Weather Research And Forecasting Research Articles

WRF-Comfort: simulating microscale variability in outdoor heat stress at the city scale with a mesoscale model

Abstract. Urban overheating and its ongoing exacerbation due to global warming and urban development lead to increased exposure to urban heat and increased thermal discomfort and heat stress. To quantify thermal stress, specific indices have been proposed that depend on air temperature, mean radiant temperature (MRT), wind speed, and relative humidity. While temperature and humidity vary on scales of hundreds of meters, MRT and wind speed are strongly affected by individual buildings and trees and vary on the meter scale. Therefore, most numerical thermal comfort studies apply microscale models to limited spatial domains (commonly representing urban neighborhoods with building blocks) with resolutions on the order of 1 m and a few hours of simulation. This prevents the analysis of the impact of city-scale adaptation and/or mitigation strategies on thermal stress and comfort. To solve this problem, we develop a methodology to estimate thermal stress indicators and their subgrid variability in mesoscale models – here applied to the multilayer urban canopy parameterization BEP-BEM within the Weather Research and Forecasting (WRF) model. The new scheme (consisting of three main steps) can readily assess intra-neighborhood-scale heat stress distributions across whole cities and for timescales of minutes to years. The first key component of the approach is the estimation of MRT in several locations within streets for different street orientations. Second, mean wind speed and its subgrid variability are downscaled as a function of the local urban morphology based on relations derived from a set of microscale LES and RANS simulations across a wide range of realistic and idealized urban morphologies. Lastly, we compute the distributions of two thermal stress indices for each grid square, combining all the subgrid values of MRT, wind speed, air temperature, and absolute humidity. From these distributions, we quantify the high and low tails of the heat stress distribution in each grid square across the city, representing the thermal diversity experienced in street canyons. In this contribution, we present the core methodology as well as simulation results for Madrid (Spain), which illustrate strong differences between heat stress indices and common heat metrics like air or surface temperature both across the city and over the diurnal cycle.

A Preliminary Study on the Parameter Configuration of Weather Research Forecasting in Tropical Peatland, Central Kalimantan

Hydrometeorological variables are sensitively regulated by atmospheric dynamics and variability. Weather research and forecasting (WRF) model is the cutting-edge tool for studying and investigating the dynamics of physical atmospheric conditions, but the configuration scheme of WRF parameters remains a research challenge for topical peatland situated in the maritime continent. Here, we evaluated WRF parametrization based on three kalibration configuration schemes, which influence rainfall, temperature, and soil moisture dynamics. We tested the WRF evaluation for Sebangau-Kahayan peatland for a wet-dry season in August 2020. The best configuration was determined based on three statistical metrics namely mean absolute error, percent bias, and coefficient of correlation. Our results showed that WRF forecasts were greatly depend on a bias correction to improve the model performance, in which it was consistently found in all configurations. Rainfall was barely predicted in station level with a low performance in term of weekly spatial distribution. Other findings revealed that all configurations showed a good performance for temperature and soil moisture forecasts. Further, our findings emphasize the important physical parameter of WRF that control rainfall formation and dynamics. Last, we highlight an urgent need of more ground stations in term of spatial distribution to validate the weather forecast.

Simulations of primary and secondary ice production during an Arctic mixed-phase cloud case from the Ny-Ålesund Aerosol Cloud Experiment (NASCENT) campaign

Abstract. The representation of Arctic clouds and their phase distributions, i.e., the amount of ice and supercooled water, influences predictions of future Arctic warming. Therefore, it is essential that cloud phase is correctly captured by models in order to accurately predict the future Arctic climate. Ice crystal formation in clouds happens through ice nucleation (primary ice production) and ice multiplication (secondary ice production). In common weather and climate models, rime splintering is the only secondary ice production process included. In addition, prescribed number concentrations of cloud condensation nuclei or cloud droplets and ice-nucleating particles are often overestimated in Arctic environments by standard model configurations. This can lead to a misrepresentation of the phase distribution and precipitation formation in Arctic mixed-phase clouds, with important implications for the Arctic surface energy budget. During the Ny-Ålesund Aerosol Cloud Experiment (NASCENT), a holographic probe mounted on a tethered balloon took in situ measurements of number and mass concentrations of ice crystals and cloud droplets in Svalbard, Norway, during fall 2019 and spring 2020. In this study, we choose one case study from this campaign that shows evidence of strong secondary ice production and use the Weather Research and Forecasting (WRF) model to simulate it at a high vertical and spatial resolution. We test the performance of different microphysical parametrizations and apply a new state-of-the-art secondary ice parametrization. We find that agreement with observations highly depends on the prescribed cloud condensation nuclei/cloud droplet and ice-nucleating particle concentrations and requires an enhancement of secondary ice production processes. Lowering mass mixing ratio thresholds for rime splintering inside the Morrison microphysics scheme is crucial to enable secondary ice production and thereby match observations for the right reasons. In our case, rime splintering is required to initiate collisional breakup. The simulated contribution from collisional breakup is larger than that from droplet shattering. Simulating ice production correctly for the right reasons is a prerequisite for reliable simulations of Arctic mixed-phase cloud responses to future temperature or aerosol perturbations.

Innovative approaches for accurate ozone prediction and health risk analysis in South Korea: The combined effectiveness of deep learning and AirQ+

Short-term exposure to ground-level ozone (O3) poses significant health risks, particularly respiratory and cardiovascular diseases, and mortality. This study addresses the pressing need for accurate O3 forecasting to mitigate these risks, focusing on South Korea. We introduce Deep Bias Correction (Deep-BC), a novel framework leveraging Convolutional Neural Networks (CNNs), to refine hourly O3 forecasts from the Community Multiscale Air Quality (CMAQ) model. Our approach involves training Deep-BC using data from 2016 to 2019, including CMAQ's 72-hour O3 forecasts, 31 meteorological variables from the Weather Research and Forecasting (WRF) model, and previous days' station measurements of 6 air pollutants. Deep-BC significantly outperforms CMAQ in 2021, reducing biases in O3 forecasts. Furthermore, we utilize Deep-BC's daily maximum 8-hour average O3 (MDA8 O3) forecasts as input for the AirQ+ model to assess O3's potential impact on mortality across seven major provinces of South Korea: Seoul, Busan, Daegu, Incheon, Daejeon, Ulsan, and Sejong. Short-term O3 exposure is associated with 0.40 % to 0.48 % of natural cause and respiratory deaths and 0.67 % to 0.81 % of cardiovascular deaths. Gender-specific analysis reveals higher mortality rates among men, particularly from respiratory causes. Our findings underscore the critical need for region-specific interventions to address air pollution's detrimental effects on public health in South Korea. By providing improved O3 predictions and quantifying its impact on mortality, this research offers valuable insights for formulating targeted strategies to mitigate air pollution's adverse effects. Moreover, we highlight the urgency of proactive measures in health policies, emphasizing the significance of accurate forecasting and effective interventions to safeguard public health from the deleterious effects of air pollution.

Effects of irrigation‐induced surface thermodynamics changes on wind speed in the Heihe River basin, Northwest China

AbstractThe Heihe River Basin, located in Northwest China, serves as a major commodity grain base in China due to its state‐of‐the‐art irrigation system. The rapid increase in soil moisture caused by irrigation can alter the land–atmosphere energy fluxes and regulate regional climate. However, the effects and mechanisms of irrigation on wind speed related to thermodynamics remain unclear. Here, we carried out two 10‐year numerical simulations using the Weather Research and Forecast (WRF) model incorporating a real‐time irrigation scheme. By comparing the simulation differences (including and excluding irrigation), we found that irrigation significantly decreased the daily mean and maximum wind speed at 10 m above ground by 0.30 and 0.55 m·s−1, respectively, in the irrigated area during the growth season. Such surface wind slowdown could be explained by irrigation‐induced surface air cooling and, hence, intensifying atmospheric column stability, weakening turbulent momentum transport, as well as opposite‐to‐prevailing winds vector anomaly caused by increased southward pressure gradient. Meanwhile, we also found wind slowdown mainly occurs below 1100 m while acceleration effect above that level. It was highlighted that the surface wind slowdown effect of irrigation substantially improved the performance of the WRF model. Due to the surface wind slowdown effect of irrigation, the positive bias of daily mean and maximum wind speed simulated by the WRF model was reduced by 15.3% and 27.4%, respectively. Our findings implicate the potential importance of irrigation in improving the performance of climate models as well as in explaining the phenomenon of global stilling.

Analysis of flood streamflow sensitivity to precipitation using the WRF-Hydro model in a humid environment

ABSTRACT Basin-scale runoff forecasting requires controlling absolute relative errors within 0.2 for accurate predictions. This study examines the sensitivity of simulated flood deviations in frequently flooded humid regions to discrepancies in precipitation driving the Weather Research and Forecasting (WRF)-Hydro model. Key parameters of various WRF-Hydro modules are calibrated from 23 flood events between 2003 and 2017. Two experiments were designed with minimized uncertainty in both model and parameterization to explore the sensitivity of streamflow changes to precipitation misestimations, both in total and graded precipitation. The sensitivity of runoff relative errors is more pronounced than that of precipitation relative errors, influenced by the magnitude, variability, and duration of rainfall. During processes involving heavy rainstorm, precipitation absolute relative errors increased by 50%, while runoff absolute relative errors increased by more than 60%. This sensitivity trend is linked to variations in components generated by misestimated precipitation. Runoff relative errors are higher when precipitation is misestimated at high rainfall levels compared to the same misestimation at low rainfall levels. For effective flood simulation and prediction, it is crucial to emphasize the accuracy of precipitation, especially during high rainfall events. Future research will incorporate more realistic precipitation forcing mechanisms and additional metrics for evaluating precipitation forecast.

Multiparameter Detection of Summer Open Fire Emissions: The Case Study of GAW Regional Observatory of Lamezia Terme (Southern Italy)

In Southern Mediterranean regions, the issue of summer fires related to agriculture practices is a periodic recurrence. It implies a significant increase in carbon dioxide (CO2) emissions and other combustion-related gaseous and particles compounds emitted into the atmosphere with potential impacts on air quality and global climate. In this work, we performed an analysis of summer fire events that occurred on August 2021. Measurements were carried out at the permanent World Meteorological Organization (WMO)/Global Atmosphere Watch (GAW) station of Lamezia Terme (Code: LMT) in Calabria, Southern Italy. The observatory is equipped with greenhouse gases and black carbon analyzers, an atmospheric particulate impactor system, and a meteo-station for atmospheric parameters to characterize atmospheric mechanisms and transport for land and sea breezes occurrences. High mole fractions of carbon monoxide (CO) and carbon dioxide (CO2) coming from quadrants of inland areas were correlated with fire counts detected via the MODIS satellite (GFED-Global Fire Emissions Database) at 1 km of spatial resolution. In comparison with the typical summer values, higher CO and CO2 were observed in August 2021. Furthermore, the growth in CO concentration values in the tropospheric column was also highlighted by the analyses of the L2 products of the Copernicus SP5 satellite. Wind fields were reconstructed via a Weather Research and Forecasting (WRF) output, the latter suggesting a possible contribution from open fire events observed at the inland region near the observatory. So far, there have been no documented estimates of the effect of prescribed burning on carbon emissions in this region. This study suggested that data collected at the LMT station can be useful in recognizing and consequently quantifying emission sources related to open fires.

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.

Heavy Rainfall Events in Selected Geographic Regions of Mexico, Associated with Hail Cannons

In this article, we document the use of hail cannons in Mexico to dispel or suppress heavy rain episodes, a common practice among farmers, without scientific evidence to support its effectiveness. This study uses two rain databases: one compiled from the Global Precipitation Measurement (GPM) mission and the other generated with the implementation of the Weather Research and Forecasting (WRF) model. The aim is to explore the association between heavy rain episodes and hail cannon locations. The analysis includes two geographic features: a pair of coordinates and a 3 km radius area of influence around each hail cannon. This dimension is based on the size and distribution of the heavy rainfall events. This study analyzes four years of half-hourly rain data using the Python ecosystem environment with machine learning libraries. The results show no relationship between the operation of hail cannons and the dissipation or attenuation of heavy rainfall events. However, this study highlights that the significant differences between the GPM and WRF databases in registering heavy rain events may be attributable to their own uncertainty. Despite the unavailability of ground-based observations, the inefficiency of hail cannons in affecting the occurrence of heavy rain events is evident. Overall, this study provides scientific evidence that hail cannons are inefficient in preventing the occurrence of heavy rain episodes.

A Drifting and Blowing Snow Scheme in the Weather Research and Forecasting Model

AbstractWind‐driven redistribution of surface snow is one of the key factors leading to heterogeneous accumulation of snow at small scales. Understanding the processes that lead to this heterogeneous accumulation is, therefore, of great importance to many glaciological and hydrological questions. High‐quality information on the wind field is necessary to realistically represent drifting snow. Here, we introduce a novel, intermediate‐complexity drifting and blowing snow module for the Weather Research and Forecasting (WRF) model that integrates seamlessly into the standard WRF infrastructure. The module also accounts for snow particle sublimation and considers the thermodynamic feedback on the atmospheric fields. In an idealized model environment the module was tested for physical consistency. Sensitivity experiments showed that drifting snow sublimation has on the one hand local effects on the erosion and deposition patterns and on the other hand non‐local effects on the larger‐scale atmosphere. The simple and computationally efficient implementation allows this module to be used for small‐scale and large‐scale simulations in polar and glaciated regions.

RaFSIP: Parameterizing Ice Multiplication in Models Using a Machine Learning Approach

AbstractAccurately representing mixed‐phase clouds (MPCs) in global climate models (GCMs) is critical for capturing climate sensitivity and Arctic amplification. Secondary ice production (SIP), can significantly increase ice crystal number concentration (ICNC) in MPCs, affecting cloud properties and processes. Here, we introduce a machine‐learning (ML) approach, called Random Forest SIP (RaFSIP), to parameterize SIP in stratiform MPCs. RaFSIP is trained on 16 grid points with 10‐km horizontal spacing derived from a 2‐year simulation with the Weather Research and Forecasting (WRF) model, including explicit SIP microphysics. Designed for a temperature range of 0 to −25°C, RaFSIP simplifies the description of rime splintering, ice‐ice collisional break‐up, and droplet‐shattering using only a limited set of inputs. RaFSIP was evaluated offline before being integrated into WRF, demonstrating its stable online performance in a 1‐year simulation keeping the same model setup as during training. Even when coupled with the 50‐km grid spacing domain of WRF, RaFSIP reproduces ICNC predictions within a factor of 3 when compared to simulations with explicit SIP microphysics. The coupled WRF‐RaFSIP scheme replicates regions of enhanced SIP and accurately maps ICNCs and liquid water content, particularly at temperatures above −10°C. Uncertainties in RaFSIP minimally impact surface cloud radiative forcing in the Arctic, resulting in radiative biases under 3 Wm−2 compared to simulations with detailed microphysics. Although the performance of RaFSIP in convective clouds remains untested, its adaptable nature allows for data set augmentation to address this aspect. This framework opens possibilities for GCM simplification and process description through physics‐guided ML algorithms.

Assimilation of Radar Reflectivity via a Full-Hydrometeor Assimilation Scheme Based on the WSM6 Microphysics Scheme in WRF 4D-Var

Abstract The microphysical parameterization scheme employed in four-dimensional variational data assimilation (4D-Var) plays an important role in the assimilation of humidity and cloud-sensitive observations. In this study, a newly developed full-hydrometeor assimilation scheme, integrating warm-rain and cold-cloud processes, has been implemented in the Weather Research and Forecasting (WRF) 4D-Var system. This scheme is based on the WRF single-moment 6-class microphysics scheme (WSM6). Its primary objective is to directly assimilate radar reflectivity observations, with the goal of evaluating its effects on model initialization and subsequent forecasting performance. Four assimilation experiments were conducted to assess the performance of the full-hydrometeor assimilation scheme against the warm-rain assimilation scheme. These experiments also investigated reflectivity assimilation using both indirect and direct methods. We found that the nonlinearity of the radar operator in the two direct reflectivity assimilation experiments requires more iterations for cost function reduction than in the indirect assimilation method. The hydrometeor fields were reasonably analyzed using the full-hydrometeor assimilation scheme, particularly improving the simulation of ice-phase hydrometeors and reflectivity above the melting layer. The assimilation of radar reflectivity led to improvements in short-term (0–3 h) precipitation forecasting with the full-hydrometeor assimilation scheme. Assimilation experiments across multiple case studies reaffirmed that assimilating radar reflectivity observations with the full-hydrometeor assimilation scheme accelerated model spinup and yielded enhancements in 0–3-h total accumulate precipitation forecasts for a range of precipitation thresholds.

The added value of Himawari-8 satellite radiance data assimilation for very heavy rainfall prediction in the Indonesian Maritime Continent (Case study: East Kalimantan)

Abstract This study evaluates the impact of assimilating Advanced Himawari Imager (AHI) radiance data on the 8-10 water vapor channel using the three-dimensional variational (3DVAR) technique for the very heavy rainfall events in East Kalimantan on June 2-4, 2019. We utilized the Weather Research and Forecasting (WRF) and WRF Data Assimilation (WRFDA) system to run assimilation and prediction model simulations. To compare the assimilated model with AHI data and without assimilation, we devised two experimental schemes: 3DVAR and NODA. We assessed the enhancement in model prediction by comparing simulation results based on convection evolution, vertically integrated moisture flux convergence (VIMFC) increment, and rain prediction skill metrics, including Threat Score (TS), Equitable Threat Score (ETS), and Fractions Skill Score (FSS). Our findings suggest that the incorporation of AHI data results in a substantial improvement of the WRF model’s prediction of the very heavy rainfall events in East Kalimantan.

The Impact on Simulated Bow Echoes of Changing Grid Spacing from 3 km to 1 km in the WRF Model

Abstract Ten bow echo events were simulated using the Weather Research and Forecasting (WRF) Model with 3- and 1-km horizontal grid spacing with both the Morrison and Thompson microphysics schemes to determine the impact of refined grid spacing on this often poorly simulated mode of convection. Simulated and observed composite reflectivities were used to classify convective mode. Skill scores were computed to quantify model performance at predicting all modes, and a new bow echo score was created to evaluate specifically the accuracy of bow echo forecasts. The full morphology score for runs using the Thompson scheme was noticeably improved by refined grid spacing, while the skill of Morrison runs did not change appreciably. However, bow echo scores for runs using both schemes improved when grid spacing was refined, with Thompson runs improving most significantly. Additionally, near storm environments were analyzed to understand why the simulated bow echoes changed as grid spacing was changed. A relationship existed between bow echo production and cold pool strength, as well as with the magnitude of microphysical cooling rates. More numerous updrafts were present in 1-km runs, leading to longer intense lines of convection which were more likely to evolve into longer-lived bow echoes in more cases. Large-scale features, such as a low-level jet orientation more perpendicular to the convective line and surface boundaries, often had to be present for bow echoes to occur in the 3-km runs.

Uncertainty and bias on velocities determined from an arc-scanning lidar

Accurate determination of wind speed offshore is important for the progression of offshore wind energy. Arc-scanning lidars offer precise measurements of both wind speed and direction. They can be placed on a fixed footing, such as a transition piece of a fixed-bottom wind turbine, or on the coast. However, the procedure to derive the wind vector relies on the assumption of homogeneous flow, i.e., that the wind vector is constant along the scanning arc. In this study, we derive a theoretical expression for the wind speed bias due to inhomogeneity in the mean flow. We show that inhomogeneity in the flow will mostly affect the wind component tangential to the arc. The dominating term in the bias equation is equal to the range gate distance times the gradient of the wind speed away from the lidar in the direction along the arc, i.e. crudely, how fast the wind component away from the lidar changes with the scan angle. Atmospheric simulations using the Weather Research and Forecast (WRF) model of flow near mountainous coasts (Madeira Island), where the wind gradients are supposed to be largest, are used to estimate the gradient and, thereby, the bias in a real case. Errors in special situations exceed 50%.

Atmospheric flow simulation over stationary waves with various steepness using WRF-LES

We investigated the capability of the Weather Research and Forecasting (WRF) model to perform large-eddy simulations (LESs) of the structure of the marine atmospheric boundary layer and turbulence exchange between the sea surface and the atmosphere. The WRF-LES model results were compared to results from Sullivan (2008) [1] for two cases: flow over a flat surface and over an idealized stationary sinusoidal surface. We also investigated the sensitivity of the WRF-LES model in simulating turbulence through the marine atmosphere under a range of horizontal grid spacings and wave steepnesses. All simulations were performed in a conventionally neutral atmosphere with a geostrophic wind of 5 m s−1, aligned with the direction of wave propagation. The simulated wind vertical profiles from the WRF-LES model exhibit similar behavior to those of Sullivan (2008) over both wavy and flat surfaces, but the differences in Sullivan’s vertical wind profiles for the two cases are considerably larger in comparison to those derived from WRF-LES. Furthermore, spectral analysis clearly shows the effect of horizontal grid spacing. The impact of wave steepness on turbulence distribution is clearly noticeable in the vertical profiles of resolved velocity variances and covariances. The impact is largest close to the surface, but it is important throughout the bulk of the boundary layer.

Enhanced shear and veer in the Taiwan Strait during typhoon passage

During typhoon passage extreme wind conditions pose a challenge to the structural integrity of wind turbines. Particularly, wind shear and wind veer can influence wind turbine loads. This study investigates how Taiwan’s central mountain range affects wind shear and wind veer in the Taiwan Strait during three westward-moving typhoon cases. The typhoons are simulated with the Weather Research and Forecasting (WRF) model. We find that wind speed, shear, and veer vary over different regions in the Taiwan Strait. In large areas, the simulated wind shear is larger than modeled over the open ocean. In particular, mountain blockage leads to a spatially confined area of several 1000 km2 downstream of Taiwan, that exhibits over several hours strongly modified shear and veer in all three analyzed cases. Shear exponents up to 0.75 and veer between 0.2 and 0.6° m−1 suggest that turbine loads are impacted by the vertical change in the wind field in this area. The shear exponent and wind veer vary strongly with height in the downstream area of Taiwan’s central mountain range. The location of the area with large wind shear and wind veer differs between the three simulated typhoon cases and primarily depends on the latitude of the typhoon track relative to the central mountain range.

Using random forest to improve EMEP4PL model estimates of daily PM2.5 in Poland

Long-term exposure to poor air quality is responsible for many diseases and increased mortality worldwide. European Environmental Agency reports that Poland is one of the most polluted countries in Europe due to high emissions associated with large coal and wood consumption and specific weather conditions. Exceedances of WHO-recommended PM2.5 thresholds are still common in Poland, so further action is needed to protect the health of the population. Atmospheric chemical transport models (CTMs) provide information on air quality to the public and are used to regulate air pollutant emissions. However, uncertainties associated with CTMs, related to e.g. physical/chemical processes and input data quality often lead to underestimation of pollutant concentrations, especially for PM2.5, and limits the applicability of CTMs e.g. for health impact studies.A hybrid approach combining the EMEP4PL chemical transport model for Poland with the Random Forest (RF) machine learning algorithm was applied to address the limitations of CTM and reduce its underestimation. We used EMEP4PL-modelled PM2.5 concentrations for period 2016–2019 as a predictor and measured daily PM2.5 from 71 monitoring stations in Poland as a dependent variable in three RF scenarios, which differed in terms of the selected predictors. The impact of different additional variables for the area was revealed, including population and emission data, dominant type of land use, Weather and Research Forecast (WRF) meteorological parameters, and temporal patterns of PM2.5 concentrations across the years. The models were evaluated in a random 5-fold and spatial leave-one-station-out cross-validations (LOSOCV), as well as for an independent test set. Our final model achieved a test set R2 of 0.71, compared to 0.38 for EMEP4PL, along with a reduction in negative bias (0.25 μg m−3 for the final RF, compared to −11 μg m−3 for EMEP4PL) and an improved ability to detect severe PM2.5 episodes. Enhanced coefficients of determination were observed in all seasons and at all monitoring sites included in the study, for both types of cross-validation and for the test set. We estimated the contribution of each group of variables separately and discovered, that the most impactful predictors for the area of Poland included temporal patterns of PM2.5 calculated based on the averages of EMEP4PL outcome (such as day of the year, week number, etc.) and meteorological modelled factors related to temperature, planetary boundary layer height, wind speed, and atmospheric pressure. The developed approach provides a basis for improved spatiotemporal estimates and forecasting of PM2.5 in the region, which is an important step toward better understanding the impact of air pollution on the local population well-being.

Influence of urbanization on meteorological conditions and ozone pollution in the Central Plains Urban Agglomeration, China

Changes in aerodynamic and thermal conditions caused by urbanization can impact regional meteorological conditions, subsequently affecting air quality. Updated Moderate-resolution Imaging Spectroradiometer (MODIS) land use data and coupled with the urban canopy models (UCMs) in the Weather Research and Forecasting (WRF) model. This enabled the impact of urban land expansion on meteorological conditions and ozone (O3) concentrations to be evaluated. Urban expansion increased the temperature at 2 m (T2) and the probability of precipitation in urban expansion areas, and enhanced the surface urban heat island at night. As the expansion areas became progressively larger, the increase in T2 became more pronounced. The proportions of urban surfaces in June 2016, 2018, and 2020 compared to 2001 increased by 0.69%, 0.83%, and 1.04%, respectively, while T2 increased by 0.12, 0.19, and 0.20 °C in urban areas, respectively. With urban expansion, the O3 concentration increased by 1.12, 1.37, and 0.76 μg/m3 (three-year averages) in urban, suburban, and rural areas, respectively. After coupling a multi-layer urban canopy model (building effect parameterization, BEP), or a multi-layer urban canopy model with a building energy model including anthropogenic heat due to air conditioning (BEP + BEM, abbreviated as BEM simulation), the O3 concentration changed significantly in the urban expansion area, compared to the results of a single-layer urban canopy model (UCM). O3 concentrations decreased most in the BEP simulation (−0.77 μg/m3), while O3 concentrations increased most in the BEM simulation (+1.85 μg/m3). The average observed O3 concentration was 108.35 μg/m3 (three-year average), while the simulated value was 75.65–83.72 μg/m3 (R = 0.69−0.77). The validation results in the BEM and Global Optimal Scenario (GOS) simulations were relatively good, with the GOS simulation producing slightly better results than the BEM. The simulation of O3 in urban agglomerations could be improved by integrating the results of the UCMs.

On Predicting Offshore Hub Height Wind Speed and Wind Power Density in the Northeast US Coast Using High-Resolution WRF Model Configurations during Anticyclones Coinciding with Wind Drought

We investigated the predictive capability of various configurations of the Weather Research and Forecasting (WRF) model version 4.4, to predict hub height offshore wind speed and wind power density in the Northeast US wind farm lease areas. The selected atmospheric conditions were high-pressure systems (anticyclones) coinciding with wind speed below the cut-in wind turbine threshold. There are many factors affecting the potential of offshore wind power generation, one of them being low winds, namely wind droughts, that have been present in future climate change scenarios. The efficiency of high-resolution hub height wind prediction for such events has not been extensively investigated, even though the anticipation of such events will be important in our increased reliance on wind and solar power resources in the near future. We used offshore wind observations from the Woods Hole Oceanographic Institution’s (WHOI) Air–Sea Interaction Tower (ASIT) located south of Martha’s Vineyard to assess the impact of the initial and boundary conditions, number of model vertical levels, and inclusion of high-resolution sea surface temperature (SST) fields. Our focus has been on the influence of the initial and boundary conditions (ICBCs), SST, and model vertical layers. Our findings showed that the ICBCs exhibited the strongest influence on hub height wind predictions above all other factors. The NAM/WRF and HRRR/WRF were able to capture the decreased wind speed, and there was no single configuration that systematically produced better results. However, when using the predicted wind speed to estimate the wind power density, the HRRR/WRF had statistically improved results, with lower errors than the NAM/WRF. Our work underscored that for predicting offshore wind resources, it is important to evaluate not only the WRF predictive wind speed, but also the connection of wind speed to wind power.