47 research outputs found

    The Lagrangian Atmospheric Radionuclide Transport Model (ARTM) — Sensitivity studies and evaluation using airborne measurements of power plant emissions

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    The Atmospheric Radionuclide Transport Model (ARTM) operates at the meso-γ-scale and simulates the dispersion of radionuclides originating from nuclear facilities under routine operation within the planetary boundary layer. This study presents the extension and validation of this Lagrangian particle dispersion model and consists of three parts: i) a sensitivity study that aims to assess the impact of key input parameters on the simulation results; ii) the evaluation of the mixing properties of five different turbulence models using the well-mixed criterion; and iii) a comparison of model results to airborne observations of carbon dioxide (CO2) emissions from a power plant and the evaluation of related uncertainties. In the sensitivity study, we analyse the effects of stability class, roughness length, zero-plane displacement factor and source height on the three-dimensional plume extent as well as the distance between source and maximum concentration at the ground. The results show that the stability class is the most sensitive input parameter as expected. The five turbulence models are the default turbulence models of ARTM 2.8.0 and ARTM 3.0.0, one alternative built-in turbulence model of ARTM and two further turbulence models implemented for this study. The well-mixed condition tests showed that all five turbulence models are able to preserve an initially well-mixed atmospheric boundary layer reasonably well. The models deviate only 6% from the expected uniform concentration below 80% of the mixing layer height except for the default turbulence model of ARTM 3.0.0 with deviations by up to 18%, respectively. CO2 observations along a flight path in the vicinity of the lignite power plant Bełchatów, Poland measured by the DLR Cessna aircraft during the CoMet campaign in 2018 allow to evaluate the model performance for the different turbulence models under unstable boundary layer conditions

    CO2Image retrieval studies and performance analysis

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    Current and planned satellite missions such as the Japanese GOSAT (Greenhouse Gases Observing Satellite) and NASA's OCO (Orbiting Carbon Observatory) series and the upcoming Copernicus Carbon Dioxide Monitoring (CO2M) mission aim to constrain national and regional-scale emissions down to scales of urban agglomerations and large point sources. The CO2Image demonstrator mission of the German Aerospace Center (DLR) is specifically designed to detect and quantify carbon dioxide (CO2) and methane (CH4) emissions from medium-size point sources. To this end its COSIS (Carbon dioxide Sensing Imaging Spectrometer) push-broom grating spectrometer measures reflected solar radiation with a high spatial resolution of 50x50 m2, covering tiles of ~50x50 km2 extent. The instrument has a moderate spectral resolution of approximately ~1 nm and observes in a single spectral window in the 2 µm region. Here we present and discuss the impact of the expected COSIS performance on the retrieved level-2 data. The level-1 data (spectra) are generated using the Py4CAtS (Python for Computational ATmospheric Spectroscopy) line-by-line radiative transfer model and the COSIS SIMulator (COSIS-SIM). Based on the COSIS instrument parameters the analysis examines the retrieval errors related to noise which allows to estimate the detection and quantification limit of CO2 and CH4 emission rates at the instrument's spatial and spectral resolution. We further discuss the effect of heterogeneous scenes, i.e. high contrast surfaces that cause an effective distortion of the spectral response function by non-uniform illumination of the entrance slit. Finally, we assess the influence of initial guess values for the plume's vertical extent and shape on the retrieval

    The CO2Image mission: retrieval studies and performance analysis

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    The CO2Image satellite mission, led by the German Aerospace Center (DLR), aims to demonstrate the feasibility of quantifying carbon dioxide (CO2) and methane (CH4) emissions from medium-size point sources. Several DLR institutes are currently working on the reliminary design phase (Phase B) of the mission. Here we present a performance analysis based on the current instrument specifications. The Beer InfraRed Retrieval Algorithm (BIRRA), the line-by-line radiative transfer model Py4CAtS (Python for Computational ATmospheric Spectroscopy) and a COSIS (Carbon dioxide Sensing Imaging Spectrometer) instrument model are employed to infer CO2 and CH4 concentrations from synthetic COSIS spectra. We evaluate the instrument's performance and determine if it meets the intended requirements. The study assesses uncertainties in the retrieved concentrations as well as errors in point source emission estimates caused by instrument noise. First results suggest that the detection and quantification limits stated in the mission requirements document are justified. The analysis also demonstrates that retrieval errors tend to increase when the signal-to-noise ratio is low, complicating the distinction between emission sources and background concentrations. Furthermore, we discuss non-instrumental effects and demonstrate that the fit quality significantly improves if a low-level plume is scaled instead of a background reference profile that covers the atmosphere's full vertical extent. The analysis on heterogeneous scenes (high albedo contrast) reveals that the various instrument setups perform similarly for both molecules
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