Search Results (35)

Recent trends of intense forest fires in the Korean Peninsula have increased concerns about more extreme burning in the future under a warming climate. Accurate and reliable fire weather information has become more critical to reduce the risk of forest-related disasters over complex terrain. In this study, two parameterizations reflecting complex topographic effects were implemented in the Weather Research and Forecasting (WRF) model. The model performance was evaluated over the mountainous region in Gangwon-do, South Korea’s most significant forest area. The simulation results of the wildfire case in 2019 show that subgrid-scale orographic parameterization considerably improves model performance regarding wind speed, with a lower root mean square error (RMSE) and bias by 53% and 57%, respectively. Another parameterization, reflecting slope and shading, effectively reflected sunrise and sunset effects. The second parametrization produced little effect on the daily averages of meteorological elements. However, thermodynamic components such as temperature and heat flux show more realistic values during sunset or sunrise when the solar altitude angle is low. The results imply that applying topographic parameterizations is required in numerical simulations, especially for hazardous weather conditions over complex terrain in mountainous regions. Full article

The northern slope of the Tibetan Plateau (TP) is the crucial affected area for dust originating from the Taklimakan Desert (TD). However, few studies have focused on the meteorological element responses to TD dust over different surface types near the TP. Satellite data and the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) were used to analyze the dust being transported from the TD to the TP and its effect from 30 July to 2 August 2016. In the TD, the middle-upper dust layer weakened the solar radiation reaching the lower dust layer, which reduced the temperature within the planetary boundary layer (PBL) during daytime. At night, the dust’s thermal preservation effect increased temperatures within the PBL and decreased temperatures at approximately 0.5 to 2.5 km above PBL. In the TP without snow cover, dust concentration was one-fifth of the TD, while the cooling layer intensity was comparable to the TD. However, within the PBL, the lower concentration and thickness of dust allowed dust to heat atmospheric continuously throughout the day. In the TP with snow cover, dust diminished planetary albedo, elevating temperatures above 6 km, hastening snow melting, which absorbed latent heat and increased the atmospheric water vapor content, consequently decreasing temperatures below 6 km. Surface meteorological element responses to dust varied significantly across different surface types. In the TD, 2 m temperature (T₂) decreased by 0.4 °C during daytime, with the opposite nighttime variation. In the TP without snow cover, T₂ was predominantly warming. In the snow-covered TP, T₂ decreased throughout the day, with a maximum cooling of 1.12 °C and decreased PBL height by up to 258 m. Additionally, a supplementary simulation of a dust event from 17 June to 19 June 2016 further validated our findings. The meteorological elements response to dust is significantly affected by the dust concentration, thickness, and surface type, with significant day–night differences, suggesting that surface types and dust distribution should be considered in dust effect studies to improve the accuracy of climate predictions. Full article

The accurate estimation of solar radiation is crucial for optimizing solar energy deployment and advancing the global energy transition. This study pioneers the development of a hybrid model combining Recurrent Neural Networks (RNNs) and the Weather Research and Forecasting (WRF) model to estimate solar radiation in tropical regions characterized by scarce and low-quality data. Using datasets from Sumedang and Jakarta across five locations in West Java, Indonesia, the RNN model achieved moderate accuracy, with R² values of 0.68 and 0.53 and RMSE values of 159.87 W/m² and 125.53 W/m², respectively. Additional metrics, such as Mean Bias Error (MBE) and relative MBE (rMBE), highlight limitations due to input data constraints. Incorporating spatially resolved GHI data from the WRF model into the RNN framework significantly enhanced accuracy under both clear and cloudy conditions, accounting for the region’s complex topography. While the results are not yet comparable to best practices in data-rich regions, they demonstrate promising potential for advancing solar radiation modeling in tropical climates. This study establishes a critical foundation for future research on hybrid solar radiation estimation techniques in challenging environments, supporting the growth of renewable energy applications in the tropics. Full article

The urbanization effect (UE) on local or regional climate is a prominent research topic in the research field of urban climates. However, there is little research on the UE of Urumqi, a typical arid region city, concerning various climatic factors and their spatio–temporal characteristics. This study quantitatively investigates the UE of Urumqi on multiple climatic factors in summer based on a decade-long period of WRF–UCM (Weather Research and Forecasting model coupled with the Urban Canopy Model) simulation data. The findings reveal that the UE of Urumqi has resulted in a reduction in the diurnal temperature range (DTR) within the urban area by causing an increase in night-time minimum temperatures, with the maximum decrease reaching −2.5 °C. Additionally, the UE has also led to a decrease in the water vapor mixing ratio (WVMR) and relative humidity (RH) at 2 m, with the maximum reductions being 0.45 g kg⁻¹ and −6.5%, respectively. Furthermore, the UE of Urumqi has led to an increase in planetary boundary layer height (PBLH), with a more pronounced effect in the central part of the city than in its surroundings, reaching a maximum increase of over 750 m at 19:00 Local Solar Time (LST, i.e., UTC + 6). The UE has also resulted in an increase in precipitation in the northern part of the city by up to 7.5 mm while inhibiting precipitation in the southern part by more than 6 mm. Moreover, the UE of Urumqi has enhanced precipitation both upstream and downstream of the city, with a maximum increase of 7.9 mm. The UE of Urumqi has also suppressed precipitation during summer mornings while enhancing it in summer afternoons. The UE has exerted certain influences on the aforementioned climatic factors, with the UE varying across different directions for each factor. Except for precipitation and PBLH, the UE on the remaining factors exhibit a greater magnitude in the northern region compared to the southern region of Urumqi. Full article

Statistical characteristics of atmospheric circulation patterns (ACPs) and associated diurnal variation characteristics (DVCs) of precipitation in summer (June–August) from 2015 to 2019 over the complex terrain in northern Xinjiang (NX), northwestern arid region of China, were investigated based on NCEP FNL reanalysis data and Weather Research and Forecasting model simulation data from Nanjing University (WRF-NJU). The results show that six different ACPs (Type 1–6) were identified based on the Simulated ANealing and Diversified RAndomization (SANDRA), exhibiting significant differences in major-influencing synoptic systems and basic meteorological environments. Types 5, 3, and 2 were the most prevalent three patterns, accounting for 21.6%, 19.7%, and 17.7%, respectively. Type 5 mainly occurred in June and July, while Types 3 and 2 mainly occurred in August and July, respectively. From the perspective of DVCs, Type 1 reached its peak at midnight, while Type 5 was most frequent in the afternoon and morning. The overall DVCs of hourly precipitation intensity and frequency demonstrated a unimodal structure, with a peak occurring at around 16 Local Solar Time (LST). Basic meteorological elements in various terrain regions exhibit significant diurnal variation, with marked differences between mountainous and basin areas under different ACPs. In Types 3 and 6, meteorological elements significantly influence precipitation enhancement by promoting the convergence and uplift of low-level wind fields and maintaining high relative humidity (RH). The Altay Mountains region and Western Mountainous regions experience dominant westerly winds under these conditions, while the Junggar Basin and Ili River Valley regions benefit from counterclockwise water vapor transport associated with the Iranian Subtropical High in Type 6, which increases RH. Collectively, these factors facilitate the formation and development of precipitation. Full article

Northwestern Mexico has a desert climate with high solar resources. Clear skies and low humidity during most of the year favor their use. In winter, the arrival of cold air masses from the polar latitudes cause instability and abrupt changes in atmospheric variables, increasing the error of short-term forecasts. This work focuses on the evaluation of the Weather Research and Forecasting (WRF) model for predicting the global horizontal irradiance (GHI), considering different parameterizations of shortwave and longwave solar radiation during the influence of five cold fronts that affected the desert region of northwestern Mexico. The simulation was carried out under four main shortwave configurations and the results were evaluated with surface measurements and compared with climate information from NASA-POWER. The GHI predicted with the Dudhia parameterization showed an overestimation of the WRF model during most of the analyzed events; the most accurate predictions obtained correlation values between 0.85 and 0.91 and a mean absolute error between 15 and 45 W m⁻². In periods where intermittent clouds prevailed, the mean error increased by almost 20%. An evaluation of the different proposed configurations shows advantages with the shortwave Dudhia and longwave RRTM parameterizations, providing a useful meteorological tool for predicting short-range variations in the GHI to improve the operability of solar power generation systems. Full article

This study evaluates nine Planetary Boundary Layer (PBL) turbulence parameterization schemes from the Weather Research and Forecasting (WRF) mesoscale meteorological model, comparing hourly values of meteorological variables observed and simulated at the surface of the Metropolitan Region of São Paulo (MRSP). The numerical results were objectively compared with high-quality observations carried out on three micrometeorological platforms representing typical urban, suburban, and rural land use areas of the MRSP, during the 2013 summer and winter field campaigns as part of the MCITY BRAZIL project. The main objective is to identify which PBL scheme best represents the diurnal evolution of conventional meteorological variables (temperature, relative and specific humidity, wind speed, and direction) and unconventional (sensible and latent heat fluxes, net radiation, and incoming downward solar radiation) on the surface. During the summer field campaign and over the suburban area of the MRSP, most PBL scheme simulations exhibited a cold and dry bias and overestimated wind speed. They also overestimated sensible heat flux, with high agreement index and correlation values. In general, the PBL scheme simulations performed well for latent heat flux, displaying low mean bias error and root square mean error values. Both incoming downward solar radiation and net radiation were also accurately simulated by most of them. The comparison of the nine PBL schemes indicated the local Mellor-Yamada-Janjic (MYJ) scheme performed best during the summer period, particularly for conventional meteorological variables for the land use suburban in the MRSP. During the winter field campaign, simulation outcomes varied significantly based on the site’s land use and the meteorological variable analyzed. The MYJ, Bougeault-Lacarrère (BouLac), and Mellor-Yamada Nakanishi-Niino (MYNN) schemes effectively simulated temperature and humidity, especially in the urban land use area. The MYNN scheme also simulated net radiation accurately. There was a tendency to overestimate sensible and latent heat fluxes, except for the rural land use area where they were consistently underestimated. However, the rural area exhibited superior correlations compared to the urban area. Overall, the MYJ scheme was deemed the most suitable for representing the convectional and nonconventional meteorological variables on the surface in all urban, suburban, and rural land use areas of the MRSP. Full article

Cumulus parameterization schemes model the subgrid-scale effects of moist convection, affecting the prognosis of cloud formation, rainfall, energy levels reaching the surface, and air quality. Working with a spatial resolution of 1 km, we studied the influence of cumulus parameterization schemes coded in the Weather Research and Forecasting with Chemistry Version 3.2 (WRF-Chem 3.2) for modeling in an Andean city in Southern Ecuador (Cuenca, 2500 masl), during September 2014. To assess performance, we used meteorological records from the urban area and stations located mainly over the Cordillera, with heights above 3000 masl, and air quality records from the urban area. Firstly, we did not use any cumulus parameterization (0 No Cumulus). Then, we considered four schemes: 1 Kain–Fritsch, 2 Betts–Miller–Janjic, 3 Grell–Devenyi, and 4 Grell-3 Ensemble. On average, the 0 No Cumulus option was better for modeling meteorological variables over the urban area, capturing 66.5% of records and being the best for precipitation (77.8%). However, 1 Kain–Fritsch was better for temperature (78.7%), and 3 Grell–Devenyi was better for wind speed (77.0%) and wind direction (37.9%). All the options provided acceptable and comparable performances for modeling short-term and long-term air quality variables. The results suggested that using no cumulus scheme could be beneficial for holistically modeling meteorological and air quality variables in the urban area. However, all the options, including deactivating the cumulus scheme, overestimated the total amount of precipitation over the Cordillera, implying that its modeling needs to be improved, particularly for studies on water supply and hydrological management. All the options also overestimated the solar radiation levels at the surface. New WRF-Chem versions and microphysics parameterization, the other component directly related to cloud and rainfall processes, must be assessed. In the future, a more refined inner domain, or an inner domain that combines a higher resolution (less than 1 km) over the Cordillera, with 1 km cells over the urban area, can be assessed. Full article

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. Full article

An accurate forecast of Global Horizontal solar Irradiance (GHI) and Direct Normal Irradiance (DNI) in cloudy conditions remains a major challenge in the solar energy industry. This study focuses on the impact of cloud microphysics on GHI and its partition into DNI and Diffuse Horizontal Irradiance (DHI) using the Weather Research and Forecasting model specifically designed for solar radiation applications (WRF-Solar) and seven microphysical schemes. Three stratocumulus (Sc) and five shallow cumulus (Cu) cases are simulated and evaluated against measurements at the US Department of Energy’s Atmospheric Radiation Measurement (ARM) user facility, Southern Great Plains (SGP) site. Results show that different microphysical schemes lead to spreads in simulated solar irradiance components up to 75% and 350% from their ensemble means in the Cu and Sc cases, respectively. The Cu cases have smaller microphysical sensitivity due to a limited cloud fraction and smaller domain-averaged cloud water mixing ratio compared to Sc cases. Cloud properties also influence the partition of GHI into DNI and DHI, and the model simulates better GHI than DNI and DHI due to a non-physical error compensation between DNI and DHI. The microphysical schemes that produce more accurate liquid water paths and effective radii of cloud droplets have a better overall performance. Full article

Climate change poses a significant threat to humanity. Achieving net-zero emissions is a key goal in many countries. Among various energy resources, solar power generation is one of the prominent renewable energy sources. Previous studies have demonstrated that post-processing techniques such as bias correction can enhance the accuracy of solar power forecasting based on numerical weather prediction (NWP) models. To improve the post-processing technique, this study proposes a new day-ahead forecasting framework that integrates weather research and forecasting solar (WRF-Solar) irradiances and the total solar power generation measurements for five cities in northern, central, and southern Taiwan. The WRF-Solar irradiances generated by the Taiwan Central Weather Bureau (CWB) were first subjected to bias correction using the decaying average (DA) method. Then, the effectiveness of this correction method was verified, which led to an improvement of 22% in the forecasting capability from the WRF-Solar model. Subsequently, the WRF-Solar irradiances after bias correction using the DA method were utilized as inputs into the transformer model to predict the day-ahead total solar power generation. The experimental results demonstrate that the application of bias-corrected WRF-Solar irradiances enhances the accuracy of day-ahead solar power forecasts by 15% compared with experiments conducted without bias correction. These findings highlight the necessity of correcting numerical weather predictions to improve the accuracy of solar power forecasts. Full article

Accurately predicting the power produced during solar power generation can greatly reduce the impact of the randomness and volatility of power generation on the stability of the power grid system, which is beneficial for its balanced operation and optimized dispatch and reduces operating costs. Solar PV power generation depends on the weather conditions, such as temperature, relative humidity, rainfall (precipitation), global solar radiation, wind speed, etc., and it is prone to large fluctuations under different weather conditions. Its power generation is characterized by randomness, volatility, and intermittency. Recently, the demand for further investigation into the uncertainty of short-term solar PV power generation prediction and its effective use in many applications in renewable energy sources has increased. In order to improve the predictive accuracy of the output power of solar PV power generation and develop a precise predictive model, the authors used predictive algorithms for the output power of a solar PV power generation system. Moreover, since short-term solar PV power forecasting is an important aspect of optimizing the operation and control of renewable energy systems and electricity markets, this review focuses on the predictive models of solar PV power generation, which can be verified in the daily planning and operation of a smart grid system. In addition, the predictive methods identified in the reviewed literature are classified according to the input data source, and the case studies and examples proposed are analyzed in detail. The contributions, advantages, and disadvantages of the predictive probabilistic methods are compared. Finally, future studies on short-term solar PV power forecasting are proposed. Full article

The present study is aimed at the development of a novel empirical base for application to ground-based astronomical telescopes. A Weather Research and Forecasting (WRF) model is used for description of atmospheric flow structure with a high spatial resolution within the Baikal Astrophysical Observatory (BAO) region. Mesoscale vortex structures are found within the atmospheric boundary layer, which affect the quality of astronomical images. The results of simulations show that upward air motions in the lower atmosphere are suppressed both above the cold surface of Lake Baikal and inside mesoscale eddy structures. A model of the outer scale of turbulence for BAO is developed. In this work, we consider the seeing parameter that represents the full width at half-maximum of the point spread function. Optical turbulence profiles are obtained and daytime variations of seeing are estimated. Vertical profiles of optical turbulence are optimized taking into account data from direct optical observations of solar images. Full article

The amount of solar irradiation that reaches Earth’s surface is a key quantity of solar energy research and is difficult to predict, because it is directly affected by the changing constituents of the atmosphere. The numerical weather prediction (NWP) model performs computational simulations of the evolution of the entire atmosphere to forecast the future state of the atmosphere based on the current state. The Weather Research and Forecasting (WRF) model is a mesoscale NWP. WRF solar is an augmented feature of WRF, which has been improved and configured specifically for solar energy applications. The aim of this paper is to evaluate the performance of the high resolution WRF solar model and compare the results with the low resolution WRF solar and Global Forecasting System (GFS) models. We investigate the performance of WRF solar for a high-resolution spatial domain of resolution 1 × 1 km and compare the results with a 3 × 3 km domain and GFS. The results show error metrices rMAE {23.14%, 24.51%, 27.75%} and rRMSE {35.69%, 36.04%, 37.32%} for high resolution WRF solar, coarse domain WRF solar and GFS, respectively. This confirms that high resolution WRF solar performs better than coarse domain and in general. WRF solar demonstrates statistically significant improvement over GFS. Full article

This article discusses the quality of astronomical images under conditions of moderate small-scale turbulence and varying meso-scale airflows above the Baikal Astrophysical Observatory (BAO). We applied a Weather Research and Forecasting (WRF) Model, as well as statistical estimations of the Fried parameter from the differential motion of the solar images. The simulations were performed with a fairly high horizontal resolution within a large area of 1600 × 1600 km. A high horizontal resolution provides representative estimations of atmospheric characteristics and correct accounting of large-scale air advection. We considered the influence of atmospheric motions over the cold water area of Lake Baikal, as well as meso-scale vortex structures over rough terrain on solar image quality. A better understanding of structured turbulent small-scale motions and optical turbulence over rough terrain may help to develop advanced methods for diagnostics and prediction of image quality. For the first time, we have shown that the BAO is located at the periphery of a meso-scale atmospheric vortex structure with an anticyclonic direction of airflows in the daytime. An increase in image quality was associated with weakening airflows over Lake Baikal and a decrease in the intensity of wind speed fluctuations. Calculated spectra of atmospheric turbulence in the daytime were close to the classical form. At night and in the morning, the spectra had a steeper slope on small scales. Deformations of the spectra were due to the suppression of turbulence under stable stratification of the atmosphere. The characteristic horizontal scales of the transition from “−5/3” to ∼“−3” spectral slope were 2–2.5 km. The results obtained using the WRF model and analysis of optical turbulence strength (namely, the Fried parameter) indicated that the parameterization schemes used in the WRF model were accurate. Full article