ARL Weekly News – September 16, 2022
WMO Releases Air Quality and Climate Update
The 2022 WMO Air Quality and Climate Bulletin No. 2, released on September 7, provides an update on the global distribution of particulate matter for 2021, highlighting the contribution of extreme wildfire events. The Bulletin also underscores the relationship between air quality and climate change and a range of possible outcomes as the climate warms under various emissions scenarios. The launch coincided with the United Nations International Day of Clean Air for Blue Skies. ARL Director Ariel Stein is an editor of the bulletin.
Employee Resources Group Presentation.
Rick Saylor, Acting Director of ARL’s Atmospheric Turbulence and Diffusion Division (ATDD), gave a presentation on September 15th to the GREEN Employee Resources Group of Fresenius Medical Care. The presentation gave an overview of boundary-layer related research at ATDD and how this research helps NOAA improve its capabilities in weather and air quality forecasting and climate prediction. The GREEN Employee Resources Group is an employee interest group focused on environmental topics and hosts periodic presentations from a variety of subject matter experts in environmental fields.
Alice Crawford, Ariel Stein, Tianfeng Chai, and Fantine Ngan, contributed to the Third international challenge to model the medium- to long-range transport of radioxenon to four Comprehensive Nuclear-Test-Ban Treaty monitoring stations, Journal of Environmental Radioactivity
Citation: Maurer, S. Galmarini, E. Solazzo, J. Kuśmierczyk-Michulec, J. Baré, M. Kalinowski, M. Schoeppner, P. Bourgouin, A. Crawford, A. Stein, T. Chai, F. Ngan, A. Malo, P. Seibert, A. Axelsson, A. Ringbom, R. Britton, A. Davies, M. Goodwin, P.W. Eslinger, T.W. Bowyer, L.G. Glascoe, D.D. Lucas, S. Cicchi, P. Vogt, Y. Kijima, A. Furuno, P.K. Long, B. Orr, A. Wain, K. Park, K.-S. Suh, A. Quérel, O. Saunier, D. Quélo, Third international challenge to model the medium- to long-range transport of radioxenon to four Comprehensive Nuclear-Test-Ban Treaty monitoring stations, Journal of Environmental Radioactivity, Volume 255, 2022, 106968, ISSN 0265-931X, https://doi.org/10.1016/j.jenvrad.2022.106968. Abstract: In 2015 and 2016, atmospheric transport modeling challenges were conducted in the context of the Comprehensive Nuclear-Test-Ban Treaty (CTBT) verification, however, with a more limited scope with respect to emission inventories, simulation period and number of relevant samples (i.e., those above the Minimum Detectable Concentration (MDC)) involved. Therefore, a more comprehensive atmospheric transport modeling challenge was organized in 2019. Stack release data of Xe-133 were provided by the Institut National des Radioéléments/IRE (Belgium) and the Canadian Nuclear Laboratories/CNL (Canada) and accounted for in the simulations over a three (mandatory) or six (optional) months period. Best estimate emissions of additional facilities (radiopharmaceutical production and nuclear research facilities, commercial reactors or relevant research reactors) of the Northern Hemisphere were included as well. Model results were compared with observed atmospheric activity concentrations at four International Monitoring System (IMS) stations located in Europe and North America with overall considerable influence of IRE and/or CNL emissions for evaluation of the participants’ runs. Participants were prompted to work with controlled and harmonized model set-ups to make runs more comparable, but also to increase diversity. It was found that using the stack emissions of IRE and CNL with daily resolution does not lead to better results than disaggregating annual emissions of these two facilities taken from the literature if an overall score for all stations covering all valid observed samples is considered. A moderate benefit of roughly 10% is visible in statistical scores for samples influenced by IRE and/or CNL to at least 50% and there can be considerable benefit for individual samples. Effects of transport errors, not properly characterized remaining emitters and long IMS sampling times (12–24 h) undoubtedly are in contrast to and reduce the benefit of high-quality IRE and CNL stack data. Complementary best estimates for remaining emitters push the scores up by 18% compared to just considering IRE and CNL emissions alone. Despite the efforts undertaken the full multi-model ensemble built is highly redundant. An ensemble based on a few arbitrary runs is sufficient to model the Xe-133 background at the stations investigated. The effective ensemble size is below five. An optimized ensemble at each station has on average slightly higher skill compared to the full ensemble. However, the improvement (maximum of 20% and minimum of 3% in RMSE) in skill is likely being too small for being exploited for an independent period.
McCandless, T., Gagne, D.J., Kosović, B., Haupt, S.E., Yang, B., Becker, C., and Schreck, J. Machine Learning for Improving Surface-Layer-Flux Estimates. Boundary-Layer Meteorol (2022). https://doi.org/10.1007/s10546-022-00727-4
Abstract: Flows in the atmospheric boundary layer are turbulent, characterized by a large Reynolds number, the existence of a roughness sublayer and the absence of a well-defined viscous layer. Exchanges with the surface are therefore dominated by turbulent fluxes. In numerical models for atmospheric flows, turbulent fluxes must be specified at the surface; however, surface fluxes are not known a priori and therefore must be parametrized. Atmospheric flow models, including global circulation, limited area models, and large-eddy simulation, employ Monin–Obukhov similarity theory (MOST) to parametrize surface fluxes. The MOST approach is a semi-empirical formulation that accounts for atmospheric stability effects through universal stability functions. The stability functions are determined based on limited observations using simple regression as a function of the non-dimensional stability parameter representing a ratio of distance from the surface and the Obukhov length scale (Obukhov in Trudy Inst Theor Geofiz AN SSSR 1:95–115, 1946), z/Lz/L. However, simple regression cannot capture the relationship between governing parameters and surface-layer structure under the wide range of conditions to which MOST is commonly applied. We therefore develop, train, and test two machine-learning models, an artificial neural network (ANN) and random forest (RF), to estimate surface fluxes of momentum, sensible heat, and moisture based on surface and near-surface observations. To train and test these machine-learning algorithms, we use several years of observations from the Cabauw mast in the Netherlands and from the National Oceanic and Atmospheric Administration’s Field Research Division tower in Idaho. The RF and ANN models outperform MOST. Even when we train the RF and ANN on one set of data and apply them to the second set, they provide more accurate estimates of all of the fluxes compared to MOST. Estimates of sensible heat and moisture fluxes are significantly improved, and model interpretability techniques highlight the logical physical relationships we expect in