 weather station locations. The results indicate a noticeable improvement in WRF simulations in all station locations. The average of annual index of agreement for surface wind, precipitation, surface pressure, temperature, dewpoint temperature, and relative humidity of all stations is 0.85 (WRF:0.67), 0.62 (WRF:0.56), 0.91 (WRF:0.69), 0.99 (WRF:0.98), 0.98 (WRF:0.98), and 0.92 (WRF:0.87), respectively. While this study focuses on South Korea, the proposed approach can be applied for any measured weather parameters at any location.

Simulating the Impacts of Wind Farm Wake under the Changes in MYNN Planetary Boundary Layer Scheme in High Resolution Weather Research and Forecasting Model

This study focuses on simulating the impacts of wind farm wake due to changes in the Mellor-Yamanda-Nakanishi-Niino (MYNN) planetary boundary layer (PBL) scheme in a high-resolution mesoscale Weather Research and Forecasting (WRF) model for a non-flat region in Turkey. This is the first study with a comprehensive evaluation of simulated wind farm wake impact responses to changes in the MYNN PBL scheme in the WRF model. Our results show that the WRF-WFP solutions for the wind farm wake impact significantly change with a change in the MYNN PBL scheme. In addition, the incorrect TKE advection and the correction factor of 0.25 for the TKE coefficient in wind farm parametrization (WFP) cause incorrect wind farm wake impacts especially on TKE and air temperature. Our study also shows that modifications in the mixing length create greater changes in simulated wind farm wake impacts than activation of the mass-flux scheme. In this study, the relative contributions of WFP’s components are also evaluated.

Multiscale Simulation of Offshore Wind Variability During Frontal Passage: Brief Implication on Turbines’ Wakes and Load

Enhancing the performance of offshore wind park power production requires, to a large extent, a better understanding of the interactions of wind farms and individual wind turbines with the atmospheric boundary layer over a wide range of spatiotemporal scales. In this study, we use a multiscale atmospheric model chain coupled offline with the aeroelastic Fatigue, Aerodynamics, Structures, and Turbulence (FAST) code. The multiscale model contains two different components in which the nested mesoscale Weather and Research Forecast (WRF) model is coupled offline with the Parallelized Large-eddy Simulation Model (PALM). Such a multiscale framework enables to study in detail the turbine behaviour under various atmospheric forcing conditions, particularly during transient atmospheric events.

Statistical Analysis of the Spatiotemporal Distribution of Lower Atmospheric Ducts over the Seas Adjacent to China, Based on the ECMWF Reanalysis Dataset

On the basis of 12 years of the European Centre for Mesoscale Weather Forecasts (ECMWF) reanalysis dataset, we statistically analyzed the spatiotemporal distribution of lower atmospheric ducts over the seas around China, and we investigated the possible generation mechanisms. The results show that the ducts’ occurrence had obvious seasonal and regional variations. Ducting events were more likely to occur in spring and summer, and the maximum occurrence rate reached 45.6%, which was closely related to the East Asian monsoon. The ducts’ altitude in continental coastal areas was lower than that far from the coast due to the dominance of surface ducts. The ducts’ thickness varied between 50 m and 450 m, and the thicker ducts were mainly concentrated in the South China Sea and the Pacific Ocean on the east side of the East China Sea near the Philippines and Taiwan. Except for a few areas, the ducts’ intensity was less than 10 M-units (an M-unit is the unit of atmospheric modified refractivity) and the diurnal variations were less pronounced. The duct formation in the lower atmosphere was related to factors such as monsoons, tropical cyclones, ocean currents, radiative cooling, and sea–land breezes.