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... of all, we have realized experiments modifying physical options. A total of 18 experiments have been eva- luated progressively, as Table 1 shows. Two of them by varying microphysics schemes, four of them by varying radiation scheme options, three by varying cumulus scheme and eight of them by varying PBL and surface layer schemes (at the same time due to model restrictions). ...
Citations
... The motive behind the specific choices is given by Chin [31]. Arasa et al. [32] and Aligo et al. [33] also present detailed information on parametrization and vertical level selection. ...
The current work focuses on establishing the parameters that influence the wind’s behavior over the Aegean and Ionian Seas and estimating the wind potential in the region based on long-term historic climate data. Combining a downscaling technique performed with the well-founded WRF-ARW computational algorithm and a number of simultaneous meteorological station time series, an attempt is made to investigate how regional changes may affect low-altitude wind speed distribution at hub height (100 m a.s.l.). The provided time-series coastal data span the entire region of interest from north to south. WRF-ARW v.3.9 is utilized to associate the geostrophic wind distribution obtained from long-term Copernicus ERA5 wind data with the localized wind potential over lower altitudes. Evaluation and correlation of the observational data to the predicted wind climate are performed, and the statistical differences that arise are investigated. High-accuracy wind resource potential maps are thus obtained in the region. Also, a few distinctive flow patterns are identified, such as wind speed cut-off regions and very high wind speed distributions, which are presented in specific southern regions of the Aegean Sea.
... They performed sensitive analyses of the physical parameterization options used in the NWP, and found the optimum combination of schemes of the options for their purpose. Arasa et al. 15) and Verbois et al. 16) also employed the same analysis and improved the weather forecasting in southern Spain and Singapore, respectively. The authors focused on solar irradiance forecasting in Thailand and found the optimal schemes with the same analysis as them. ...
As photovoltaic (PV) power generation systems become more widespread instability of the electric power grids with PV connection is becoming an issue. For appropriate management of the grids, probability prediction of solar irradiance is proposed. Lagged Average Forecast (LAF) method is used for ensemble forecasting. The 72-hour ahead forecasting of solar irradiance is operated in Thailand once a day, and it contains intra-day, next-day and 2-day ahead forecastings. The ensemble forecasting has three ensemble members. The accuracy of intra-day forecasting is higher than the other members, and it is employed as the most probable value of the forecasting. The relation between spread and forecasting error is analyzed. From the result, the confidence intervals of the predictions are derived for arbitrary reliability. The probability prediction is performed with the most probable value and the confidence intervals. The interval changes its width due to spread changes and captures the observation in it.
... Various schemes are available for each option, and appropriate schemes are selected and used for accurate weather forecasting according to the target meteorological parameter, target region and other computational conditions. 15,16) Baki et al. 17,18) Chinta et al. 19,20) Di et al. 21) and Mohanty et al. 22) mentioned the impact of the different meteorological data used as initial and boundary conditions of the simulation to the simulation results and their accuracy, and pointed out that the performance of NWP models depends on the quality, reliability and representativeness of the initial values and lateral boundary conditions. 22) Consequently, seeking the appropriate combination of schemes for the parameterization option is important. ...
... Seeking the appropriate combination of schemes is performed and reported by researchers and operators of weather forecasting. Arasa et al. 15) performed NWP computations repeatedly by changing schemes of six parameterization options, and found their optimal combination for weather forecasting in southern Spain. Their objective variable is wind on the ground, but temperature and relative humidity on the ground are also improved by the optimization. ...
... 3.1. Seeking optimal combination of schemes of physical parameterization options We seek the optimal combination of schemes of physical parameterization options in a similar way to Arasa et al. 15) A total of 20 simulations with different schemes have been performed progressively. The combinations of schemes for the simulations are listed in Table II the options PBL, surface layer, cumulus, shortwave radiation, longwave radiation and microphysics, respectively. ...
Many Photovoltaic (PV) systems are connected to electric power grids, and the grids get risk of instability due to the fluctuations of PV output. Numerical Weather Prediction (NWP) models are used for forecasting the solar irradiance and proper grid management. NWPs usually have many physical parameterization options, and appropriate schemes of the options should be selected for accurate forecasting. The options should be changed by regional and climatic conditions and other factors. The target country is Thailand which is in the tropics. At there, cumulus and cumulonimbus appear frequently, and their behavior makes weather forecasting difficult. The optimal combination of schemes in the tropics is found through the sensitivity analysis of the options. By the optimization, the forecasting accuracy increases from 0.773 to 0.814 in correlation coefficient with the observation. It’s also found that contributions of surface layer and planetary boundary layer processes are significant for improvement of accuracy.
... It is worth noting that in sensitivity studies, there are uncertainties in determining the threshold of values to determine if the model is performing well. In this study, the recommended values in Table 3, as adopted from the studies [49][50][51][52], will be used as a reference. Table 3. Recommended values of statistical tests for the near surface variables adopted in this study. ...
This present study aims to determine the performance of using the Weather Research Forecasting (WRF) Model, coupled with the Urban Canopy Models (UCMs), in simulating the 2-m air temper-ature and 2-m relative humidity in Metro Manila. The simulation is done from April 22-29, 2018, which coincides with the dry season in the Philippines. Four model options were used in this study, which includes the bulk (no urban), SLUCM, BEP, and BEM. After model validation, the Urban Heat Island (UHI) is then characterized to determine the spatial-temporal variations in the cities of Metro Manila. Statistical results show that the WRF simulation for 2-m air temperature agrees with measurement with a RMSE of < 3.0C, mean bias error of < 2.0C, and index of agreement of >0.80. WRF simulation for relative humidity still presents a challenge where simula-tion errors are higher than the acceptable range. The addition of UCMs does not necessarily im-prove the simulation for 2-m air temperature, while the use of BEP improved 2-m relative humidi-ty simulation. Result shows the importance of using actual urban morphology values in WRF to accurately simulate near-surface variables. On the other hand, WRF simulation shows the pres-ence of urban heat island, notably in the northwest and central area of Metro Manila during day-time, extending throughout Metro Manila during nighttime. Lower air temperature was consist-ently observed in areas near Laguna Lake, while higher air temperature due to stagnant winds was observed in the northwest area of Metro Manila. High heat index was also observed throughout Metro Manila from daytime until nighttime especially in areas near bodies of water like Manila Bay and Laguna Lake due to high humidity.
... For this purpose, the typically accepted logarithmic wind profile method [39][40][41] was applied to extrapolate the measured winds by the buoy from 3 to 10 m over the sea. Although there are several methods and variations used for this purpose, e.g., stress equivalent winds [42] that consider the air mass density and stability, for practical reasons, here we used the logarithmic wind profile method, which is suitable for our aim, requiring only WS and WD measurements and proven to be consistent over the first 30 m of sea surface [43,44]. ...
... The dynamical setup of the simulation was based on the optimised design presented by [45] after performing 4150 daily simulations over southern Spain (Table 2). Unlike previous studies in the area [40,44,45], which consider constant sea surface temperature (SST), in our study, the SST was updated every 6 h. Although the overall impact is expected to be small, it is a more realistic approach and might have an impact under specific conditions or in specific areas [46]. ...
... Prior to the comparisons, the WRF wind data were linearly interpolated to the position of in situ instruments, as well as to the S3A/B along-track measurement positions. Several statistical parameters were used to compare the wind speed and direction from the altimeter and model, according to previous studies [44,47,48]. Root mean square error (RMSE) (1), normalised root mean square error (NRMSE) (2), bias (3), and Pearson's correlation coefficient (r) (4) were used to evaluate wind speed, while bias and standard deviation error (STDE) (5) were applied to the wind direction comparisons results. ...
This work presents the quality performance and the capabilities of altimetry derived wind speed (WS) retrievals from the altimeters on-board Copernicus satellites Sentinel-3A/B (S3A/B) for the spatial assessment of WS outputs from the weather research and forecasting (WRF) model over the complex area of the Gulf of Cádiz (GoC), Spain. In order to assess the applicability of the altimetry data for this purpose, comparisons between three different WS data sources over the area were evaluated: in situ measurements, S3A/B 20 Hz altimetry data, and WRF model outputs. Sentinel-3A/B WS data were compared against two different moored buoys to guarantee the quality of the data over the GoC, resulting in satisfying scores (average results: RMSE = 1.21 m/s, r = 0.93 for S3A and RMSE = 1.36 m/s, r = 0.89 for S3B). Second, the WRF model was validated with in situ data from four different stations to ensure the correct performance over the area. Finally, the spatial variability of the WS derived from the WRF model was compared with the along-track altimetry-derived WS. The analysis was carried out under different wind synoptic conditions. Qualitative and quantitative results (average RMSE < 1.0 m/s) show agreement between both data sets under low/high wind regimes, proving that the spatial coverage of satellite altimetry enables the spatial assessment of high-resolution numerical weather prediction models in complex water-covered zones.
... The WRF model was used to generate a regional atmospheric simulation on a 10 km × 10 km grid, covering the eastern part of the Mediterranean Sea, with 179 × 179 grid cells centered over Lebanon's mountains (Fig. 1). In the vertical dimension, the domain was composed of 35 levels, with the top set to 50 hPa, similar to other studies over Mediterranean regions (Arasa et al., 2016). The simulation covers the period from 1 January 2010 to 30 June 2017, using the first 9 months as spin-up period allowing for physical equilibrium between the external forcings and the land model (Montavez et al., 2017). ...
The snowpack over the Mediterranean mountains constitutes a key water resource for the downstream populations. However, its dynamics have not been studied in detail yet in many areas, mostly because of the scarcity of snowpack observations. In this work, we present a characterization of the snowpack over the two mountain ranges of Lebanon. To obtain the necessary snowpack information, we have developed a 1 km regional-scale snow reanalysis (ICAR_assim) covering the period 2010–2017. ICAR_assim was developed by means of an ensemble-based data assimilation of Moderate Resolution Imaging Spectroradiometer (MODIS) fractional snow-covered area (fSCA) through an energy and mass snow balance model, the Flexible Snow Model (FSM2), using the particle batch smoother (PBS). The meteorological forcing data were obtained by a regional atmospheric simulation from the Intermediate Complexity Atmospheric Research model (ICAR) nested inside a coarser regional simulation from the Weather Research and Forecasting model (WRF). The boundary and initial conditions of WRF were provided by the ERA5 atmospheric reanalysis. ICAR_assim showed very good agreement with MODIS gap-filled snow products, with a spatial correlation of R=0.98 in the snow probability (P(snow)) and a temporal correlation of R=0.88 on the day of peak snow water equivalent (SWE). Similarly, ICAR_assim has shown a correlation with the seasonal mean SWE of R=0.75 compared with in situ observations from automatic weather stations (AWSs). The results highlight the high temporal variability in the snowpack in the Lebanese mountain ranges, with the differences between Mount Lebanon and the Anti-Lebanon Mountains that cannot only be explained by hypsography as the Anti-Lebanon Mountains are in the rain shadow of Mount Lebanon. The maximum fresh water stored in the snowpack is in the middle elevations, approximately between 2200 and 2500 m a.s.l. (above sea level). Thus, the resilience to further warming is low for the snow water resources of Lebanon due to the proximity of the snowpack to the zero isotherm.
... The resolutions were defined to follow a ratio of 1:3 between them and the ERA5 mother data resolution, as suggested by the literature (Dudhia and Wang, 2014). The simulation parameters (Table SM4) were chosen following the guidelines published in sensitivity studies in the area (Arasa et al., 2016). ...
Two radon measurement stations located to the north and south of a NORM (Naturally Occurring Radioactive Materials) repository of phosphogypsum (southwest of Europe) were used to monitor radon behavior during 2018. The stations are located at opposing sides of the repository, one in Huelva City to the north and other one in a rural area to the south. This setup aimed to identify the influence of the NORM repository on each station and use radon levels as a marker of atmospheric transport in the local area. To achieve this, a comparison was carried out with other coastal stations in the south of Spain, finding higher average concentrations in Huelva City, ∼3.3 Bq m⁻³. Hierarchical clustering was applied to identify days with different radon patterns at each Huelva station, detecting possible local radon transport events from the repository. Three events were investigated with WRF (Weather Research and Forecasting) and FLEXPART-WRF (FLEXible PARTicle dispersion model). It was found that both sampling sites required atmospheric stagnant conditions to reach high radon concentration. However, under these conditions the urban station showed high radon regardless of wind direction while the rural station also required radon transport from the repository, either directly or indirectly.
... For WRF results, the R values of T2 and RH2 are 0.98 and 0.88 (p < 0.05), respectively, and 0.99 and 0.94 for IA. The IA of T2 meets the statistical benchmark for the temperature (≥0.8) [34]. The performance of T2 and RH2 is better than that of WS and PRE. ...
... The RMSEs of WS and WD reach 1.70 m s −1 and 69.79 • , and the HRs are 26.78% and 76.78%, respectively. Though the IA of WS meets the statistical benchmark (≥0.6) suggested by previous study [34], WRF significantly overestimates WS based on variance analysis (p < 0.05), with MB is 1.50 m s −1 . Moreover, the observed STD of WS is smaller than the simulated STD, indicating that WRF overestimates the fluctuation range of WS. ...
... For T2 and RH2, the simulations of nine circulation types all meet the statistical benchmark (IA > 0.8) [34], and their MBs have small values, indicating that the WRF model could basically simulate the temperature and relative humidity characteristics of different circulation types. However, relative to other types of circulation, the simulation results under CT1, CT4, and CT7 are relatively poor. ...
In this study, National Centers for Environmental Prediction (NCEP) Final (FNL) operational global analysis data and meteorological observation data from 2013 to 2017 were used to evaluate the impact of seasonal changes and different circulation classifications on the dynamical downscaling simulation results of Weather Research and Forecasting (WRF) in the Pearl River Delta (PRD) region. The results show that the dynamical downscaling method can accurately simulate the time variation characteristics of the near-surface meteorological field and the hit rates of a 2-m temperature, 2-m relative humidity, 10-m wind speed, and 10-m wind direction are 92.66%, 93.98%, 26.78%, and 76.78%, respectively. The WRF model slightly underestimates the temperature and relative humidity, and overestimates the wind speed and precipitation. For precipitation, the WRF model can better simulate the variation characteristics of light rain and heavy rain, with the probability of detection are 0.59 and 0.69, respectively. For seasonal factors, the WRF model can conduct a perfect simulation in autumn and winter, followed by spring, while summer is vulnerable to extreme weather, so the result of the simulation is relatively poor. The circulation type is an important parameter of downscaling assessment. When the PRD is controlled by high pressure, the simulated results of WRF are good, and when the PRD is affected by low pressure or extreme weather, the simulation results are relatively poor.
... In addition, different techniques have been used in Spain in order to predict the precipitation caused by MCSs using the limited area model Weather Research and Forecasting (WRF) described in Skamarock et al. (2008). Mercader (2010) ran the WRF model with different microphysical combinations, Trapero et al. (2013) studied the influence of the Pyrenees mountain range on the precipitation processes using the WRF model with a high resolution in Catalonia and Arasa et al. (2016) employed the WRF model with assimilation of soundings and irradiance (from satellites) in the Port of Huelva. Nevertheless, we are not aware of previous studies carried out in Spain with radar data assimilation. ...
This study uses the Weather Research and Forecasting model (WRF) and the three-dimensional variational data assimilation system (WRF 3DVAR), in cold and warm starts, with the aim of finding out an appropriate nowcasting method that would have improved the forecast of precipitation maxima in the mesoscale convective system that occurred in Catalonia (NE Spain) on March 21, 2012 at 20 UTC. We assimilated radar data using different configurations, qualitatively verifying the increase of rainwater produced by the assimilation of reflectivity. While in cold starts the best result was obtained with a length scale of 0.75, in warm starts it was necessary to use a length scale of 0.25. We got better results in all cases when radar data assimilation was used, and although one of the cold starts achieved the best result and correctly located precipitation maxima, the forecast amount was still lower than the observations. © 2018, Associacio Catalana de Meteorologia. All rights reserved.
