Mesoscale inversion: first results from the CERES campaign with synthetic data

Abstract. We investigate the ability of a mesoscale model to reconstruct CO2 fluxes at regional scale. Formally, we estimate the reduction of error for a CO2 flux inversion at 8 km resolution in the South West of France, during four days of the CarboEurope Regional Experiment (CERES) in spring 2005. Measurements from two towers and two airplanes are available for this campaign. The lagrangian particle dispersion model LPDM was coupled to the non-hydrostatic model Meso-NH and integrated in a matrix inversion framework. Impacts of aircraft and tower measurements are quantified separately and together. We find that the configuration with both towers and aircraft is able to significantly reduce uncertainties on the 4-day averaged CO2 fluxes over about half of the 300×300 km domain. Most of this reduction comes from the tower measurements, even though the impact of aircraft measurements remains noticeable. The noise contributed by imperfect knowledge of boundary inflows does not significantly impair the resolution. We test alternative strategies to improve the impact of aircraft measurements and find that most information comes from measurements inside the boundary layer.

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Mesoscale inversion: first results from the CERES campaign with synthetic data

Atmospheric Chemistry and Physics ◽

10.5194/acp-8-3459-2008 ◽

2008 ◽

Vol 8 (13) ◽

pp. 3459-3471 ◽

Cited By ~ 73

Author(s):

T. Lauvaux ◽

M. Uliasz ◽

C. Sarrat ◽

F. Chevallier ◽

P. Bousquet ◽

...

Keyword(s):

Mesoscale Model ◽

Dispersion Model ◽

Regional Scale ◽

Error Reduction ◽

Particle Dispersion ◽

Surface Fluxes ◽

Co2 Fluxes ◽

Aircraft Measurements ◽

Imperfect Knowledge ◽

The Impact

Abstract. We investigate the ability of a mesoscale model to reconstruct CO2 fluxes at regional scale. Formally, we estimate the reduction of error for a CO2 flux inversion at 8 km resolution in the South West of France, during four days of the CarboEurope Regional Experiment Strategy (CERES) in spring 2005. Measurements from two towers and two airplanes are available for this campaign. The lagrangian particle dispersion model LPDM was coupled to the non-hydrostatic model Meso-NH and integrated in a matrix inversion framework. Impacts of aircraft and tower measurements are quantified separately and together. We find that the configuration with both towers and aircraft is able to significantly reduce uncertainties on the 4-day averaged CO2 fluxes over about half of the 300×300 km2 domain. Most of this reduction comes from the tower measurements, even though the impact of aircraft measurements remains noticeable. Imperfect knowledge of boundary conditions does not significantly impact the error reduction for surface fluxes. We test alternative strategies to improve the impact of aircraft measurements and find that most information comes from measurements inside the boundary layer. We find that there would be a large improvement in error reduction if we could improve our ability to model nocturnal concentrations at tower sites.

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On using radon-222 and CO2 to calculate regional-scale CO2 fluxes

Atmospheric Chemistry and Physics ◽

10.5194/acp-7-3737-2007 ◽

2007 ◽

Vol 7 (14) ◽

pp. 3737-3747 ◽

Cited By ~ 19

Author(s):

A. I. Hirsch

Keyword(s):

Dispersion Model ◽

Regional Scale ◽

Radioactive Decay ◽

Weighted Average ◽

Co2 Flux ◽

Particle Dispersion ◽

Spatial Average ◽

Co2 Fluxes ◽

Tracer Method ◽

Average Flux

Abstract. Because of its ubiquitous release on land and well-characterized atmospheric loss, radon-222 has been very useful for deducing fluxes of greenhouse gases such as CO2, CH4, and N2O. It is shown here that the radon-tracer method, used in previous studies to calculate regional-scale greenhouse gas fluxes, returns a weighted-average flux (the flux field F weighted by the sensitivity of the measurements to that flux field, f) rather than an evenly-weighted spatial average flux. A synthetic data study using a Lagrangian particle dispersion model and modeled CO2 fluxes suggests that the discrepancy between the sensitivity-weighted average flux and evenly-weighted spatial average flux can be significant in the case of CO2, due to covariance between F and f for biospheric CO2 fluxes during the growing season and also for anthropogenic CO2 fluxes in general. A technique is presented to correct the radon-tracer derived fluxes to yield an estimate of evenly-weighted spatial average CO2 fluxes. A new method is also introduced for correcting the CO2 flux estimates for the effects of radon-222 radioactive decay in the radon-tracer method.

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On using radon-222 and CO2 to calculate regional-scale CO2 fluxes

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-6-10929-2006 ◽

2006 ◽

Vol 6 (6) ◽

pp. 10929-10958 ◽

Cited By ~ 1

Author(s):

A. I. Hirsch

Keyword(s):

Dispersion Model ◽

Regional Scale ◽

Radioactive Decay ◽

Weighted Average ◽

Co2 Flux ◽

Particle Dispersion ◽

Spatial Average ◽

Co2 Fluxes ◽

Tracer Method ◽

Average Flux

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Impact of optimized mixing heights on simulated regional atmospheric transport of CO2

Atmospheric Chemistry and Physics ◽

10.5194/acp-14-7149-2014 ◽

2014 ◽

Vol 14 (14) ◽

pp. 7149-7172 ◽

Cited By ~ 24

Author(s):

R. Kretschmer ◽

C. Gerbig ◽

U. Karstens ◽

G. Biavati ◽

A. Vermeulen ◽

...

Keyword(s):

Dispersion Model ◽

Synthetic Data ◽

Wrf Model ◽

Horizontal Resolution ◽

Particle Dispersion ◽

Random Errors ◽

Tracer Transport ◽

European Continent ◽

Co2 Transport ◽

The Impact

Abstract. The mixing height (MH) is a crucial parameter in commonly used transport models that proportionally affects air concentrations of trace gases with sources/sinks near the ground and on diurnal scales. Past synthetic data experiments indicated the possibility to improve tracer transport by minimizing errors of simulated MHs. In this paper we evaluate a method to constrain the Lagrangian particle dispersion model STILT (Stochastic Time-Inverted Lagrangian Transport) with MH diagnosed from radiosonde profiles using a bulk Richardson method. The same method was used to obtain hourly MHs for the period September/October 2009 from the Weather Research and Forecasting (WRF) model, which covers the European continent at 10 km horizontal resolution. Kriging with external drift (KED) was applied to estimate optimized MHs from observed and modelled MHs, which were used as input for STILT to assess the impact on CO2 transport. Special care has been taken to account for uncertainty in MH retrieval in this estimation process. MHs and CO2 concentrations were compared to vertical profiles from aircraft in situ data. We put an emphasis on testing the consistency of estimated MHs to observed vertical mixing of CO2. Modelled CO2 was also compared with continuous measurements made at Cabauw and Heidelberg stations. WRF MHs were significantly biased by ~10–20% during day and ~40–60% during night. Optimized MHs reduced this bias to ~5% with additional slight improvements in random errors. The KED MHs were generally more consistent with observed CO2 mixing. The use of optimized MHs had in general a favourable impact on CO2 transport, with bias reductions of 5–45% (day) and 60–90% (night). This indicates that a large part of the found CO2 model–data mismatch was indeed due to MH errors. Other causes for CO2 mismatch are discussed. Applicability of our method is discussed in the context of CO2 inversions at regional scales.

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Regional-scale geostatistical inverse modeling of North American CO2 fluxes: a synthetic data study

Atmospheric Chemistry and Physics ◽

10.5194/acp-10-6151-2010 ◽

2010 ◽

Vol 10 (13) ◽

pp. 6151-6167 ◽

Cited By ~ 48

Author(s):

S. M. Gourdji ◽

A. I. Hirsch ◽

K. L. Mueller ◽

V. Yadav ◽

A. E. Andrews ◽

...

Keyword(s):

North America ◽

Flux Distribution ◽

Regional Scale ◽

Measurement Data ◽

Synthetic Data ◽

Surface Fluxes ◽

Co2 Fluxes ◽

Actual Measurement ◽

Diurnal Variability ◽

The Impact

Abstract. A series of synthetic data experiments is performed to investigate the ability of a regional atmospheric inversion to estimate grid-scale CO2 fluxes during the growing season over North America. The inversions are performed within a geostatistical framework without the use of any prior flux estimates or auxiliary variables, in order to focus on the atmospheric constraint provided by the nine towers collecting continuous, calibrated CO2 measurements in 2004. Using synthetic measurements and their associated concentration footprints, flux and model-data mismatch covariance parameters are first optimized, and then fluxes and their uncertainties are estimated at three different temporal resolutions. These temporal resolutions, which include a four-day average, a four-day-average diurnal cycle with 3-hourly increments, and 3-hourly fluxes, are chosen to help assess the impact of temporal aggregation errors on the estimated fluxes and covariance parameters. Estimating fluxes at a temporal resolution that can adjust the diurnal variability is found to be critical both for recovering covariance parameters directly from the atmospheric data, and for inferring accurate ecoregion-scale fluxes. Accounting for both spatial and temporal a priori covariance in the flux distribution is also found to be necessary for recovering accurate a posteriori uncertainty bounds on the estimated fluxes. Overall, the results suggest that even a fairly sparse network of 9 towers collecting continuous CO2 measurements across the continent, used with no auxiliary information or prior estimates of the flux distribution in time or space, can be used to infer relatively accurate monthly ecoregion scale CO2 surface fluxes over North America within estimated uncertainty bounds. Simulated random transport error is shown to decrease the quality of flux estimates in under-constrained areas at the ecoregion scale, although the uncertainty bounds remain realistic. While these synthetic data inversions do not consider all potential issues associated with using actual measurement data, e.g. systematic transport errors or problems with the boundary conditions, they help to highlight the impact of inversion setup choices, and help to provide a baseline set of CO2 fluxes for comparison with estimates from future real-data inversions.

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Impact of optimized mixing heights on simulated regional atmospheric transport of CO2

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-14-4627-2014 ◽

2014 ◽

Vol 14 (4) ◽

pp. 4627-4685

Author(s):

R. Kretschmer ◽

C. Gerbig ◽

U. Karstens ◽

G. Biavati ◽

A. Vermeulen ◽

...

Keyword(s):

Dispersion Model ◽

Synthetic Data ◽

Wrf Model ◽

Horizontal Resolution ◽

Particle Dispersion ◽

Random Errors ◽

Tracer Transport ◽

European Continent ◽

Co2 Transport ◽

The Impact

Abstract. The mixing height (MH) is a crucial parameter in commonly used transport models that proportionally affects air concentrations of trace gases with sources/sinks near the ground and on diurnal scales. Past synthetic data experiments indicated the possibility to improve tracer transport by minimizing errors of simulated MHs. In this paper we evaluate a method to constrain the Langrangian particle dispersion model STILT (Stochastic Time-Inverted Lagrangian Transport) with MH diagnosed from radiosonde profiles using a bulk Richardson method. The same method was used to obtain hourly MHs for the period September/October 2009 from the Weather Research and Forecasting (WRF) model, which covers the European continent at 10 km horizontal resolution. Kriging with External Drift (KED) was applied to estimate optimized MHs from observed and modelled MHs, which were used as input for STILT to assess the impact on CO2 transport. Special care has been taken to account for uncertainty in MH retrieval in this estimation process. MHs and CO2 concentrations were compared to vertical profiles from aircraft in-situ data. We put an emphasis on testing the consistency of estimated MHs to observed vertical mixing of CO2. Modelled CO2 was also compared with continuous measurements made at Cabauw and Heidelberg stations. WRF MHs were significantly biased by ~10–20% during day and ~40–60% during night. Optimized MHs reduced this bias to ~5% with additional slight improvements in random errors. The KED MHs were generally more consistent with observed CO2 mixing. The use of optimized MHs had in general a favourable impact on CO2 transport, with bias reductions of 5–45% (day) and 60–90% (night). This indicates that a large part of the found CO2 model-data mismatch was indeed due to MH errors. Other causes for CO2 mismatch are discussed. Applicability of our method is discussed in the context of CO2 inversions at regional scales.

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Joint inverse estimation of fossil fuel and biogenic CO2 fluxes in an urban environment: An observing system simulation experiment to assess the impact of multiple uncertainties

Elem Sci Anth ◽

10.1525/elementa.138 ◽

2018 ◽

Vol 6 ◽

Cited By ~ 9

Author(s):

Kai Wu ◽

Thomas Lauvaux ◽

Kenneth J. Davis ◽

Aijun Deng ◽

Israel Lopez Coto ◽

...

Keyword(s):

High Resolution ◽

Urban Environment ◽

Co2 Emissions ◽

Fossil Fuel ◽

Atmospheric Transport ◽

System Simulation ◽

Co2 Flux ◽

Point Sources ◽

Co2 Fluxes ◽

The Impact

The Indianapolis Flux Experiment aims to utilize a variety of atmospheric measurements and a high-resolution inversion system to estimate the temporal and spatial variation of anthropogenic greenhouse gas emissions from an urban environment. We present a Bayesian inversion system solving for fossil fuel and biogenic CO2 fluxes over the city of Indianapolis, IN. Both components were described at 1 km resolution to represent point sources and fine-scale structures such as highways in the a priori fluxes. With a series of Observing System Simulation Experiments, we evaluate the sensitivity of inverse flux estimates to various measurement deployment strategies and errors. We also test the impacts of flux error structures, biogenic CO2 fluxes and atmospheric transport errors on estimating fossil fuel CO2 emissions and their uncertainties. The results indicate that high-accuracy and high-precision measurements produce significant improvement in fossil fuel CO2 flux estimates. Systematic measurement errors of 1 ppm produce significantly biased inverse solutions, degrading the accuracy of retrieved emissions by about 1 µmol m–2 s–1 compared to the spatially averaged anthropogenic CO2 emissions of 5 µmol m–2 s–1. The presence of biogenic CO2 fluxes (similar magnitude to the anthropogenic fluxes) limits our ability to correct for random and systematic emission errors. However, assimilating continuous fossil fuel CO2 measurements with 1 ppm random error in addition to total CO2 measurements can partially compensate for the interference from biogenic CO2 fluxes. Moreover, systematic and random flux errors can be further reduced by reducing model-data mismatch errors caused by atmospheric transport uncertainty. Finally, the precision of the inverse flux estimate is highly sensitive to the correlation length scale in the prior emission errors. This work suggests that improved fossil fuel CO2 measurement technology, and better understanding of both prior flux and atmospheric transport errors are essential to improve the accuracy and precision of high-resolution urban CO2 flux estimates.

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The potential for regional-scale bias in top-down CO2 flux estimates due to atmospheric transport errors

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-14-23681-2014 ◽

2014 ◽

Vol 14 (16) ◽

pp. 23681-23709

Author(s):

S. M. Miller ◽

I. Fung ◽

J. Liu ◽

M. N. Hayek ◽

A. E. Andrews

Keyword(s):

Boundary Layer ◽

Atmospheric Transport ◽

Regional Scale ◽

Co2 Flux ◽

Atmospheric Co2 ◽

Co2 Fluxes ◽

Atmospheric Measurements ◽

Meteorological Model ◽

Co2 Transport

Abstract. Estimates of CO2 fluxes that are based on atmospheric data rely upon a meteorological model to simulate atmospheric CO2 transport. These models provide a quantitative link between surface fluxes of CO2 and atmospheric measurements taken downwind. Therefore, any errors in the meteorological model can propagate into atmospheric CO2 transport and ultimately bias the estimated CO2 fluxes. These errors, however, have traditionally been difficult to characterize. To examine the effects of CO2 transport errors on estimated CO2 fluxes, we use a global meteorological model-data assimilation system known as "CAM–LETKF" to quantify two aspects of the transport errors: error variances (standard deviations) and temporal error correlations. Furthermore, we develop two case studies. In the first case study, we examine the extent to which CO2 transport uncertainties can bias CO2 flux estimates. In particular, we use a common flux estimate known as CarbonTracker to discover the minimum hypothetical bias that can be detected above the CO2 transport uncertainties. In the second case study, we then investigate which meteorological conditions may contribute to month-long biases in modeled atmospheric transport. We estimate 6 hourly CO2 transport uncertainties in the model surface layer that range from 0.15 to 9.6 ppm (standard deviation), depending on location, and we estimate an average error decorrelation time of ∼2.3 days at existing CO2 observation sites. As a consequence of these uncertainties, we find that CarbonTracker CO2 fluxes would need to be biased by at least 29%, on average, before that bias were detectable at existing non-marine atmospheric CO2 observation sites. Furthermore, we find that persistent, bias-type errors in atmospheric transport are associated with consistent low net radiation, low energy boundary layer conditions. The meteorological model is not necessarily more uncertain in these conditions. Rather, the extent to which meteorological uncertainties manifest as persistent atmospheric transport biases appears to depend, at least in part, on the energy and stability of the boundary layer. Existing CO2 flux studies may be more likely to estimate inaccurate regional fluxes under those conditions.

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An extended Kalman-filter for regional scale inverse emission estimation

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-11-29195-2011 ◽

2011 ◽

Vol 11 (10) ◽

pp. 29195-29249 ◽

Cited By ~ 3

Author(s):

D. Brunner ◽

S. Henne ◽

C. A. Keller ◽

S. Reimann ◽

M. K. Vollmer ◽

...

Keyword(s):

Kalman Filter ◽

Dispersion Model ◽

Regional Scale ◽

Estimation Method ◽

Particle Dispersion ◽

Montreal Protocol ◽

Correlated Noise ◽

Temporal Correlations ◽

Emission Estimation ◽

Hfc 152A

Abstract. A Kalman-filter based inverse emission estimation method for long-lived trace gases is presented for use in conjunction with a Lagrangian particle dispersion model like FLEXPART. The sequential nature of the approach allows tracing slow seasonal and interannual changes rather than estimating a single period-mean emission field. Other important features include the estimation of a slowly varying concentration background at each measurement station, the possibility to constrain the solution to non-negative emissions, the quantification of uncertainties, the consideration of temporal correlations in the residuals, and the applicability to potentially large inversion problems. The method is first demonstrated for a set of synthetic observations created from a prescribed emission field with different levels of (correlated) noise, which closely mimics true observations. It is then applied to real observations of the three halocarbons HFC-125, HFC-152a and HCFC-141b at the remote research stations Jungfraujoch and Mace Head for the quantification of emissions in Western European countries from 2006 to 2010. Estimated HFC-125 emissions are mostly consistent with national totals reported to the Kyoto protocol and show a generally increasing trend over the considered period. Results for HFC-152a are much more variable with estimated emissions being both higher and lower in different countries. The highest emissions of the order of 1000 Mg yr−1 are estimated for Italy which so far does not report HFC-152a emissions. Emissions of HCFC-141b show a continuing strong decrease as expected due to its ban under the Montreal Protocol. Emissions from France, however, were still rather large (near 1000 Mg yr−1) in the years 2006 and 2007 but strongly declined thereafter.

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Identification of Aerosol Sources in Siberia and Study of Aerosol Transport at Regional Scale by Airborne and Space-Borne Lidar Measurement

EPJ Web of Conferences ◽

10.1051/epjconf/202023702014 ◽

2020 ◽

Vol 237 ◽

pp. 02014

Author(s):

Antonin Zabukovec ◽

Gérard Ancellet ◽

Jacques Pelon ◽

J.D. Paris ◽

Iogannes E. Penner ◽

...

Keyword(s):

Forest Fires ◽

Dispersion Model ◽

Regional Scale ◽

Particle Dispersion ◽

Airborne Lidar ◽

Orthogonal Polarization ◽

Lidar Measurement ◽

Imaging Spectrometer ◽

Modis Aod ◽

Moderate Resolution

Airborne lidar measurements were carried out over Siberia in July 2013 and June 2017. Aerosol optical properties are derived using the Lagrangian FLEXible PARTicle dispersion model (FLEXPART) simulations and Moderate Resolution Imaging Spectrometer (MODIS) AOD. Comparison with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol products is used to validate the CALIOP aerosol type identification above Siberia. Two case studies are discussed : a mixture of dust and pollution from Northern Kazakhstan and smoke plumes from forest fires. Comparisons with the CALIOP backscatter ratio show that CALIOP algorithm may overestimate the LR for a dusty mixture if not constrained by an independent AOD measurement.

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Mesoscale inversion: first results from the CERES campaign with synthetic data