Inverse Modelling for Identification of Multiple-Point Releases from Atmospheric Concentration Measurements

2012 ◽  
Vol 146 (2) ◽  
pp. 277-295 ◽  
Author(s):  
Sarvesh Kumar Singh ◽  
Maithili Sharan ◽  
Jean-Pierre Issartel
Keyword(s):  
Inverse Modelling ◽  
Multiple Point ◽  
Atmospheric Concentration ◽  
Concentration Measurements

scholarly journals Atmospheric CO2 measurements with a 2-μm DIAL instrument

2018 ◽  
Vol 176 ◽  
pp. 05045 ◽  
Author(s):  
Erwan Cadiou ◽  
Jean-Baptiste Dherbecourt ◽  
Guillaume Gorju ◽  
Jean-Michel Melkonian ◽  
Antoine Godard ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Long Range ◽  
Direct Detection ◽  
Atmospheric Co2 ◽  
Scattered Signal ◽  
Differential Absorption ◽  
Differential Absorption Lidar ◽  
Atmospheric Concentration ◽  
Concentration Measurements ◽  
Parametric Source

We report on ground-based atmospheric concentration measurements of carbon dioxide, using a pulsed direct detection differential absorption lidar operating at 2051 nm. The transmitter is based on a tunable parametric source emitting 10-mJ energy, 10-ns duration Fourier-limited pulses. Range resolved concentration measurements have been carried out on the aerosol back-scattered signal. Cloud signals have been used to get long range integrated-path measurements.

scholarly journals Exploring new methods of estimating deposition using atmospheric concentration measurements: A modeling case study of ammonia downwind of a feedlot

2020 ◽  
Vol 290 ◽  
pp. 107989
Author(s):  
William Lassman ◽  
Jeffrey L. Collett ◽  
Jay M. Ham ◽  
Azer P. Yalin ◽  
Kira B. Shonkwiler ◽  
...  
Keyword(s):  
Atmospheric Concentration ◽  
New Methods ◽  
Concentration Measurements

scholarly journals Inverse modelling of the Chernobyl source term using atmospheric concentration and deposition measurements

2017 ◽  
Author(s):  
Nikolaos Evangeliou ◽  
Thomas Hamburger ◽  
Anne Cozic ◽  
Yves Balkanski ◽  
Andreas Stohl
Keyword(s):  
Soviet Union ◽  
Former Soviet Union ◽  
Source Term ◽  
Main Direction ◽  
Inverse Modelling ◽  
Spent Fuel ◽  
Atmospheric Concentration ◽  
The Public ◽  
The Usa ◽  
Modelling Techniques

Abstract. The present paper describes the results of an inverse modelling study for the determination of the source term of the radionuclides 134Cs, 137Cs and 131I released after the Chernobyl accident. The accident occurred on 26 April 1986 in the Former Soviet Union and released about 1019 Bq of radioactive materials that were transported as far away as the USA and Japan. Thereafter, several attempts to assess the real magnitude of the emissions were made that were based on the knowledge of the core inventory and the levels of the spent fuel. More recently, when modelling tools were further developed, inverse modelling techniques were applied to the Chernobyl case for source term quantification. However, because radioactivity is a sensitive topic for the public and attracts a lot of attention, high quality measurements, that are essential for inverse modelling, were not made available except for a few sparse activity concentration measurements far from the source and far from the main direction of the radioactive fallout. For the first time, we apply Bayesian inversion of the Chernobyl source term using not only activity concentrations, but also deposition measurements from the most recent public dataset. These observations refer to a data rescue attempt that started more than 10 years ago, with a final goal to give such kind of measurements into anyone interested. As regards to our inverse modelling results, emissions of 134Cs were estimated to be 80 PBq or 30–50 % higher than what was previously published. From the released amount of 134Cs, about 70 PBq were deposited all over Europe. Similar to 134Cs, emissions of 137Cs were estimated as 86 PBq, in the same order with previously reported results. Finally, 131I emissions of 1365 PBq were found, which are about 10 % less than the prior total releases. The inversion pushes the injection heights of the three radionuclides to higher altitudes (up to about 3 km) than previously assumed (≈ 2.2 km) in order to better match both concentration and deposition observations over Europe. The results were of the present inversion were confirmed using an independent Eulerial model, for which deposition patterns were also improved when using the estimated posterior releases. Although the independent model tends to underestimate deposition in countries that are not in the main direction of the plume, it reproduces country levels of deposition very efficiently. The results were also tested for robustness against different set-ups of the inversion through sensitivity runs. The source term data from this study are made publically available.

An inversion technique for the retrieval of single-point emissions from atmospheric concentration measurements

2009 ◽  
Vol 465 (2107) ◽  
pp. 2069-2088 ◽  
Author(s):  
Maithili Sharan ◽  
Jean-Pierre Issartel ◽  
Sarvesh Kumar Singh ◽  
Pramod Kumar
Keyword(s):  
Point Source ◽  
Dispersion Model ◽  
Single Point ◽  
Point Sources ◽  
Average Error ◽  
Diffusion Experiment ◽  
Theoretical Point ◽  
Monitoring Networks ◽  
Atmospheric Concentration ◽  
Concentration Measurements

The aim of the study is to propose a technique for the retrieval of point sources of atmospheric trace species from concentration measurements. The inverse problem of identifying the parameters of a point source is addressed within the assimilative framework of renormalization recently proposed for the identification of distributed emissions. This theory has been extended for the point sources based on the property that these are associated with the maximum of the renormalized estimate computed from the observations. This approach along with an analytic dispersion model is used for point source identification, and the sensitivity of the samplers is described by the same model in backward mode. The proposed technique is illustrated not only with synthetic measurements but also with seven sets of observations, corresponding to convective conditions, taken from the low-wind tracer diffusion experiment conducted at the Indian Institute of Technology Delhi in 1991. The position and intensity of the source are retrieved exactly with the synthetic measurements in all the sets validating the technique. The position of the source is retrieved with an average error of 17 m, mostly along wind; its intensity is estimated within a factor 2 for all the sets of real observations. From a theoretical point of view, the link established between point and distributed sources clarifies new concepts for the exploitation of monitoring networks. In particular, the influence of the noise on the identification of a source is related to the relative visibility of the various regions described with a renormalizing weight function. The geometry of the environment modified according to the weights is interpreted as an apparent geometry. It is analogous to the apparent flatness of the starry sky in eye's view, usually considered an impression rather than a scientific fact.

scholarly journals An attempt at estimating Paris area CO<sub>2</sub> emissions from atmospheric concentration measurements

2014 ◽  
Vol 14 (7) ◽  
pp. 9647-9703 ◽  
Author(s):  
F. M. Bréon ◽  
G. Broquet ◽  
V. Puygrenier ◽  
F. Chevallier ◽  
I. Xueref-Rémy ◽  
...  
Keyword(s):  
Boundary Conditions ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Current System ◽  
A Priori ◽  
Temporal Correlation ◽  
Atmospheric Concentration ◽  
Sources And Sinks ◽  
Paris Area ◽  
Concentration Measurements

Abstract. Atmospheric concentration measurements are used to adjust the daily to monthly budget of CO2 emissions from the AirParif inventory of the Paris agglomeration. We use 5 atmospheric monitoring sites including one at the top of the Eiffel tower. The atmospheric inversion is based on a Bayesian approach, and relies on an atmospheric transport model with a spatial resolution of 2 km with boundary conditions from a global coarse grid transport model. The inversion tool adjusts the CO2 fluxes (anthropogenic and biogenic) with a temporal resolution of 6 h, assuming temporal correlation of emissions uncertainties within the daily cycle and from day to day, while keeping the a priori spatial distribution from the emission inventory. The inversion significantly improves the agreement between measured and modelled concentrations. However, the amplitude of the atmospheric transport errors is often large compared to the CO2 gradients between the sites that are used to estimate the fluxes, in particular for the Eiffel tower station. In addition, we sometime observe large model-measurement differences upwind from the Paris agglomeration, which confirms the large and poorly constrained contribution from distant sources and sinks included in the prescribed CO2 boundary conditions These results suggest that (i) the Eiffel measurements at 300 m above ground cannot be used with the current system and (ii) the inversion shall rely on the measured upwind-downwind gradients rather than the raw mole fraction measurements. With such setup, realistic emissions are retrieved for two 30 day periods. Similar inversions over longer periods are necessary for a proper evaluation of the results.

scholarly journals Comparing the CarbonTracker and TM5-4DVar data assimilation systems for CO<sub>2</sub> surface flux inversions

2015 ◽  
Vol 15 (6) ◽  
pp. 8883-8932 ◽  
Author(s):  
A. Babenhauserheide ◽  
S. Basu ◽  
S. Houweling ◽  
W. Peters ◽  
A. Butz
Keyword(s):  
Data Assimilation ◽  
Co2 Flux ◽  
Surface Flux ◽  
Co2 Concentration ◽  
Surface Fluxes ◽  
Filter Method ◽  
Operational Parameters ◽  
Atmospheric Concentration ◽  
The North ◽  
Concentration Measurements

Abstract. Data assimilation systems allow for estimating surface fluxes of greenhouse gases from atmospheric concentration measurements. Good knowledge about fluxes is essential to understand how climate change affects ecosystems and to characterize feedback mechanisms. Based on assimilation of more than one year of atmospheric in-situ concentration measurements, we compare the performance of two established data assimilation models, CarbonTracker and TM5-4DVar, for CO2 flux estimation. CarbonTracker uses an Ensemble Kalman Filter method to optimize fluxes on ecoregions. TM5-4DVar employs a 4-D variational method and optimizes fluxes on a 6° × 4° longitude/latitude grid. Harmonizing the input data allows analyzing the strengths and weaknesses of the two approaches by direct comparison of the modelled concentrations and the estimated fluxes. We further assess the sensitivity of the two approaches to the density of observations and operational parameters such as temporal and spatial correlation lengths. Our results show that both models provide optimized CO2 concentration fields of similar quality. In Antarctica CarbonTracker underestimates the wintertime CO2 concentrations, since its 5-week assimilation window does not allow for adjusting the far-away surface fluxes in response to the detected concentration mismatch. Flux estimates by CarbonTracker and TM5-4DVar are consistent and robust for regions with good observation coverage, regions with low observation coverage reveal significant differences. In South America, the fluxes estimated by TM5-4DVar suffer from limited representativeness of the few observations. For the North American continent, mimicking the historical increase of measurement network density shows improving agreement between CarbonTracker and TM5-4DVar flux estimates for increasing observation density.

scholarly journals Comparing the CarbonTracker and TM5-4DVar data assimilation systems for CO<sub>2</sub> surface flux inversions

2015 ◽  
Vol 15 (17) ◽  
pp. 9747-9763 ◽  
Author(s):  
A. Babenhauserheide ◽  
S. Basu ◽  
S. Houweling ◽  
W. Peters ◽  
A. Butz
Keyword(s):  
Data Assimilation ◽  
Transport Model ◽  
Time Window ◽  
Co2 Flux ◽  
Surface Fluxes ◽  
Variational Model ◽  
Filter Method ◽  
Operational Parameters ◽  
Atmospheric Concentration ◽  
Concentration Measurements

Abstract. Data assimilation systems allow for estimating surface fluxes of greenhouse gases from atmospheric concentration measurements. Good knowledge about fluxes is essential to understand how climate change affects ecosystems and to characterize feedback mechanisms. Based on the assimilation of more than 1 year of atmospheric in situ concentration measurements, we compare the performance of two established data assimilation models, CarbonTracker and TM5-4DVar (Transport Model 5 – Four-Dimensional Variational model), for CO2 flux estimation. CarbonTracker uses an ensemble Kalman filter method to optimize fluxes on ecoregions. TM5-4DVar employs a 4-D variational method and optimizes fluxes on a 6° × 4° longitude–latitude grid. Harmonizing the input data allows for analyzing the strengths and weaknesses of the two approaches by direct comparison of the modeled concentrations and the estimated fluxes. We further assess the sensitivity of the two approaches to the density of observations and operational parameters such as the length of the assimilation time window. Our results show that both models provide optimized CO2 concentration fields of similar quality. In Antarctica CarbonTracker underestimates the wintertime CO2 concentrations, since its 5-week assimilation window does not allow for adjusting the distant surface fluxes in response to the detected concentration mismatch. Flux estimates by CarbonTracker and TM5-4DVar are consistent and robust for regions with good observation coverage, regions with low observation coverage reveal significant differences. In South America, the fluxes estimated by TM5-4DVar suffer from limited representativeness of the few observations. For the North American continent, mimicking the historical increase of the measurement network density shows improving agreement between CarbonTracker and TM5-4DVar flux estimates for increasing observation density.

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