Lagrangian Particle Dispersion Model Research Articles

A model sensitivity study of the impact of clouds on satellite detection and retrieval of volcanic ash

Abstract. Volcanic ash is commonly observed by infrared detectors on board Earth-orbiting satellites. In the presence of ice and/or liquid-water clouds, the detected volcanic ash signature may be altered. In this paper the sensitivity of detection and retrieval of volcanic ash to the presence of ice and liquid-water clouds was quantified by simulating synthetic equivalents to satellite infrared images with a 3-D radiative transfer model. The sensitivity study was made for the two recent eruptions of Eyjafjallajökull (2010) and Grímsvötn (2011) using realistic water and ice clouds and volcanic ash clouds. The water and ice clouds were taken from European Centre for Medium-Range Weather Forecast (ECMWF) analysis data and the volcanic ash cloud fields from simulations by the Lagrangian particle dispersion model FLEXPART. The radiative transfer simulations were made both with and without ice and liquid-water clouds for the geometry and channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI). The synthetic SEVIRI images were used as input to standard reverse absorption ash detection and retrieval methods. Ice and liquid-water clouds were on average found to reduce the number of detected ash-affected pixels by 6–12%. However, the effect was highly variable and for individual scenes up to 40% of pixels with mass loading >0.2 g m−2 could not be detected due to the presence of water and ice clouds. For coincident pixels, i.e. pixels where ash was both present in the FLEXPART (hereafter referred to as "Flexpart") simulation and detected by the algorithm, the presence of clouds overall increased the retrieved mean mass loading for the Eyjafjallajökull (2010) eruption by about 13%, while for the Grímsvötn (2011) eruption ash-mass loadings the effect was a 4% decrease of the retrieved ash-mass loading. However, larger differences were seen between scenes (standard deviations of ±30 and ±20% for Eyjafjallajökull and Grímsvötn, respectively) and even larger ones within scenes. The impact of ice and liquid-water clouds on the detection and retrieval of volcanic ash, implies that to fully appreciate the location and amount of ash, hyperspectral and spectral band measurements by satellite instruments should be combined with ash dispersion modelling.

Validation of ash cloud modelling with satellite retrievals: a case study of the 16–17 June 1996 Mount Ruapehu eruption

Volcanic ash can be hazardous to aviation and human health; hence, there is a need to be able to accurately forecast the dispersion of ash clouds in real time. This relies, in part, on the choice of suitable input parameters for the modelling of a given eruption event, often when data from the actual eruption are not yet available. In this paper, the sensitivity of a coupled modelling system, consisting of the Lagrangian particle dispersion model FLEXPART and the Weather Research and Forecasting (WRF) model, namely FLEXPART-WRF, to varying eruption source parameters is examined through comparison of modelled ash clouds with satellite data for the 16–17 June 1996 eruption of Mount Ruapehu, New Zealand. The model is evaluated using the probability of detection, false alarm ratio and Critical Success Index statistical analyses. The eruption source parameters considered in the ash cloud modelling are: the eruption duration, the mass eruption rate, the particle density, the number of particles, the plume height, the particle size distribution and the plume ratio (defined as the ratio of the thickness of the laterally spreading ash cloud at the plume top to the height of the plume). We allowed the plume height, plume ratio and particle size distribution to vary in order to carry out the sensitivity experiments. Our results indicate that for this eruption: (1) the plume ratio has a large effect on the model prediction of ash clouds dispersion; (2) the uncertainties associated with the plume heights retrieved from satellite data do not have a significant impact on the model prediction of ash cloud dispersion; and (3) the particle size distribution of fine ash has little effect on the model prediction of ash cloud dispersion. Moreover, the results show that the ash cloud forecasts are only accurate for a comparatively short period of time (up to 11 h) due to the limitations associated with WRF model. Further analysis is needed to investigate the general applicability of the method.

Formulation of stability-dependent empirical relations for turbulent intensities from surface layer turbulence measurements for dispersion parameterization in a lagrangian particle dispersion model

Season- and stability-dependent turbulence intensity (σu/u*, σv/u*, σw/u*) relationships are derived from experimental turbulence measurements following surface layer scaling and local stability at the tropical coastal site Kalpakkam, India for atmospheric dispersion parameterization. Turbulence wind components (u′, v′, w′) measured with fast response UltraSonic Anemometers during an intense observation campaign for wind field modeling called Round Robin Exercise are used to formulate the flux–profile relationships using surface layer similarity theory and Fast Fourier Transform technique. The new relationships (modified Hanna scheme) are incorporated in a Lagrangian Particle Dispersion model FLEXPART-WRF and tested by conducting simulations for a field tracer dispersion experiment at Kalpakkam. Plume dispersion analysis of a ground level hypothetical release indicated that the new turbulent intensity formulations provide slightly higher diffusivity across the plume relative to the original Hanna scheme. The new formulations for σu, σv, σw are found to give better agreement with observed turbulent intensities during both stable and unstable conditions under various seasonal meteorological conditions. The simulated concentrations using the two methods are compared with those obtained from a classical Gaussian model and the observed SF6 concentration. It has been found that the new relationships provide comparatively higher diffusion across the plume relative to the model default Hanna scheme and provide downwind concentration results in better agreement with observations.

Mesoscale atmospheric flow-field simulations for air quality modeling over complex terrain region of Ranchi in eastern India using WRF

In the present study mesoscale meteorological flow and planetary boundary layer (PBL) parameters over the complex topographic region of Jharkhand state of tropical India are simulated using high resolution Advanced Research WRF (ARW) mesoscale model to study their role in the air pollution dispersion characteristics using a Lagrangian Particle Dispersion Model (FLEXPART). Eight fair weather days in different seasons (winter, pre-monsoon, monsoon, and post-monsoon) are chosen. Sensitivity experiments are conducted with two non-local [Yonsei University (YSU), Asymmetric Convective Model version 2 (ACM2)] and three local turbulence kinetic energy (TKE) closure [Mellor- Yamada Nakanishi and Niino Level 2.5 PBL (MYNN2), Mellor-Yamada-Janjic (MYJ), and quasi-normal scale elimination (QNSE)] PBL parameterisations to study the evolution of PBL parameters and thermodynamic structure. Simulated parameters are validated with available in situ meteorological observations at three stations in the study region. Results indicate that the low-level flow field is highly influenced by the topography and widely varies in different seasons. Simulated vertical PBL structure varied across seasons with shallow boundary layers (1–1.7 km) during winter, monsoon and post-monsoon seasons, deep mixed layers (2–2.7 km) in pre-monsoon season. Simulations in various seasons revealed that ACM2 followed by MYNN2 and YSU reproduced various PBL features such as topographic flows, surface layer fluxes, meteorological variables and the thermo-dynamical structure reasonably well indicating the suitability of the above PBL schemes for air quality simulations over the region. This is corroborated with the error statistics as well. Simulations with FLEXPART using ARW derived meteorology revealed higher dilution potential of the atmosphere in monsoon and pre-monsoon compared to post monsoon and winter seasons over the region. Results also indicate the ACM2, MYNN2 and YSU produce relatively larger dispersion than the QNSE and MYJ.

Greenhouse gas network design using backward Lagrangian particle dispersion modelling – Part 2: Sensitivity analyses and South African test case

Abstract. This is the second part of a two-part paper considering a measurement network design based on a stochastic Lagrangian particle dispersion model (LPDM) developed by Marek Uliasz, in this case for South Africa. A sensitivity analysis was performed for different specifications of the network design parameters which were applied to this South African test case. The LPDM, which can be used to derive the sensitivity matrix used in an atmospheric inversion, was run for each candidate station for the months of July (representative of the Southern Hemisphere winter) and January (summer). The network optimisation procedure was carried out under a standard set of conditions, similar to those applied to the Australian test case in Part 1, for both months and for the combined 2 months, using the incremental optimisation (IO) routine. The optimal network design setup was subtly changed, one parameter at a time, and the optimisation routine was re-run under each set of modified conditions and compared to the original optimal network design. The assessment of the similarity between network solutions showed that changing the height of the surface grid cells, including an uncertainty estimate for the ocean fluxes, or increasing the night-time observation error uncertainty did not result in any significant changes in the positioning of the stations relative to the standard design. However, changing the prior flux error covariance matrix, or increasing the spatial resolution, did. Large aggregation errors were calculated for a number of candidate measurement sites using the resolution of the standard network design. Spatial resolution of the prior fluxes should be kept as close to the resolution of the transport model as the computing system can manage, to mitigate the exclusion of sites which could potentially be beneficial to the network. Including a generic correlation structure in the prior flux error covariance matrix led to pronounced changes in the network solution. The genetic algorithm (GA) was able to find a marginally better solution than the IO procedure, increasing uncertainty reduction by 0.3 %, but still included the most influential stations from the standard network design. In addition, the computational cost of the GA compared to IO was much higher. Overall the results suggest that a good improvement in knowledge of South African fluxes is available from a feasible atmospheric network, and that the general features of this network are invariable under several reasonable choices in a network design study.

Evaluation of black carbon emission inventories using a Lagrangian dispersion model – a case study over southern India

Abstract. We evaluated three emission inventories of black carbon (BC) using Lagrangian particle dispersion model simulations and BC observations from a rural site in southern India (Gadanki; 13.48° N, 79.18° E) from 2008 to 2012. We found that 93 to 95% of the BC load at the observation site originated from emissions in India and the rest from the neighbouring countries and shipping. A substantial fraction (33 to 43%) of the BC was transported from northern India. Wet deposition is found to play a minor role in reducing BC mass at the site because of its proximity to BC sources during rainy season and relatively short rainy season over western and northern parts of India. Seasonally, the highest BC concentration (approx. 3.3 μg m−3) is observed during winter, followed by spring (approx. 2.8 μg m−3). While the model reproduced well the seasonal cycle, the modelled BC concentrations are significantly lower than observed values, especially in spring. The model bias is correlated to fire radiative power – a proxy of open biomass burning activity. Using potential emission sensitivity maps derived using the model, we suggest that underestimation of BC mass in the model during spring is due to the underestimation of BC fluxes over southern India (possibly from open-biomass-burning/forest-fires). The overall performance of the model simulations using three different emission inventories (SAFAR-India, ECLIPSE and RETRO) is similar, with ECLIPSE and SAFAR-India performing marginally better as both have about 30% higher emissions for India than RETRO. The ratio of observed to modelled annual mean BC concentration was estimated as 1.5 for SAFAR, 1.7 for ECLIPSE and 2.4 for RETRO.

Wintertime characteristics of aerosols at middle Indo-Gangetic Plain: Impacts of regional meteorology and long range transport

To develop a coherent picture of possible origin of Asian aerosol, transport and meteorological interaction; wintertime aerosol (January, 1 to March, 31, 2014 (n = 90)) were measured in middle IGP in terms of aerosol mass loading, optical properties, altitudinal distributions and both high and low altitude transportation. Both space-borne passive (Aqua and Terra MODIS) and active sensor (CALIPSO-CALIOP) based measurements were concurrently used over the selected transect (25°10′–25°19′N and 82°54′–83°4′E). Exceptionally high aerosol mass loading was recorded for PM10 (233 ± 58.37 μg m−3) and PM2.5 (138 ± 47.12 μg m−3). Daily variations of PM2.5/PM10 persist in a range of 0.25–0.97 (mean = 0.60 ± 0.14; n = 90) and were in accordance to computed Angstrom exponent (0.078–1.407; mean: 1.002 ± 0.254) explaining concurrent contribution of both PM2.5 and PM10 for the region. Space borne (Aqua MODIS-AOD: 0.259–2.194) and ground based (MTP-AOD: 0.066–1.239) AODs revealed significant temporal variability and moderate association in terms of PM10 (MODIS-AOD: 0.46; MTP-AOD: 0.56) and PM2.5 (MODIS-AOD: 0.54; MTP-AOD: 0.39). Varying association of AOD and aerosol mass loading was also explained in terms of meteorological variables. CALIPSO altitude-orbit-cross-section profiles revealed presence of non-spherical coarse particulates (altitude: 1.2–5.4 km) and dominance of spherical fine particulates (altitude: 0.1–4.2 km). Contribution of trans-boundary aerosols transportation to mass loadings at middle IGP were recognized through lagrangian particle dispersion model, synoptic vector wind profiles at different geopotential heights and satellite images.

Source identification of total mercury (TM) wet deposition using a Lagrangian particle dispersion model (LPDM)

Event-based precipitation samples for total mercury (TM) were collected with a modified MIC-B sampler concurrent with atmospheric gaseous oxidized mercury (GOM) and particulate bound mercury (PBM) concentrations in Seoul, Korea from January 2006 to December 2009 to: 1) determine the seasonal variations in TM wet deposition, 2) investigate contributions of GOM and PBM scavenging to mercury wet deposition, and 3) identify source areas contributing to the high TM wet deposition using a Lagrangian particle dispersion model (LPDM). The volume-weighted mean (VWM) TM concentration was highest in winter, while the wet deposition flux was highest in summer. Multiple linear regression showed that the scavenging coefficient (SC) for GOM was much higher than the SC for PBM indicating that GOM was more effectively scavenged by wet deposition than PBM. Joint-probability LPDM (JP-LPDM) indicated that the main sources of TM wet deposition were the industrial and coastal areas in China and Japan.

Long-term high frequency measurements of ethane, benzene and methyl chloride at Ragged Point, Barbados: Identification of long-range transport events

Abstract Here we present high frequency long-term observations of ethane, benzene and methyl chloride from the AGAGE Ragged Point, Barbados, monitoring station made using a custom built GC-MS system. Our analysis focuses on the first three years of data (2005–2007) and on the interpretation of periodic episodes of high concentrations of these compounds. We focus specifically on an exemplar episode during September 2007 to assess if these measurements are impacted by long-range transport of biomass burning and biogenic emissions. We use the Lagrangian Particle Dispersion model, NAME, run forwards and backwards in time to identify transport of air masses from the North East of Brazil during these events. To assess whether biomass burning was the cause we used hot spots detected using the MODIS instrument to act as point sources for simulating the release of biomass burning plumes. Excellent agreement for the arrival time of the simulated biomass burning plumes and the observations of enhancements in the trace gases indicates that biomass burning strongly influenced these measurements. These modelling data were then used to determine the emissions required to match the observations and compared with bottom up estimates based on burnt area and literature emission factors. Good agreement was found between the two techniques highlight the important role of biomass burning. The modelling constrained by in situ observations suggests that the emission factors were representative of their known upper limits, with the in situ data suggesting slightly greater emissions of ethane than the literature emission factors account for. Further analysis was performed concluding only a small role for biogenic emissions of methyl chloride from South America impacting measurements at Ragged Point. These results highlight the importance of long-term high frequency measurements of NMHC and ODS and highlight how these data can be used to determine sources of emissions 1000’s km away.

A method to quantitatively apportion pollutants at high spatial and temporal resolution: the Stochastic Lagrangian Apportionment Method (SLAM).

We introduce a method to quantify upwind contributions to concentrations of atmospheric pollutants. The Stochastic Lagrangian Apportionment Method (SLAM) carries out the following: (1) account for chemical transformations and depositional losses; (2) incorporate the effects of turbulent dispersion; (3) simulate the locations of the sources with high spatial and temporal resolution; and (4) minimize the impact from numerical diffusion. SLAM accomplishes these four features by using a time-reversed Lagrangian particle dispersion model and then simulating chemical changes forward in time, while tagging and keeping track of different sources. As an example of SLAM's application, we show its use in apportioning sources contributing to ammonia (NH3) and ammonium particulates (p-NH4(+)) at a site in southern Ontario, Canada. Agricultural emissions are seen to dominate contributions to NH3 and p-NH4(+) at the site. The source region of NH3 was significantly smaller than that of p-NH4(+), which covered numerous states of the American Midwest. The source apportionment results from SLAM were compared against those from zeroing-out individual sources ("brute force method"; BFM). The comparisons show SLAM to produce almost identical results as BFM for NH3, but higher concentrations of p-NH4(+), likely due to indirect effects that affect BFM. Finally, uncertainties in the SLAM approach and ways to address such shortcomings by combining SLAM with inverse methods are discussed.

A regional CO<sub>2</sub> observing system simulation experiment for the ASCENDS satellite mission

Abstract. Top–down estimates of the spatiotemporal variations in emissions and uptake of CO2 will benefit from the increasing measurement density brought by recent and future additions to the suite of in situ and remote CO2 measurement platforms. In particular, the planned NASA Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) satellite mission will provide greater coverage in cloudy regions, at high latitudes, and at night than passive satellite systems, as well as high precision and accuracy. In a novel approach to quantifying the ability of satellite column measurements to constrain CO2 fluxes, we use a portable library of footprints (surface influence functions) generated by the Stochastic Time-Inverted Lagrangian Transport (STILT) model in combination with the Weather Research and Forecasting (WRF) model in a regional Bayesian synthesis inversion. The regional Lagrangian particle dispersion model framework is well suited to make use of ASCENDS observations to constrain weekly fluxes in North America at a high resolution, in this case at 1° latitude × 1° longitude. We consider random measurement errors only, modeled as a function of the mission and instrument design specifications along with realistic atmospheric and surface conditions. We find that the ASCENDS observations could potentially reduce flux uncertainties substantially at biome and finer scales. At the grid scale and weekly resolution, the largest uncertainty reductions, on the order of 50%, occur where and when there is good coverage by observations with low measurement errors and the a priori uncertainties are large. Uncertainty reductions are smaller for a 1.57 μm candidate wavelength than for a 2.05 μm wavelength, and are smaller for the higher of the two measurement error levels that we consider (1.0 ppm vs. 0.5 ppm clear-sky error at Railroad Valley, Nevada). Uncertainty reductions at the annual biome scale range from ~40% to ~75% across our four instrument design cases and from ~65% to ~85% for the continent as a whole. Tests suggest that the quantitative results are moderately sensitive to assumptions regarding a priori uncertainties and boundary conditions. The a posteriori flux uncertainties we obtain, ranging from 0.01 to 0.06 Pg C yr−1 across the biomes, would meet requirements for improved understanding of long-term carbon sinks suggested by a previous study.

Modelled atmospheric contribution to nitrogen eutrophication in the English Channel and the southern North Sea

Eutrophication of the coastal waters results in algal blooms which may be harmful to the marine ecosystem and coastal economy. The main sources of nutrients are the rivers but an unquantified amount of nitrogen is also transported from ground sources via the atmosphere and deposited to the sea directly by rain and turbulent diffusion. A Lagrangian Particle Dispersion (LPD) model based on the open source code FLEXPART (http://flexpart.eu) is described that quantifies the dissolved nitrogen coming from the air in the English Channel and Southern North Sea (the ‘2Seas’ geographical region). The model uses meteorological records, emissions data and LPD computations to simulate the motion and deposition of nitrogen compounds. The emission sources contributing to the deposition are individually identified, and calculated concentrations are compared with ground measurements in selected locations. The highest calculated atmospheric depositions to the sea in the considered region are found to be along the Belgium–Netherlands coast.

Uncertainty in Lagrangian pollutant transport simulations due to meteorological uncertainty from a mesoscale WRF ensemble

Abstract. Lagrangian particle dispersion models require meteorological fields as input. Uncertainty in the driving meteorology is one of the major uncertainties in the results. The propagation of uncertainty through the system is not simple, and it has not been thoroughly explored. Here, we take an ensemble approach. Six different configurations of the Weather Research and Forecast (WRF) model drive otherwise identical simulations with FLEXPART-WRF for 49 days over eastern North America. The ensemble spreads of wind speed, mixing height, and tracer concentration are presented. Uncertainty of tracer concentrations due solely to meteorological uncertainty is 30–40%. Spatial and temporal averaging reduces the uncertainty marginally. Tracer age uncertainty due solely to meteorological uncertainty is 15–20%. These are lower bounds on the uncertainty, because a number of processes are not accounted for in the analysis.

A new circulation type classification based upon Lagrangian air trajectories

A new classification method of the large-scale circulation characteristic for a specific target area (NW Iberian Peninsula) is presented, based on the analysis of 90-h backward trajectories arriving in this area calculated with the 3-D Lagrangian particle dispersion model FLEXPART. A cluster analysis is applied to separate the backward trajectories in up to five representative air streams for each day. Specific measures are then used to characterise the distinct air streams (e.g., curvature of the trajectories, cyclonic or anticyclonic flow, moisture evolution, origin and length of the trajectories). The robustness of the presented method is demonstrated in comparison with the Eulerian Lamb weather type classification. A case study of the 2003 heatwave is discussed in terms of the new Lagrangian circulation and the Lamb weather type classifications. It is shown that the new classification method adds valuable information about the pertinent meteorological conditions, which are missing in an Eulerian approach. The new method is climatologically evaluated for the five-year time period from December 1999 to November 2004. The ability of the method to capture the inter-seasonal circulation variability in the target region is shown. Furthermore, the multi-dimensional character of the classification is shortly discussed, in particular with respect to inter-seasonal differences. Finally, the relationship between the new Lagrangian classification and the precipitation in the target area is studied.

FLEXINVERT: an atmospheric Bayesian inversion framework for determining surface fluxes of trace species using an optimized grid

Abstract. We present a new modular Bayesian inversion framework, called FLEXINVERT, for estimating the surface fluxes of atmospheric trace species. FLEXINVERT can be applied to determine the spatio-temporal flux distribution of any species for which the atmospheric loss (if any) can be described as a linear process and can be used on continental to regional and even local scales with little or no modification. The relationship between changes in atmospheric mixing ratios and fluxes (the so-called source–receptor relationship) is described by a Lagrangian Particle Dispersion Model (LPDM) run in a backwards-in-time mode. In this study, we use FLEXPART but any LPDM could be used. The framework determines the fluxes on a nested grid of variable resolution, which is optimized based on the source–receptor relationships for the given observation network. Background mixing ratios are determined by coupling FLEXPART to the output of a global Eulerian model (or alternatively, from the observations themselves) and are also optionally optimized in the inversion. Spatial and temporal error correlations in the fluxes are taken into account using a simple model of exponential decay with space and time and, additionally, the aggregation error from the variable grid is accounted for. To demonstrate the use of FLEXINVERT, we present one case study in which methane fluxes are estimated in Europe in 2011 and compare the results to those of an independent inversion ensemble.

Locating and quantifying greenhouse gas emissions at a geological CO2storage site using atmospheric modeling and measurements

The Cooperative Research Centre for Greenhouse Gas Technologies (CO2CRC) Otway Project is Australia's first demonstration of the geological storage of carbon dioxide (CO2), where about 65,000 metric tons of fluid consisting of 92% CO2 and 8% methane (CH4) by mass have been injected underground. As part of the project objective of developing methodologies to detect, locate, and quantify potential leakage of the stored fluid into the atmosphere, we formulate an inverse atmospheric model based on a Bayesian probabilistic framework coupled to a state-of-the-art backward Lagrangian particle dispersion model. A Markov chain Monte Carlo method is used for efficiently sampling the posterior probability distribution of the source parameters. Controlled experiments used to test the model involved releases of the injected fluid from one of the nearby wells and were staggered over 1 month. Atmospheric measurements of CO2 and CH4 concentrations were taken at two stations installed in an upwind-downwind configuration. Modeling both the emission rate and the source location using the concentration measurements from only two stations is difficult, but the fact that the emission rate was constant, which is not an unrealistic scenario for potential geological leakage, allows us to compute both parameters. The modeled source parameters compare reasonably well with the actual values, with the CH4 tracer constraining the source better than CO2, largely as a result of its 6 times higher signal-to-noise ratio. The results lend confidence in the ability of atmospheric techniques to quantify potential leakage from CO2 storage as well as other source types.

Greenhouse gas network design using backward Lagrangian particle dispersion modelling − Part 1: Methodology and Australian test case

Abstract. This paper describes the generation of optimal atmospheric measurement networks for determining carbon dioxide fluxes over Australia using inverse methods. A Lagrangian particle dispersion model is used in reverse mode together with a Bayesian inverse modelling framework to calculate the relationship between weekly surface fluxes, comprising contributions from the biosphere and fossil fuel combustion, and hourly concentration observations for the Australian continent. Meteorological driving fields are provided by the regional version of the Australian Community Climate and Earth System Simulator (ACCESS) at 12 km resolution at an hourly timescale. Prior uncertainties are derived on a weekly timescale for biosphere fluxes and fossil fuel emissions from high-resolution model runs using the Community Atmosphere Biosphere Land Exchange (CABLE) model and the Fossil Fuel Data Assimilation System (FFDAS) respectively. The influence from outside the modelled domain is investigated, but proves to be negligible for the network design. Existing ground-based measurement stations in Australia are assessed in terms of their ability to constrain local flux estimates from the land. We find that the six stations that are currently operational are already able to reduce the uncertainties on surface flux estimates by about 30%. A candidate list of 59 stations is generated based on logistic constraints and an incremental optimisation scheme is used to extend the network of existing stations. In order to achieve an uncertainty reduction of about 50%, we need to double the number of measurement stations in Australia. Assuming equal data uncertainties for all sites, new stations would be mainly located in the northern and eastern part of the continent.

Analysis of the major atmospheric moisture sources affecting three sub-regions of East China

In this study, the atmospheric moisture sources affecting three sub-regions of East China, which are South China (SC), the middle and lower Yangtze River Valleys (YRV), and North China (NC), are identified and compared based on the simulation results of a Lagrangian particle dispersion model, FLEXPART, for the period 2000–2009. The investigation is conducted for ‘non-precipitation’ and ‘precipitation’ cases of summertime (May–September) and wintertime (October–April). Furthermore, the contributions of moisture originating from the land area, western Pacific Ocean, and Indian Ocean to the precipitation in the three sub-regions of East China are quantitatively estimated. The results indicate that during summertime, the transport of moisture from the Indian Ocean and western Pacific dominates the precipitation in SC, with almost equivalent contributions; evaporation over the land area seems to be the key contributor to the precipitation in YRV and NC, followed by the evaporation over the western Pacific. During wintertime, the precipitated moisture in SC originates largely from the western Pacific, while evaporation over the land area is the predominant contributor to the precipitation in YRV and NC. The results of this study indicate that the precipitation in the three sub-regions of East China reflect the contributions of the Indian monsoon and East Asian summer monsoon to different degrees.

Estimating volcanic ash hazard in European airspace

The widespread disruption of European air traffic in late April 2010, during the eruption of Eyjafjallajökull, showed the importance of early assessment of volcanic hazard from explosive eruptions. In this study, we focus on the short-term hazard of airborne ash from a climatological perspective, focusing on eruptions on Iceland. By studying eruptions of different intensity and frequency, we estimate the overall probability that ash concentration levels considered hazardous to aviation are exceeded over different parts of Europe.The method involves setting up a range of eruption scenarios based on the eruptive history of Icelandic volcanoes, and repeated simulation of these scenarios for 2years' worth of meteorological data. Simulations are conducted using meteorological data from the ERA-Interim reanalysis set, which is downscaled using the Weather Research and Forecasting (WRF) model. The weather data are then used to drive the Lagrangian particle dispersion model FLEXPART-WRF for each of the eruption scenarios. A set of threshold values, commonly used in Volcanic Ash Advisories, are used to analyze concentration data from the dispersion model.We see that the dispersion of ash is highly dominated by the mid-latitude westerlies and mainly affect northern UK and the Scandinavian peninsula. The occurrence of high ash levels from Icelandic volcanoes is lower over continental Europe but should not be neglected for eruptions when the release rate of fine ash (<16μ m) is in the order of 107 kg s −1 or higher.There is a clear seasonal variation in the ash hazard. During the summer months, the dominating dispersion direction is less distinct with some plumes extending to the northwest and Greenland. In contrast, during the winter months, the strong westerly winds tend to transport most of the emissions eastwards. The affected area of a winter-time eruption is likely to be larger as high concentrations can be found at a further distance downwind from the volcano, effectively increasing the probability of hazardous levels of ash reaching the European continent.The concentration thresholds for aviation, which were adopted after the Eyjafjallajökull eruption in 2010, have strong influence on the hazard estimates for weaker eruptions but is less important for larger eruptions; thus ash forecasts for weaker eruptions are likely more uncertain in comparison to larger eruptions.

Transport effects on the vertical distribution of tropospheric ozone over western India

AbstractIn situ tropospheric ozone measurements by balloon‐borne electrochemical concentration cell (ECC) sensors above Ahmedabad in western India from May 2003 to July 2007 are presented, along with an analysis of the transport processes responsible for the observed vertical ozone distribution. This analysis is supported by 12 day back trajectory calculations using the FLEXPART Lagrangian particle dispersion model. Lowest ozone (~20 ppbv) is observed near the surface during September at the end of the Asian summer monsoon season. Average midtropospheric (5–10 km above sea level) ozone is greatest (70–75 ppbv) during April–June and lowest (40–50 ppbv) during winter. Ozone variability is greatest in the upper troposphere with higher ozone during March–May. The FLEXPART retroplume results show that the free tropospheric vertical ozone distribution above this location is affected by long‐range transport from the direction of North Africa and North America. Ozone levels are also affected by transport from the stratosphere particularly during March–April. The lower tropospheric (&lt;3 km) ozone distribution during the Asian summer monsoon is affected by transport from the Indian Ocean via the east coast of Africa and the Arabian Sea. Influence from deep convection in the upper troposphere confined over central Asia has been simulated by FLEXPART. Lower ozone levels are observed during August–November than in any other season at 10–14 km above sea level. These in situ observations are in contrast to other studies based on satellite data which show that the lowest ozone values at these altitudes occur during the Asian summer monsoon.