Marino Palacios

Eulerian And Photochemical Modelling OverMadrid Area In A Mesoscale Context

This paper presents an Eulerian 3D Dispersion Urban Site Model (EDUSM) which is an integrated system for air quality simulations. It has been specifically designed for an urban environment. The model has been integrated with a resistance deposition model, a photochemical package (for accounting the effects of solar radiation over NO, NO^ and 03, in to 502 as a passive pollutant) and a detailed dynamic emission model based on CO BIN AIR and EPA data.

Impacts of the 4.5 and 8.5 RCP global climate scenarios on urban meteorology and air quality

Climate change is expected to influence urban living conditions and challenge the ability of cities to adapt and mitigate climate change. This paper describes a new modelling system for climate change impact assessments on urban climate and air quality with feasible computational costs (the expected CPU time is too large for actual supercomputer platforms). The system takes the outputs from a global climate model, which are injected into a dynamical regional climate model (WRF-Chem) with the nested capability activated, with 25 km spatial resolution. In addition, the system uses a diagnostic meteorological model (CALMET) to produce urban detailed information (with 200 m spatial resolution) using this downscaling procedure. At the city level, a simplified chemical-transport model (based on CMAQ and using linear chemistry) is used to map the spatial distribution of the pollutants. The system is applied to five European cities: Madrid, Antwerp, Milan, Helsinki and London (Kensington-Chelsea area). The modelling system was used to simulate the climate and air quality for present year (2011) and future years (2030, 2050 and 2100) using 2011 emissions as control run, because we want to investigate the effects on the global climate on the actual (2011) cities. Effects on temperature, precipitation, and ozone are also considered. We compare the climate and air concentrations in future years 2030, 2050 and 2100 with the control year (2011). Comparison of simulations for present situation (using NNRP reanalysis 2011 data sets) shows acceptable agreement with measurements which give us strong confidence on the results for the RCP IPCC climate future simulations for 4.5 and 8.5 scenarios. Impacts of global climate on urban scale are showed for 2030, 2050 and 2100 for 4.5 and 8.5 RCP IPCC climate scenarios.Dynamical and diagnostic downscaling processes are properly combined.Two RCP scenarios are considered: 4.5 and 8.5.Present (2011) and future (2030, 2050 and 210) years are simulated.

Using Space-Based Downstream Services for Urban Management in Smart Cities

Data collected through remote sensing (for instance geo-satellite) provides necessary stimulus for developing smart solutions for climate change & public health, energy efficiency and land monitoring in an urban environment. The velocity, variety, volume and veracity of high resolution data produced by geo-satellites provide big opportunity for planning and decision making in a smart city context. However, processing and integrating remote sensing data with auxiliary data sources require proper data management and elastic computational resources to derive necessary information intelligence (or knowledge) for decision making. This paper presents prototype of selected Decumanus services and highlights strengths & weaknesses of climate change, energy efficiency and land monitoring applications for the different European cities. The analysis of the early results indicate that the amount of computation resources required to process data for above applications make cloud computing a suitable technology but also face challenges in adopting it due to its recency, impact on green computing and reluctance to transform from legacy computing systems to new paradigms like cloud computing. We critically discuss these challenges and suggest possible solutions.

Analysis of fire behaviour simulations over Spain with WRF-FIRE

Wildland fire spread and behaviour are complex phenomena owing to both the number of involved physico-chemical factors, and the non-linear relationship between variables. Spain is plagued by forest and brush fires every summer, when the extremely dry weather sets in along with high temperatures. The use of fire behaviour models requires the availability of high resolution environmental and fuel data; in the absence of real data, errors on the simulated fire spread can be compounded to affect the spatial and temporal accuracy of predicted data. The effect of input values on the accuracy of WRF-FIRE simulations was evaluated to assess the capabilities of the new system for wildland fire in accurately forecasting fire behaviour. The results confirm that the use of accurate meteorological data and a custom fuel moisture content model is crucial to obtain precise simulations of fire behaviour.

Comparison between different dynamical downscaling methods using WRF-Chem for urban applications: Madrid case study

The European Centre for Medium-Range Weather Forecasts is used to provide boundary conditions for the mesoscale model WRF-Chem that has been run over Europe with 23 km spatial resolution. We have used a full one-way nesting approach to produce simulations centred over the city of Madrid (Spain) with 4.6 km spatial resolution, 0.92 km spatial resolution and 0.184 km spatial resolution. In last level, we have run the CMAQ (full chemistry) model to produce chemical pollution data. This is called the control reference simulation. The simplified and faster downscaling procedure used in this experiment is the CALMET-CMAQL (linear chemistry) model. Both downscaling techniques are compared using meteorological and air pollution monitoring station. The comparison between both downscaling techniques shows that the CALMET-CMAQL model is much faster and computationally cheap; the results are good enough to consider this tool for climate purposes.

Climate Downscaling Processes Using WRF/Chem and CALMET Modelling Systems: Madrid Case Study

Downscaling techniques are very important to assure the robustness and credibility of climate modelling exercises. Regional climate simulations use boundary conditions and initial conditions from global climate and meteorological models. The regional climate simulations (WRF/chem model) have much higher spatial resolution and using nesting approaches can be used to derive climate indicators at urban level. Dynamical nesting approaches -- also known as dynamical downscaling procedures -- use a substantial amount of computer power, particularly for urban applications, other alternatives such as CALMET diagnostic model (for meteorological applications) and CMAQ model (with linear chemistry) produce results faster and can be used for climate applications with reasonable required computer power. In this contribution, we are using the European Centre for Medium-Range Weather Forecasts (ECMWF) model data sets to provide boundary conditions for the mesoscale model WRF/Chem (NOAA, US) that has been ran over Europe with 23 km spatial resolution and 33 vertical levels up to 50 hPa. We have used the full nesting approach defined into WRF model to produce simulations centered over the city of Madrid (Spain) with 4.6 km spatial resolution (nesting level 1, l1), 0.92 km spatial resolution (nesting level 2, l2) and 0.184 km spatial resolution (nesting level 3, l3). In l3, we have run the CMAQ (full chemistry) model (EPA, US) to produce chemical pollution data. We have applied both downscaling techniques over Madrid area using Retiro meteorological and air pollution monitoring station as observational station. The comparison between both downscaling techniques shows that CALMET-CMAQL (linear chemistry) model is much faster and the results are good enough (compared with other simulations results) to consider this tool, when the number of simulations for climate purposes is very high (due to many years and several climate scenarios) and the application of the WRF/chem model (dynamical downscaling) is prohibited computationally.

The Impact of a Wildland Fire on Air Pollution Concentrations Using WRF/Chem/Fire: An Application over Murcia (Spain)

In this contribution we will show the impact on air pollution concentration of a Fire developed in the Surrounding area of Murcia (Spain). The impact on air pollution concentrations has been done using the WRF/Chem model developed by NCAR (US) and the Fire model implemented into WRF in on-line mode. The fire spread has been considered in on-line mode to change the use of the different land use grid cells as the fire spreads. This process changes the land use and affects substantially to the Fuel Moisture Content (FMC) modifying the surface turbulent energy balance and the pollution dispersion consequently. A new fuel moisture content model has been developed. The new module allows each time step to calculate the fuel moisture content of the dead fuels (1 h, 10 h, and 100 h) and live fuels. Two simulations have been performed over the Murcia area on September, 7th, 2010 with 9 h of fire starting at 19:09 over an area of 7 km × 7 km: (a) with a fire of 9 h simulation we run the WRF/Chem with 200 m spatial resolution over the fire domain in on-line mode with FIRE model. Emissions from fire have been accounted on; and (b) the same simulation than in case (a) but without fire (no emissions and no changes in the land use).