the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The Lagrangian Atmospheric Radionuclide Transport Model (ARTM) – Sensitivity studies and evaluation using airborne measurements of power plant emissions
Dominik Brunner
Christiane Voigt
Alina Fiehn
Anke Roiger
Margit Pattantyús-Ábrahám
Abstract. 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 prop- erties 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 sensitiv- ity 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 turbu- lence 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. All simulated mixing ratios are in the same order of magnitude as the airborne in situ data. An extensive uncertainty analysis using probability distribution functions, statistical tests and direct spatio-temporal comparisons of measurements and model results help to quantify the model uncertainties. With the default turbulence setups of ARTM version 2.8.0 and 3.0.0, the plume widths are underestimated by up to 50 % resulting in a strong overestimation of the maximum plume CO2 mixing ratios. The comparison of the three alternative turbulence models shows a good agreement of the peak plume CO2 concentrations, the CO2 distribution within the plumes and the plume width with 30 % deviations in peak CO2 concentration and less than 25 % deviation of the measured CO2 plume width. Uncertainties of the simulations may arise from the different spatial and temporal resolution of simulations and measurements in addition to the turbulence parametrisation and boundary conditions. The results of this work may help to improve the accurate representa- tion of real plumes in very unstable atmospheric conditions by the selection of distinct turbulence models. Further comparisons at different stability regimes are required for a final assessment of model uncertainties.
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Robert Hanfland et al.
Status: final response (author comments only)
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RC1: 'Comment on egusphere-2023-245', Anonymous Referee #1, 26 May 2023
Hanfland et al. present an extensive evaluation of a Lagrangian radionuclide transport model, incorporating sensitivity analyses, tests of adherence to the well-mixed criterion, and comparisons against real-world observations downwind of power plant emissions. Overall, the analyses are solid and the presentation is fine.
I only have minor comments before suggesting the paper for publication:
Line 15: reword to "allow evaliation of model performance"
Line 95: "Sensitiviy analysis (SA)..."
Line 171: "correspond to typical"
Line 346: what is meant by "quite high"?
Line 386: "As tracer 14C in its bounded form as CO2 is used" doesn't seem to make sense
Line 459: "simulations' uncertainties"
Citation: https://doi.org/10.5194/egusphere-2023-245-RC1 -
RC2: 'Comment on egusphere-2023-245', Anonymous Referee #2, 11 Sep 2023
GENERAL COMMENTS
This contribution is certainly of interest to the (Lagrangian, but not only) dispersion-modelling community, since, even if in application to a single model, it proposes an approach and a methodology to sensitivity studies and validation that may be adopted by other researchers.
After a sensitivity study of the input parameters for the standard version of the model ARTM (presumably, with ARTM2 turbulence parameterization: this could be specified in Section 3, then referring to Section 4 for its description), the rest of the article is dedicated to the evaluation of five different parameterization schemes for the Lagrangian turbulent variables.
A main concern for me is the partial use of Hanna's parameterization for the MODHANNA combination. Hanna (1982) determined different formulations for the three stratifications for both the wind velocity fluctuations (sigmas) and the Lagrangian time scales. For consistency and homogeneity, I would find it preferable to adopt for both the sigmas and the timescales the full set of Hanna's formulations, which are based on a scale analysis, surface- and boundary-layer parameters. Picking up only the horizontal sigmas might end up being an ad-hoc adjustment based on some improvement in results, which might not be likewise effective in other cases. Hanna's formulation for the vertical sigma in the unstable case varies depending on four different ranges of the ratio between the actual height and the PBL height, yet they are quite simple.
The analysis in Section 5 is indeed thorough and rigorous. However, the observational data may be affected by a large uncertainty, given their origin from aircraft measurements. This is anticipated in the introductory part of the Section, but maybe it should be better addressed and somehow a 'quantitative' indication of the uncertainty of the observations could be provided. This, because the observations are then used to test and 'rank' the performance of the turbulence parameterizations, whose mutual differences might occur to lie inside the uncertainty range of the observations themselves.
In the conclusions, some suggestions and some findings that may be generalised are provided. Following validations of the model, in its different configurations and parameterizations, for the other atmospheric stratifications will be welcome, especially for stable conditions where turbulence parameterizations face their main challenge.
In the following there are some specific comments, referred to the Line Number "L XX". I think the manuscript can be considered for publication after revision.
SPECIFIC COMMENTS (AND SOME TECHNICAL NOTES)
L 87-92. In addition to citing the differences with other models using prognostic meteo fields, it would be worth including some references of similar approaches based on a diagnostic mass-consistent model driving an LPDM
L 91. (...) 'in the vicinity': how much close?
L 95. Maybe better to define 'SA' here instead of L 39.
L 162. The top of the domain is at 300 m: was it high enough to resolve the (very)unstable conditions and their effect on the opening of the plume volume by turbulent diffusion?
L 169. In general, in the boundary-layer formulas, it is common to use the zero-plane displacement (say, zd in m), while here its 'factor d' is introduced: it would be worth making explicit the relationship between zd and d, which can be inferred only later by Table 4.
L 220. "The strong influence of hs on this target quantity is intuitively understandable, but interestingly SC is still more important."
This can be somehow expected, since the stability conditions determine the potential vertical dispersion of the plume, thus having an influence on the effectiveness of the horizontal transport and, consequently, on the location of the maximum at the ground. As known, in stable conditions, being vertical motions suppressed, the plume may travel longer distances thus maximum at the ground may be found farther from the source; whereas, in unstable stratification the downdrafts due to the thermals may bring the plume released from high sources to hit the ground relatively close to the source itself.
L 236. It would be worth explaining here why only the unstable formulations are presented, even if this becomes clear later. This, also in view of the fact that stable conditions are critical for their impact, given that the pollutant tends to remain inside a shallow boundary layer.
L 238-239. "For simplicity the zero-plane displacement is not taken into account in the following equations."
Not clear to me whether this is just to simplify the formulations as reported in the manuscript or if the zero-plane displacement is not used in the formulations of the ARTM model itself.
L 249. Since, as the authors state, the ARTM2 model is not widely used, some more information on what is based on and on how the coefficients are derived would be useful.
L 260. What are the implications of 'mixing' the ARTM2 vertical sigma with the horizontal sigmas by Hanna's model? Why not use the complete Hanna formulations, also with his Lagrangian time scales?
L 306. A dot missing after "are used"
L 311. It would be worth specifying whether the profile assigned to the wind velocity is the actual one for unstable stratification; the authors may consider including a figure for it, even just in the supplementary material. Also, a wind speed of 1 m/s at 10-m height corresponds to rather low-wind conditions, is there a reason for this choice?
L 394-395 Wind-meandering is generally associated with non-turbulent oscillations of the horizontal wind velocity, so in principle it cannot be expected to be represented in the turbulence spectrum, therefore to be resolved by turbulence parameterisations. The authors might comment on this aspect.
L 449-450: I am not sure how to interpret the sentence:
"However, in Fig. 7 the highest maximum mixing ratios at wall 1 occur at different transects for the simulations and the observation"
Should it be Fig. 8 instead of Fig. 7? Wall 1 corresponds to transect 2 (Fig. 6), while looking at Fig. 7 the maximum mixing ratios are found, for both simulated and observed data, in transect 1: is this the meaning?
L 532-533: "The mixing properties of the ARTM3 model may bias simulation results when handling with γ-cloud-shine or wet deposition."
This sentence sounds a bit out of (this) context: consider explaining and justifying it more, or removing it.
L 553-561. The authors might find of interest some other comparisons between Hanna and Degrazia parameterizations, in the following papers:
Carvalho et al., 2002. Atmospheric Environment, 36, n. 7, 1147-1161
Trini Castelli et al. 2014. Quart J Roy Meteorol Soc., 140, 2023-2036
Trini Castelli et al. 2014. In: Steyn DG, Builtjes P (eds) Air pollution modeling and its application XXII. Springer, Berlin, 529–534
Citation: https://doi.org/10.5194/egusphere-2023-245-RC2
Robert Hanfland et al.
Robert Hanfland et al.
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