Inversion System Research Articles

Sequential joint inversion of gravity and magnetic data via the cross‐gradient constraint

ABSTRACTDifferent geophysical methods use different model parameterizations and inversion algorithms. Thus, combining these different inversion systems and yet adding the nonlinear cross‐gradient constraint in a joint inversion framework might be a big challenge, for instance, as explained further by Moorkamp et al. in 2011, there is a complex interaction between the data misfit terms, regularization and cross‐gradient terms and an imperfect fit to the data is expected. In this paper, we use a sequential algorithm for a two‐dimensional joint inversion of gravity and magnetic data, which tries to avoid these issues by decoupling the gravity inversion, the magnetic inversion and the cross‐gradient minimization processes. The efficiency of the algorithm and developed code is demonstrated by the joint inversion of noisy synthetic data. The results show a significant improvement in the respective models obtained by introducing the cross‐gradient joint inversion over the models obtained by separate inversions for synthetic data and then for field data targeting potash ore source in the AjiChai salt deposit in north‐western Iran.In this application case, the lower density of salt minerals such as potash, compared to its surrounding sedimentary sequences, motivates a gravity study. Furthermore, the relative lower susceptibility of these salt minerals, alongside their diamagnetic effect, makes them a suitable target for magnetic surveys. Separate gravity and magnetic studies had been performed over the deposit; however, a constitutive relationship between density and magnetization within the area of interest supporting a joint inversion had not been established. In this paper, we apply the sequential cross‐gradient approach to perform the first full joint inversion for the AjiChai salt deposit. The magnetic inversion here is performed to recover the magnetization amplitudes rather than the magnetization vector. In fact, we assume there is no remanent magnetization and, therefore, that the magnetization vector is constant and parallel to the geomagnetic field direction. The constructed density and magnetization models are of high concordance with available geological information and previous studies including drilling results. In addition, unlike previous separate inversion models, the models are structurally and geometrically similar.

Evaluation of CarbonTracker's Inverse Estimates of North American Net Ecosystem Exchange of CO2 From Different Observing Systems Using ACT‐America Airborne Observations

AbstractQuantification of regional terrestrial carbon dioxide (CO2) fluxes is critical to our understanding of the carbon cycle. We evaluate inverse estimates of net ecosystem exchange (NEE) of CO2 fluxes in temperate North America, and their sensitivity to the observational data used to drive the inversions. Specifically, we consider the state‐of‐the‐science CarbonTracker global inversion system, which assimilates (a) in situ measurements (IS), (b) the Orbiting Carbon Observatory‐2 (OCO‐2) v9 column CO2 (XCO2) retrievals over land (LNLG), (c) OCO‐2 v9 XCO2 retrievals ocean‐glint (OG), and (d) a combination of all these observational constraints (LNLGOGIS). We use independent CO2 observations from the Atmospheric Carbon and Transport (ACT)—America aircraft mission to evaluate the inversions. We diagnose errors in the flux estimates using the differences between modeled and observed biogenic CO2 mole fractions, influence functions from a Lagrangian transport model, Bayesian inference, and root‐mean‐square error (RMSE) and bias metrics. The IS fluxes have the smallest RMSE among the four products, followed by LNLG. Both IS and LNLG outperform the OG and LNLGOGIS inversions with regard to RMSE. Regional errors do not differ markedly across the four sets of posterior fluxes. The CarbonTracker inversions appear to overestimate the seasonal cycle of NEE in the Midwest and Western Canada, and overestimate dormant season NEE across the Central and Eastern US. The CarbonTracker inversions may overestimate annual NEE in the Central and Eastern US. The success of the LNLG inversion with respect to independent observations bodes well for satellite‐based inversions in regions with more limited in situ observing networks.

Far-field biogenic and anthropogenic emissions as a dominant source of variability in local urban carbon budgets: A global high-resolution model study with implications for satellite remote sensing

With the launch of the Observing Carbon Observatories (OCO-2 and OCO-3), we have entered a new era of greenhouse gas (GHG) data collection where sub-city scale data can be collected at varying times across the entire globe. An increasing focus on quantifying urban emissions of GHGs from policy makers has begun to spur new research into how best to use this unique and very high resolution data set. While, historically, this line of research has been the domain of limited domain mesoscale and regional models, an increasing understanding and respect for boundary inflow uncertainty and bias has led people to search for an alternative modeling framework that both characterizes high frequency variability of CO2 in space and time as well as honor mass conservation requirements globally and be seamless with respect to boundaries. To monitor local anthropogenic emissions from space, the influence of atmospheric signals originating from outside the local area of interest needs to be quantified. In our first step towards building a comprehensive multi-scale CO2 inversion system, we use free running simulations of the Ocean Land Atmosphere Model (OLAM), a variable-resolution general circulation model, to explore the signal-to-noise statistics of anthropogenic urban emissions of CO2 versus the background inflow for approximately 40 of the largest cities across the globe. We show that signal-to-noise levels are much better in winter time than summer but also that the winter biological inflow is far from negligible, suggesting that the commonly held assumption that biology can be ignored in winter time urban emission estimates is probably incorrect. Simulated pressure-weighted column average CO2 (XCO2) is also used to evaluate the ability of fixed location XCO2 measurements to provide background inflow estimates. Results show why Los Angeles, a heavily instrumented and studied urban center, is likely one of the easiest cities to observe globally from space, despite its relatively complex meteorology. Lastly, we discuss challenges and possible research paths forward to continue to advance the notion of multi-scale global CO2 flux inversion systems capable of simultaneously optimizing local urban emissions and large-scale CO2 transport patterns.

Development and evaluation of CO<sub>2</sub> transport in MPAS-A v6.3

Abstract. Chemistry transport models (CTMs) play an important role in understanding fluxes and atmospheric distribution of carbon dioxide (CO2). They have been widely used for modeling CO2 transport through forward simulations and inferring fluxes through inversion systems. With the increasing availability of high-resolution observations, it has been become possible to estimate CO2 fluxes at higher spatial resolution. In this work, we implemented CO2 transport in the Model for Prediction Across Scales – Atmosphere (MPAS-A). The objective is to use the variable-resolution capability of MPAS-A to enable a high-resolution CO2 simulation in a limited region with a global model. Treating CO2 as an inert tracer, we implemented in MPAS-A (v6.3) the CO2 transport processes, including advection, vertical mixing by boundary layer scheme, and convective transport. We first evaluated the newly implemented model's tracer mass conservation and then its CO2 simulation accuracy. A 1-year (2014) MPAS-A simulation is evaluated at the global scale using CO2 measurements from 50 near-surface stations and 18 Total Carbon Column Observing Network (TCCON) stations. The simulation is also compared with two global models: National Oceanic and Atmospheric Administration (NOAA) CarbonTracker v2019 (CT2019) and European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). A second set of simulation (2016–2018) is used to evaluate MPAS-A at regional scale using Atmospheric Carbon and Transport – America (ACT-America) aircraft CO2 measurements over the eastern United States. This simulation is also compared with CT2019 and a 27 km WRF-Chem simulation. The global-scale evaluations show that MPAS-A is capable of representing the spatial and temporal CO2 variation with a comparable level of accuracy as IFS of similar horizontal resolution. The regional-scale evaluations show that MPAS-A is capable of representing the observed atmospheric CO2 spatial structures related to the midlatitude synoptic weather system, including the warm versus cold sector distinction, boundary layer to free troposphere difference, and frontal boundary CO2 enhancement. MPAS-A's performance in representing these CO2 spatial structures is comparable to the global model CT2019 and regional model WRF-Chem.

Iterated inversion system: an algorithm for efficiently visualizing Kleinian groups and extending the possibilities of fractal art

Kleinian group theory is a branch of mathematics. A visualized Kleinian group often presents a beautiful fractal structure and provides clues for understanding Möbius transformations the mathematical properties of the group. However, it often takes much time to render images of Kleinian groups on a computer. Thus, we propose an efficient algorithm for visualizing some kinds of Kleinian groups: the Iterated Inversion System (IIS), which enables us to render images of Kleinian groups composed of inversions as circles or spheres in real-time. Real-time rendering has various applications; for example, the IIS can be used for experimentation in Kleinian group theory and the creation of mathematical art. The algorithm can also be used to draw both two-dimensional and three-dimensional fractals. The algorithm can extend the possibilities of art originating from Kleinian groups. In this paper, we discuss Kleinian fractals from an artistic viewpoint.

Speech acoustics and mental health assessment

According to the World Health Organization, more than 264 million people worldwide suffer from Major Depression Disorder (MDD) and another 20 million have schizophrenia. MDD and schizophrenia are among the most common precursors to suicide and, according to a 2018 CDC report, suicidality is the second leading cause of death in youth and young adults between 10 and 34 years of age. While suicidality has historically been at low rates in the black community, it has recently become a crisis for black youth. It is the second leading cause for the death of black children between 10 and 14 years of age, and it is the third leading cause of death for black adolescents between 15 and 19 years of age. Our work is focused on understanding how a person's mental health status is reflected in their coordination of speech gestures. Speech articulation is a complex activity that requires finely timed coordination across articulators, i.e., tongue, jaw, lips, velum, and larynx. In a depressed or a psychotic state, this coordination changes and, in turn, modifies the perceived speech signal. In this talk, I will discuss a speech inversion system we developed that maps the acoustic signal to vocal tract variables (TVs). The trajectories of the TVs show the timing and spatial movement of speech gestures. Next, I will discuss how we use machine learning techniques to compute articulatory coordination features (ACFs) from the TVs. The ACFs serve as an input into a deep learning model for mental health classification. Finally, I will illustrate the key acoustic differences between speech produced by subjects when they are mentally ill relative to when they are in remission and relative to healthy controls. The ultimate goal of this research is the development of a technology (perhaps an app) for patients that can help them, their therapists and caregivers monitor their mental health status between therapy sessions.Q&A will follow the presentation

Revealing the sulfur dioxide emission reductions in China by assimilating surface observations in WRF-Chem

Abstract. The anthropogenic emission of sulfur dioxide (SO2) over China has significantly declined as a consequence of the clean air actions. In this study, we have developed a new emission inversion system based on a four-dimensional local ensemble transform Kalman filter (4D-LETKF) and the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to dynamically update the SO2 emission grid by grid over China by assimilating the ground-based hourly SO2 observations. Sensitivity tests for the assimilation system have been conducted firstly to tune four system parameters: ensemble size, horizontal and temporal localization lengths, and perturbation size. Our results reveal that the same random perturbation factors used throughout the whole model grids with assimilating observations within about 180 km can efficiently optimize the SO2 emission, whereas the ensemble size has only little effect. The temporal localization by assimilating only the subsequent hourly observations can reveal the diurnal variation of the SO2 emission, which is better than updating the magnitude of SO2 emission every 12 h by assimilating all the observations within the 12 h window. The inverted SO2 emission over China in November 2016 has declined by an average of 49.4 % since 2010, which is well in agreement with the bottom-up estimation of 48.0 %. Larger reductions of SO2 emission are found over the a priori higher source regions such as the Yangtze River Delta (YRD). The simulated SO2 surface mass concentrations using two distinguished chemical reaction mechanisms are both much more comparable to the observations with the newly inverted SO2 emission than those with the a priori emission. These indicate that the newly developed emission inversion system can efficiently update the SO2 emissions based on the routine surface SO2 observations. The reduced SO2 emission induces the sulfate and PM2.5 surface concentrations to decrease by up to 10 µg m−3 over central China.

Regional‐Scale, Sector‐Specific Evaluation of Global CO2 Inversion Models Using Aircraft Data From the ACT‐America Project

AbstractWe use 148 airborne vertical profiles of CO2 for frontal cases from the summer 2016 Atmospheric Carbon and Transport—America (ACT‐America) campaign to evaluate the skill of 10 global CO2 in situ inversion models from the version 7 Orbiting Carbon Observatory–2 (OCO‐2) Model Intercomparison Project (MIP). Model errors (model posterior—observed CO2 dry air mole fractions) were categorized by region (Mid‐Atlantic, Midwest, and South), frontal sector (warm or cold), and transport model (predominantly Tracer Model 5 (TM5) and Goddard Earth Observing System—Chemistry [GEOS‐Chem]). All inversions assimilated the same CO2 observations. Overall, the median inversion profiles reproduce the general structures of the observations (enhanced/depleted low‐level CO2 in warm/cold sectors), but 1) they underestimate the magnitude of the warm/cold sector mole fraction difference, and 2) the spread among individual inversions can be quite large (>5 ppm). Uniquely in the Mid‐Atlantic, inversion biases segregated according to atmospheric transport model, where TM5 inversions biases were −3 to −4 ppm in warm sectors, while those of GEOS‐Chem were +2 to +3 ppm in cold sectors. The large spread among the mean posterior CO2 profiles is not explained by the different atmospheric transport models. These results show that the inversion systems themselves are the dominant cause of this spread, and that the aircraft campaign data are clearly able to identify these large biases. Future controlled experiments should identify which inversions best reproduce mid‐latitude CO2 mole fractions, and how inversion system components are linked to system performance.

Carbon Monitoring System Flux Net Biosphere Exchange 2020 (CMS-Flux NBE 2020)

Abstract. Here we present a global and regionally resolved terrestrial net biosphere exchange (NBE) dataset with corresponding uncertainties between 2010–2018: Carbon Monitoring System Flux Net Biosphere Exchange 2020 (CMS-Flux NBE 2020). It is estimated using the NASA Carbon Monitoring System Flux (CMS-Flux) top-down flux inversion system that assimilates column CO2 observations from the Greenhouse Gases Observing Satellite (GOSAT) and NASA's Observing Carbon Observatory 2 (OCO-2). The regional monthly fluxes are readily accessible as tabular files, and the gridded fluxes are available in NetCDF format. The fluxes and their uncertainties are evaluated by extensively comparing the posterior CO2 mole fractions with CO2 observations from aircraft and the NOAA marine boundary layer reference sites. We describe the characteristics of the dataset as the global total, regional climatological mean, and regional annual fluxes and seasonal cycles. We find that the global total fluxes of the dataset agree with atmospheric CO2 growth observed by the surface-observation network within uncertainty. Averaged between 2010 and 2018, the tropical regions range from close to neutral in tropical South America to a net source in Africa; these contrast with the extra-tropics, which are a net sink of 2.5±0.3 Gt C/year. The regional satellite-constrained NBE estimates provide a unique perspective for understanding the terrestrial biosphere carbon dynamics and monitoring changes in regional contributions to the changes of atmospheric CO2 growth rate. The gridded and regional aggregated dataset can be accessed at https://doi.org/10.25966/4v02-c391 (Liu et al., 2020).

Real-time forward modeling and inversion of logging-while-drilling electromagnetic measurements in horizontal wells

Based on the pseudo-analytical equation of electromagnetic log for layered formation, an optimal boundary match method is proposed to adaptively truncate the encountered formation structures. An efficient integral method is put forward to significantly accelerate the convergence of Sommerfeld integral. By asymptotically approximating and subtracting the first reflection/transmission waves from the scattered field, the new Sommerfeld integral method has addressed difficulties encountered by the traditional digital filtering method, such as low computational precision and limited operating range, and realized the acceleration of the computation speed of logging-while-drilling electromagnetic measurements (LWD EM). By making use of the priori information from the offset/pilot wells and interactively adjusting the formation model, the optimum initial guesses of the inversion model is determined in order to predict the nearby formation boundaries. The gradient optimization algorithm is developed and an interactive inversion system for the LWD EM data from the horizontal wells is established. The inverted results of field data demonstrated that the real-time interactive inversion method is capable of providing the accurate boundaries of layers around the wellbore from the LWD EM, and it will benefit the wellbore trajectory optimization and reservoir interpretation.

Technical note: A high-resolution inverse modelling technique for estimating surface CO<sub>2</sub> fluxes based on the NIES-TM–FLEXPART coupled transport model and its adjoint

Abstract. We developed a high-resolution surface flux inversion system based on the global Eulerian–Lagrangian coupled tracer transport model composed of the National Institute for Environmental Studies (NIES) transport model (TM; collectively NIES-TM) and the FLEXible PARTicle dispersion model (FLEXPART). The inversion system is named NTFVAR (NIES-TM–FLEXPART-variational) as it applies a variational optimization to estimate surface fluxes. We tested the system by estimating optimized corrections to natural surface CO2 fluxes to achieve the best fit to atmospheric CO2 data collected by the global in situ network as a necessary step towards the capability of estimating anthropogenic CO2 emissions. We employed the Lagrangian particle dispersion model (LPDM) FLEXPART to calculate surface flux footprints of CO2 observations at a spatial resolution of 0.1∘×0.1∘. The LPDM is coupled with a global atmospheric tracer transport model (NIES-TM). Our inversion technique uses an adjoint of the coupled transport model in an iterative optimization procedure. The flux error covariance operator was implemented via implicit diffusion. Biweekly flux corrections to prior flux fields were estimated for the years 2010–2012 from in situ CO2 data included in the Observation Package (ObsPack) data set. High-resolution prior flux fields were prepared using the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) for fossil fuel combustion, the Global Fire Assimilation System (GFAS) for biomass burning, the Vegetation Integrative SImulator for Trace gases (VISIT) model for terrestrial biosphere exchange, and the Ocean Tracer Transport Model (OTTM) for oceanic exchange. The terrestrial biospheric flux field was constructed using a vegetation mosaic map and a separate simulation of CO2 fluxes at a daily time step by the VISIT model for each vegetation type. The prior flux uncertainty for the terrestrial biosphere was scaled proportionally to the monthly mean gross primary production (GPP) by the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD17 product. The inverse system calculates flux corrections to the prior fluxes in the form of a relatively smooth field multiplied by high-resolution patterns of the prior flux uncertainties for land and ocean, following the coastlines and fine-scale vegetation productivity gradients. The resulting flux estimates improved the fit to the observations taken at continuous observation sites, reproducing both the seasonal and short-term concentration variabilities including high CO2 concentration events associated with anthropogenic emissions. The use of a high-resolution atmospheric transport in global CO2 flux inversions has the advantage of better resolving the transported mixed signals from the anthropogenic and biospheric sources in densely populated continental regions. Thus, it has the potential to achieve better separation between fluxes from terrestrial ecosystems and strong localized sources, such as anthropogenic emissions and forest fires. Further improvements in the modelling system are needed as our posterior fit was better than that of the National Oceanic and Atmospheric Administration (NOAA)'s CarbonTracker for only a fraction of the monitoring sites, i.e. mostly at coastal and island locations where background and local flux signals are mixed.

Simulated Annealing Algorithm-Based Inversion Model To Interpret Flow Rate Profiles and Fracture Parameters for Horizontal Wells in Unconventional Gas Reservoirs

SummaryWith the increasing application of distributed temperature sensing (DTS) in downhole monitoring for multifractured horizontal wells (MFHWs), well performance interpretation by inversing DTS data has become a popular topic around the world. However, because of the lack of efficient inversion models, great challenges still exist in interpreting flow rate profiles and fracture parameters for MFHWs in unconventional gas reservoirs from DTS data.In this paper, a robust inversion system is developed to interpret flow rate profiles and fracture parameters for MFHWs in unconventional gas reservoirs by inversion of DTS data. A temperature prediction model serves as a forward model to simulate the temperature behaviors of MFHWs. A new inversion model based on a simulated annealing (SA) algorithm is proposed to find inversion solutions to flow rate profiles and fracture parameters. The simulated results of temperature behaviors indicate that the temperature profile of each MFHW is irregularly serrated, and the temperature drop in each serration is positively correlated with the inflow rate and fracture half-length. These results provide an excellent method to identify and locate effective hydraulic fractures for field MFHWs. Because of the far more significant influence of fracture half-length than conductivity on a temperature profile, fracture half-length was chosen as the inversion target parameter when performing the inversion of DTS data for MFHWs. Then a synthetic inversion task was accomplished using the SA algorithm-based inversion system, and it took only 110 iterations to reach the target inversion accuracy (10−6 level). Real-time inversion error distributions indicate that this novel inversion system shows great advantages in computational efficiency. Finally, a field application in a shale gas reservoir is presented to validate the reliability of the new inversion model. Based on accurate identification of effective fractures from DTS profiles, satisfactory inversion solutions (the maximum temperature deviation of less than 0.03 K) are obtained. The absolute error of the inversed gas production rate is less than 4 m3/d. The SA algorithm-based inversion system proves reliable to interpret flow rate profiles and fracture parameters, which is a great help to postfracturing evaluation and productivity improvement for MFHWs in unconventional gas reservoirs.

A local- to national-scale inverse modeling system to assess the potential of spaceborne CO<sub>2</sub> measurements for the monitoring of anthropogenic emissions

Abstract. This work presents a flux inversion system which assesses the potential of new satellite imagery measurements of atmospheric CO2 for monitoring anthropogenic emissions at scales ranging from local intense point sources to regional and national scales. Such imagery measurements will be provided by the future Copernicus Anthropogenic Carbon Dioxide Monitoring Mission (CO2M). While the modeling framework retains the complexity of previous studies focused on individual and large cities, this system encompasses a wide range of sources to extend the scope of the analysis. This atmospheric inversion system uses a zoomed configuration of the CHIMERE regional transport model which covers most of western Europe with a 2 km resolution grid over northern France, western Germany and Benelux. For each day of March and May 2016, over the 6 h before a given satellite overpass, the inversion separately controls the hourly budgets of anthropogenic emissions in this area from ∼ 300 cities, power plants and regions. The inversion also controls hourly regional budgets of the natural fluxes. This enables the analysis of results at the local to regional scales for a wide range of sources in terms of emission budget and spatial extent while accounting for the uncertainties associated with natural fluxes and the overlapping of plumes from different sources. The potential of satellite data for monitoring CO2 fluxes is quantified with posterior uncertainties or uncertainty reductions (URs) from prior inventory-based statistical knowledge. A first analysis focuses on the hourly to 6 h budgets of the emissions of the Paris urban area and on the sensitivity of the results to different characteristics of the images of vertically integrated CO2 (XCO2) corresponding to the spaceborne instrument: the pixel spatial resolution, the precision of the XCO2 retrievals per pixel and the swath width. This sensitivity analysis provides a correspondence between these parameters and thresholds on the targeted precisions of emission estimates. However, the results indicate a large sensitivity to the wind speed and to the prior flux uncertainties. The analysis is then extended to the large ensemble of point sources, cities and regions in the study domain, with a focus on the inversion system's ability to separately monitor neighboring sources whose atmospheric signatures overlap and are also mixed with those produced by natural fluxes. Results highlight the strong dependence of uncertainty reductions on the emission budgets, on the wind speed and on whether the focus is on point or area sources. With the system hypothesis that the atmospheric transport is perfectly known, the results indicate that the atmospheric signal overlap is not a critical issue. All of the tests are conducted considering clear-sky conditions, and the limitations from cloud cover are ignored. Furthermore, in these tests, the inversion system is perfectly informed about the statistical properties of the various sources of errors that are accounted for, and systematic errors in the XCO2 retrievals are ignored; thus, the scores of URs are assumed to be optimistic. For the emissions within the 6 h before a satellite overpass, URs of more than 50 % can only be achieved for power plants and cities whose annual emissions are more than ∼ 2 MtC yr−1. For regional budgets encompassing more diffuse emissions, this threshold increases up to ∼ 10 MtC yr−1. The results therefore suggest an imbalance in the monitoring capabilities of the satellite XCO2 spectro-imagery towards high and dense sources.

MIIDAPS-AI: An Explainable Machine-Learning Algorithm for Infrared and Microwave Remote Sensing and Data Assimilation Preprocessing - Application to LEO and GEO Sensors

In this article, we leverage and apply state-of-the-art artificial intelligence (AI) techniques to satellite remote sensing of temperature, moisture, surface, and cloud parameters in all-weather, all-surface conditions, from both microwave and infrared sensors. The multi-instrument inversion and data assimilation preprocessing system, artificial intelligence version, or MIIDAPS-AI for short, is valid for both polar and geostationary microwave and infrared sounders and imagers as well as for pairs of combined infrared and microwave sounders. The algorithm produces vertical profiles of temperature and moisture as well as surface temperature, surface emissivity, and cloud parameters. Additional products from hyperspectral infrared sensors include selected trace gases. From microwave sensors, additional products such as rainfall rate, first year/multiyear sea ice concentration, and soil moisture can be derived from primary products. The MIIDAPS-AI algorithm is highly efficient with no noticeable decrease in accuracy compared to traditional operational sounding algorithms. The automatically generated Jacobians from this deep-learning algorithm could provide an explainability mechanism to build trustworthiness in the algorithm, and to quantify uncertainties of the algorithm's outputs. The computation gain is estimated to be two orders of magnitude, which opens the door to either 1) process massively larger amounts of satellite data, or to 2) offer improvements in timeliness and significant saving in computing power (and therefore cost) if the same amount of data is processed. Here, we present an overview of the MIIDAPS-AI implementation, discuss its applicability to various sensors and provide an initial performance assessment for a select number of sensors and geophysical parameters.

Application of near-infrared CO<sub>2</sub> concentration inversion system based on empirical modal decomposition

Application of near-infrared CO<sub>2</sub> concentration inversion system based on empirical modal decomposition

PMIF v1.0: assessing the potential of satellite observations to constrain CO<sub>2</sub> emissions from large cities and point sources over the globe using synthetic data

Abstract. This study assesses the potential of satellite imagery of vertically integrated columns of dry-air mole fractions of CO2 (XCO2) to constrain the emissions from cities and power plants (called emission clumps) over the whole globe during 1 year. The imagery is simulated for one imager of the Copernicus mission on Anthropogenic Carbon Dioxide Monitoring (CO2M) planned by the European Space Agency and the European Commission. The width of the swath of the CO2M instruments is about 300 km and the ground horizontal resolution is about 2 km resolution. A Plume Monitoring Inversion Framework (PMIF) is developed, relying on a Gaussian plume model to simulate the XCO2 plumes of each emission clump and on a combination of overlapping assimilation windows to solve for the inversion problem. The inversion solves for the 3 h mean emissions (during 08:30–11:30 local time) before satellite overpasses and for the mean emissions during other hours of the day (over the aggregation between 00:00–08:30 and 11:30–00:00) for each clump and for the 366 d of the year. Our analysis focuses on the derivation of the uncertainty in the inversion estimates (the “posterior uncertainty”) of the clump emissions. A comparison of the results obtained with PMIF and those from a previous study using a complex 3-D Eulerian transport model for a single city (Paris) shows that the PMIF system provides the correct order of magnitude for the uncertainty reduction of emission estimates (i.e., the relative difference between the prior and posterior uncertainties). Beyond the one city or few large cities studied by previous studies, our results provide, for the first time, the global statistics of the uncertainty reduction of emissions for the full range of global clumps (differing in emission rate and spread, and distance from other major clumps) and meteorological conditions. We show that only the clumps with an annual emission budget higher than 2 MtC yr−1 can potentially have their emissions between 08:30 and 11:30 constrained with a posterior uncertainty smaller than 20 % for more than 10 times within 1 year (ignoring the potential to cross or extrapolate information between 08:30–11:30 time windows on different days). The PMIF inversion results are also aggregated in time to investigate the potential of CO2M observations to constrain daily and annual emissions, relying on the extrapolation of information obtained for 08:30–11:30 time windows during days when clouds and aerosols do not mask the plumes, based on various assumptions regarding the temporal auto-correlations of the uncertainties in the emission estimates that are used as a prior knowledge in the Bayesian framework of PMIF. We show that the posterior uncertainties of daily and annual emissions are highly dependent on these temporal auto-correlations, stressing the need for systematic assessment of the sources of uncertainty in the spatiotemporally resolved emission inventories used as prior estimates in the inversions. We highlight the difficulty in constraining the total budget of CO2 emissions from all the cities and power plants within a country or over the globe with satellite XCO2 measurements only, and calls for integrated inversion systems that exploit multiple types of measurements.

The Impact of Height-Independent Errors in State Variables on the Determination of the Daytime Atmospheric Boundary Layer Depth Using the Bulk Richardson Approach

AbstractRawinsonde observations have long been used to estimate the atmospheric boundary layer depth (BLD), which is an important parameter for monitoring air quality, dispersion studies, weather forecast models, and inversion systems for estimating regional surface–atmosphere fluxes of tracers. Although many approaches exist for deriving the BLDs from rawinsonde observations, the bulk Richardson approach has been found to be most appropriate. However, the impact of errors in the measured thermodynamic and kinematic fields on the estimated BLDs remains unexplored. We argue that quantifying BLD error (δBLD) estimates is equally as important as the BLDs themselves. Here we quantified δBLD by applying the bulk Richardson method to 35 years of rawinsonde data obtained from three stations in the United States: Sterling, Virginia; Amarillo, Texas; and Salt Lake City, Utah. Results revealed similar features in terms of their respective errors. A −2°C bias in temperature yielded a mean δBLD ranging from −15 to 200 m. A +2°C bias in temperature yielded a mean δBLD ranging from −214 to +18 m. For a −5% relative humidity bias, the mean δBLD ranged from −302 to +7 m. For a +5% relative humidity bias, the mean δBLD ranged from +2 to +249 m. Differences of ±2 m s−1 in the winds yielded BLD errors of ~±300 m. The δBLD increased as a function of BLD when introducing errors to the thermodynamic fields and decreased as a function of BLD when introducing errors to the kinematic fields. These findings expand upon previous work evaluating rawinsonde-derived δBLD by quantifying δBLD arising from rawinsonde-derived thermodynamic and kinematic measurements. Knowledge of δBLD is critical in, for example, intercomparison studies where rawinsonde-derived BLDs are used as references.

The regional European atmospheric transport inversion comparison, EUROCOM: first results on European-wide terrestrial carbon fluxes for the period 2006–2015

Abstract. Atmospheric inversions have been used for the past two decades to derive large-scale constraints on the sources and sinks of CO2 into the atmosphere. The development of dense in situ surface observation networks, such as ICOS in Europe, enables in theory inversions at a resolution close to the country scale in Europe. This has led to the development of many regional inversion systems capable of assimilating these high-resolution data, in Europe and elsewhere. The EUROCOM (European atmospheric transport inversion comparison) project is a collaboration between seven European research institutes, which aims at producing a collective assessment of the net carbon flux between the terrestrial ecosystems and the atmosphere in Europe for the period 2006–2015. It aims in particular at investigating the capacity of the inversions to deliver consistent flux estimates from the country scale up to the continental scale. The project participants were provided with a common database of in situ-observed CO2 concentrations (including the observation sites that are now part of the ICOS network) and were tasked with providing their best estimate of the net terrestrial carbon flux for that period, and for a large domain covering the entire European Union. The inversion systems differ by the transport model, the inversion approach, and the choice of observation and prior constraints, enabling us to widely explore the space of uncertainties. This paper describes the intercomparison protocol and the participating systems, and it presents the first results from a reference set of inversions, at the continental scale and in four large regions. At the continental scale, the regional inversions support the assumption that European ecosystems are a relatively small sink (-0.21±0.2 Pg C yr−1). We find that the convergence of the regional inversions at this scale is not better than that obtained in state-of-the-art global inversions. However, more robust results are obtained for sub-regions within Europe, and in these areas with dense observational coverage, the objective of delivering robust country-scale flux estimates appears achievable in the near future.

Evaluation of the uncertainties in two-dimensional inversion of the Russian electric logging signals using statistical modeling

Based on previously presented Russian electric logging measurements inversion system AlondraWL, we developed software prototype for estimating one- and two-dimensional inversion results uncertainties suitable for galvanic and induction sounding measurements. The algorithm is based on solving a statistically significant number of inverse problems from various initial models in the automatic mode. The software was tested on synthetic and practical high-frequency electromagnetic and galvanic practical logs. The test results show a significant advantage of the developed algorithm over the approach based on the analysis of sensitivity functions.

The Impact of Ensemble Meteorology on Inverse Modeling Estimates of Volcano Emissions and Ash Dispersion Forecasts: Grímsvötn 2011

Volcanic ash can interact with the earth system on many temporal and spatial scales and is a significant hazard to aircraft. In the event of a volcanic eruption, fast and robust decisions need to be made by aviation authorities about which routes are safe to operate. Such decisions take into account forecasts of ash location issued by Volcanic Ash Advisory Centers (VAACs) which are informed by simulations from Volcanic Ash Transport and Dispersion (VATD) models. The estimation of the time-evolving vertical distribution of ash emissions for use in VATD simulations in real time is difficult which can lead to large uncertainty in these forecasts. This study presents a method for constraining the ash emission estimates by combining an inversion modeling technique with an ensemble of meteorological forecasts, resulting in an ensemble of ash emission estimates. These estimates of ash emissions can be used to produce a robust ash forecast consistent with observations. This new ensemble approach is applied to the 2011 eruption of the Icelandic volcano Grímsvötn. The resulting emission profiles each have a similar temporal evolution but there are differences in the magnitude of ash emitted at different heights. For this eruption, the impact of precipitation uncertainty (and the associated wet deposition of ash) on the estimate of the total amount of ash emitted is larger than the impact of the uncertainty in the wind fields. Despite the differences that are dominated by wet deposition uncertainty, the ensemble inversion provides confidence that the reduction of the unconstrained emissions (a priori), particularly above 4 km, is robust across all members. In this case, the use of posterior emission profiles greatly reduces the magnitude and extent of the forecast ash cloud. The ensemble of posterior emission profiles gives a range of ash column loadings much closer in agreement with a set of independent satellite retrievals in comparison to the a priori emissions. Furthermore, airspace containing volcanic ash concentrations deemed to be associated with the highest risk (likelihood of exceeding a high concentration threshold) to aviation are reduced by over 85%. Such improvements could have large implications in emergency response situations. Future research will focus on quantifying the impact of uncertainty in precipitation forecasts on wet deposition in other eruptions and developing an inversion system that makes use of the state-of-the-art meteorological ensembles which has the potential to be used in an operational setting.