Academic literature on the topic 'Flexpart modeling'

Abstract. The FLEXible PARTicle dispersion model FLEXPART, first released in 1998, is a Lagrangian particle dispersion model developed to simulate atmospheric transport over large and mesoscale distances. Due to FLEXPART's success and its open source nature, different limited area model versions of FLEXPART were released making it possible to run FLEXPART simulations by ingesting WRF (Weather Research Forecasting model), COSMO (Consortium for Small-scale Modeling) or MM5 (mesoscale community model maintained by Penn State university) meteorological fields on top of the ECMWF (European Centre for Medium-Range Weather Forecasts) and GFS (Global Forecast System) meteorological fields. Here, we present a new FLEXPART limited area model that is compatible with the AROME mesoscale meteorological forecast model (the Applications of Research to Operations at Mesoscale model).1 FLEXPART-AROME was originally developed to study mesoscale transport around La Réunion, a small volcanic island in the southwest Indian Ocean with a complex orographic structure, which is not well represented in current global operational models. We present new turbulent modes in FLEXPART-AROME. They differ from each other by dimensionality, mixing length parameterization, turbulent transport constraint interpretation and time step configuration. A novel time step was introduced in FLEXPART-AROME. Performances of new turbulent modes are compared to the ones in FLEXPART-WRF by testing the conservation of well-mixedness by turbulence, the dispersion of a point release at the surface and the marine boundary layer evolution around Réunion. The novel time step configuration proved necessary to conserve the well-mixedness in the new turbulent modes. An adaptive vertical turbulence time step was implemented, allowing the model to adapt on a finer timescale when significant changes in the local turbulent state of the atmosphere occur.

Abstract. The Lagrangian particle dispersion model FLEXPART was originally (about 8 years ago) designed for calculating the long-range and mesoscale dispersion of air pollutants from point sources, such as after an accident in a nuclear power plant. In the meantime FLEXPART has evolved into a comprehensive tool for atmospheric transport modeling and analysis. Its application fields were extended from air pollution studies to other topics where atmospheric transport plays a role (e.g., exchange between the stratosphere and troposphere, or the global water cycle). It has evolved into a true community model that is now being used by at least 25 groups from 14 different countries and is seeing both operational and research applications. A user manual has been kept actual over the years and was distributed over an internet page along with the model's source code. However, so far there was no citeable description of FLEXPART. In this note we provide a description of FLEXPART's latest version (6.2).

Abstract. The Lagrangian particle dispersion model FLEXPART was originally designed for calculating long-range and mesoscale dispersion of air pollutants from point sources, such that occurring after an accident in a nuclear power plant. In the meantime, FLEXPART has evolved into a comprehensive tool for atmospheric transport modeling and analysis at different scales. A need for further multiscale modeling and analysis has encouraged new developments in FLEXPART. In this paper, we present a FLEXPART version that works with the Weather Research and Forecasting (WRF) mesoscale meteorological model. We explain how to run this new model and present special options and features that differ from those of the preceding versions. For instance, a novel turbulence scheme for the convective boundary layer has been included that considers both the skewness of turbulence in the vertical velocity as well as the vertical gradient in the air density. To our knowledge, FLEXPART is the first model for which such a scheme has been developed. On a more technical level, FLEXPART-WRF now offers effective parallelization, and details on computational performance are presented here. FLEXPART-WRF output can either be in binary or Network Common Data Form (NetCDF) format, both of which have efficient data compression. In addition, test case data and the source code are provided to the reader as a Supplement. This material and future developments will be accessible at http://www.flexpart.eu.

Abstract. The Lagrangian particle dispersion model FLEXPART was originally (about 8 years ago) designed for calculating the long-range and mesoscale dispersion of air pollutants from point sources, such as after an accident in a nuclear power plant. In the meantime FLEXPART has evolved into a comprehensive tool for atmospheric transport modeling and analysis. Its application fields were extended from air pollution studies to other topics where atmospheric transport plays a role (e.g., exchange between the stratosphere and troposphere, or the global water cycle). It has evolved into a true community model that is now being used by at least 25 groups from 14 different countries and is seeing both operational and research applications. A user manual has been kept actual over the years and was distributed over an internet page along with the model's source code. In this note we provide a citeable technical description of FLEXPART's latest version (6.2).

Abstract. The Lagrangian particle dispersion model FLEXPART in its original version in the mid-1990s was designed for calculating the long-range and mesoscale dispersion of hazardous substances from point sources, such as those released after an accident in a nuclear power plant. Over the past decades, the model has evolved into a comprehensive tool for multi-scale atmospheric transport modeling and analysis and has attracted a global user community. Its application fields have been extended to a large range of atmospheric gases and aerosols, e.g., greenhouse gases, short-lived climate forcers like black carbon and volcanic ash, and it has also been used to study the atmospheric branch of the water cycle. Given suitable meteorological input data, it can be used for scales from dozens of meters to global. In particular, inverse modeling based on source–receptor relationships from FLEXPART has become widely used. In this paper, we present FLEXPART version 10.4, which works with meteorological input data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) and data from the United States National Centers of Environmental Prediction (NCEP) Global Forecast System (GFS). Since the last publication of a detailed FLEXPART description (version 6.2), the model has been improved in different aspects such as performance, physicochemical parameterizations, input/output formats, and available preprocessing and post-processing software. The model code has also been parallelized using the Message Passing Interface (MPI). We demonstrate that the model scales well up to using 256 processors, with a parallel efficiency greater than 75 % for up to 64 processes on multiple nodes in runs with very large numbers of particles. The deviation from 100 % efficiency is almost entirely due to the remaining nonparallelized parts of the code, suggesting large potential for further speedup. A new turbulence scheme for the convective boundary layer has been developed that considers the skewness in the vertical velocity distribution (updrafts and downdrafts) and vertical gradients in air density. FLEXPART is the only model available considering both effects, making it highly accurate for small-scale applications, e.g., to quantify dispersion in the vicinity of a point source. The wet deposition scheme for aerosols has been completely rewritten and a new, more detailed gravitational settling parameterization for aerosols has also been implemented. FLEXPART has had the option of running backward in time from atmospheric concentrations at receptor locations for many years, but this has now been extended to also work for deposition values and may become useful, for instance, for the interpretation of ice core measurements. To our knowledge, to date FLEXPART is the only model with that capability. Furthermore, the temporal variation and temperature dependence of chemical reactions with the OH radical have been included, allowing for more accurate simulations for species with intermediate lifetimes against the reaction with OH, such as ethane. Finally, user settings can now be specified in a more flexible namelist format, and output files can be produced in NetCDF format instead of FLEXPART's customary binary format. In this paper, we describe these new developments. Moreover, we present some tools for the preparation of the meteorological input data and for processing FLEXPART output data, and we briefly report on alternative FLEXPART versions.

Abstract. The Lagrangian particle dispersion model FLEXPART was originally designed for calculating long-range and mesoscale dispersion of air pollutants from point sources, such as after an accident in a nuclear power plant. In the meantime FLEXPART has evolved into a comprehensive tool for atmospheric transport modeling and analysis at different scales. This multiscale need has encouraged new developments in FLEXPART. In this document, we present a FLEXPART version that works with the Weather Research and Forecasting (WRF) mesoscale meteorological model. We explain how to run and present special options and features that differ from its predecessor versions. For instance, a novel turbulence scheme for the convective boundary layer has been included that considers both the skewness of turbulence in the vertical velocity as well as the vertical gradient in the air density. To our knowledge, FLEXPART is the first model for which such a scheme has been developed. On a more technical level, FLEXPART-WRF now offers effective parallelization and details on computational performance are presented here. FLEXPART-WRF output can either be in binary or Network Common Data Form (NetCDF) format with efficient data compression. In addition, test case data and the source code are provided to the reader as Supplement. This material and future developments will be accessible at http://www.flexpart.eu.

AbstractIn this study, the Flexible Particle (FLEXPART)-WRF, a Lagrangian particle dispersion model, is employed to simulate pollutant dispersion in and near the Lehigh Gap, a gap in a prominent ridgeline in eastern Pennsylvania. FLEXPART-WRF is used to evaluate the diagnostic value of the ventilation index (VI), an index that describes the potential for smoke or other pollutants to ventilate away from a source, for indicating dispersion potential in complex terrain. Little is known about the effectiveness of the ventilation index in diagnosing dispersion potential in complex terrain. The modeling approach used in this study is to release a dense cloud of particles across a portion of the model domain and evaluate particle behavior and VI diagnostic value in areas of the domain with differing terrain characteristics. Although both horizontal and vertical dispersion are examined, the study focuses primarily on horizontal dispersion, assessed quantitatively by calculating horizontal residence time (HRT) within a 1-km-radius circle surrounding the particle release point. Analysis of HRT across the domain reveals horizontal dispersion patterns that are influenced by the ridgeline and the Lehigh Gap. Comparison of VI and HRT in different areas of the domain reveals a robust relationship windward of the ridgeline and a weak relationship leeward of the ridgeline and in the vicinity of the Lehigh Gap. The results of this study suggest that VI users should consider whether they are windward or leeward of topographic features, and highlight the need for an alternative metric that better takes into account the influence of the terrain on dispersion.

In this study, we evaluated estimates and predictions of the PM2.5 (fine particulate matter) concentrations and emissions in Xuzhou, China, using a coupled Lagrangian particle dispersion modeling system (FLEXPART-WRF). A Bayesian inversion method was used in FLEXPART-WRF to improve the emission calculation and mixing ratio estimation for PM2.5. We first examined the inversion modeling performance by comparing the model predictions with PM2.5 concentration observations from four stations in Xuzhou. The linear correlation analysis between the predicted PM2.5 concentrations and the observations shows that our inversion forecast system is much better than the system before calibration (with correlation coefficients of R = 0.639 vs. 0.459, respectively, and root mean square errors of RMSE = 7.407 vs. 9.805 µg/m3, respectively). We also estimated the monthly average emission flux in Xuzhou to be 4188.26 Mg/month, which is much higher (by ~10.12%) than the emission flux predicted by the multiscale emission inventory data (MEIC) (3803.5 Mg/month). In addition, the monthly average emission flux shows obvious seasonal variation, with the lowest PM2.5 flux in summer and the highest flux in winter. This pattern is mainly due to the additional heating fuels used in the cold season, resulting in many fine particulates in the atmosphere. Although the inversion and forecast results were improved to some extent, the inversion system can be improved further, e.g., by increasing the number of observation values and improving the accuracy of the a priori emission values. Further research and analysis are recommended to help improve the forecast precision of real-time PM2.5 concentrations and the corresponding monthly emission fluxes.

Abstract. Estimation of the temporal profile of an atmospheric release, also called the source term, is an important problem in environmental sciences. The problem can be formalized as a linear inverse problem wherein the unknown source term is optimized to minimize the difference between the measurements and the corresponding model predictions. The problem is typically ill-posed due to low sensor coverage of a release and due to uncertainties, e.g., in measurements or atmospheric transport modeling; hence, all state-of-the-art methods are based on some form of regularization of the problem using additional information. We consider two kinds of additional information: the prior source term, also known as the first guess, and regularization parameters for the shape of the source term. While the first guess is based on information independent of the measurements, such as the physics of the potential release or previous estimations, the regularization parameters are often selected by the designers of the optimization procedure. In this paper, we provide a sensitivity study of two inverse methodologies on the choice of the prior source term and regularization parameters of the methods. The sensitivity is studied in two cases: data from the European Tracer Experiment (ETEX) using FLEXPART v8.1 and the caesium-134 and caesium-137 dataset from the Chernobyl accident using FLEXPART v10.3.

Abstract. Recently, it has been shown that mass conservation in Lagrangian models is improved by using time-average winds out of Eulerian models. In this study, we evaluate the mass conservation and trajectory uncertainties in complex terrain at mesoscale using the FLEXPART Lagrangian particle dispersion model coupled with the WRF mesoscale model. The specific form of vertical wind used is found to have a large effect. Time average wind with time average sigma dot (σ·), instantaneous wind with geometric cartesian vertical wind (w) and instantaneous wind with σ· are used to simulate mixing ratios of a passive tracer in forward and backward runs using different time interval outputs and horizontal resolutions in California. Mass conservation in the FLEXPART model was not an issue when using time-average wind or instantaneous wind with σ·. However, mass was poorly conserved using instantaneous wind with w, with a typical variation of 25% within 24 h. Uncertainties in surface residence time (a backtrajectory product commonly used in source receptor studies or inverse modeling) calculated for each backtrajectory run were also analyzed. The smallest uncertainties were systematically found when using time-average wind. Uncertainties using instantaneous wind with σ· were slightly larger, as long as the time interval of output was sufficiently small. The largest uncertainties were found when using instantaneous wind with w. Those uncertainties were found to be linearly correlated with the local average gradient of orography. Differences in uncertainty were much smaller when trajectories were calculated over flat terrain. For a typical run at mesoscale in complex terrain, 4 km horizontal resolution and 1 h time interval output, the average uncertainty and bias in surface residence time is, respectively, 8.4% and −2.5% using time-average wind, and 13% and −3.7% using instantaneous wind with σ· in complex terrain. The corresponding values for instantaneous wind with cartesian w were 24% and −11%. While the use of time-average wind systematically improves uncertainty in FLEXPART, the improvements are small, and therfore a systematic use of time-average wind in Lagrangian models is not necessarily required. Use of cartesian vertical wind in complex terrain, however, should be avoided.

Gases and particles released by volcanoes pose a serious hazard to humans and society. Emissions can be transported over long distances before being reduced to harmless concentrations. Knowing which areas are, or will be, exposed to volcanic emissions is an important part inreducing the impact on human health and society. In this thesis, the dispersion of volcanic emissions is studied using a set of atmospheric models. The work includes contribution to the development of the Lagrangian Particle Dispersion Model FLEXPART-WRF. Three case studies have been performed, one studying potential ash emissions from potential future eruptions on Iceland, a second covering SO2 emissions from Mt. Nyiragongo in D.R. Congo, and a third studying the SO2 emission rate of the Holuhraun eruption (Iceland) in 2014–2015. The first study covers volcanic ash hazard for air traffic over Europe. Three years of meteorological data are used to repeatedly simulate dispersion from different eruption scenarios. The simulations are used to study the probability of hazardous concentrations in ash in European airspace. The ash hazard shows a seasonal variation with a higher probability of efficient eastward transport in winter, while summer eruptions pose a more persistent hazard. In the second study, regional gas exposure around Mt. Nyiragongo is modelled using flux measurements to improve the description of the emission source. Gases are generally transported to the north-west in June–August and to the south-west in December–January. A diurnal variation due to land breeze around lake Kivu contributes to high concentrations of SO2 along the northern shore during the night. Potentially hazardous concentrations are occasionally reached in populated areas in the region, but mainly during the nights. The third study uses inverse dispersion modelling to determine the height and emission rates based on traverse measurements of the plume at 80–240 km from the source. The calculated source term yields better agreement with satellite observations compared to commonly used column sources. The work in this thesis presents improvements in dispersion modelling of volcanic emissions through improved models, more accurate representation of the source terms, and through incorporating new types of measurements into the modelling systems.
Gas- och partikelutsläpp från vulkaner utgör en fara för människor och för vårt samhälle. Utsläppen kan transporteras över långa avstånd innan de reduceras till oskadliga halter. Att känna till vilka områden som utsätts för, eller kommer utsättas för, utsläppen är ett viktigt verktyg föratt minska påverkan på folkhälsa och samhälle. I avhandlingen studeras spridningen av utsläpp från vulkanutbrott med hjälp av en uppsättning numeriska atmosfärsmodeller. Den Lagrangiska Partikelspridningsmodellen FLEXPART-WRF har förbättrats och applicerats för spridningsmodellering av vulkanutbrott. Tre studier har utförts, en fokuserar på vulkanaska från potentiella framtida utbrott på Island, den andra studerar SO2-ustläpp från vulkanen Nyiragongo i Demokratiska Republiken Kongo, och den tredje studerar SO2-ustläpp från utbrottet i Holuhraun (Island) 2014–2015. Den första studien uppskattar sannolikheten för att vulkanaska från framtida vulkanutbrott på Island ska överskrida de gränsvärden som tillämpas för flygtrafik. Tre år av meteorologisk data används för att simulera spridningen från olika utbrottsscenarier. Sannolikheten för skadliga halter aska varierar med årstid, med en högre sannolikhet för effektiv transport österut under vintermånaderna, sommarutbrott är istället mer benägna att orsaka långvariga problem överspecifika områden. In den andra studien undersöks spridningen av SO2 från Nyiragongo över en ettårsperiod. Flödesmätningar av plymen används för att förbättra källtermen i modellen. Gaserna transporteras i regel mot nordväst i juni–augusti och mot sydväst i december–februari En dygnsvariation, kopplad till mesoskaliga processer runt Kivusjön, bidrar till förhöjda halter av SO2 nattetid längs Kivusjöns norra kust. Potentiellt skadliga halter av SO2 uppnås av och till i befolkade områden men huvudsakligen nattetid. Den tredje studien utnyttjar inversmodellering för att avgöra plymhöjd och gasutsläpp baserat på traversmätningar av plymen runt 80–240 km från utsläppskällan. Den beräknade källtermen resulterar i bättre överensstämmelse mellan modell- och satellitdata jämfört med enklare källtermer. Arbetet i den här avhandlingen presenterar flertalet förbättringar för spridningsmodellering av vulkanutbrott genom bättre modeller, nogrannare beskrivning av källtermer, och genom nya metoder för tillämpning av olika typer av mätdata.

A novel approach to modelling the oxygen and hydrogen isotope ratios of rainfall over the British Isles is presented. The model process involves two stages. First, a Lagrangian particle dispersion model (FLEXPART) that uses European Centre for Medium Range Weather Forecasting Reanalysis (ECMWF ERA-40) fields to produce ensembles of back trajectories of the three-dimensional path of air parcels prior to rainfall events. Second, physical atmospheric parameters along these trajectories are then input in to a Mixed Cloud Isotope Model (MCIM) to predict the isotopic ratio of rainfall. Models of the movement of oxygen and hydrogen isotopes through the hydrological system are vital to gain understanding of the isotopic systems so as to improve the use of isotopes as palaeoclimate proxies to uncover information about the past. A case study comparing daily observed isotopic values with modelled values for the same day is presented for Norwich for raindays in November and December 2005. The results of this comparison are very promising for the simulation of <>180, ID and deuterium excess for events where more than 3 mm but less than 15 mm of rain fell. A positive relationship is seen between the modelled and observed values, i.e. higher modelled values correspond to higher observed values. The regression equation of this relationship for <>180 is y =0.35x -4.18, which can be compared with the ideal case of modelled =observed (y =x), with an r value of 0.84, significant at the 95% confidence level. The gradient of this relationship and the similar ones seen for ID and deuterium excess reflect the fact that the model sensitivity is too low; the full range of observed values is not captured, though the pattern of variability is reproduced by the model. Natural variability in the observed data was seen when <>180 values from precipitation collected at 5 sites around Norwich during November 2005 were compared. However, insufficient observations (only 8 days in one city) were made to allow a general sampling error to be estimated. For the days where multiple samples were collected and analysed, the standard deviation of observed 8180 values varied between 0.11 and 0.92 %0. This factor should be considered in other studies where modelled values from a grid box are compared with point observations. Similarly, variability was seen across the modelled ensemble of values. For the model runs for Norwich on the 7th November 2005 at 1200 a range of 8180 values of 6.65 %0 was seen, emphasising the importance of an ensemble of runs being conducted rather than a single trajectory. Comprehensive sensitivity tests of the model were conducted. Case studies for other U.K. locations in Dublin and Birmingham during November 2005; and for sites at Driby, Lincolnshire and Stock Hill, Somerset during 1977 to 1982 are also presented. Positive correlations were seen between modelled and observed oxygen and hydrogen isotopic ratios and deuterium excess in all cases except for Dublin where there was an insufficient observed sample size. However, as for Norwich, the model sensitivity was too low (the maximum modelled range across all sites was 3.9 times less than that of the observed values for 8180 and 3.5 times too small for 80) . This approach shows promise for modelling the isotopic composition of rainfall for mid-latitude maritime climatic regions as a complimentary method to the technique of explicitly modelling the isotopic composition ofprecipitation in General Circulation Models (GCMs). The very nature of GCMs means that it is difficult to identify which processes involved have the largest impact on an individual atmospheric component. The simpler format of model used in this study more easily allows processes to be added or removed in order to investigate the relative importance of each one. Also, smaller scale features are accounted for using the Lagrangian approach used in this study, whereas the resolution of Eulerian GCMs is still limited by the computational times involved. However, more investigation is required into the problems seen in this study in producing a large enough modelled range before this study's approach could be widely used.

During the last decades, there has been an increase in wildfires around the globe. Climate change with higher temperatures and lower humidity, due to changing precipitation patterns, is the main factor raising the fire risk. Using earth observations is an important method to detect wildfires. Especially in areas far from populated regions, satellites support the identification of wildfires and allow issuing warnings in case of a developing event. In wildfire detection methods using satellite data, the occurrence of false alarms is unavoidable. The main goal of this work is to improve the detection of wildfires by using state-of-the-art earth observation data, specifically data from Sentinel missions, together with modelling approaches in a combined new methodology. Most of the current methods to detect wildfires by earth observations mainly use a single satellite-based data source to retrieve surface information. The benefit to existing methods is that surface- and atmospheric observations from Sentinels-3 and -5P will be combined, with the aim to reduce the number of false alarms. Sentinel-3 and Sentinel-5P can be used as independent data sources, the former is able to detect thermal anomalies in the surface, and the latter is capable of detecting direct fire emissions such as CO and HCHO in the atmosphere. The combined use with the Lagrangian particle dispersion model FLEXPART will allow the backtracking of fire emissions plus the aerosol mid height from Sentinel-5P to better identify wildfires sources. For this purpose, so called ‘source-receptor sensitivities’ are calculated, that provide information on the times and areas potentially contributing to the observed plume. Finally, identified wildfires can be validated using Sentinel-2 images. The innovation of our approach is to combine sentinel observations with atmospheric smoke plume simulations, by applying a dispersion model in backward mode to backtrack the possible source region of the smoke plume.