Nested Domains Research Articles

The Role of the Atmospheric Aerosol in Weather Forecasts for the Iberian Peninsula: Investigating the Direct Effects Using the WRF-Chem Model

In the atmosphere, aerosols play an important role in climate change, the Earth’s environment and human health. The purpose of this study is to investigate the direct and semi-direct aerosol effects on weather forecasting, focusing on the Iberian Peninsula (IP). To that end, two Weather Research and Forecasting (WRF)-Chem simulations (with and without aerosol feedback) for an entire year (2015) were performed. The model setup includes two nested domains run in two-way mode, allowing the downscaling for the IP domain at a 5 × 5 km2 high-horizontal resolution. The results were explored through agreement of pairs of time series and their spatial variability in order to analyse the importance of including the online-coupled aerosol radiative effect on the meteorological variables: shortwave (solar) radiation, air temperature and precipitation. Significant variations of agreement were found when capturing both temporal and spatial patterns of the analysed meteorological variables. While the spatial distribution of temperature and precipitation is similar throughout the IP domain, with agreement values ranging from 0.87 up to 1.00, the solar radiation presents a distinct spatial pattern with lower agreement values (0.68–0.75) over ocean and higher agreement (0.75–0.98) over land regions. With regard to the spatial differences between simulations, the aerosol contributed to a considerable decrease in annual mean and maximum radiation (up to 20 and 40 Wm−2, respectively), slightly impacting the temperature variation (up to 0.5 °C). These results suggest that the aerosol feedback effects should be accounted when performing weather forecasts, and not only for purposes of air quality assessment.

Wind Potential for the Caribbean Region of Colombia: High-Resolution Validation and Assessment with Wrf

High-resolution wind power assessment is key for planning of wind farms. However, this information is scarce for the Caribbean region of Colombia. We assess the annual wind power for this region, using the Weather Research and Forecasting (WRF) model with two nested domains (9 km and 3 km) and 50 vertical levels with 15 minutes outputs for the northern coast of South America. We evaluated the performance of the YSU, MYJ and QNSE parameterization schemes for the planetary boundary layer of WRF in the simulation of near surface winds of the months with the strongest and weakest winds, compared to observations at stations and ERA5 reanalysis. We found that WRF accomplishes an accurate simulation of phase and amplitude of observed wind diurnal cycles, extreme values, and hodographs in terms of correlations, standard deviations, bias and Weibull distributions. Using the QNSE scheme, we validate the Wind Power Density (WPD) at the stations. Based on the validation, we found areas within the domain with WPD at 100 m up to 1,200 Wm-2 onshore and 2,000 Wm-2 offshore, with annual values above 400 Wm-2. In this regard, WRF provides a valuable platform for future forecasting of wind power in this Caribbean region.

Efficacy of the Cell Perturbation Method in Large-Eddy Simulations of Boundary Layer Flow over Complex Terrain

A challenge to simulating turbulent flow in multiscale atmospheric applications is the efficient generation of resolved turbulence motions over an area of interest. One approach is to apply small perturbations to flow variables near the inflow planes of turbulence-resolving simulation domains nested within larger mesoscale domains. While this approach has been examined in numerous idealized and simple terrain cases, its efficacy in complex terrain environments has not yet been fully explored. Here, we examine the benefits of the stochastic cell perturbation method (CPM) over real complex terrain using data from the 2017 Perdigão field campaign, conducted in an approximately 2-km wide valley situated between two nearly parallel ridges. Following a typical configuration for multiscale simulation using nested domains within the Weather Research and Forecasting (WRF) model to downscale from the mesoscale to a large-eddy simulation (LES), we apply the CPM on a domain with horizontal grid spacing of 150 m. At this resolution, spurious coherent structures are often observed under unstable atmospheric conditions with moderate mean wind speeds. Results from such an intermediate resolution grid are often nested down for finer, more detailed LES, where these spurious structures adversely affect the development of turbulence on the subsequent finer grid nest. We therefore examine the impacts of the CPM on the representation of turbulence within the nested LES domain under moderate mean flow conditions in three different stability regimes: weakly convective, strongly convective, and weakly stable. In addition, two different resolutions of the underlying terrain are used to explore the role of the complex topography itself in generating turbulent structures. We demonstrate that the CPM improves the representation of turbulence within the LES domain, relative to the use of high-resolution complex terrain alone. During the convective conditions, the CPM improves the rate at which smaller-scales of turbulence form, while also accelerating the attenuation of the spurious numerically generated roll structures near the inflow boundary. During stable conditions, the coarse mesh spacing of the intermediate LES domain used herein was insufficient to maintain resolved turbulence using CPM as the flow develops downstream, highlighting the need for yet higher resolution under even weakly stable conditions, and the importance of accurate representation of flow on intermediate LES grids.

Determination of Wind Pattern Inside an Urban Area Through a Mesoscale-Microscale Approach

The present paper aims to expose the Mesoscale-Microscale numerical approach adopted for studying the air fluxes inside an urban area located in the city of Pescara (Italy). The data, recorded by a real anemometer, are compared with three Mesoscale models (Pleim-Xiu, Blackadar and MRF-LSM), each of them presents five nested domains. On the bases of the monthly values of Root Mean Square Error, BIAS and Standard Deviation, the most accurate mesoscale model is identified and evaluated. On the Microscale side, instead, two cylindrical domains are studied. The first model considers only the topography of the terrain, whereas the other also adds the buildings present inside the investigated area. The domains, with a diameter of 6 [km] and a height equal to 0.5 [km], are studied by assuming the incoming wind from four different directions. Comparisons are then made among wind speeds and directional inflows obtained from the two models. Mesoscale analyses are carried out with the weather forecast software MM5, and Microscale simulations are performed with the commercial software STAR-CCM+.

BAYWRF: a high-resolution present-day climatological atmospheric dataset for Bavaria

Abstract. Climate impact assessments require information about climate change at regional and ideally also local scales. In dendroecological studies, this information has traditionally been obtained using statistical methods, which preclude the linkage of local climate changes to large-scale drivers in a process-based way. As part of recent efforts to investigate the impact of climate change on forest ecosystems in Bavaria, Germany, we developed a high-resolution atmospheric modelling dataset, BAYWRF, for this region over the thirty-year period of September 1987 to August 2018. The atmospheric model employed in this study, the Weather Research and Forecasting (WRF) model, was configured with two nested domains of 7.5 and 1.5 km grid spacing centred over Bavaria and forced at the outer lateral boundaries by ERA5 reanalysis data. Using an extensive network of observational data, we evaluate (i) the impact of using grid analysis nudging for a single-year simulation of the period of September 2017 to August 2018 and (ii) the full BAYWRF dataset generated using nudging. The evaluation shows that the model represents variability in near-surface meteorological conditions generally well, although there are both seasonal and spatial biases in the dataset that interested users should take into account. BAYWRF provides a unique and valuable tool for investigating climate change in Bavaria with high interdisciplinary relevance. Data from the finest-resolution WRF domain are available for download at daily temporal resolution from a public repository at the Open Science Framework (Collier, 2020; https://doi.org/10.17605/OSF.IO/AQ58B).

How will rainfall change over Hawai‘i in the future? High-resolution regional climate simulation of the Hawaiian Islands

To better understand the historical and projected future climatology and hydrologic impacts of rainfall over the Hawaiian Islands, regional climate simulations over the main Hawaiian Islands were conducted for two 10-year periods using the Weather Research and Forecasting (WRF) model in two nested domains. The first (historical) simulation was driven by the ERA-Interim global reanalysis data and observed sea surface temperature from October 2002 to September 2012. The second simulation used the Pseudo Global Warming (PGW) method to perturb historical conditions with climate change signals taken from global climate model averages. Eighty-one vertical layers were used to better resolve the trade wind inversion (TWI). Results show that the historical simulation reproduces the mean surface temperature, relative humidity, and winds with low biases and high spatial correlations. Additionally, for the historical simulation, the aggregated daily and hourly rainfall probability density functions (PDFs) and rainfall spatial-temporal distributions were well simulated, likely because WRF captured the TWI properties well. For the PGW (future) simulation, the mean rainfall increased by 2.2% in the dry season and 12.5% in the wet season; the average annual increase was 7.8%. The TWI base height decreases in future dry seasons leading to reduced rainfall on the upwind sides of high mountains. Interactions between the complex terrain and the TWI led to heterogeneous changes in patterns of future rainfall across Hawai‘i, including more than a 20% increase in extreme rates in the dry season and a ~ 10% increase during the wet season.

Uncovering process domains in large rivers: Patterns and potential drivers of benthic substrate heterogeneity in two North American riverscapes

Identifying and understanding functional process domains (sensu Montgomery, 1999) in rivers is paramount for linking the physical habitat template to ecosystem structure and function. To date, efforts to do this have been rare, especially in large rivers, as they require appropriate tools for quantifying habitat heterogeneity with fine-scale resolution across broad spatial extents. In this study, we used side-scan sonar technology to map riverbed substrate at six sites in the Yellowstone and Missouri rivers. Substrate maps were then analyzed and visualized using geospatial analysis to relate fine-grained spatial substrate patterns to process domain structure. Our findings revealed two distinct nested domains of substrate patchiness, suggesting that different factors are responsible for shaping patterns of substrate at different scales. Although small-scale patchiness in substrate was likely driven by internal, or autogenic, physical processes, patterns at larger segment extents (>3 km) were often driven by abrupt transitions in habitat related to exogenous factors such as lateral erosion of talus, tributary inputs, and bank armoring. Additionally, we found that heterogeneity in benthic substrate increased with spatial extent at all of our study sites; however, this relationship was lower in the Missouri River, which is altered by impoundment. Our study represents one of the first efforts to relate benthic habitat heterogeneity to nested process domain structure in large riverscapes, and offers a unique perspective for linking landscape processes, geomorphological habitat heterogeneity, and biological structure and function in large rivers.

Evaluation of the assimilation of conventional and satellite-based observations in simulating heavy rainfall event using WRFDA over the North-West Himalayan region

Present work elucidates the impact of 3DVAR data assimilation technique for the simulation of one of the heavy rainfall events reported over Kotdwara region in the North-West Himalayan (NWH) region on 4th August 2017. We have examined the impact of conventional and satellite-based radiance datasets on the simulated results with and without assimilating the observations into the Weather Research and Forecasting (WRF) model. Three experiments have been designed with 3 nested domains of variable resolutions, one without assimilation (referred as control experiment) and other two experiments after assimilating conventional and satellite radiances observations (refer as DA-OBS and DA-SAT respectively). In the present study, assimilation of surface, upper air and the satellite-based radiance observations has been carried out for the outermost domain with horizontal resolution of 9 km. Statistical analysis suggests that the correlation coefficient is high (0.55) and root mean square error (RMSE) is low (17.12) for DA-SAT experiment as compared to other two experiments. Substantial improvement in the location, pattern and intensity of extreme rainfall event is noted after assimilation of both conventional and satellite observations with respect to the observed rainfall data. However, it is noted that the assimilation of satellite radiances has greater impact in simulating better intensity of the heavy rainfall event as compared to the assimilation of conventional observations. Plausible reason behind this could be the non-availability of the conventional observations close to the extreme rainfall event affected region.

Improving 3D-MOHID water model with an upscaling algorithm

Upscaling methodologies, enforced on nested ocean modelling grids, have become a subject of more intense research due to their benefits in modelling highly dynamic coastal areas. In this paper, an upscaling algorithm is developed for the 3D-MOHID Water model, enabling TwoWay implementations, which considers the nudging of a child domain's velocities, temperature and salinity fields by a parent domain. The algorithm is validated for a schematic case, and then applied to Tagus Region of Freshwater Influence (TagusROFI, Portugal). Results for the schematic case show an improvement of the parent domain while generating minimal mass conservation loss. TagusROFI model domains under TwoWay nesting improved the salinity transition between domain boundaries when reached by the estuarine plume. This new 3D-MOHID Water model feature improves the study of extreme scenarios where OneWay downscaling produces discontinuities on the nested domain's open boundaries, and enables the implementation of higher resolution domains with smaller size grids.

Performance of MPAS-A and WRF in predicting and simulating western North Pacific tropical cyclone tracks and intensities

Performances of the Model for Prediction Across Scales-Atmosphere (MPAS-A) in predicting and the Weather Research and Forecasting (WRF) model in simulating western North Pacific (WNP) tropical cyclone (TC) tracks and intensities have been compared. Parallel simulations of the same historical storms that made landfall over southern China, namely, TCs Hope (1979), Gordon (1989), Koryn (1993), Imbudo (2003), Dujuan (2003), Molave (2009), Hato (2017) and Mangkhut (2018), were carried out using WRF and MPAS-A, with initial conditions (and, for WRF, lateral boundary conditions as well) taken from ERA-interim. For MPAS-A, the model was integrated using a standard 60-to-3-km variable-resolution global grid mesh and also on 160-to-2-km grids customized to cover the TC tracks with the highest resolution mesh. The WRF model was integrated using a 15-km/3-km nested domain. No TC bogus scheme was applied when initializing the MPAS-A and WRF simulations. It was found that while TC tracks were reasonably captured by the two models configured variously, the storm intensities were underestimated in general. Given MPAS-A runs were initial value predictions whereas WRF runs were dynamically downscaled from ERA-interim, the finding that MPAS-A has comparable (or slightly better) performance as (than) WRF is noteworthy. To further examine the sensitivity of the MPAS-A TC forecasts to the initial data, additional experiments were carried out for TCs Molave and Hope using ERA5 reanalysis as initial conditions. The ERA5 initialized runs showed significant (slight) improvement in intensity (track) evolution, suggesting that the underestimated TC intensity is likely related to inferior representation of storms in the ERA-interim initial fields. Furthermore, additional runs using another customized 60-to-2-km mesh showed a reasonable improvement in capturing the TC tracks, suggesting that the track forecast accuracy of MPAS-A in TC can be sensitive to the grid resolution in the coarsest part of the variable-resolution mesh used.

Hydrological Extremes in the Canadian Prairies in the Last Decade due to the ENSO Teleconnection—A Comparative Case Study Using WRF

In the Prairie provinces of Alberta, Saskatchewan, and Manitoba, agricultural production depends on winter and spring precipitation. There is large interannual variability related to the teleconnection between the regional hydroclimate and El Niño and La Niña in the Tropical Pacific. A modeling experiment was conducted to simulate climatic and hydrological parameters in the Canadian Prairie region during strong El Niño and La Niña events of the last decade in 2015–2016 and 2010–2011, respectively. The National Center for Atmospheric Research (NCAR) Weather Research and Forecasting (WRF) model was employed to perform two sets of sensitivity experiments with a nested domain at 10 km resolution using the European Centre for Medium-Range Weather Forecasts Reanalysis (ERA) interim data as the lateral boundary forcing. Analysis of the hourly model output provides a detailed simulation of the drier winter, with less soil moisture in the following spring, during the 2015–2016 El Niño and a wet winter during the La Niña of 2010–2011. The high-resolution WRF simulation of these recent weather events agrees well with observations from weather stations and water gauges. Therefore, we were able to take advantage of the WRF model to simulate recent weather with high spatial and temporal resolution and thus study the changes in hydrometeorological parameters across the Prairie during the two extreme hydrological events of the last decade.

Bifractal nature of chromosome contact maps

Modern biological techniques such as Hi-C permit to measure probabilities that different chromosomal regions are close in space. These probabilities can be visualised as matrices called contact maps. In this paper, we introduce a multifractal analysis of chromosomal contact maps. Our analysis reveals that Hi-C maps are bifractal, i.e. complex geometrical objects characterized by two distinct fractal dimensions. To rationalize this observation, we introduce a model that describes chromosomes as a hierarchical set of nested domains and we solve it exactly. The predicted multifractal spectrum is in excellent quantitative agreement with experimental data. Moreover, we show that our theory yields to a more robust estimation of the scaling exponent of the contact probability than existing methods. By applying this method to experimental data, we detect subtle conformational changes among chromosomes during differentiation of human stem cells.

Study on Sensitivity of Microphysics for the Simulation of Rainfall for the Month of May 2015 over Bangladesh using High Resolution WRF-ARW Model

In this research the Advanced Research WRF (ARW) model v3.8.1 has been used to simulate the rainfall of May 2015 all over Bangladesh. The model was configured in nested domain with 18 and 6 km horizontal grid spacing with 100 × 96 and 103 × 127 grids in the east-west and north-south directions, respectively with 30 vertical levels. The Lin et al., WSM6, Thomson, Morrison Double-Moment (M-2Mom), Stony Brook University (SBU), and WDM6 microphysics schemes coupling with Kain-Fritsch (KF) cumulus parameterization (CP) scheme have been used to simulate the monthly total rainfall, heavy rainfall, monthly rainy days and heavy rainy days for the month of May 2015 at all meteorological stations of Bangladesh. The simulated results are compared with the observed results of 33 meteorological stations of Bangladesh Meteorological Department (BMD) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) output. Relative standard deviation of all observed, PERSIANN and model simulated parameters have been analyzed and compared. The maximum monthly observed rain in May 2015 at Sylhet was 752 mm but WSM6, M-2Mom and WDM6 schemes have simulated 831, 788 and 742 mm for day 1 prediction; WSM6, WDM6 and SBU-Lin schemes have simulated 757, 916 and 981 mm for day 2 prediction and WSM6 and WDM6 schemes have simulated 741 and 925 mm for day 3 prediction, respectively and all other MPs have simulated much higher rainfall at domain (D1). The WDM6, M-2Mom and Lin et al. schemes have simulated 744, 807 and 923 mm for day 1 prediction, WSM6 and WDM6 schemes have simulated 714 and 877 mm for day 2 predictions and WSM6, SBU-Lin and Lin et al. schemes have simulated 802 and 913 and 998 mm, respectively for day 3 predictions at domain (D2). The relative standard deviation (RSD) has minimum at D1 and D2 for WDM6 scheme for day 1 prediction and WSM6 scheme for day 2 and day 3 predictions for the monthly total rainfall and heavy rainfall of May 2015. The results suggest that as the forecast time increased the amount of total rain and also heavy rain is increased. As a result RSD is also increased for all MPs. WDM6 scheme gives the better performance of rainfall and rainy days all over the country.
 Journal of Engineering Science 11(1), 2020, 43-60

Numerical generation of inflow turbulence by cell perturbation technique in WRF simulation

This paper discusses the applicability of a cell perturbation technique to generation of micro-scale wind speed fluctuations in nested Weather Research and Forecasting (WRF) simulation. Multi-scale nesting simulations are conducted with different treatments for turbulence at different scale resolutions. Three outer and three inner nested domains adopt a Planetary Boundary Layer (PBL) scheme and Large Eddy Simulation (LES) model, respectively. Artificial cell perturbation of potential temperature is imposed at the innermost domain to accelerate turbulence development. In addition, five perturbation amplitudes and three perturbation cell sizes of potential temperature are examined at two wind speed levels to investigate the sensitivity of cell perturbation parameters in generating turbulence. The present study shows that the cell perturbation technique works to increase micro-scale wind speed fluctuation energy. Both perturbation amplitude and perturbation cell size play an important role in reproducing the power spectra at higher frequencies and turbulence intensities. It also shows that optimization of cell perturbation parameters is necessary to achieve specified turbulence statistics. The advantage of modeling various turbulence intensity profiles makes the cell perturbation technique adaptable to complex typhoon processes.

Investigating the Impact of High-Resolution Land–Sea Masks on Hurricane Forecasts in HWRF

Realistic wind information is critical for accurate forecasts of landfalling hurricanes. In order to provide more realistic near-surface wind forecasts of hurricanes over coastal regions, high-resolution land–sea masks are considered. As a leading hurricane modeling system, the National Centers for Environmental Prediction (NCEP) Hurricane Weather Research Forecast (HWRF) system has been widely used in both operational and research environments for studying hurricanes in different basins. In this study, high-resolution land–sea mask datasets are introduced to the nested domain of HWRF, for the first time, as an attempt to improve hurricane wind forecasts. Four destructive North Atlantic hurricanes (Harvey and Irma in 2017; and Florence and Michael in 2018), which brought historic wind damage and storm surge along the Eastern Seaboard of the United States and Northeastern Gulf Coast, were selected to demonstrate the methodology of extending the capability to HWRF, due to the introduction of the high-resolution land–sea masks into the nested domains for the first time. A preliminary assessment of the numerical experiments with HWRF shows that the introduction of high-resolution land–sea masks into the nested domains produce significantly more accurate definitions of coastlines, land-use, and soil types. Furthermore, the high-resolution land–sea mask not only improves the quality of simulated wind information along the coast, but also improves the hurricane track, intensity, and storm-size predictions.

A Hydrodynamical Atmosphere/Ocean Coupled Modeling System for Multiple Tropical Cyclones

The goal of this paper is to introduce a new multi-storm atmosphere/ocean coupling scheme that was implemented and tested in the Basin-Scale Hurricane Weather Research and Forecasting (HWRF-B) model. HWRF-B, an experimental model developed at the National Oceanic and Atmospheric Administration (NOAA) and supported by the Hurricane Forecast Improvement Program, is configured with multiple storm-following nested domains to produce high-resolution predictions for several tropical cyclones (TCs) within the same forecast integration. The new coupling scheme parallelizes atmosphere/ocean interactions for each nested domain in HWRF-B, and it may be applied to any atmosphere/ocean coupled system. TC forecasts from this new hydrodynamical modeling system were produced in the North Atlantic and eastern North Pacific from 2017–2019. The performance of HWRF-B was evaluated, including forecasts of TC track, intensity, structure (e.g., surface wind radii), and intensity change, and simulated sea-surface temperatures were compared with satellite observations. Median forecast skill scores showed significant improvement over the operational HWRF at most forecast lead times for track, intensity, and structure. Sea-surface temperatures cooled by 1–8 °C for the five HWRF-B case studies, demonstrating the utility of the model to study the impact of the ocean on TC intensity forecasting. These results show the value of a multi-storm modeling system and provide confidence that the multi-storm coupling scheme was implemented correctly. Future TC models within NOAA, especially the Unified Forecast System’s Hurricane Analysis and Forecast System, would benefit from the multi-storm coupling scheme whose utility and performance are demonstrated in HWRF-B here.

Sensitivity studies of cloud microphysical schemes of WRF-ARW model in the simulation of trailing stratiform squall lines over the Gangetic West Bengal region

In this paper an attempt has been made to investigate the impact of different microphysical (MP) parameterization schemes in simulation of leading convective line with trailing stratiform (TS) squall lines observed over the Gangetic West Bengal (GWB) region of India during the pre-monsoon season of March–May using Advanced Research Weather Research and Forecasting (WRF-ARW). The model is employed to study the features associated with three TS squall line (May 15, 2009, May 5, 2010 and May 7, 2010) observed over the study region. The model sensitivity is evaluated with four different microphysics schemes of the model (Goddard, WDM6, Thompson and Morrison). Model simulations are carried out with three two-way nested domains with horizontal resolution of 18, 6 and 2 km grid spacing and explicit convection in the innermost domain. The model simulated results are compared with available observations (Surface data, rainfall, hydrometeors and Doppler Weather Radar products). The analysis demonstrates that the simulation of TS squall lines are very sensitive to the parameterization of microphysical processes of different MP schemes, in spite of using similar initial and boundary conditions and model configuration. The model provides better simulations of rainfall, maximum reflectivity and vertical profiles of hydrometeors with the Morrison MP Scheme. The study reveals that snow and graupel particles has significant impacts on the simulation of stratiform precipitation region of the TS squall line. The study suggests that simulation of TS squall lines using a high-resolution mesoscale model with suitable cloud microphysics scheme is useful for the prediction of such severe events.

Evaluating the performance of a WRF initial and physics ensemble over Eastern Black Sea and Mediterranean regions in Turkey

The physics- and initial-based ensemble approach for Weather Research and Forecasting (WRF) model is applied for predicting four different extreme precipitation events that occurred in summer and autumn over the two most flood-prone regions of Turkey, namely; the Eastern Black Sea (EBS) and Mediterranean (MED). A total of 48 runs, each of which includes two nested domains, is designed considering four microphysics (MP), three cumulus (CU), two planetary boundary layer (PBL) schemes, and two initial forcing datasets (ERA5 and Global Forecast System (GFS)) for each event. The forecast skill of the runs is measured through the hierarchic application of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method using five categorical and four statistical metrics. The best ten members for each event are validated on the independent events. The MP scheme is the most influential on precipitation estimates between the schemes, especially for the autumn events. However, the choice of a specific MP scheme varies with season and region. Similarly, PBL and CU schemes show a high dependency on event timing and location. Forecasts based on ERA5 yield the highest accuracy over EBS, while GFS-based forecasts are better in the MED region. Most of the top ten members are represented by runs from the 9-km domain. The model parameterization generally shows a more significant impact than model initiation on precipitation variability through spatiotemporal runs. Also, the dynamic downscaling of ERA5 through the WRF model indeed produces better precipitation estimates than ERA5 products over topographically complex regions.

Evaluating Multiple WRF Configurations and Forcing over the Northern Patagonian Icecap (NPI) and Baker River Basin

The use of numerical weather prediction (NWP) model to dynamically downscale coarse climate reanalysis data allows for the capture of processes that are influenced by land cover and topographic features. Climate reanalysis downscaling is useful for hydrology modeling, where catchment processes happen on a spatial scale that is not represented in reanalysis models. Selecting proper parameterization in the NWP for downscaling is crucial to downscale the climate variables of interest. In this work, we are interested in identifying at least one combination of physics in the Weather Research Forecast (WRF) model that performs well in our area of study that covers the Baker River Basin and the Northern Patagonian Icecap (NPI) in the south of Chile. We used ERA-Interim reanalysis data to run WRF in twenty-four different combinations of physics for three years in a nested domain of 22.5 and 4.5 km with 34 vertical levels. From more to less confident, we found that, for the planetary boundary layer (PBL), the best option is to use YSU; for the land surface model (LSM), the best option is the five-Layer Thermal, RRTM for longwave, Dudhia for short wave radiation, and Thompson for the microphysics. In general, the model did well for temperature (average, minimum, maximum) for most of the observation points and configurations. Precipitation was good, but just a few configurations stood out (i.e., conf-9 and conf-10). Surface pressure and Relative Humidity results were not good or bad, and it depends on the statistics with which we evaluate the time series (i.e., KGE or NSE). The results for wind speed were inferior; there was a warm bias in all of the stations. Once we identify the best configuration in our experiment, we run WRF for one year using ERA5 and FNL0832 climate reanalysis. Our results indicate that Era-interim provided better results for precipitation. In the case of temperature, FNL0832 gave better results; however, all of the models’ performances were good. Therefore, working with ERA-Interim seems the best option in this region with the physics selected. We did not experiment with changes in resolution, which may have improved results with ERA5 that has a better spatial and temporal resolution.

Diesel, petrol or electric vehicles: What choices to improve urban air quality in the Ile-de-France region? A simulation platform and case study

Abstract Air pollution from road traffic and its mitigation is a major concern in most cities. A platform for simulating pollutant emissions and concentrations was developed and applied to the Ile-de-France Region (Greater Paris) of France, taking account of anthropogenic and natural sources and ‘imported’ pollution from elsewhere in France and Europe. Four technological scenarios for 2025 were studied and compared to the 2014 reference situation (1-REF). These scenarios included the current evolution of the park with widespread adoption of diesel particulate filters (DPFs) (2-BAU), decline in the sale of diesel vehicles and a corresponding increase in petrol vehicle sales (3-PET), promotion of electric vehicles in urban areas (4-ELEC), and a combinaison with a decrease in traffic of about 15% in the densely populated area inside the A86 outer ring road (5-AIR). The corresponding vehicle fleets were determined using a fleet simulation model. Traffic pollutant emissions were computed with the COPERT4 European methodology and hourly traffic data over the Ile-de-France road network. Particulate matter (PM10, PM2,5 and PM1,0), particles number (PN), black carbon (BC), organic matter (OM), nitrogen oxides (NOx) and nitrogen dioxide (NO2), non-methane volatile organic compounds (VOC), ammonia (NH3), carbon monoxide (CO) and carbon dioxide (CO2) were considered. Emissions for other sectors were taken from a regional inventory. Emissions outside the Ile-de-France region (Europe and France) were derived from the European and French emission inventories. Pollutant concentrations (PM2,5, PM10, organic and inorganic PM10, PN, BC, NO2 and O3) were simulated over nested domains (Europe, France and Ile-de-France) using the Polyphemus platform for two scenarios (2-BAU and 3-PET). Methodological aspects and results for Ile-de-France are discussed here. All scenarios led to a sharp decrease in traffic emissions in Ile-de-France (−30% to −60%) by 2025. The decline in diesel induced a stronger renewal of the fleet. PM and NOx emissions were more strongly reduced than VOC or NH3. Traffic reduction reduced all emissions in the densely populated area within the A86 outer ring road (−20% to −45% for exhaust particles and gaseous pollutants). The 2-BAU and 3-PET scenarios lowered annual average concentrations, especially for NO2 and BC, and more strongly influenced daily-peak than daily-average concentrations. In Ile-de-France, PM of diameter Differences between 2-BAU and 3-PET scenarios were slight. For PM and NO2 concentrations, the petrol scenario was slightly more favorable than the “business-as-usual” scenario with diesel vehicles and DPF; differences were strong for primary particles and NO2 and weak for secondary compounds. This slight advantage was due to lower emissions and accelerated fleet renewal (higher proportion of Euro 5 & 6).