WRF Meteorological Model Research Articles

Wildfire-smoke-precipitation interactions in Siberia: Insights from a regional model study

Powerful wildfires occurring in Siberia each summer emit large amounts of smoke aerosol that, according to studies of the environmental impacts of biomass burning (BB) aerosol in different regions of the world, can affect precipitation and other weather parameters and induce feedback on fires. However, the knowledge of smoke-weather interactions and fire-weather feedback in Siberia is presently limited. To advance this knowledge, we performed coupled-meteorology-chemistry simulations of aerosols and weather in a Siberian region covering taiga and tundra using the CHIMERE chemistry-transport model and the WRF meteorological model. We addressed a monthly period of July 2016 and considered several modeling scenarios in which aerosol-radiation interaction (ARI) and aerosol-cloud interaction (ACI) were taken into account jointly or separately. The simulation results were combined with emission and precipitation data retrieved from satellite observations. The joint analysis of the simulated precipitation fields and satellite-observation-based data revealed that in the taiga, the inhibiting effect of Siberian smoke on precipitation induced a significant positive feedback on BB aerosol emissions that, according to our estimates, enhanced by 27 (±7) % respective to a hypothetical situation in which smoke-weather interactions were absent. At the same time, an increase of precipitation over active fire spots due to ACI and ARI in tundra led to the formation of a negative feedback loop between fire emissions and BB smoke, resulting in a reduction of BB aerosol emissions there by 14 (±6) %. Hence, this study revealed evidence for significant feedback of smoke-induced precipitation changes on fire emissions in Siberia. Given the global importance of Siberia as a major carbon sink, this feedback needs to be studied further and accurately taken into account in projections of climate change both on regional and global scales.

Application of the CHIMERE-WRF Model Complex to Study the Radiative Effects of Siberian Smoke Aerosol in the Eastern Arctic

We describe a computational technology for studying the effects of the aerosol-radiation interaction and calculating regional estimates of the direct (DRE) and semidirect radiative effects (SDRE) of biomass burning (BB) aerosol based on simulations with the CHIMERE chemistry transport model coupled with the WRF meteorological model. The technology was applied to numerical studying the radiative effects of Siberian biomass burning aerosol in the eastern Arctic in the period of July 16–31, 2016. The model simulations show that Siberian smokes, on the whole, had a significant cooling effect on the atmosphere in the eastern Arctic in that period due to the DRE, the value of which at top of the atmosphere was, on average, −6.0 W m−2, being minimal over the snow-ice cover of the ocean (−1.2 W m−2). At the same time, the contribution of the Siberian BB aerosol DRE to the radiative balance of the Arctic atmosphere is found to be offset to a certain extent by the SDRE, which is positive (2.0 W m−2), on average. The SDRE is formed as a result of the multihour aerosol effect on meteorological processes and plays the most important role over the snow-ice Arctic covers, where it exceeds the DRE in absolute value. It has been shown that the SDRE of Siberian BBA in our numerical experiments is mainly due to scattering (rather than absorption) of radiation by aerosol particles.

Numerical simulation of the radioactive contamination of Ukraine after the Chornobyl disaster: the influence of the input meteorological data on the results uncertainty

In this article we assess the sensitivity of the numerical simulations of the radioactive 137Cs contamination of Ukraine caused by the Chornobyl nuclear power plant accident in 1986 to the input meteorological data. The atmospheric transport, dispersion, and deposition (dry scavenging and rain washout) of the radioactive aerosols was simulated using the CALPUFF dispersion model. The source parameterization of the 137Cs emissions during the active phase of the catastrophe (26 April—May 5 of 1986) was adopted from the previously published literature results. Seventeen different versions/realizations of the input meteorology for CALPUFF simulations were prepared with the regional prognostic meteorological model WRF by combining the available global atmospheric reanalyses for 1986 (NNRP, ERA-Interim, ERA5, CFSR) and the model’s physical parameterizations (microphysics, radiation processes, boundary/surface layer physics). The assessment of the simulation uncertainty was carried out in two different ways. In the first approach, the uncertainty was estimated as the width of the distribution of the calculated 137Cs surface concentrations (adjusted to the logarithmic scale), which were obtained with different versions of the input meteorology. The second approach was based on the statistical comparison of the calculated 137Cs contaminations and the corresponding measured values obtained during a complex assessment of the aftermath of the disaster made at the beginning of 1990s. Two statistical metrics were used: the geometric mean bias and the geometric mean variance. The results of our study demonstrate that even when using somewhat unified meteorological data (atmospheric reanalysis), the results of the radioactive contamination calculations at the same spatial locations can differ by several orders of magnitude. We find that the uncertainty depends not only on the distance to the source of the emissions but also on the physical mechanism (wet or dry deposition) responsible for the formation of the local contamination

Performance of Hourly Rainfall Simulations using WRF Meteorological Model for Calculation of Streamflow to Ta Trach Reservoir During 2020 Flood Season

More and more extreme weather events are frequently observed in Thua Thien Hue province in recent years, especially in 2020. Only 7 days from October 6-12, 2020, total rainfall measured at Thuong Nhat station is approximately 1900 mm, accounting for more than 50% of the total annual rainfall. So, it is necessary to ensure the effective operation for Ta Trach reservoir, a large reservoir with a basin area of ​​717 km2 in Thua Thien Hue. This study is initially to clarify the performance of hourly rainfall simulations and streamflow to Ta Trach reservoir using WRF and HEC-HMS hydro-meteorological models during the 2020 flood season, respectively. The results showed that on the scale of daily, the correlation between simulated and measured rainfall is quite good, most of the stations could be reached about 0,8. On the scale of hourly, these figures are about 0,5 and the trend of cumulative hourly rainfall simulated is consistent with measured values. Especially, the trend of simulated streamflow using directly hourly rainfall from WRF model to Ta Trach reservoir is consistent with the measurement streamflow although the errors are still seen to extent with the NASH index of about 0,65.

Meteonetwork: An Open Crowdsourced Weather Data System

Citizen science has shown great potential for bringing large groups of people closer to science, thanks in part to cooperation with universities and research centers. In this context, amateur weather networks played a major role in the last few decades thanks to a constant growth in technology. An example is given by the Meteonetwork association, born in 2002, and mainly composed by atmospheric science enthusiasts, who built up in time a huge weather station network in Italy. In recent years, they have enlarged their horizons over Europe, displaying real time observations and daily maps coming from both personal weather stations and official networks. This study described how Meteonetwork has set up an open crowdsourced weather data system, how data are validated, and which products are generated and freely accessible for scientists and stakeholders for their own purposes. Two concrete use cases were described as examples: the Weatherness Project, which selects a subnet of Meteonetwork data for biometeorological and health purposes and the data assimilation process implemented to improve the initial conditions into the WRF meteorological model for daily weather forecasts.

Association between extreme ambient temperatures and general indistinct and work-related road crashes. A nationwide study in Italy

Despite the relevance of road crashes and their impact on social and health care costs, the effects of extreme temperatures on road crashes risk have been scarcely investigated, particularly for those occurring in occupational activities. A nationwide epidemiological study was carried out to estimate the risk of general indistinct and work-related road crashes related with extreme temperatures and to identify crash and occupation parameters mostly involved. Data about road crashes, resulting in death or injury, occurring during years 2013–2015 in Italy, were collected from the National Institute of Statistics, for general indistinct road crashes, and from the compensation claim applications registered by the national workers' compensation authority, for work-related ones. Time series of hourly temperature were derived from the results provided by the meteorological model WRF applied at a national domain with 5 km resolution. To consider the different spatial-temporal characteristics of the two road crashes archives, the association with extreme temperatures was estimated by means of a case-crossover time-stratified approach using conditional logistic regression analysis, and a time-series analysis, using over-dispersed Poisson generalized linear regression model, for general indistinct and work-related datasets respectively. The analyses were controlled for other covariates and confounding variables (including precipitation). Non-linearity and lag effects were considered by using a distributed lag non-linear model. Relative risks were calculated for increment from 75th to 99th percentiles (hot) and from 25 to first percentile (cold) of temperature. Results for general indistinct crashes show a positive association with hot temperature (RR = 1.12, 95 % CI: 1.09–1.16) and a negative one for cold (RR = 0.93, 95 % CI: 0.91−0.96), while for work-related crashes a positive association was found for both hot and cold (RR = 1.06 (95 % CI: 1.01–1.11) and RR = 1.10 (95 % CI: 1.05–1.16). The use of motorcycles, the location of accident (urban vs out of town), presence of crossroads, as well as occupational factors like the use of a vehicle on duty were all found to produce higher risks of road crashes during extreme temperatures. Mitigation and prevention measures are needed to limit social and health care costs.

Simulating summer mixing heights in California's San Joaquin Valley using the WRF meteorological model with three land surface modules

During the 1990 SARMAP/AUSPEX study a network of rawinsondes was flown at 3- and 6-h intervals to evaluate the meteorological conditions conducive high ozone concentrations in the Central Valley of California. Vertical profiles of air temperature, humidity, and pressure from 9 of the sites were used to determine maximum daily mixing heights in California’s San Joaquin Valley (SJV) during the period August 3–6, 1990 and to evaluate their spatial distribution. The analysis showed a general pattern of increasing mixing heights from north to south within the SJV. But, it also showed that mixing heights were lower in areas in proximity to irrigated agriculture. The WRF prognostic meteorological model was used to simulate mixing heights during this period using three land surface modules (LSM): thermal diffusion (TD), Noah, and Pleim-Xiu (PXU). The maximum daily mixing heights simulated using the TD LSM were generally negatively biased; while those simulated using the Noah and PXU LSMs were positively biased. The differences in simulated mixing heights were attributed to differences in how crop land irrigation water is represented within each LSM. The TD LSM effectively assumes a fixed soil water content, but of sufficient magnitude to reflect latent heat fluxes associated with crop land irrigation. The PXU and Noah LSMs include comprehensive descriptions of the soil water budget. However, soil water may be depleted in the arid summer climate and these LSMs provide no direct means of accounting for water added through irrigation. The LSMs were also sensitive to estimates of ground cover in irrigated areas. For all 9 sites, the root mean square error (RMSE) for maximum daily mixing heights for the TD LSM, the Noah LSM, and the PXU LSM was 269, 350, and 366 m, respectively; and the biases for the 9 sites were − 161, 355, and 437 m, respectively. Using the assumption of well-managed irrigation and leaf area index (LAI) estimates from AVHRR remote sensing data sets, adjustments to the WRF input files were made to update soil water contents and ground cover for irrigated crop land use. Using the Noah LSM with the adjusted files, site-specific mixing height biases were reduced up to a factor of 4 and, for all sites, the RMSE for maximum daily mixing heights were reduced from 350 to 260 m; for the PXU LSM RMSEs were reduced from 366 to 238 m; and for the TD LSM from 269 to 231 m. This analysis illustrates the importance of accounting for the effects of applied irrigation water and ground cover in the simulation of mixing heights during the arid summer within the SJV. Irrigation can influence actual mixing heights and their distribution within a domain. It also illustrates that the choice of LSM is of less importance than setting LSM parametrizations to values appropriate for the modeling domain.

Моделювання вторинного радіоактивного забруднення повітря в Україні внаслідок вітрового під-йому радіонуклідів.

The paper presents the results of the simulation of secondary air pollution scenario in Ukraine due to the wind lift of anthropogenic radionuclides during a dust storm in April 2020. A variant of the Bagnold formula was used to parameterize the intensity of radionuclide resuspension. To set the initial pollution of the territory of Ukraine, the reconstruction of meteorological conditions and fallout of Cs-137 after the disaster at the Chernobyl nuclear power plant was carried out through the use of the RODOS nuclear emergency response system and the WRF meteorological model. For the normalized root-mean-square error of the calculated total fallout in the 50-km zone around the Chernobyl NPP the value NMSE=4,5 was obtained. A decrease in the levels of pollution of the Earth's surface during the time after the accident due to radioactive decay and other environmental processes was estimated. The distribution of contamination of the Earth's surface obtained in this way was used to assess the intensity of wind rise and atmospheric transport of radionuclides during a dust storm on April 16–18, 2020. The calculations were carried out using the CALPUFF model. The input meteorological data were the results of the WRF-Ukraine weather forecasting system. In the calculation of secondary contamination, the effect of fires in the Chernobyl Exclusion Zone (ChEZ) was not taken into account. The calculated average daily concentrations of Cs-137 in the air were maximum for the first day of the dust storm (April 16), when the wind speed was maximum (13 m/s with gusts up to 19 m/s). Average daily concentrations on April 16 ranged from the background values (5,8·10-6 Bq/m3 in Kyiv) to 2,2·10-3 Bq/m3 (ChNPP). The obtained estimates are much less than the permissible concentrations (0.8 Bq/m3). At the same time consideravle exceedance of background values were predicted in a large part of Ukraine – from Rivne NPP (2,2·10-5 Bq/m3) to Kharkiv (1,3·10-5 Bq / m3). In the vicinity of the ChEZ in the cities of Chernihiv and Slavutich, the obtained estimates of daily average concentration were 1,6·10-4 Bq/m3 and 3,2·10-4 Bq/m3 respectively.

Towards a regional dust modeling system in the central Middle East: Evaluation, uncertainties and recommendations

This study aims towards an accurate regional dust modeling system in the central Middle East area (CME), through the implementation of the state-of-art dust parameterizations. The modeling system consists of the natural emission model NEMO, the meteorological model WRF and the chemistry transport model CAMx. An extensive evaluation of 16 different configurations has been realized, incorporating all the combinations of the components utilized in the state-of-art dust modeling approaches, namely the drag partition, the sandblasting efficiency, the horizontal mass flux, as well two commonly used soil particle size distributions. Daily mean PM10 measurements in Doha, as well the satellite AOD products of MODIS have been used for the quantitative and the qualitative assessment of the simulations. Noteworthy, each of these assessments did not yield to the exact same ranking of the configurations (e.g. best five) but they assisted on identifying clear patterns. For example, a consistent overestimation of the daily mean PM10, when the MacKinnon's drag partition scheme is utilized, was found. On the other hand, the assessments led to best three performing configurations, with common components the Raupach's drag partition scheme and Alfaro and Gomes sandblasting efficiency. Although their overall performance is good, several issues were found i.e. on individual dust events and a mean underestimation during the studied period, ranging from −49 to −75 μg/m3. One cause of this underestimation could be the aerodynamic entrainment, a mechanism usually neglected from the dust modeling approaches. Another cause could be missed regional and/or local sources or underestimation of their emission rates.

Reconstruction of the hazardous flood of 2014 in Magadan city based on coupled hydrometeorological modelling

The study assessed the possibility of using a deterministic distributed hydrological model Hydrograph to calculate the maximum discharge of catastrophic flood at the Magadanka River (48.5 km2, city of Magadan, North-East of Russia) in 2014. The model parameters were not calibrated but borrowed from previously performed regional modelling studies. To verify the Hydrograph model streamflow simulations with daily time step were carried out for the period 1971-2015. The median value of Nash-Sutcliffe efficiency was 0.42 for a period of 44 years, which, given the lack of a meteorological station within the catchment, made it possible to evaluate the results as satisfactory. For the catastrophic flood calculation, two types of precipitation data were used: hourly data on precipitation from the nearest weather station and distribution of precipitation for the watershed from the meteorological model WRF. The flood hydrographs were estimated for the initial and corrected sets of the model parameters. The initial set of the model parameters allowed for proper timing of the flood peak but underestimated “observed” maximum value. We introduced the decreasing correction coefficient to the infiltration parameter of the model to “stretch out” the peak and volume of hydrographs. The results have shown that combining the meteorological input from weather station and regional meteorological model may allow for successful flood simulations in ensemble mode.

화학 수송 모델과 기상 모델을 이용한 석탄 화력 발전소에서 배출되는 미세먼지의 거동 분석

A coal-fired power plant is considered as one of the main sources for fine particulate matters. Particulate matter generated from the power plant can be classified into the primary particulate matter (PM) and the secondary PM. The primary PM is ejected as a solid-state and the secondary PM is ejected as a gaseous state called a precursor and becomes a solid-state by chemical reactions in the atmosphere. Since the size of the secondary PM is smaller than 2.5 micrometers, it can cause severe health problems. Therefore, accurate prediction of advection and diffusion of PM generated from a coal-fired power plant is highly necessary. In the present study, the WRF-Chem (Weather Research and Forecasting–Chemistry) model is used to simulate air quality near a coal-fired power plant. The Carbon-bond mechanism version Z (CBMZ), is used to predict formation of the secondary PM. An aerosol model, which simulates aerosol interactions and chemistry (MOSAIC), is used to predict the transport of fine particles. The meteorological model WRF is used to predict dispersion of fine particles and gaseous components.

Air quality, emissions, and source contributions analysis for the Greater Bengaluru region of India

Bengaluru - capital of the state of Karnataka is the original “Silicon Valley” of India. In this paper, we present a comprehensive snapshot of the state of air quality in Bengaluru, along with an emissions inventory for the pollutants necessary for chemical transport modeling at 0.01° grid resolution (approximately 1-km), for an urban airshed covering 60 × 60 grids (4300 km2). For 2015, emission estimates for the city are 31,300 tons of PM2.5, 67,100 tons of PM10, 5300 tons of SO2, 56,900 tons of NOx, 335,550 tons of CO, and 83,500 tons of NMVOCs. Overall, transport is the key emission source for Bengaluru - vehicle exhaust and on-road dust resuspension account for a combined 56% and 70% of total PM2.5 and PM10 emissions; followed by industries (17.8% including the brick kilns), open waste burning (11.0%), and domestic cooking, heating, and lighting (6.5%), in case of PM2.5. We conducted particulate pollution source apportionment of local and non-local sources, using WRF meteorological model and CAMx chemical transport modeling system. A comparison of range of 24-hr average modeled PM2.5 concentrations (36.5 ± 9.0 μg/m3) and monitored PM2.5 concentrations (32.3 ± 24.2 μg/m3) by month, shows that the model catches the quantitative ranges and qualitative trends. The modeled source contributions highlight the vehicle exhaust (28%) and dust (including on-road resuspended dust and construction activities) (23%), and open waste burning (14%), as the key air pollution sources. Unless there is an aggressive strategy to improve urban planning and public transport options, pollutant emissions under the business as usual scenario are expected to increase at least 50% in 2030 and doubling the urban area with PM2.5 annual averages above the national ambient standard of 40 μg/m3.

WRF-SMOKE-CMAQ modeling system for air quality evaluation in São Paulo megacity with a 2008 experimental campaign data.

Atmospheric pollutants are strongly affected by transport processes and chemical transformations that alter their composition and the level of contamination in a region. In the last decade, several studies have employed numerical modeling to analyze atmospheric pollutants. The objective of this study is to evaluate the performance of the WRF-SMOKE-CMAQ modeling system to represent meteorological and air quality conditions over São Paulo, Brazil, where vehicular emissions are the primary contributors to air pollution. Meteorological fields were modeled using the Weather Research and Forecasting model (WRF), for a 12-day period during the winter of 2008 (Aug. 10th-Aug. 22nd), using three nested domains with 27-km, 9-km, and 3-km grid resolutions, which covered the most polluted cities in São Paulo state. The 3-km domain was aligned with the Sparse Matrix Operator Kernel Emissions (SMOKE), which processes the emission inventory for the Models-3 Community Multiscale Air Quality Modeling System (CMAQ). Data from an aerosol sampling campaign was used to evaluate the modeling. The PM10 and ozone average concentration of the entire period was well represented, with correlation coefficients for PM10, varying from 0.09 in Pinheiros to 0.69 in ICB/USP, while for ozone, the correlation coefficients varied from 0.56 in Pinheiros to 0.67 in IPEN. However, the model underestimated the concentrations of PM2.5 during the experiment, but with ammonium showing small differences between predicted and observed concentrations. As the meteorological model WRF underestimated the rainfall and overestimated the wind speed, the accuracy of the air quality model was expected to be below the desired value. However, in general, the CMAQ model reproduced the behavior of atmospheric aerosol and ozone in the urban area of São Paulo.

Air-Temperature Response to Neighborhood-Scale Variations in Albedo and Canopy Cover in the Real World: Fine-Resolution Meteorological Modeling and Mobile Temperature Observations in the Los Angeles Climate Archipelago

To identify and characterize localized urban heat- and cool-island signals embedded within the temperature field of a large urban-climate archipelago, fine-resolution simulations with a modified urbanized version of the WRF meteorological model were carried out as basis for siting fixed weather monitors and designing mobile-observation transects. The goal was to characterize variations in urban heat during summer in Los Angeles, California. Air temperatures measured with a shielded sensor mounted atop an automobile in the summers of 2016 and 2017 were compared to model output and also correlated to surface physical properties focusing on neighborhood-scale albedo and vegetation canopy cover. The study modeled and measured the temperature response to variations in surface properties that already exist in the real world, i.e., realistic variations in albedo and canopy cover that are attainable through current building and urban design practices. The simulated along-transect temperature from a modified urbanized WRF model was compared to the along-transect observed temperature from 15 mobile traverses in one area near downtown Los Angeles and another in an inland basin (San Fernando Valley). The observed transect temperature was also correlated to surface physical properties characterizations that were developed for input to the model. Both comparisons were favorable, suggesting that (1) the model can reliably be used in siting fixed weather stations and designing mobile-transect routes to characterize urban heat and (2) that except for a few cases with opposite co-varying influences, the correlations between observed temperature and albedo and between observed temperature and canopy cover were each negative, ranging from −1.0 to −9.0 °C per 0.1 increase in albedo and from −0.1 to −2.2 °C per 0.1 increase in canopy cover. Observational data from the analysis domains pointed to a wind speed threshold of 3 m/s. Below this threshold the variations in air temperature could be explained by land use and surface properties within a 500-m radius of each observation point. Above the threshold, air temperature was influenced by the properties of the surface within a 1-km upwind fetch. Of relevance to policy recommendations, the study demonstrates the significant real-world cooling effects of increasing urban albedo and vegetation canopy cover. Based on correlations between the observed temperature (from mobile transects) and surface physical properties in the study domains, the analysis shows that neighborhood-scale (500-m) cooling of up to 2.8 °C during the daytime can be achieved by increasing albedo. A neighborhood can also be cooled by up to 2.3 °C during the day and up to 3.3 °C at night by increasing canopy cover. The analysis also demonstrates the suitability of using fine-resolution meteorological models to design mobile-transect routes or site-fixed weather monitors in order to quantify urban heat and the efficacy of albedo and canopy cover countermeasures. The results also show that the model is capable of accurately predicting the geographical locations and the magnitudes of localized urban heat and cool islands. Thus the model results can also be used to devise urban-heat mitigation measures.

Four dimensional data assimilation (FDDA) impacts on WRF performance in simulating inversion layer structure and distributions of CMAQ-simulated winter ozone concentrations in Uintah Basin

Four-dimensional data assimilation (FDDA) was applied in WRF-CMAQ model sensitivity tests to study the impact of observational and analysis nudging on model performance in simulating inversion layers and O3 concentration distributions within the Uintah Basin, Utah, U.S.A. in winter 2013. Observational nudging substantially improved WRF model performance in simulating surface wind fields, correcting a 10 °C warm surface temperature bias, correcting overestimation of the planetary boundary layer height (PBLH) and correcting underestimation of inversion strengths produced by regular WRF model physics without nudging. However, the combined effects of poor performance of WRF meteorological model physical parameterization schemes in simulating low clouds, and warm and moist biases in the temperature and moisture initialization and subsequent simulation fields, likely amplified the overestimation of warm clouds during inversion days when observational nudging was applied, impacting the resulting O3 photochemical formation in the chemistry model. To reduce the impact of a moist bias in the simulations on warm cloud formation, nudging with the analysis water mixing ratio above the planetary boundary layer (PBL) was applied. However, due to poor analysis vertical temperature profiles, applying analysis nudging also increased the errors in the modeled inversion layer vertical structure compared to observational nudging. Combining both observational and analysis nudging methods resulted in unrealistically extreme stratified stability that trapped pollutants at the lowest elevations at the center of the Uintah Basin and yielded the worst WRF performance in simulating inversion layer structure among the four sensitivity tests. The results of this study illustrate the importance of carefully considering the representativeness and quality of the observational and model analysis data sets when applying nudging techniques within stable PBLs, and the need to evaluate model results on a basin-wide scale.

Calculation of precipitation during period of catastrophic flood 21-27 july 2008 in Ukrainian Carpathians

Intense precipitation event happened in Ukraine on 21-27 July 2008 leading to extreme flash floods in the Ukrainian Carpathians which are characterized by the return period of approximately 50 years. Besides favourable synoptic conditions leading to quasi-stationary low situated over Balkans and South-West part of Ukraine precipitation during this event was intensified by mountains. This lead to formation of the specific precipitation patterns in the Ukrainian Carpathians which was not adequately resolved by the existing measurement network.
 The purpose of this publication is application of the mesoscale meteorological model WRF with high resolution (1 km) for reconstruction of precipitation during flood in July, 2008 in the Ukrainian Carpathians and evaluation of the simulated results against measurements.
 Calculation of precipitation during the period of catastrophic summer flood in 2008 at the territory of Ukrainian Carpathians had been carried out with the use of meteorological model WRF. The precipitation field was well simulated for the period of maximum floods (July, 25-th) as compared to precipitation data measured at meteorological stations. However the second and lower peak of precipitation which happened during July, 27-th was underestimated by WRF. The reason of such underestimation is possibly the influence of boundary conditions on simulated results. The precipitation field formed during July, 25-th is elongated along the main ridge of the Ukrainian Carpathians and precipitation maximums on that date, reaching up to 70 mm/3 hours are situated above foothills of the Carpathians. On July, 27-th the precipitation maximums are shifted south-eastward, close to the boundary of the computational domain. The vertical crossections of the calculated meteorological fields demonstrate characteristic system of gravitational waves occurring in flow above mountains and sequence of convective cells situated mostly above the windward (north-eastern) hills and coinciding with the precipitation maximums. The depth of some of the convective cells reaches 10 km, which is supported by satellite data. The indirect evaluation of the calculated precipitation field is performed using the measured water discharge data at the watersheds of the rivers in Carpathians. For the period of maximum flood the relative precipitation amount at each watershed (over total precipitation amount at all water-sheds) appears to be approximately equal to relative water discharge of the corresponding watershed.
 In conclusion we could state that the precipitation field was well simulated by WRF for the period of maximum floods (25 July). High quality of simulated results is supported by comparison of the calculated and measured precipitation as well as with indirect juxtaposition of the calculated integral precipitation at the watersheds of Carpathian rivers and water discharges in the corresponding river outlets. The calculated results demonstratre that existing measurement network is too sparse to capture the details of the orographically enhanced precipitation field. The scarcity of the measurement network makes it difficult to evaluate integral and maximum characteristics of precipitation happening in the Ukrainian Carpathians.
 Directions for further research should include usage of the coupled mesoscale meteorological-distributed hydrological model chain for calculation and forecasting of meteorological and hydrological characteristics of floods.

Using 3DVAR data assimilation to measure offshore wind energy potential at different turbine heights in the West Mediterranean

In this article, offshore wind energy potential is measured around the Iberian Mediterranean coast and the Balearic Islands using the WRF meteorological model without 3DVAR data assimilation (the N simulation) and with 3DVAR data assimilation (the D simulation). Both simulations have been checked against the observations of six buoys and a spatially distributed analysis of wind based on satellite data (second version of Cross-Calibrated Multi-Platform, CCMPv2), and compared with ERA-Interim (ERAI). Three statistical indicators have been used: Pearson’s correlation, root mean square error and the ratio of standard deviations. The simulation with data assimilation provides the best fit, and it is as good as ERAI, in many cases at a 95% confidence level. Although ERAI is the best model, in the spatially distributed evaluation versus CCMPv2 the D simulation has more consistent indicators than ERAI near the buoys. Additionally, our simulation’s spatial resolution is five times higher than ERAI. Finally, regarding the estimation of wind energy potential, we have represented the annual and seasonal capacity factor maps over the study area, and our results have identified two areas of high potential to the north of Menorca and at Cabo Begur, where the wind energy potential has been estimated for three turbines at different heights according to the simulation with data assimilation.

Aerosol–radiation interaction modelling using online coupling between the WRF 3.7.1 meteorological model and the CHIMERE 2016 chemistry-transport model, through the OASIS3-MCT coupler

Abstract. The presence of airborne aerosols affects the meteorology as it induces a perturbation in the radiation budget, the number of cloud condensation nuclei and the cloud micro-physics. Those effects are difficult to model at regional scale as regional chemistry-transport models are usually driven by a distinct meteorological model or data. In this paper, the coupling of the CHIMERE chemistry-transport model with the WRF meteorological model using the OASIS3-MCT coupler is presented. WRF meteorological fields along with CHIMERE aerosol optical properties are exchanged through the coupler at a high frequency in order to model the aerosol–radiation interactions. The WRF-CHIMERE online model has a higher computational burden than both models run separately in offline mode (up to 42 % higher). This is mainly due to some additional computations made within the models such as more frequent calls to meteorology treatment routines or calls to optical properties computation routines. On the other hand, the overall time required to perform the OASIS3-MCT exchanges is not significant compared to the total duration of the simulations. The impact of the coupling is evaluated on a case study over Europe, northern Africa, the Middle East and western Asia during the summer of 2012, through comparisons of the offline and two online simulations (with and without the aerosol optical properties feedback) to observations of temperature, aerosol optical depth (AOD) and surface PM10 (particulate matter with diameters lower than 10 µm) concentrations. The result shows that using the optical properties feedback induces a radiative forcing (average forcing of −4.8 W m−2) which creates a perturbation in the average surface temperatures over desert areas (up to 2.6° locally) along with an increase in both AOD and PM10 concentrations.

Sensitivity to the use of 3DVAR data assimilation in a mesoscale model for estimating offshore wind energy potential. A case study of the Iberian northern coastline

In this work, the WRF meteorological model is run in three different modes to estimate the wind energy potential in the Bay of Biscay for the 1990–2001 period. The first simulation (NODA) involves a typical use of the WRF model and it does not use data assimilation. The second one (12hDA) performs 3DVAR data assimilation at 00 UTC and 12 UTC. Finally, 6hDA uses 3DVAR data assimilation at 00 UTC, 06 UTC, 12 UTC and 18 UTC. Verification for the three simulations has been carried out at a preliminary stage using wind data from buoys, and then a spatially distributed analysis has been conducted of surface wind based on satellite data from the Cross-Calibrated Multi-Platform (CCMP). To that purpose, the spatial correlation and error patterns over our study area have been used as statistical indicators. The results indicate that the wind values obtained with data assimilation every six hours (6hDA) yield the best verification scores at a 95% confidence level, thereby being the most accurate at reproducing wind observations in the area. Regarding the estimation of wind energy potential, at a second stage, we tested the calculation’s sensitivity to the use of data assimilation. The most reliable simulation with data assimilation (6hDA) estimates 21% less energy potential than the simulation without data assimilation. In the absence of historical wind observation records of the sea with sufficient time and space resolution, meteorological models such as WRF provide an estimation of the wind values in tentative areas for offshore wind farms. In this line, our study highlights the need to use meteorological models with data assimilation, as future wind energy production can then be more realistically estimated beforehand. This may also contribute to a more accurate economic and technical evaluation of the risks and benefits for future investments in offshore wind energy.

Improving of local ozone forecasting by integrated models

This paper discuss the problem of forecasting the maximum ozone concentrations in urban microlocations, where reliable alerting of the local population when thresholds have been surpassed is necessary. To improve the forecast, the methodology of integrated models is proposed. The model is based on multilayer perceptron neural networks that use as inputs all available information from QualeAria air-quality model, WRF numerical weather prediction model and onsite measurements of meteorology and air pollution. While air-quality and meteorological models cover large geographical 3-dimensional space, their local resolution is often not satisfactory. On the other hand, empirical methods have the advantage of good local forecasts. In this paper, integrated models are used for improved 1-day-ahead forecasting of the maximum hourly value of ozone within each day for representative locations in Slovenia. The WRF meteorological model is used for forecasting meteorological variables and the QualeAria air-quality model for gas concentrations. Their predictions, together with measurements from ground stations, are used as inputs to a neural network. The model validation results show that integrated models noticeably improve ozone forecasts and provide better alert systems.