Transport of NOxin East Asia identified by satellite and in situ measurements and Lagrangian particle dispersion model simulations

2014 ◽  
Vol 119 (5) ◽  
pp. 2574-2596 ◽  
Author(s):  
H.-J. Lee ◽  
S.-W. Kim ◽  
J. Brioude ◽  
O. R. Cooper ◽  
G. J. Frost ◽  
...  
Keyword(s):  
East Asia ◽  
Dispersion Model ◽  
Particle Dispersion ◽  
In Situ Measurements ◽  
Lagrangian Particle ◽  
Model Simulations

scholarly journals Synergy between satellite observations and model simulations during extreme events

2018 ◽  
Author(s):  
Anne Wiese ◽  
Joanna Staneva ◽  
Johannes Schultz-Stellenfleth ◽  
Arno Behrens ◽  
Luciana Fenoglio-Marc ◽  
...  
Keyword(s):  
Satellite Data ◽  
Extreme Events ◽  
Wave Model ◽  
Coastal Areas ◽  
In Situ Measurements ◽  
Model Simulations ◽  
Significant Wave ◽  
Operational Analysis

Abstract. In this study, the quality of wind and wave data provided by the new Sentinel-3A satellite is evaluated. We focus on coastal areas, where altimeter data are of lower quality than those for the open ocean. The satellite data of Sentinel-3A, Jason-2 and CryoSat-2 are assessed in a comparison with in situ measurements and spectral wave model (WAM) simulations. The sensitivity of the wave model to wind forcing is evaluated using data with different temporal and spatial resolution, such as ERA-Interim and ERA5 reanalyses, ECMWF operational analysis and short-range forecasts, German Weather Service (DWD) forecasts and regional atmospheric model simulations -coastDat. Numerical simulations show that both the wave model forced using the ERA5 reanalyses and that forced using the ECMWF operational analysis/forecast demonstrate the best capability over the whole study period, as well as during extreme events. To further estimate the variance of the significant wave height of ensemble members for different wind forcings, especially during extreme events, an empirical orthogonal function (EOF) analysis is performed. Intercomparisons between remote sensing and in situ observations demonstrate that the overall quality of the former is good over the North Sea and Baltic Sea throughout the study period, although the significant wave heights estimated based on satellite data tend to be greater than the in situ measurements by 7 cm to 26 cm. The quality of all satellite data near the coastal area decreases; however, within 10 km off the coast, Sentinel-3A performs better than the other two satellites. Analyses in which data from satellite tracks are separated in terms of onshore and offshore flights have been carried out. No substantial differences are found when comparing the statistics for onshore and offshore flights. Moreover, no substantial differences are found between satellite tracks under various metocean conditions. Furthermore, the satellite data quality does not depend on the wind direction relative to the flight direction. Thus, the quality of the data obtained by the new Sentinel-3A satellite over coastal areas is improved compared to that of older satellites.

Tracking source area of Shangdianzi station using Lagrangian particle dispersion model of FLEXPART

2013 ◽  
Vol 21 (3) ◽  
pp. 466-473 ◽  
Author(s):  
Xingqin An ◽  
Bo Yao ◽  
Yan Li ◽  
Nan Li ◽  
Lingxi Zhou
Keyword(s):  
Dispersion Model ◽  
Source Area ◽  
Particle Dispersion ◽  
Lagrangian Particle

Simulation of a dense gas chlorine release with a Lagrangian particle dispersion model (LPDM)

2021 ◽  
Vol 244 ◽  
pp. 117791 ◽  
Author(s):  
Félix Gomez ◽  
Bruno Ribstein ◽  
Laurent Makké ◽  
Patrick Armand ◽  
Jacques Moussafir ◽  
...  
Keyword(s):  
Dispersion Model ◽  
Particle Dispersion ◽  
Dense Gas ◽  
Lagrangian Particle

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

2014 ◽  
Vol 14 (17) ◽  
pp. 9363-9378 ◽  
Author(s):  
T. Ziehn ◽  
A. Nickless ◽  
P. J. Rayner ◽  
R. M. Law ◽  
G. Roff ◽  
...  
Keyword(s):  
Network Design ◽  
Fossil Fuel ◽  
Dispersion Model ◽  
Surface Flux ◽  
Particle Dispersion ◽  
Surface Fluxes ◽  
Test Case ◽  
Lagrangian Particle ◽  
Gas Network ◽  
Australian Continent

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.

Evaluation of the Lagrangian particle dispersion model DIPCOT against data from wind tunnel simulations of quasi two-dimensional turbulent flow

2005 ◽  
Vol 24 (1/2/3/4) ◽  
pp. 114 ◽  
Author(s):  
Efstratios Davakis ◽  
Spyros Andronopoulos ◽  
George A. Sideridis ◽  
Eleftherios G. Kastrinakis ◽  
Stavros G. Nychas ◽  
...  
Keyword(s):  
Wind Tunnel ◽  
Turbulent Flow ◽  
Dispersion Model ◽  
Particle Dispersion ◽  
Two Dimensional ◽  
Lagrangian Particle

scholarly journals Boundary layer and free-tropospheric dimethyl sulfide in the Arctic spring and summer

2017 ◽  
Vol 17 (14) ◽  
pp. 8757-8770 ◽  
Author(s):  
Roghayeh Ghahremaninezhad ◽  
Ann-Lise Norman ◽  
Betty Croft ◽  
Randall V. Martin ◽  
Jeffrey R. Pierce ◽  
...  
Keyword(s):  
Dimethyl Sulfide ◽  
Transport Model ◽  
Dispersion Model ◽  
Open Water ◽  
Particle Dispersion ◽  
The Arctic ◽  
Chemical Transport Model ◽  
Model Simulations ◽  
Baffin Bay ◽  
Mixing Ratios

Abstract. Vertical distributions of atmospheric dimethyl sulfide (DMS(g)) were sampled aboard the research aircraft Polar 6 near Lancaster Sound, Nunavut, Canada, in July 2014 and on pan-Arctic flights in April 2015 that started from Longyearbyen, Spitzbergen, and passed through Alert and Eureka, Nunavut, and Inuvik, Northwest Territories. Larger mean DMS(g) mixing ratios were present during April 2015 (campaign mean of 116  ±  8 pptv) compared to July 2014 (campaign mean of 20  ±  6 pptv). During July 2014, the largest mixing ratios were found near the surface over the ice edge and open water. DMS(g) mixing ratios decreased with altitude up to about 3 km. During April 2015, profiles of DMS(g) were more uniform with height and some profiles showed an increase with altitude. DMS reached as high as 100 pptv near 2500 m. Relative to the observation averages, GEOS-Chem (www.geos-chem.org) chemical transport model simulations were higher during July and lower during April. Based on the simulations, more than 90 % of the July DMS(g) below 2 km and more than 90 % of the April DMS(g) originated from Arctic seawater (north of 66° N). During April, 60 % of the DMS(g), between 500 and 3000 m originated from Arctic seawater. During July 2014, FLEXPART (FLEXible PARTicle dispersion model) simulations locate the sampled air mass over Baffin Bay and the Canadian Arctic Archipelago 4 days back from the observations. During April 2015, the locations of the air masses 4 days back from sampling were varied: Baffin Bay/Canadian Archipelago, the Arctic Ocean, Greenland and the Pacific Ocean. Our results highlight the role of open water below the flight as the source of DMS(g) during July 2014 and the influence of long-range transport (LRT) of DMS(g) from further afield in the Arctic above 2500 m during April 2015.

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