scholarly journals WOMBAT v1.0: a fully Bayesian global flux-inversion framework

2022 ◽  
Vol 15 (1) ◽  
pp. 45-73
Author(s):  
Andrew Zammit-Mangion ◽  
Michael Bertolacci ◽  
Jenny Fisher ◽  
Ann Stavert ◽  
Matthew Rigby ◽  
...  
Keyword(s):  
Transport Model ◽  
Atmospheric Transport ◽  
Trace Gases ◽  
Remotely Sensed Data ◽  
Bayesian Hierarchical ◽  
Statistical Framework ◽  
Correlated Error ◽  
Out Of Sample ◽  
Fully Bayesian ◽  
Observing System Simulation Experiment

Abstract. WOMBAT (the WOllongong Methodology for Bayesian Assimilation of Trace-gases) is a fully Bayesian hierarchical statistical framework for flux inversion of trace gases from flask, in situ, and remotely sensed data. WOMBAT extends the conventional Bayesian synthesis framework through the consideration of a correlated error term, the capacity for online bias correction, and the provision of uncertainty quantification on all unknowns that appear in the Bayesian statistical model. We show, in an observing system simulation experiment (OSSE), that these extensions are crucial when the data are indeed biased and have errors that are spatio-temporally correlated. Using the GEOS-Chem atmospheric transport model, we show that WOMBAT is able to obtain posterior means and variances on non-fossil-fuel CO2 fluxes from Orbiting Carbon Observatory-2 (OCO-2) data that are comparable to those from the Model Intercomparison Project (MIP) reported in Crowell et al. (2019). We also find that WOMBAT's predictions of out-of-sample retrievals obtained from the Total Column Carbon Observing Network (TCCON) are, for the most part, more accurate than those made by the MIP participants.

scholarly journals WOMBAT v1.0: A fully Bayesian global flux-inversion framework

2021 ◽  
Author(s):  
Andrew Zammit-Mangion ◽  
Michael Bertolacci ◽  
Jenny Fisher ◽  
Ann Stavert ◽  
Matthew L. Rigby ◽  
...  
Keyword(s):  
Transport Model ◽  
Atmospheric Transport ◽  
Trace Gases ◽  
Chem Phys ◽  
Remotely Sensed Data ◽  
Bayesian Hierarchical ◽  
Correlated Error ◽  
Out Of Sample ◽  
Fully Bayesian ◽  
Observing System Simulation Experiment

Abstract. WOMBAT (the WOllongong Methodology for Bayesian Assimilation of Trace-gases) is a fully Bayesian hierarchical statistical framework for flux inversion of trace gases from flask, in situ, and remotely sensed data. WOMBAT extends the conventional Bayesian-synthesis framework through the consideration of a correlated error term, the capacity for online bias correction, and the provision of uncertainty quantification on all unknowns that appear in the Bayesian statistical model. We show, in an observing system simulation experiment (OSSE), that these extensions are crucial when the data are indeed biased and have errors that are spatio-temporally correlated. Using the GEOS-Chem atmospheric transport model, we show that WOMBAT is able to obtain posterior means and variances on non-fossil-fuel CO2 fluxes from Orbiting Carbon Observatory-2 (OCO-2) data that are comparable to those from the Model Intercomparison Project (MIP) reported in Crowell et al. (2019, Atmos. Chem. Phys., vol. 19). We also find that WOMBAT's predictions of out-of-sample retrievals obtained from the Total Column Carbon Observing Network are, for the most part, more accurate than those made by the MIP participants.

Recovery after stroke: the severely impaired are a distinct group

2021 ◽  
pp. jnnp-2021-327211
Author(s):  
Anna K Bonkhoff ◽  
Tom Hope ◽  
Danilo Bzdok ◽  
Adrian G Guggisberg ◽  
Rachel L Hawe ◽  
...  
Keyword(s):  
Distinct Group ◽  
Recovery Process ◽  
Prediction Performance ◽  
Stroke Recovery ◽  
Bayesian Hierarchical ◽  
Statistical Framework ◽  
Process Framework ◽  
Out Of Sample ◽  
Out Of Sample Prediction ◽  
Different Levels

IntroductionStroke causes different levels of impairment and the degree of recovery varies greatly between patients. The majority of recovery studies are biased towards patients with mild-to-moderate impairments, challenging a unified recovery process framework. Our aim was to develop a statistical framework to analyse recovery patterns in patients with severe and non-severe initial impairment and concurrently investigate whether they recovered differently.MethodsWe designed a Bayesian hierarchical model to estimate 3–6 months upper limb Fugl-Meyer (FM) scores after stroke. When focusing on the explanation of recovery patterns, we addressed confounds affecting previous recovery studies and considered patients with FM-initial scores <45 only. We systematically explored different FM-breakpoints between severe/non-severe patients (FM-initial=5–30). In model comparisons, we evaluated whether impairment-level-specific recovery patterns indeed existed. Finally, we estimated the out-of-sample prediction performance for patients across the entire initial impairment range.ResultsRecovery data was assembled from eight patient cohorts (n=489). Data were best modelled by incorporating two subgroups (breakpoint: FM-initial=10). Both subgroups recovered a comparable constant amount, but with different proportional components: severely affected patients recovered more the smaller their impairment, while non-severely affected patients recovered more the larger their initial impairment. Prediction of 3–6 months outcomes could be done with an R2=63.5% (95% CI=51.4% to 75.5%).ConclusionsOur work highlights the benefit of simultaneously modelling recovery of severely-to-non-severely impaired patients and demonstrates both shared and distinct recovery patterns. Our findings provide evidence that the severe/non-severe subdivision in recovery modelling is not an artefact of previous confounds. The presented out-of-sample prediction performance may serve as benchmark to evaluate promising biomarkers of stroke recovery.

scholarly journals The Environmental Effects of the April 2020 Wildfires and the Cs-137 Re-Suspension in the Chernobyl Exclusion Zone: A Multi-Hazard Threat

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 467
Author(s):  
Rocío Baró ◽  
Christian Maurer ◽  
Jerome Brioude ◽  
Delia Arnold ◽  
Marcus Hirtl
Keyword(s):  
Air Quality ◽  
Nuclear Power ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Point Of View ◽  
Plume Dispersion ◽  
Exclusion Zone ◽  
Fire Plume ◽  
Mean Values ◽  
The Impact

This paper demonstrates the environmental impacts of the wildfires occurring at the beginning of April 2020 in and around the highly contaminated Chernobyl Exclusion Zone (CEZ). Due to the critical fire location, concerns arose about secondary radioactive contamination potentially spreading over Europe. The impact of the fire was assessed through the evaluation of fire plume dispersion and re-suspension of the radionuclide Cs-137, whereas, to assess the smoke plume effect, a WRF-Chem simulation was performed and compared to Tropospheric Monitoring Instrument (TROPOMI) satellite columns. The results show agreement of the simulated black carbon and carbon monoxide plumes with the plumes as observed by TROPOMI, where pollutants were also transported to Belarus. From an air quality and health perspective, the wildfires caused extremely bad air quality over Kiev, where the WRF-Chem model simulated mean values of PM2.5 up to 300 µg/m3 (during the first fire outbreak) over CEZ. The re-suspension of Cs-137 was assessed by a Bayesian inverse modelling approach using FLEXPART as the atmospheric transport model and Ukraine observations, yielding a total release of 600 ± 200 GBq. The increase in both smoke and Cs-137 emissions was only well correlated on the 9 April, likely related to a shift of the focus area of the fires. From a radiological point of view even the highest Cs-137 values (average measured or modelled air concentrations and modelled deposition) at the measurement site closest to the Chernobyl Nuclear Power Plant, i.e., Kiev, posed no health risk.

Modelling nitrogen transport across compartments over northern Europe

2021 ◽  
Author(s):  
Stefan Hagemann ◽  
Ute Daewel ◽  
Volker Matthias ◽  
Tobias Stacke
Keyword(s):  
Baltic Sea ◽  
Land Surface ◽  
Climate Model ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Regional Climate ◽  
Marine Ecosystem ◽  
Coupled System ◽  
Nutrient Concentrations ◽  
Nutrient Loads

&lt;p&gt;River discharge and the associated nutrient loads are important factors that influence the functioning of the marine ecosystem. Lateral inflows from land carrying fresh, nutrient-rich water determine coastal physical conditions and nutrient concentration and, hence, dominantly influence primary production in the system. Since this forms the basis of the trophic food web, riverine nutrient concentrations impact the variability of the whole coastal ecosystem. This process becomes even more relevant in systems like the Baltic Sea, which is almost decoupled from the open ocean and land-borne nutrients play a major role for ecosystem productivity on seasonal up to decadal time scales.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;In order to represent the effects of climate or land use change on nutrient availability, a coupled system approach is required to simulate the transport of nutrients across Earth system compartments. This comprises their transport within the atmosphere, the deposition and human application at the surface, the lateral transport over the land surface into the ocean and their dynamics and transformation in the marine ecosystem. In our study, we combine these processes in a modelling chain within the GCOAST (Geesthacht Coupled cOAstal model SysTem) framework for the northern European region. This modelling chain comprises:&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;ul&gt;&lt;li&gt;Simulation of emissions, atmospheric transport and deposition with the chemistry transport model CMAQ at 36 km grid resolution using atmospheric forcing from the coastDat3 data that have been generated with the regional climate model COSMO-CLM over Europe at 0.11&amp;#176; resolution using ERA-Interim re-analyses as boundary conditions&lt;/li&gt; &lt;li&gt;Simulation of inert processes at the land surface with the global hydrology model HydroPy (former MPI-HM), i.e. considering total nitrogen without any chemical reactions&lt;/li&gt; &lt;li&gt;Riverine transport with the Hydrological Discharge (HD) model at 0.0833&amp;#176; spatial resolution&lt;/li&gt; &lt;li&gt;Simulation of the North Sea and Baltic Sea ecosystems with 3D coupled physical-biogeochemical NPZD-model ECOSMO II at about 10 km resolution&lt;/li&gt; &lt;/ul&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;We will present first results and their validation from this exercise.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals Inverse modelling of CF<sub>4</sub> and NF<sub>3</sub> emissions in East Asia

2018 ◽  
Vol 18 (18) ◽  
pp. 13305-13320 ◽  
Author(s):  
Tim Arnold ◽  
Alistair J. Manning ◽  
Jooil Kim ◽  
Shanlan Li ◽  
Helen Webster ◽  
...  
Keyword(s):  
South Korea ◽  
East Asia ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Atmospheric Gases ◽  
Atmospheric Measurements ◽  
Small Uncertainty ◽  
Measurement Location ◽  
Atmospheric Transport Model ◽  
Good Agreement

Abstract. Decadal trends in the atmospheric abundances of carbon tetrafluoride (CF4) and nitrogen trifluoride (NF3) have been well characterised and have provided a time series of global total emissions. Information on locations of emissions contributing to the global total, however, is currently poor. We use a unique set of measurements between 2008 and 2015 from the Gosan station, Jeju Island, South Korea (part of the Advanced Global Atmospheric Gases Experiment network), together with an atmospheric transport model, to make spatially disaggregated emission estimates of these gases in East Asia. Due to the poor availability of good prior information for this study, our emission estimates are largely influenced by the atmospheric measurements. Notably, we are able to highlight emission hotspots of NF3 and CF4 in South Korea due to the measurement location. We calculate emissions of CF4 to be quite constant between the years 2008 and 2015 for both China and South Korea, with 2015 emissions calculated at 4.3±2.7 and 0.36±0.11 Gg yr−1, respectively. Emission estimates of NF3 from South Korea could be made with relatively small uncertainty at 0.6±0.07 Gg yr−1 in 2015, which equates to ∼1.6 % of the country's CO2 emissions. We also apply our method to calculate emissions of CHF3 (HFC-23) between 2008 and 2012, for which our results find good agreement with other studies and which helps support our choice in methodology for CF4 and NF3.

scholarly journals Measurements of hydrogen cyanide (HCN) and acetylene (C<sub>2</sub>H<sub>2</sub>) from the Infrared Atmospheric Sounding Interferometer (IASI)

2013 ◽  
Vol 6 (4) ◽  
pp. 917-925 ◽  
Author(s):  
V. Duflot ◽  
D. Hurtmans ◽  
L. Clarisse ◽  
Y. R'honi ◽  
C. Vigouroux ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Hydrogen Cyanide ◽  
Atmospheric Transport ◽  
Trace Gases ◽  
First Order ◽  
Line Mixing ◽  
Atmospheric Sounding ◽  
First Time ◽  
Order Comparison ◽  
Emission Ratios

Abstract. Hydrogen cyanide (HCN) and acetylene (C2H2) are ubiquitous atmospheric trace gases with medium lifetime, which are frequently used as indicators of combustion sources and as tracers for atmospheric transport and chemistry. Because of their weak infrared absorption, overlapped by the CO2 Q branch near 720 cm−1, nadir sounders have up to now failed to measure these gases routinely. Taking into account CO2 line mixing, we provide for the first time extensive measurements of HCN and C2H2 total columns at Reunion Island (21° S, 55° E) and Jungfraujoch (46° N, 8° E) in 2009–2010 using observations from the Infrared Atmospheric Sounding Interferometer (IASI). A first order comparison with local ground-based Fourier transform infraRed (FTIR) measurements has been carried out allowing tests of seasonal consistency which is reasonably captured, except for HCN at Jungfraujoch. The IASI data shows a greater tendency to high C2H2 values. We also examine a nonspecific biomass burning plume over austral Africa and show that the emission ratios with respect to CO agree with previously reported values.

scholarly journals Properties of aerosols and formation mechanisms over southern China during the monsoon season

2016 ◽  
Author(s):  
Weihua Chen ◽  
Xuemei Wang ◽  
Jason Blake Cohen ◽  
Shengzhen Zhou ◽  
Zhisheng Zhang ◽  
...  
Keyword(s):  
Aqueous Phase ◽  
Atmospheric Aerosols ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Southern China ◽  
Monsoon Season ◽  
Sea Salt ◽  
Urban Site ◽  
Chemical Components ◽  
Formation Mechanisms

Abstract. Measurements of size-resolved aerosols from 0.25 to 18 μm were conducted at three sites (urban, suburban and background sites) and used in tandem with an atmospheric transport model to study the size distribution and formation of atmospheric aerosols in southern China during the monsoon season (May–June) in 2010. The mass distribution showed the majority of chemical components were found in the smaller size bins (< 2.5 μm). Sulfate, was found to be strongly correlated with aerosol water, and anti-correlated with atmospheric SO2, hinting at aqueous-phase reactions being the main formation pathway. Nitrate was the only major species that showed a bi-modal distribution at the urban site, and was dominated by the coarse mode in the other two sites, suggesting that an important component of nitrate formation is chloride depletion of sea salt transported from the South China Sea. In addition to these aqueous-phase reactions and interactions with sea salt aerosols, new particle formation, chemical aging, and long-range transport from upwind urban or biomass burning regions were also found to be important in at least some of the sights on some of the days. This work therefore summarizes the different mechanisms that significantly impact the aerosol chemical composition during the Monsoon over southern China.

scholarly journals Study of the main processes driving atmospheric CH<sub>4</sub> variability in a rural Spanish region

2017 ◽  
Author(s):  
Claudia Grossi ◽  
Felix R. Vogel ◽  
Roger Curcoll ◽  
Alba Àgueda ◽  
Arturo Vargas ◽  
...  
Keyword(s):  
Transport Model ◽  
Atmospheric Transport ◽  
Daily Basis ◽  
Atmospheric Variability ◽  
Absolute Deviation ◽  
Monthly Basis ◽  
Bottom Up ◽  
Methane Fluxes ◽  
Carbon Dioxide Co2 ◽  
Atmospheric Concentrations

Abstract. Atmospheric concentrations of the two main greenhouse gases (GHGs), carbon dioxide (CO2) and methane (CH4), are continuously measured since November 2012 at the Spanish rural station of Gredos (GIC3), within the climate network ClimaDat, together with atmospheric radon (222Rn) tracer and meteorological parameters. The atmospheric variability of CH4 concentrations measured from 2013 to 2015 at GIC3 has been analyzed in this study. It is interpreted in relation to the variability of measured 222Rn concentrations, modelled 222Rn fluxes and modelled heights of the planetary boundary layer (PBLH) in the same period. In addition, nocturnal fluxes of CH4 were estimated using two methods: the Radon Tracer Method (RTM) and one based on the EDGARv4.2 bottom-up emission inventory. Both previous methods have been applied using the same footprints, calculated with the atmospheric transport model FLEXPARTv6.2. Results show that daily and seasonal changes in atmospheric concentrations of 222Rn (and the corresponding fluxes) can help to understand the atmospheric CH4 variability. On daily basis, the variation in the PBLH mainly drives changes in 222Rn and CH4 concentrations while, on monthly basis, their atmospheric variability seems to depend on changes in their emissions. The median value of RTM based methane fluxes (FR_CH4) is 0.17 mg CH4 m−2 h−1 with an absolute deviation of 0.08 mg CH4 m−2 h−1. Median methane fluxes based on bottom-up inventory (FE_CH4) is of 0.32 mg CH4 m−2 h−1 with an absolute deviation of 0.06 mg CH4 m−2 h−1. Monthly FR_CH4 flux shows a seasonality which is not observed in the monthly FE_CH4 flux. During January–May FR_CH4 fluxes present a median value of 0.08 mg CH4 m−2 h−1 with an absolute deviation of 0.05 mg CH4 m−2 h−1 and a median value of 0.19 mg CH4 m−2 h−1 with an absolute deviation of 0.06 mg CH4 m−2 h−1 during June–December. This seasonal doubling of the median methane fluxes calculated by RTM at the GIC3 area seems to be mainly related to the alternate presence of transhumant livestock in the GIC3 area. The results obtained in this study highlight the benefit of applying independent RTM to improve the seasonality of the emission factors from bottom-up inventories.

scholarly journals A European-wide <sup>222</sup>Radon and <sup>222</sup>Radon progeny comparison study

2016 ◽  
Author(s):  
Dominik Schmithüsen ◽  
Scott Chambers ◽  
Bernd Fischer ◽  
Stefan Gilge ◽  
Juha Hatakka ◽  
...  
Keyword(s):  
Transport Model ◽  
Atmospheric Transport ◽  
Ground Level ◽  
Data Sets ◽  
Correction Factors ◽  
Comparison Study ◽  
Data Set ◽  
Parallel Data ◽  
Linear Regressions ◽  
Atmospheric Transport Model

Abstract. A European-wide 222Radon/222Radon progeny comparison study has been conducted in order to determine correction factors that could be applied to existing atmospheric 222Radon data sets for quantitative use of this tracer in atmospheric transport model validation. Two compact and easy-to-transport Heidelberg Radon Monitors (HRM) were moved around to run for at least one month at each of the nine European measurement stations that were included in the comparison. Linear regressions between parallel data sets were calculated, yielding correction factors relative to the HRM ranging from 0.68 to 1.45. A calibration bias between ANSTO (Australian Nuclear Science and Technology Organisation) two-filter radon monitors and the HRM of ANSTO/HRM = 1.11 ± 0.05 was found. For continental stations, which use one-filter systems, preliminary 214Po/222Rn disequilibrium values were estimated to lie between 0.8 at mountain stations (e.g. Schauinsland) and 0.9 at non-mountain sites for sampling heights around 20 to 30 m above ground level. Respective corrections need to be applied to obtain a consistent European 222Radon data set for further applications.

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