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This study investigates the predictability of downslope windstorms located in Santa Barbara County, California, locally referred to as Sundowner winds, from both observed relationships and a high-resolution, operational numerical weather prediction model. We focus on April 2022, during which the Sundowner Winds Experiment (SWEX) was conducted. We further refine our study area to the Montecito region owing to some of the highest wind measurements occurring at or near surface station MTIC1, situated on the coast-facing slope overlooking the area. Fires are not uncommon in this area, and the difficulty of egress makes the population particularly vulnerable. Area forecasters often use the sea-level pressure difference (Δ SLP) between Santa Barbara Airport (KSBA) and locations to the north such as Bakersfield (KBFL) to predict Sundowner windstorm occurrence. Our analysis indicates that Δ SLP by itself is prone to high false alarm rates and offers little information regarding downslope wind onset, duration, or magnitude. Additionally, our analysis shows that the high-resolution rapid refresh (HRRR) model has limited predictive skill overall for forecasting winds in the Montecito area. The HRRR, however, skillfully predicts KSBA-KBFL Δ SLP, as does GraphCast, a machine learning weather prediction model. Using a logistic regression model we were able to predict the occurrence of winds exceeding 9 m s − 1 with a high probability of detection while minimizing false alarm rates compared to other methods analyzed. This provides a refined and easily computed algorithm for operational applications. [ABSTRACT FROM AUTHOR]

The strong drying expected in the Eastern Mediterranean with climate change could cause mass migration of people already living under water shortages. On the other hand, precipitation is expected to increase toward the region's north, particularly those along the interior of the eastern Black Sea coasts, which could worsen existing floods. However, this double‐sided adverse phenomenon and its underlying reasons have been investigated by relatively low‐resolution models in the Eastern Mediterranean Black Sea (EMBS) region, where orographic precipitation prevails. This study performs 4 km resolution Weather Research and Forecasting (WRF) model simulations with and without climate change signals retrieved from a CMIP6 GCM ensemble via pseudo‐global‐warming (PGW). The WRF simulations captured the large‐scale dynamics affecting the EMBS fairly well: an anticyclonic low‐level circulation and enhancing subsidence stem from anomalous ridge development over the central Mediterranean in winter (DJF), and a cyclonic low‐level circulation and weakening subsidence rise from heat‐low development over the Eastern Mediterranean in summer (JJA). The resulting picture of future warming and drying over the area generally supports the literature, although new insights emerge in anomalous precipitation increase, especially in the summer season over the Greater Caucasus and nearby regions. Most likely, the warmer‐than‐expected Caspian Sea induces a large increase in specific humidity and, thus, a large moisture source in the lower troposphere and an extension of the heat‐low effect in the mid‐troposphere. In addition, the high‐resolution WRF simulations provide added value over the complex topography of the Caucasian Mountain range for new insights into this region. Plain Language Summary: One of the most vulnerable parts of the Mediterranean climate change hotspot is the Eastern Mediterranean Black Sea (EMBS) region. The future climate change signal of the state‐of‐the‐art CMIP6 GCM ensemble between 2071–2100 and 1985–2014 periods was retrieved for the region under the Shared Socioeconomic Pathway (SSP5‐8.5). The signal was used to perturb ERA5 reanalysis data for the IPCC AR6 baseline period, 1995–2014, in the context of the pseudo‐global warming (PGW) method. The Weather Research and Forecasting (WRF) model was used to obtain very high‐resolution (4 km) climate data. According to the study, the future winters were as expected: anomalous ridge formation over southern Italy, resultant northeasterlies over the EMBS, the large‐scale mid‐ and upper tropospheric subsidence, attenuation of the land‐sea temperature contrast and finally, strong drying. The summers exhibit negative vertical pressure velocity changes, strengthening Etesians, and strong surface warming in line with the expectations of the literature. However, summers brought anomalous precipitation to the Greater Caucasus and nearby areas in our high‐resolution WRF simulations. We think these new insights are primarily caused by the warmer‐than‐expected Caspian Sea stemming from the SST availability among the ensemble, a very convective environment for the region and better‐resolved complex‐orography‐induced precipitation. Key Points: The WRF‐PGW simulations perfectly capture the low‐level circulation change and warming/drying signals from CMIP6 for the EMBS in winterThe WRF‐PGW simulations show heat‐low formation over the EMBS in summer, in line with the CMIP6 GCM ensembleThe new insights appear in future summer precipitation increases over the Greater Caucasus and nearby regions [ABSTRACT FROM AUTHOR]

Abstract The Tibetan Plateau (TP) is the highest and one of the most extensive plateaus in the world and serves as a hotspot of climate change. In the context of climate warming, changes in evapotranspiration (ET) and external water vapor transport have a significant impact on assessing atmospheric water cycle processes over the TP. By using the Weather Research and Forecasting (WRF) model for long-term simulations and the finer box model for the calculation of water vapor along the boundary of the TP, the external atmospheric water vapor transport and its spatiotemporal characteristics over the TP are finely described. The simulated precipitation and ET are well-simulated compared with observation. Research results show that: (1) The total water path on the TP decreases from southeast to northwest. Water vapor is mainly transported into the TP from the western and southern boundaries. The net water vapor flux transported from the western boundary to the TP by westerly wind is negative, while the net water vapor flux transported from the southern boundary to the TP by southerly wind is positive. (2) In spring and winter, water vapor is mainly transported into the TP by mid-latitude westerlies from the western boundary. In summer, water vapor transport controlled by mid-latitude westerlies weakens, and water vapor is mainly transported into the TP from the southern boundary. In autumn, water vapor controlled by mid-latitude westerlies gradually strengthens, and water vapor is mainly transported into the TP from the western boundary. In addition, the ratio of ET to precipitation on the TP is about 0.48, and the moisture recycling is about 0.37. Water vapor mainly comes from external water vapor transport.

The Tibetan Plateau (TP) is the highest and one of the most extensive plateaus in the world and serves as a hotspot of climate change. In the context of climate warming, changes in evapotranspiration (ET) and external water vapor transport have a significant impact on assessing atmospheric water cycle processes over the TP. By using the Weather Research and Forecasting (WRF) model for long-term simulations and the finer box model for the calculation of water vapor along the boundary of the TP, the external atmospheric water vapor transport and its spatiotemporal characteristics over the TP are finely described. The simulated precipitation and ET are well-simulated compared with observation. Research results show that: (1) The total water path on the TP decreases from southeast to northwest. Water vapor is mainly transported into the TP from the western and southern boundaries. The net water vapor flux transported from the western boundary to the TP by westerly wind is negative, while the net water vapor flux transported from the southern boundary to the TP by southerly wind is positive. (2) In spring and winter, water vapor is mainly transported into the TP by mid-latitude westerlies from the western boundary. In summer, water vapor transport controlled by mid-latitude westerlies weakens, and water vapor is mainly transported into the TP from the southern boundary. In autumn, water vapor controlled by mid-latitude westerlies gradually strengthens, and water vapor is mainly transported into the TP from the western boundary. In addition, the ratio of ET to precipitation on the TP is about 0.48, and the moisture recycling is about 0.37. Water vapor mainly comes from external water vapor transport. [ABSTRACT FROM AUTHOR]

We investigate the influence of bias correction of Global Climate Models (GCMs) prior to dynamical downscaling using regional climate models (RCMs), on the change in climate projected. We use 4 GCMs which are bias corrected against ERA-Interim re-analysis as a surrogate truth, and carry out bias corrected and non-bias corrected simulations over the CORDEX Australasia domain using the Weather Research and Forecasting model. Our results show that when considering the effect of bias correction on current and future climate separately, bias correction has a large influence on precipitation and temperature, especially for models which are known to have large biases. However, when considering the change in climate, i.e the Δ change (future minus current), we found that while differences between bias-corrected and non-corrected RCM simulations can be substantial (e.g. more than 1 ∘ C for temperatures) these differences are generally smaller than the models' inter-annual variability. Overall, averaged across all variables, bias corrected boundary conditions produce an overall reduction in the range, standard deviation and mean absolute deviation of the change in climate projected by the 4 models tested, over 61.5%, 62% and 58% of land area, with a larger reduction for precipitation as compared to temperature indices. In addition, we show that changes in the Δ change for DJF tasmax are broadly linked to precipitation changes and consequently soil moisture and surface sensible heat flux and changes in the Δ changefor JJA tasmin are linked to downward longwave heat flux. This study shows that bias correction of GCMs against re-analysis prior to dynamical downscaling can increase our confidence in projected future changes produced by downscaled ensembles. [ABSTRACT FROM AUTHOR]

This study simulated the wind energy density distribution in the Jiaodong Peninsula region using the Weather Research and Forecasting (WRF) Model. The impacts of different boundary-layer and near-surface parameterization schemes on the simulated wind speed and direction were investigated. The results indicate that the Yonsei University (YSU) scheme and the Quasi-Normal Scale Elimination (QNSE) scheme performed optimally for wind speed and wind direction. We also conducted a sensitivity test of the simulation results for atmospheric pressure, air temperature, and relative humidity. The statistical analysis showed that the YSU scheme performed optimally, while the MRF and BL schemes performed poorly. Following this, the wind energy distribution in the coastal hilly areas of the Jiaodong Peninsula was simulated using the YSU boundary-layer parameterization scheme. The modeled wind energy density in the mountainous and hilly areas of the Jiaodong Peninsula were higher than that in other regions. The wind energy density exhibits a seasonal variation, with the highest values in spring and early summer and the lowest in summer. In spring, the wind energy density over the Bohai Sea is higher than over the Yellow Sea, while the opposite trend is modeled in summer. [ABSTRACT FROM AUTHOR]

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Copyright of New Architecture is the property of New Architecture Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Study region: The Khorasan Razavi Province represents a variety of climatic types in the northeastern region of Iran. It lies between 56° 19′ and 61° 16′ east longitude and 33° 52′–37° 42′ north latitude, which is one of the largest and most densely populated provinces in Iran. Study focus: The aim of this study is using the Weather Research and Forecasting (WRF) model to evaluate and simulate rainfall in the northeastern region of Iran based on the climatic conditions in order to rainfall forecasting, also warn about and control probable floods. New hydrological insights for the region: Following the climatic classification of the study area, the six WRF model schemas were employed to implement the model in this area: Purdue-Lin (Lin), WRF Single-Moment class 3, 5, 6 (WSM3, WSM5, WSM6), and WRF Double-Moment class 5, 6 (WDM5, WDM6). The results revealed that the Threat Score (TS) coefficients for the WDM5 and Lin schemas in the desert climate are 1 and 0.97, respectively; the TS coefficient for each one of the Lin, WSM3, and WDM6 have the value of 0.60 in the semi-desert climate. The Lin scheme performs the best in all climates of the study area. The results revealed that the WRF model accurately simulates rainfall in the climatic conditions of the region. Hydrologists and planners will be able to use this meteorological model as an input to hydrological models based on the output of the WRF model, which can provide early flood warnings within the next 24 h based on these findings.

The present study introduces a new boundary layer parameterization for weather and forecasting models. It is implemented here as a boundary layer module in Weather Research and Forecasting (WRF) model. The main novelty in the new scheme is that it includes prognostic equations for the heat flux and temperature variance, being the first WRF boundary layer scheme with that feature. This is specially aimed at improving the representation of nocturnal stable boundary layer and of its turbulence regimes, weakly and very stable. The effort is supported by previous studies that found that the two regimes and the transitions between them are better represented by simplified numerical schemes that represent the interactions between the surface and the air adjacent to it when the heat flux and temperature variance are solved prognostically. The results show that the two regimes are adequately simulated by the new scheme. Such an evaluation is presented in terms of the relationship between the turbulence velocity scale and mean wind speed, of the dependence of the potential temperature gradient near the surface and the mean wind speed, and by the relationship between flux and gradient Richardson numbers. In the new scheme, the relationship between thermal structure and the mean and turbulent flows arises naturally from the heat flux prognostic equation, not being arbitrarily imposed by an empirical stability function. [ABSTRACT FROM AUTHOR]

The impact of large reservoirs on regional climate extremes, such as heatwaves, is not yet well understood, primarily due to the complex interactions of heat, moisture, and momentum over mountainous terrains and the dynamic nature of water surfaces. In this study, we investigate the role of the Miyun Reservoir (MYR), one of the largest reservoirs in northern China, covering an extensive water surface area of 180 km2, in influencing a severe heatwave event that occurred southwest of MYR from 2nd to 6th July 2010. Using high-resolution simulations from the Weather Research and Forecasting Model, our findings reveal contrasting thermodynamic effects of MYR on near-surface air temperatures, with up to three degrees of warming during night-time and up to five degrees of cooling during daytime, with a spatial extent of impact reaching up to 20 km away from the reservoir. These impacts are attributed to a combination of synergized land-lake breeze and mountain-valley winds during the night, leading to the transport of warm air plumes downwind towards the plain and intensifying the heatwave. Furthermore, we observe an increase in surface water vapor brought to higher atmospheric levels due to strong vertical mixing, resulting in lowered surface-specific humidity. Our study emphasizes the dual role of large reservoirs in exacerbating remote heatwaves while providing cooling effects in their proximity. Gaining a comprehensive understanding of the intricate interactions between reservoirs, topography, and regional climate extremes is of paramount importance for accurate climate risk assessments and the formulation of effective mitigation strategies..

The solar power industry is a rapidly growing sector of renewable energy, and it is crucial that the available energy is accurately forecast. Using numerical weather prediction models, we can forecast the global horizontal irradiance on which the amount of energy produced by photovoltaic systems depends. This study presents the forecast effects for one of the most challenging weather conditions in modelling, occurring in central and eastern Europe. The dates of the synoptic situations were selected from 2021 and 2022. Simulations were carried out for 18 days with a cold front and, in order to verify the model configuration, for 2 days with a warm front, 2 days with an occlusion front and 2 days with a high pressure situation. Overall, 24 forecasts were made for each of the three parameterizations of the Weather Research and Forecasting model. The data were compared with the values measured in situ at the station performing the actinometric measurements belonging to Germany's National Meteorological Service. This paper presents the spatial distribution of the global horizontal irradiance parameters for several terms to explain the differences between the results of the different simulations. [ABSTRACT FROM AUTHOR]

The study uses numerical weather prediction models to predict the occurrence of heavy convective rainfall associated with the passage of the African Easterly Wave (AEW) during the period 23–26 August 2017 over Nigeria. Fraction skill score (FSS) and method for object‐based diagnostic evaluation (MODE) verification techniques were applied to verify how well the models predict the high‐impact event and to demonstrate how these tools can support operational forecasting. Ensemble model forecasts at a convective scale from UK Met Office Unified Model (MetUM) and a one‐way nested weather research and forecasting (WRF) model were compared with the integrated multisatellite retrievals for global precipitation measurement (IMERG GPM). The purpose is to examine skills of improved model resolution and ensemble in reproducing rainfall forecasts on useful scales and how the skill varies with spatial scale. WRF 2 and 6 km model forecasts show comparable skill at smaller grid scales. The skill of MetUM improves dramatically when the verification statistics are applied to the ensemble mean of the binary fields of the individual member forecast. The object‐based analysis reveals a similar structure as observed, although displaced eastwards. Most improvement occurred for heavier rainfall events associated with the passage of the AEW. WRF 6 km compares reasonably well with WRF 2 km in terms of shape and structure of rainfall underscoring the ability of the model to reasonably represent convection at 6 km horizontal resolution. The ensemble members in MetUM explicitly reproduce convection at 4 km resolution but are displaced at about 166 km behind observed rainfall. [ABSTRACT FROM AUTHOR]

Abstract The thermal heterogeneity between the land and sea might affect the wind patterns within wind farms (WF) located near seashores. This condition was modeled with a large‐eddy simulation of a numerical weather prediction model (Weather Research and Forecasting) that included the wind turbine actuator disk model (ADM). The assumed condition was that the downstream surface temperature was relatively higher (unstably stratified condition) than the neutrally stratified upstream wind. Under this condition, a thermal internal boundary layer (TIBL) was developed from an area where a step‐changed surface temperature was implemented. The combined effect of the wake deficit due to the WF and velocity recovery as a result of enhanced mixing under unstable stratification showed significant modulation of the wind speed at the hub height when local atmospheric stability affected the wind turbine (WT). We show that TIBL height depends on the variables to be evaluated as the threshold. A precise prediction of the TIBL height is beneficial for better estimation of power generation. A prediction model was proposed as an extension of the internal boundary layer (IBL) model for neutral stratification, and the results tracked TIBL development reasonably well. The effects of WFs on surface properties (e.g., friction velocity, heat flux, and Obukhov length) and the tendency of IBL growth were minor. A single WT wake was also assessed under several TIBL developmental stages (i.e., location) and thermal stratification conditions. The standard deviation of the wake deficit increased vertically during the development stage of the TIBL. In contrast, the coefficients in the horizontal and vertical directions were comparable when the WT was deep inside the TIBL.

Over the past few decades, the ground-based global navigation satellite systems (GNSSs) tropospheric sounding technique has undergone rapid development and has proven to be highly effective in sensing atmospheric variables. Through the utilization of the maturing data assimilation technique, GNSS products, e.g., precipitable water vapor (PWV), can be effectively integrated into a numerical weather prediction (NWP) model, thereby significantly bolstering its performance. In this study, to further refine this practice, a comprehensive investigation of the optimal spatial resolutions for assimilating near real-time PWV into the Weather Research and Forecasting model to improve the accuracy of atmospheric humidity field was conducted under different weather conditions in the context of Victoria, Australia. Results revealed that the optimal spatial resolutions under the heavy rainfall and normal conditions were 46.40 and 55.10 km, respectively. Therefore, the overall optimal spatial resolution for assimilating PWV was ultimately determined as 46.40 km, which can capture the necessary details and improve the accuracy of humidity field across different weather scenarios. Specifically, by incorporating PWV into an NWP model using the optimal spatial resolution, the accuracy of atmospheric humidity fields under the heavy rainfall and normal conditions were significantly improved by 26.0% and 24.7%, respectively. Therefore, the findings have considerable implications for further advancing the assimilation technique and offer valuable insights for the construction and deployment of GNSS ground infrastructure in future scenarios.

This study simulated the wind energy density distribution in the Jiaodong Peninsula region using the Weather Research and Forecasting (WRF) Model. The impacts of different boundary-layer and near-surface parameterization schemes on the simulated wind speed and direction were investigated. The results indicate that the Yonsei University (YSU) scheme and the Quasi-Normal Scale Elimination (QNSE) scheme performed optimally for wind speed and wind direction. We also conducted a sensitivity test of the simulation results for atmospheric pressure, air temperature, and relative humidity. The statistical analysis showed that the YSU scheme performed optimally, while the MRF and BL schemes performed poorly. Following this, the wind energy distribution in the coastal hilly areas of the Jiaodong Peninsula was simulated using the YSU boundary-layer parameterization scheme. The modeled wind energy density in the mountainous and hilly areas of the Jiaodong Peninsula were higher than that in other regions. The wind energy density exhibits a seasonal variation, with the highest values in spring and early summer and the lowest in summer. In spring, the wind energy density over the Bohai Sea is higher than over the Yellow Sea, while the opposite trend is modeled in summer.

Accurate tropical cyclone (TC) track simulations are required to mitigate property damage and casualties. Previous studies have generally simulated TC tracks using numerical models, which tend to experience systematic errors due to model imperfections, although the model accuracy has improved over time. Recently, machine-learning methods have been applied to correct such errors. In this study, we used an artificial neural network (ANN) to correct TC tracks hindcasted by the Weather Research and Forecasting (WRF) model from 2006 to 2018 over the western North Pacific. TC categories that are stronger than tropical depressions (i.e., tropical storms, severe tropical storms, and typhoons) were selected from June to November, and a bias correction was made to target TC positions at 72 h. The WRF-simulated tracks were used as input variables for training and testing the ANN using the best track and reanalysis data. To obtain a reliable corrected result, the number of neurons in the ANN structure was optimized for TCs during 2006–2015, and the optimized ANN was verified for TCs from 2016–2018. Because the performance of the numerical model differed according to the TC track, the ANN was assessed by cluster analysis. The results of the ANN were analyzed using k-means clustering to classify TCs into eight clusters. Overall, ANN with post-processing improved the WRF performance by 4.34%. The WRF error was corrected by 8.81% for clusters where the ANN was most applicable. [ABSTRACT FROM AUTHOR]

In the warm season, El Niño/Southern Oscillation (ENSO) causes periods with more rain in Northern Mexico during its positive phase, while less rainfall is recorded in the southern regions during the negative phase. This research study evaluates the variability of summer (July–September) precipitation and moisture fluxes under different ENSO scenarios in the Mexican Altiplano and coast of the state of Nayarit. The catchment of Rio San Pedro-Mezquital (SPM-RB) connects both regions. Using the Oceanic Niño Index (ONI), the years that signal change from El Niño to La Niña (1998), neutral conditions (2005), and strong (moderate) La Niña (1999) were selected to get an insight of ENSO impact on summer precipitation. For anomalies in the Altiplano, two additional contrasting years were analyzed—2006 (mostly dry) and 2010 (wet)—to determine moisture sources. Summer rainfall conditions in 1998 and 1999 had an opposite behavior between coastal Nayarit (wet) and the Altiplano (dry), while in 2005, rainfall deficits were observed in both regions. The moisture fluxes showed large divergence areas over central Mexico and the Southeastern United States in years of intense drought (1998 and 1999) caused by two high-pressure cells at middle levels of the troposphere (500 hPa). The moisture transport mechanisms into the Altiplano were related to atmospheric circulation at the upper level (200 hPa). The variations of the moisture fluxes from 2006 to 2010 are less strong at middle levels. The Eastern Pacific moisture convergence along the western coast of Mexico favors above-average rainfall anomalies in the coastal region but below-average anomalies in the Altiplano. [ABSTRACT FROM AUTHOR]

Sea breezes are important in a coastal urban climate; however, the impact of urban development on the effects of sea breezes, which decrease air temperature and increase humidity, has not been understood quantitatively. To quantitatively evaluate this impact in Sendai, Japan over the past twenty years, this study analyzed the heat balance mechanisms in urban spaces based on the simulation results of the Weather Research and Forecasting (WRF) model coupled with Local Climate Zone (LCZ) maps. Compared to the observation data on air temperature, specific humidity, and wind in August 2002, results of the numerical simulation, using the 2002 LCZ map and the meteorological conditions of August 2002, confirmed that the WRF model could reproduce meteorological factors well. Thereafter, two numerical simulations using the LCZ maps from 2002 and 2022 were conducted based on the same meteorological condition, from 25 July to 1 September 2008, to extract the impact of urban development on the effects of sea breeze. Consequently, when land use changed from urban built-up land to natural land cover, both the effects of sea breeze—decreasing air temperature and increasing humidity—decreased. Additionally, increases in LCZ 3 (compact low rise), mainly from LCZ 6 (open low rise) and LCZ 9 (sparsely built), decreased the effects of sea breeze (decreasing air temperature and increasing humidity) by 5% and 10%, respectively, in areas around Sendai Station. This was because the consumption of the sea breeze's potential to decrease air temperature and increase humidity increased and the wind speed of sea breezes decreased in the windward areas of Sendai Station. These results provide new insights into the impact of urban development on the effects of sea breeze and quantitatively reveal changes in the effects of sea breeze. [ABSTRACT FROM AUTHOR]

Point precipitation frequency estimates (PPFEs) are used in the design of stormwater drainage infrastructures. The PPFEs for 1-year to 1,000-year recurrence intervals with durations ranging from 5 min to 60 days are published in the National Oceanic and Atmospheric Administration (NOAA) Atlas 14. NOAA's published PPFEs are accepted as a reliable resource for the design of urban drainage infrastructures, yet they are based on the stationary climate assumption. However, future climate change may affect the distribution, frequency, and intensity of precipitation events. To evaluate the potential impacts of climate change on the stationary climate assumption, a weather research and forecasting (WRF) model was developed based on the medium climate change emission scenario. Then, the PPFEs were determined for 2010–2015, 2030–2035, 2050–2055, and 2070–2075 at six different locations in the state of Arizona. The comparison of the published and simulated PPFEs revealed a varying trend in depth and frequency values dependent on location and climate. Additionally, depth values were diminished for

To date, the majority of numerical modelling [computational fluid dynamics (CFD)] studies on long-span bridges have been carried out on scaled physical models, and without field-data for validation. For the first time, a full-scale bridge aerodynamic CFD study was conducted in this paper. A full-scale three-dimensional CFD model of the middle span and central tower of the Queensferry Crossing, United Kingdom, was created. The aim of this work was accurately simulating the wind field around the bridge. The CFD simulations were developed in OpenFOAM with the k − ω SST turbulence model. Atmospheric boundary layer inflows were configured based on wind profiles provided by a full-scale Weather Research and Forecasting (WRF) model. CFD predictions were validated with field data which were collected from an on-site Structural Health Monitoring System. The simulated fluctuating wind field closely satisfied the characteristic of field data and demonstrated that the modelling approach had good potential to be used in practical bridge aerodynamic studies. Meanwhile, comparisons and sensitivity analyses on mesh density provided a reference modelling approach for any future works on full-scale bridge aerodynamic models. Additionally, a cylindrical-like domain was applied in bridge aerodynamics for the first time and verified as being a convenient and reliable way to be used in bridge studies that involve changes in yaw angle. [ABSTRACT FROM AUTHOR]

In recent years, the use of deep learning techniques to forecast the weather has increased significantly; however, existing machine learning methods based on observed data are only suitable for very short-term forecasting. Numerical models are more stable for short- and medium-term forecasting, but the results may deviate from the observed data. This study proposes a deep learning method to improve the performance of numerical weather prediction models. In this method, the transformation relationship between the output of the numerical model and the observed data is learned by a generative adversarial network, which is then used to correct the forecasts of the numerical model. Experiments on 9 months of paired numerical model data and observed radar data demonstrate that correction of the forecast data using this method improves prediction performance, especially of heavy rainfall events. The proposed method provides a practical approach to combining conventional numerical weather prediction with data-driven deep learning models. [ABSTRACT FROM AUTHOR]

Extreme temperatures were considered natural hazards because they could increase morbidity and mortality. Understanding the extreme temperature changes at different warming levels is crucial to climate change mitigation and adoption for human health. This study projected climate change effects on the intensity, occurrence, and duration of extreme temperatures in Taiwan with 2 and 4°C global warming scenarios using the Weather Research and Forecasting model. The future climate simulations were conducted with the pseudo‐global warming approach, and the future climate changes were obtained from the ensemble mean of simulations in the Coupled Model Intercomparison Project 5. The simulated daily mean temperature increased by 1.40 and 3.09°C under 2 and 4°C global warmings. In a warming world, the daily maximum temperature was projected to increase by 1.35–3.00°C, whereas the daily minimum temperature was even higher, leading to weaker diurnal temperature variation in most regions. The simulation results show that intensified heatwaves with frequent and prolonged durations become par for the course, whereas extremely cold days disappear gradually. The occurrence of heatwaves in the future is projected to be five times that in the current climate. Comparing the global warming impacts over different land‐use types, the heatwave occurrence over urban areas rose more quickly than over other land‐use types; forests are less vulnerable to global warming. On the contrary, the changes in extremely cold days over urban areas were weaker than over other land‐use types. Overall, the effects of global warming on temperature revealed that extreme events were more severe with increased temperature than with the mean state of air temperature. Nonlinear behaviours indicated that global warming should be limited to 2°C, and the additional 2°C warming (from 2 to 4°C) should be addressed carefully. [ABSTRACT FROM AUTHOR]

The thermal heterogeneity between the land and sea might affect the wind patterns within wind farms (WF) located near seashores. This condition was modeled with a large‐eddy simulation of a numerical weather prediction model (Weather Research and Forecasting) that included the wind turbine actuator disk model (ADM). The assumed condition was that the downstream surface temperature was relatively higher (unstably stratified condition) than the neutrally stratified upstream wind. Under this condition, a thermal internal boundary layer (TIBL) was developed from an area where a step‐changed surface temperature was implemented. The combined effect of the wake deficit due to the WF and velocity recovery as a result of enhanced mixing under unstable stratification showed significant modulation of the wind speed at the hub height when local atmospheric stability affected the wind turbine (WT). We show that TIBL height depends on the variables to be evaluated as the threshold. A precise prediction of the TIBL height is beneficial for better estimation of power generation. A prediction model was proposed as an extension of the internal boundary layer (IBL) model for neutral stratification, and the results tracked TIBL development reasonably well. The effects of WFs on surface properties (e.g., friction velocity, heat flux, and Obukhov length) and the tendency of IBL growth were minor. A single WT wake was also assessed under several TIBL developmental stages (i.e., location) and thermal stratification conditions. The standard deviation of the wake deficit increased vertically during the development stage of the TIBL. In contrast, the coefficients in the horizontal and vertical directions were comparable when the WT was deep inside the TIBL. [ABSTRACT FROM AUTHOR]

Land use data with building characteristics are important for modeling the impacts of urban morphology on local climate. In this study, an extreme heat event in Shanghai, China, was simulated by using a WRF/BEP + BEM (Weather Research and Forecasting/Building Effect Parameterization + Building Energy Model) model. We incorporated local climate zone (LCZ) land use data that resolved urban morphology using 10 classes of building parameters. The simulation was compared to a control case based on MODIS (Moderate-resolution Imaging Spectroradiometer) land use data. The findings are as follows: (1) the LCZ data performed better than the MODIS data for simulating 10-m wind speed. An increase in building height led to the wind speed to decrease by 0.6–1.4 m s−1 in the daytime and by 0.2–0.7 m s−1 at nighttime. (2) High-rise buildings warmed the air by trapping radiation in the urban canyon. This warming effect was partially offset by the cooling effect of building shadows in the day. As a result, the 2-m temperature increased by 0.8°C at night but only by 0.4°C during the day. (3) Heterogeneous urban surfaces increased the 50-m turbulent kinetic energy by 0.4 m2 s−2, decreased the 10-m wind speed by 1.8 m s−1 in the daytime, increased the surface net radiation by 45.1 W m−2, and increased the 2-m temperature by 1.5°C at nighttime. (4) The LCZ data modified the atmospheric circulation between land and ocean. The shadowing effect reduced the air temperature differences between land and ocean and weakened the sea breeze. Moreover, high-rise buildings obstructed sea breezes, restricting their impact to a smaller portion (10 km along the wind direction) of inland areas compared to that with MODIS. [ABSTRACT FROM AUTHOR]

To improve the forecast accuracy of the annual energy production from wind power, the characteristics of the atmospheric stability distribution are summarized using data from wind observation towers at ten sites in Japan. The bulk Richardson numbers indicate that stable conditions prevailed at seven out of ten locations. An analysis of each met mast indicated that the prevailing stability varied by season and differed not only according to the area but also according to the wind farm scale. These differences are thought to result from the distance to the sea and the wind shear. In addition, we compared the results according to the observational data and the bulk Richardson number calculated using the Weather Research and Forecasting model（WRF） during each season. The prevailing atmospheric stability at the observation points is affected by the atmospheric stability of the land and the surrounding ocean. The results of previous research and this study indicate that considering the atmospheric stability enables more correct estimations of the annual wind power energy production. If possible, seasonal and local differences in the atmospheric stability should be considered to better estimate the wind power generation. [ABSTRACT FROM AUTHOR]

The solar power industry is a rapidly growing sector of renewable energy, and it is crucial that the available energy is accurately forecast. Using numerical weather prediction models, we can forecast the global horizontal irradiance on which the amount of energy produced by photovoltaic systems depends. This study presents the forecast effects for one of the most challenging weather conditions in modelling, occurring in central and eastern Europe. The dates of the synoptic situations were selected from 2021 and 2022. Simulations were carried out for 18 days with a cold front and, in order to verify the model configuration, for 2 days with a warm front, 2 days with an occlusion front and 2 days with a high pressure situation. Overall, 24 forecasts were made for each of the three parameterizations of the Weather Research and Forecasting model. The data were compared with the values measured in situ at the station performing the actinometric measurements belonging to Germany’s National Meteorological Service. This paper presents the spatial distribution of the global horizontal irradiance parameters for several terms to explain the differences between the results of the different simulations.

Warm-sector heavy rainfall in South China is a frequent type of precipitation in summer in the Pearl River Delta region. The complexity of the mechanisms involved in the triggering of convection, especially the effects of urbanization, has greatly increased the uncertainty of numerical simulations of warm-sector heavy rainfall. In this study, five new surface parameters with five new anthropogenic heat (AH) parameters were constructed and coupled with the urban canopy model (UCM) of the Weather Research and Forecasting model, version 4.1, based on the local climate zone system over the Pearl River Delta Urban Agglomeration (PRDUA). Taking a typical warm-sector heavy rainfall process that occurred in the PRDUA on 20 April 2019 as an example, five groups of experiments involving different schemes were compared and analyzed, revealing that the precipitation simulated using the localized UCM with the new AH parameters was the best (closest to observations). The localized UCM successfully simulated the increase in 2 m temperature and sensible heat flux and the resultant thermal forcing in urban areas, which promoted the convergence of low-level southerly winds with water vapor and the lifting of the lower-layer warm and humid water vapor to the upper layers in the urban center, leading to a significant increase of precipitation. The improved AH parameters enhanced the anthropogenic heat and its vertical conduction in urban areas, but contributed only marginally to the convergence of 10 m winds. Compared with observations from wind profile radar, it was found that the localized UCM enhanced the accuracy of the simulated horizontal wind field convergence at upper and lower levels, while the improved AH parameters enhanced the accuracy of the simulated low-level jet intensity and vertical movement, which are important drivers for the spatial variation in warm-sector heavy rainfall over the PRDUA. The current findings will be helpful for improving the model skill in simulating warm-sector heavy rainfall over high-density urban areas, as well as enhancing understanding of the impact mechanism of urbanization on the occurrence and development of warm-sector heavy rainfall.

Point precipitation frequency estimates (PPFEs) are used in the design of stormwater drainage infrastructures. The PPFEs for 1-year to 1,000-year recurrence intervals with durations ranging from 5 min to 60 days are published in the National Oceanic and Atmospheric Administration (NOAA) Atlas 14. NOAA's published PPFEs are accepted as a reliable resource for the design of urban drainage infrastructures, yet they are based on the stationary climate assumption. However, future climate change may affect the distribution, frequency, and intensity of precipitation events. To evaluate the potential impacts of climate change on the stationary climate assumption, a weather research and forecasting (WRF) model was developed based on the medium climate change emission scenario. Then, the PPFEs were determined for 2010-2015, 2030-2035, 2050-2055, and 2070-2075 at six different locations in the state of Arizona. The comparison of the published and simulated PPFEs revealed a varying trend in depth and frequency values dependent on location and climate. Additionally, depth values were diminished for, 3-h events at a majority of the stations. [ABSTRACT FROM AUTHOR]

Estimation of probable maximum flood (PMF) is a crucial process in water resources management and in the design of large hydraulic structures. However, there are uncertainties in the estimation of hydrologic conditions that contribute to extreme floods. In particular, this is the case in snow‐dominated regions, as surface air temperature and wind speed are understood to have a substantial effect on the magnitude of a flood during a storm event. Motivated by the development of a new approach to investigate and estimate reliable PMF values and in an attempt to resolve the uncertainty issues, this study introduces a physically based modeling approach. For the case study, seven watersheds located in the Sierra‐Nevada mountain range of California, including Cosumnes, Mokelumne, Stanislaus, Tuolumne, Merced, Upper San Joaquin, and Upper Kings were selected. The hydroclimate model was first implemented over the physical boundaries of the study region, and then utilized to simulate possible maximum flood conditions with input from 10 extreme precipitation scenarios. The study results provide evidence of a nonlinear atmospheric–hydrologic system; the extreme 72‐h basin‐averaged precipitation depth was found not to be linearly proportional to 72‐h flow volume equivalent depth. It can also be concluded that a large precipitation depth may not be the sole reason for a large flood event. Temperature and other atmospheric variables also contribute significantly to the production of snowfall and liquid water available for runoff, and to the resulting hydrologic response, such as the flood peak discharge and volume. [ABSTRACT FROM AUTHOR]

This paper presents incorporation of the microphysical piggybacking into the Weather Research and Forecasting (WRF) model. Microphysical piggybacking is to run a single simulation applying two microphysical schemes, the first scheme driving the simulation and the second piggybacking this simulated flow. "Driving the simulation" means that the simulated microphysical processes, affect the cloud buoyancy and thus force the simulated flow. In contrast, the piggybacking variables are advected by the simulated flow and undergo microphysical transformation, but they do not affect the simulated flow (like in prescribed flow—kinematic—simulations). The two sets of variables (driver and piggybacker) include temperature, water vapor mixing ratio, and all microphysical variables. We provide details of implementing piggybacking into the WRF model, illustrate its applications, and demonstrate the benefits of this methodology in two idealized three‐dimensional cases: (a) a squall line case applying two microphysics schemes, the Thompson bulk microphysics scheme and the University of Pécs/NCAR bin (UPNB) scheme. The piggybacking simulations revealed that the microphysics‐dynamics interaction plays a more important role than the pure microphysical size sorting effect in the transition zone formation. (b) A case of daytime shallow‐to‐deep convective development over land. This case uses the UPNB scheme and contrasts convection developing in environments with either pristine or polluted cloud condensation nuclei (CCN). The piggybacking results indicated that the increase of cloud cover and decrease of supersaturation are mainly associated with the microphysical effect of increasing CCN while the change of precipitation on the ground is also influenced by microphysics‐dynamics interactions. Plain Language Summary: Weather forecast, especially precipitation and cloud formation prediction is a challenging part of numerical modeling. They have to interpret processes that take place on a wide range of scales in space and time (dynamical and microphysical processes). A novel simulation methodology was implemented in a widely used weather prediction model (WRF). This method helps us to understand better the interactions between different processes. On one hand, this unique technique operates as a normal weather prediction model, which drives the simulation (the different processes interact with each other in this "driver" run). On the other hand, a second package of model variables just follows the flow produced by the "driver," which is called piggybacking the simulation. This second set of variables just go through the same calculations as in a normal model run, except that they do not have any feedback on the simulated flow. We have tested various cases to investigate the interactions in different types of weather situations. We have found that it's a useful technique to understand the relationship between dynamic and microphysical processes. It also helps us better understand how weather works so we can make more accurate predictions. Key Points: Piggybacking methodology implemented into the Weather Research and Forecasting model to examine microphysics‐dynamics interactionsIdealized squall line case presented the importance of microphysics‐dynamics interactions in transition zone formationThe advantage of the method is the gridpoint‐by‐gridpoint comparison, for example, to state the cloud condensation nuclei effect on microphysics [ABSTRACT FROM AUTHOR]

Projected changes to the El Niño Southern Oscillation (ENSO) climate mode have been explored using global Earth system models (ESMs). Regional expressions of such changes have yet to be fully advanced and may require the use of regional downscaling. Here, we employ regional climate modeling (RCM) using the Weather Research and Forecasting (WRF) model at convection-permitting resolution and nested in output from the HadGEM2 ESM. We quantify ENSO teleconnections to temperature and precipitation anomalies in historical and future climate scenarios over eastern North America. Two paired simulations are run, a strong El Niño (positive ENSO phase) and a weak La Niña (negative ENSO phase), for the historical and future years. The HadGEM2 direct output and HadGEM2-WRF simulation output are compared to the anomalies derived from the NOAA ENSO Climate Normals dataset. The near-surface temperature and precipitation differences by ENSO phase, as represented by the HadGEM2-WRF historical simulations, show a poor degree of association with the NOAA ENSO Climate Normals, in part because of the large biases in the HadGEM2 model. Downscaling with the WRF model does improve the agreement with the observations, and large discrepancies remain. The model chain HadGEM2-WRF reverses the sign of the ENSO phase response over eastern North America under simulations of the future climate with high greenhouse gas forcing, but due to the poor agreement with the NOAA ENSO Climate Normals it is difficult to assign confidence to this prediction. [ABSTRACT FROM AUTHOR]

Abstract Heatwaves are acknowledged to be the major meteorological disaster, causing a noticeable impact on humans and animals’ lives during the last few decades. The number, frequency, duration, intensity, and areal extent of the heatwaves are on the rise during recent years. The Maximum temperature data of 2013 is analyzed to assess the synoptic nature, intensity, frequency, and various significant facets of the heatwave over the south peninsular states of Andhra Pradesh and Telangana. Indian subcontinent experienced a major heatwave during 2013, which claimed 1216 human lives. Even though the highest intensity of maximum temperatures is observed in May over major areas of India, the increasing (incidence, duration, number of spells, and the sweltering temperatures) number of heatwaves are observed over many parts of the country. The northwest and southeast coastal regions are the two heat wave prone regions. The advection of heat from the northwest with the aid of north-westerly winds causes heatwaves over northwest India to sweep or move towards India’s southeast and east coast. The heatwave record over south-eastern India, i.e, Andhra Pradesh and the adjoining Telangana state during May 22–24, 2013 were described in this study. Maximum temperatures above 40°C are observed with a sudden rise by 6 to 7°C over the study region. An attempt is made to predict the maximum temperatures 72 hours before the existence of a heatwave at 3 km horizontal resolution using the Advanced core of the Weather Research and Forecasting (WRF) model. Model predicted temperature values match with observations and the statistical metrics show a high index of the agreement, lower values for root-mean-square error and mean absolute error. Atmospheric circulation patterns associated with this heatwave are also presented. The arrest of sea breeze, the hovering of diabatic heat because of subsidence is the factor that abetted the heatwave blockade over the south-eastern part of the country. The WRF model forecasts could present the occurance of the heat wave over AP and Telangana region with 72 hour lead time with high accuracy.

Abstract Estimation of probable maximum flood (PMF) is a crucial process in water resources management and in the design of large hydraulic structures. However, there are uncertainties in the estimation of hydrologic conditions that contribute to extreme floods. In particular, this is the case in snow‐dominated regions, as surface air temperature and wind speed are understood to have a substantial effect on the magnitude of a flood during a storm event. Motivated by the development of a new approach to investigate and estimate reliable PMF values and in an attempt to resolve the uncertainty issues, this study introduces a physically based modeling approach. For the case study, seven watersheds located in the Sierra‐Nevada mountain range of California, including Cosumnes, Mokelumne, Stanislaus, Tuolumne, Merced, Upper San Joaquin, and Upper Kings were selected. The hydroclimate model was first implemented over the physical boundaries of the study region, and then utilized to simulate possible maximum flood conditions with input from 10 extreme precipitation scenarios. The study results provide evidence of a nonlinear atmospheric–hydrologic system; the extreme 72‐h basin‐averaged precipitation depth was found not to be linearly proportional to 72‐h flow volume equivalent depth. It can also be concluded that a large precipitation depth may not be the sole reason for a large flood event. Temperature and other atmospheric variables also contribute significantly to the production of snowfall and liquid water available for runoff, and to the resulting hydrologic response, such as the flood peak discharge and volume.

In the warm season, El Niño/Southern Oscillation (ENSO) causes periods with more rain in Northern Mexico during its positive phase, while less rainfall is recorded in the southern regions during the negative phase. This research study evaluates the variability of summer (July–September) precipitation and moisture fluxes under different ENSO scenarios in the Mexican Altiplano and coast of the state of Nayarit. The catchment of Rio San Pedro-Mezquital (SPM-RB) connects both regions. Using the Oceanic Niño Index (ONI), the years that signal change from El Niño to La Niña (1998), neutral conditions (2005), and strong (moderate) La Niña (1999) were selected to get an insight of ENSO impact on summer precipitation. For anomalies in the Altiplano, two additional contrasting years were analyzed—2006 (mostly dry) and 2010 (wet)—to determine moisture sources. Summer rainfall conditions in 1998 and 1999 had an opposite behavior between coastal Nayarit (wet) and the Altiplano (dry), while in 2005, rainfall deficits were observed in both regions. The moisture fluxes showed large divergence areas over central Mexico and the Southeastern United States in years of intense drought (1998 and 1999) caused by two high-pressure cells at middle levels of the troposphere (500 hPa). The moisture transport mechanisms into the Altiplano were related to atmospheric circulation at the upper level (200 hPa). The variations of the moisture fluxes from 2006 to 2010 are less strong at middle levels. The Eastern Pacific moisture convergence along the western coast of Mexico favors above-average rainfall anomalies in the coastal region but below-average anomalies in the Altiplano.

Sea breezes are important in a coastal urban climate; however, the impact of urban development on the effects of sea breezes, which decrease air temperature and increase humidity, has not been understood quantitatively. To quantitatively evaluate this impact in Sendai, Japan over the past twenty years, this study analyzed the heat balance mechanisms in urban spaces based on the simulation results of the Weather Research and Forecasting (WRF) model coupled with Local Climate Zone (LCZ) maps. Compared to the observation data on air temperature, specific humidity, and wind in August 2002, results of the numerical simulation, using the 2002 LCZ map and the meteorological conditions of August 2002, confirmed that the WRF model could reproduce meteorological factors well. Thereafter, two numerical simulations using the LCZ maps from 2002 and 2022 were conducted based on the same meteorological condition, from 25 July to 1 September 2008, to extract the impact of urban development on the effects of sea breeze. Consequently, when land use changed from urban built-up land to natural land cover, both the effects of sea breeze—decreasing air temperature and increasing humidity—decreased. Additionally, increases in LCZ 3 (compact low rise), mainly from LCZ 6 (open low rise) and LCZ 9 (sparsely built), decreased the effects of sea breeze (decreasing air temperature and increasing humidity) by 5% and 10%, respectively, in areas around Sendai Station. This was because the consumption of the sea breeze’s potential to decrease air temperature and increase humidity increased and the wind speed of sea breezes decreased in the windward areas of Sendai Station. These results provide new insights into the impact of urban development on the effects of sea breeze and quantitatively reveal changes in the effects of sea breeze.

Unique data from a 100-m meteorological mast located on the windward side of the Dinaric Alps, Croatia, are compared to high-resolution Weather Research and Forecasting (WRF) model simulations. This was performed for an especially strong and long-lasting (more than 20 days) wintertime bora event. The agreement between the measurements on the mast and the respective WRF simulation was generally very good, even with respect to the time series of the turbulence kinetic energy. Based on this finding, which validates the WRF model suitability for numerical simulations of transient winds in windward areas, this approach can be used in future studies to explore the severe bora upwind of the coastal mountains, which has been studied inadequately thus far. In this context, some of the preliminary results are outlined here. [ABSTRACT FROM AUTHOR]

At the local and regional climate scale, one of the most studied environmental issues is urban heat island (UHI). UHI is a thermal anomaly caused by temperature differences between urban and rural settings, which adds heat to the atmosphere and makes people feel uncomfortable. This study explores the influence of new land-cover data on UHI simulations using the high-resolution Weather Research and Forecasting (WRF) model coupled with the single-layer urban canopy model (SLUCM) in the city of Harbin. A comparison was performed between the new Tsinghua University (TU) land cover dataset with the default United States Geological Survey (USGS) and Moderate Resolution Imaging Spectroradiometer (MODIS) land cover datasets. The results of this study revealed that the new TU land cover data had better representation and more realistic land cover changes than the default datasets. The diurnal, seasonal, and long-term nighttime UHIs of air and surface temperatures were higher than the daytime UHIs for both downtown Harbin and the satellite towns. We discovered that coal-burning during winter had a significant influence on UHI in Harbin. Moreover, the results from our buffer revealed a rapid increase in the UHIs of satellite towns, thus revealing the need to focus on the effects of UHI in satellite towns in the future. Therefore, the timely updating of land cover datasets in the WRF model and implementing mitigation strategies will help improve the urban climatic comfort. [ABSTRACT FROM AUTHOR]

Understanding the physical processes that affect the turbulent structure of the nocturnal urban boundary layer (UBL) is essential for improving forecasts of air quality and the air temperature in urban areas. Low-level jets (LLJs) have been shown to affect turbulence in the nocturnal UBL. We investigate the interaction of a mesoscale LLJ with the UBL during a 60-h case study. We use observations from two Doppler lidars and results from two high-resolution numerical-weather-prediction models (Weather Research and Forecasting model, and the Met Office Unified Model for limited-area forecasts for the U.K.) to study differences in the occurrence frequency, height, wind speed, and fall-off of LLJs between an urban (London, U.K.) and a rural (Chilbolton, U.K.) site. The LLJs are elevated (≈ 70 m) over London, due to the deeper UBL, while the wind speed and fall-off are slightly reduced with respect to the rural LLJ. Utilizing two idealized experiments in the WRF model, we find that topography strongly affects LLJ characteristics, but there is still a substantial urban influence. Finally, we find that the increase in wind shear under the LLJ enhances the shear production of turbulent kinetic energy and helps to maintain the vertical mixing in the nocturnal UBL. [ABSTRACT FROM AUTHOR]

Convection-permitting models (CPMs) enable the representation of meteorological variables at horizontal high-resolution spatial scales (≤ 4 km), where convection plays a significant role. In this regard, physical schemes need to be evaluated considering factors in the studied region such as orography and climate variability. This study investigates the sensitivity of the Weather Research and Forecasting (WRF) model as CPM to the use of different physics schemes on Andalusia, a complex orography region in the southern part of the Iberian Peninsula (IP). To do that, a set of 1-year WRF simulations was completed based on two "one-way" nested domains: the parent domain (d01) spanning the entire IP with 5 km spatial resolution and the nested domain (d02) for the region of Andalusia at 1 km of spatial resolution. 12 physic schemes were examined from combinations of microphysics (MP) schemes including THOMPSON, WRF single moment 6-class (WSM6), and WRF single moment 7-class (WSM7), and different options for the convection in d01, the Grell 3D (G3), Grell-Freitas (GF), Kain-Fritsch (KF), and deactivated cumulus parameterization (OFF). The simulated precipitation and 2-m temperature for the year 2018, characterized to be a very wet year, were compared with observational datasets from different sources to determine the optimal WRF configuration, including point-to-point and station-point comparisons at different time aggregations (from annual to hourly). In general, greater differences were shown when comparing the results of convection schemes in d01. Simulations completed with GF or OFF presented better performance compared to the reference datasets. Concerning the MP, although THOMPSON showed a better fit in high mountain areas, it generally presents a worse agreement with the reference datasets. In terms of temperature, the results were very similar and, therefore, the selection of the "best" configuration was based mainly on the precipitation results with the WSM7-GF scheme being suitable for Andalusia region. • WRF fits better to AEMET than CHIRPS, CMORPH, and IMERG over Andalusia. • The convection has a greater impact on simulations than microphysics in Andalusia. • WRF captures well the annual cycle of pr and temperature, with regional variations. • WRF shows high capability in representing extreme pr events. • WSM7-GF is optimal for WRF in simulating Andalusia's climate. [ABSTRACT FROM AUTHOR]

In this study, a scheme was developed to quantitatively assess the effectiveness of measures against hot outdoor environments under future climate conditions. In this scheme, each component of heat balance (i.e. advection, turbulent diffusion, anthropogenic heat release) were analyzed based on mesoscale simulations. By comparing the current and future values of the component of the heat balance corresponding to the measure being targeted, it is possible to clarify how the effectiveness of the measure changes. To illustrate the application of the scheme, it was applied to a coastal city (Sendai, Japan) and the advection component was analyzed to assess the effectiveness of introducing a sea breeze into a coastal city as a measure against hot outdoor environments under future climate conditions. Mesoscale simulations were conducted using WRF (Weather Research and Forecasting model) and pseudo global warming method was adopted to create boundary conditions. The increase in air temperature and humidity from the 2000s to the 2050s was more significant in the coastal area than in the inland areas, resulting in increased incoming sensible and latent heat fluxes generated by advection. It was quantitatively demonstrated that while it is currently effective to introduce the sea breeze as a measure against the hot outdoor environment in coastal cities, the effectiveness will not be as significant in the future. • A scheme to assess the measures against hot outdoor environments was developed. • Introducing a sea breeze into a city under future climate conditions was assessed. • In the future, the introduction of the sea breeze will not be as effective as current. [ABSTRACT FROM AUTHOR]

Urban artificial water dissipation is a concomitant process of human water use in built‐up areas that can absorb heat through evapotranspiration, reduce air temperature, transfer surface water to the atmosphere, and participate in the urban water cycle. In the context of increased urbanization, the impact of artificial water dissipation on urban climates cannot be ignored. In this study, calculation models for artificial water dissipation from different underlying surface types (buildings, hardened ground, soil, and vegetation) were introduced into an urban canopy model and coupled with the weather research and forecasting (WRF) model for mesoscale weather simulations of the Beijing area. Observational datasets of temperature and humidity from automatic weather stations in Beijing were used for validation. Results showed that the coupled model could reproduce the temperature and humidity of urban weather stations in Beijing more accurately compared to simulations that did not employ an urban canopy model or that adopted a traditional urban canopy model. In the Beijing urban area, the latent heat flux of NON, urban canopy model, and artificial water dissipation into the UCM are approximately 0, 35, and 65 W/m2, respectively, and the anthropogenic latent heat is about 30 W/m2. Incorporating urban artificial water dissipation into the urban canopy model reduced the urban air temperature by 0.2°C and increased the specific humidity by 0.6 g/kg, alleviating urban heat island effects and increasing humidity. These findings indicate that artificial urban water dissipation plays an important role in urban weather and climate that should be considered by urban canopy models. In the future, this model can be coupled into WRF for more accurate mesoscale weather simulations. [ABSTRACT FROM AUTHOR]

Due to climate change and associated longer and more frequent droughts, the risk of forest fires increases. To address this, the Institute of Meteorology and Water Management implemented a system for forecasting fire weather in Poland. The Fire Weather Index (FWI) system, developed in Canada, has been adapted to work with meteorological fields derived from the high-resolution (2.5 km) Weather Research and Forecasting (WRF) model. Forecasts are made with 24- and 48-h lead times. The purpose of this work is to present the validation of the implemented system. First, the results of the WRF model were validated using in situ observations from ~70 synoptic stations. Second, we used the correlation method and Eastaugh's percentile analysis to assess the quality of the FWI index. The data covered the 2019 fire season and were analysed for the whole forest area in Poland. Based on the presented results, it can be concluded that the FWI index (calculated based on the WRF model) has a very high predictive ability of fire risk. However, the results vary by region, distance from human habitats, and size of fire. [ABSTRACT FROM AUTHOR]

High Wind Speed Shutdown (HWSS) events pose a significant threat to power system security for grids with a high penetration of wind energy. To date, however, the forecasting of wind speed events has received limited attention in literature. This study proposes a hybrid WRF-ANN forecasting model to address the research objective of modelling wind speed events at turbine level, using meso-scale wind speed forecasts as inputs. A multi-model WRF ensemble model is used for the short-term meso-scale wind speed forecasts, whereas the ANN model uses the forecast as the input in order to predict the wind speed events for individual turbines. The performance of the model is evaluated for wind speed events which are derived using two wind speed thresholds for a large utility-scale wind farm. All significant events are accurately predicted by the model with few false alarms. [ABSTRACT FROM AUTHOR]