scholarly journals Regional CO<sub>2</sub> inversions with LUMIA, the Lund University Modular Inversion Algorithm, v1.0

2019 ◽  
Author(s):  
Guillaume Monteil ◽  
Marko Scholze
Keyword(s):  
Large Scale ◽  
Transport Model ◽  
Inverse Modelling ◽  
Current Status ◽  
Model Errors ◽  
Inversion Algorithm ◽  
New Developments ◽  
In Situ Data ◽  
Modular Inversion

Abstract. Atmospheric inversions are commonly used for estimating large-scale (continental to regional) net sources and sinks of CO2 and other stable atmospheric tracers from their observed concentrations. Recently, there has been an increasing demand from stakeholders for robust estimates of greenhouse gases at country-scale (or higher) resolution, in particular in the framework of the Paris agreement. This increase in resolution is in theory enabled by the growing availability of observations from surface in-situ networks (such as ICOS in Europe) and from remote sensing products (OCO-2, GOSAT-2). The increase in the resolution of inversions is also a necessary step to provide efficient feedback to the process-based (bottom-up) modelling community (vegetation models, fossil fuel emission inventories). This, however, calls for new developments in the inverse modelling systems, mainly in terms of diversification of the inversion approaches, shift from global to regional inversions, and improvement in the computational efficiency, We have developed the Lund University Modular Inversion Algorithm (LUMIA) as a tool to address some of these new developments. LUMIA is meant to be a platform for inverse modelling developments at Lund University. It aims at being a flexible, yet simple and easy to maintain set of tools that modellers can combine to build inverse modelling experiments. It is in particular designed to be transport model agnostic, which should facilitate isolating the transport model errors from those introduced by the inversion setup itself. Here, we briefly describe the motivations for developing LUMIA as well as the underlying development principles, current status and future prospects. We present a first LUMIA inversion setup for a regional CO2 inversions over Europe, based on a new coupling between the Lagrangian FLEXPART (high resolution foreground transport) and the global coarse resolution TM5 transport models, using in-situ data from surface and tall tower observation sites.

scholarly journals Regional CO<sub>2</sub> inversions with LUMIA, the Lund University Modular Inversion Algorithm, v1.0

2021 ◽  
Vol 14 (6) ◽  
pp. 3383-3406
Author(s):  
Guillaume Monteil ◽  
Marko Scholze
Keyword(s):  
Inverse Modeling ◽  
Transport Model ◽  
Dispersion Model ◽  
Particle Dispersion ◽  
Model Errors ◽  
Inversion Algorithm ◽  
In Situ Data ◽  
Increasing Demand ◽  
Modular Inversion

Abstract. Atmospheric inversions are used to derive constraints on the net sources and sinks of CO2 and other stable atmospheric tracers from their observed concentrations. The resolution and accuracy that the fluxes can be estimated with depends, among other factors, on the quality and density of the observational coverage, on the precision and accuracy of the transport model used by the inversion to relate fluxes to observations, and on the adaptation of the statistical approach to the problem studied. In recent years, there has been an increasing demand from stakeholders for inversions at higher spatial resolution (country scale), in particular in the framework of the Paris agreement. This step up in resolution is in theory enabled by the growing availability of observations from surface in situ networks (such as ICOS in Europe) and from remote sensing products (OCO-2, GOSAT-2). The increase in the resolution of inversions is also a necessary step to provide efficient feedback to the bottom-up modeling community (vegetation models, fossil fuel emission inventories, etc.). However, it calls for new developments in the inverse models: diversification of the inversion approaches, shift from global to regional inversions, and improvement in the computational efficiency. In this context, we developed LUMIA, the Lund University Modular Inversion Algorithm. LUMIA is a Python library for inverse modeling built around the central idea of modularity: it aims to be a platform that enables users to construct and experiment with new inverse modeling setups while remaining easy to use and maintain. It is in particular designed to be transport-model-agnostic, which should facilitate isolating the transport model errors from those introduced by the inversion setup itself. We have constructed a first regional inversion setup using the LUMIA framework to conduct regional CO2 inversions in Europe using in situ data from surface and tall-tower observation sites. The inversions rely on a new offline coupling between the regional high-resolution FLEXPART Lagrangian particle dispersion model and the global coarse-resolution TM5 transport model. This test setup is intended both as a demonstration and as a reference for comparison with future LUMIA developments. The aims of this paper are to present the LUMIA framework (motivations for building it, development principles and future prospects) and to describe and test this first implementation of regional CO2 inversions in LUMIA.

AI4GEO: An automatic 3D geospatial information capability

2020 ◽  
Author(s):  
Simon Baillarin ◽  
Pierre-Marie Brunet ◽  
Pierre Lassalle ◽  
Gwenael Souille ◽  
Laurent Gabet ◽  
...  
Keyword(s):  
Big Data ◽  
Value Chain ◽  
Large Scale ◽  
Value Added ◽  
Current Status ◽  
Geospatial Information ◽  
Automatic Production ◽  
In Situ Data ◽  
Big Data Technologies

&lt;p&gt;The availability of &lt;strong&gt;3D Geospatial information&lt;/strong&gt; is a key stake for many expanding sectors such as autonomous vehicles, business intelligence and urban planning.&lt;/p&gt;&lt;p&gt;The availability of huge volumes of satellite, airborne and in-situ data now makes this production feasible on a large scale. It needs nonetheless a certain level of skilled manual intervention to secure a certain level of quality, which prevents mass production.&lt;/p&gt;&lt;p&gt;New artificial intelligence and big data technologies &lt;strong&gt;are key in lifting these obstacles. &lt;/strong&gt;&lt;/p&gt;&lt;p&gt;The AI4GEO project aims at developing an automatic solution for producing 3D geospatial information and offer new value-added services leveraging innovative methods adapted to 3D imagery.&lt;/p&gt;&lt;p&gt;The AI4GEO consortium consists of &lt;strong&gt;institutional partners &lt;/strong&gt;(CNES, IGN, ONERA)&lt;strong&gt; and industrial groups&lt;/strong&gt; (CS-SI, AIRBUS, CLS, GEOSAT, QWANT, QUANTCUBE) covering the whole value chain of Geospatial Information.&lt;/p&gt;&lt;p&gt;With a 4 years&amp;#8217; timeline, the project is structured around 2 R&amp;D axes which will progress simultaneously and feed each other.&lt;/p&gt;&lt;p&gt;The first axis consists in &lt;strong&gt;developing a set of technological bricks allowing the automatic production of qualified 3D maps composed of 3D objects and associated semantics&lt;/strong&gt;. This &lt;strong&gt;collaborative work&lt;/strong&gt; benefits from the latest research from all partners in the field of AI and Big Data technologies as well as from an &lt;strong&gt;unprecedented database&lt;/strong&gt; (satellite and airborne data (optics, radars, lidars) combined with cartographic and in-situ data).&lt;/p&gt;&lt;p&gt;The second axis consists in deriving from these technological bricks &lt;strong&gt;a variety of services for different fields&lt;/strong&gt;: 3D semantic mapping of cities, macroeconomic indicators, decision support for water management, autonomous transport, consumer search engine.&lt;/p&gt;&lt;p&gt;Started in 2019, the first axis of the project has already produced very promising results. A first version of the platform and technological bricks are now available.&lt;/p&gt;&lt;p&gt;This paper will first introduce AI4GEO initiative: context and overall objectives.&lt;/p&gt;&lt;p&gt;It will then present the current status of the project and in particular it will focus on the innovative approach to handle big 3D datasets for analytics needs and it will present the first results of 3D semantic segmentations on various test sites and associated perspectives.&lt;/p&gt;

scholarly journals Basic Mechanical Parameter Relations of Soft Clay in Pearl River Delta

2014 ◽  
Vol 8 (1) ◽  
pp. 320-325 ◽  
Author(s):  
Zhangming Li ◽  
Na Qi ◽  
Zhibin Masumi ◽  
Weidi Lin
Keyword(s):  
Shear Strength ◽  
Pearl River Delta ◽  
Large Scale ◽  
Soft Clay ◽  
River Delta ◽  
Pearl River ◽  
Cylindrical Cavity Expansion ◽  
In Situ Data ◽  
The Pearl River

Basic parameters relations among CPT parameters, un-drained strength and other mechanical parameters of soft clay are presented based on an elastic-plastic solution for cylindrical cavity expansion for soil investigation in energy engineering. The relation between CPT parameters and shear strength from vane test is also presented based on the result. Thus, the CPT parameters can be determined directly by elastic parameters and shear strength or vane shear parameters and vice versa. That makes it possible to save the high test costs and provide theoretical formulas to avoid some tests which are limited due to the site and/or other condition. Results are compared between the relations and in situ data at a large-scale project in the Pearl River Delta. The results showed consistency between the relation and in situ data.

scholarly journals Using ship-borne observations of methane isotopic ratio in the Arctic Ocean to understand methane sources in the Arctic

2019 ◽  
Author(s):  
Antoine Berchet ◽  
Isabelle Pison ◽  
Patrick M. Crill ◽  
Brett Thornton ◽  
Philippe Bousquet ◽  
...  
Keyword(s):  
Arctic Ocean ◽  
Large Scale ◽  
Transport Model ◽  
Isotopic Ratio ◽  
The Arctic ◽  
Isotopic Ratios ◽  
Air Masses ◽  
Remote Areas ◽  
The Arctic Ocean

Abstract. Due to the large variety and heterogeneity of sources in remote areas hard to document, the Arctic regional methane budget remain very uncertain. In situ campaigns provide valuable data sets to reduce these uncertainties. Here we analyse data from the SWERUS-C3 campaign, on-board the icebreaker Oden, that took place during summer 2014 in the Arctic Ocean along the Northern Siberian and Alaskan shores. Total concentrations of methane, as well as isotopic ratios were measured continuously during this campaign for 35 days in July and August 2014. Using a chemistry-transport model, we link observed concentrations and isotopic ratios to regional emissions and hemispheric transport structures. A simple inversion system helped constraining source signatures from wetlands in Siberia and Alaska and oceanic sources, as well as the isotopic composition of lower stratosphere air masses. The variation in the signature of low stratosphere air masses, due to strongly fractionating chemical reactions in the stratosphere, was suggested to explain a large share of the observed variability in isotopic ratios. These points at required efforts to better simulate large scale transport and chemistry patterns to use isotopic data in remote areas. It is found that constant and homogeneous source signatures for each type of emission in the region (mostly wetlands and oil and gas industry) is not compatible with the strong synoptic isotopic signal observed in the Arctic. A regional gradient in source signatures is highlighted between Siberian and Alaskan wetlands, the later ones having a lighter signatures than the first ones. Arctic continental shelf sources are suggested to be a mixture of methane from a dominant thermogenic origin and a secondary biogenic one, consistent with previous in-situ isotopic analysis of seepage along the Siberian shores.

scholarly journals Impact of transport model errors on the global and regional methane emissions estimated by inverse modelling

2013 ◽  
Vol 13 (19) ◽  
pp. 9917-9937 ◽  
Author(s):  
R. Locatelli ◽  
P. Bousquet ◽  
F. Chevallier ◽  
A. Fortems-Cheney ◽  
S. Szopa ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Transport Model ◽  
Initial Conditions ◽  
Spatial Scales ◽  
Global Scale ◽  
Methane Emissions ◽  
Inverse Modelling ◽  
Model Errors ◽  
Inverse Systems ◽  
The Impact

Abstract. A modelling experiment has been conceived to assess the impact of transport model errors on methane emissions estimated in an atmospheric inversion system. Synthetic methane observations, obtained from 10 different model outputs from the international TransCom-CH4 model inter-comparison exercise, are combined with a prior scenario of methane emissions and sinks, and integrated into the three-component PYVAR-LMDZ-SACS (PYthon VARiational-Laboratoire de Météorologie Dynamique model with Zooming capability-Simplified Atmospheric Chemistry System) inversion system to produce 10 different methane emission estimates at the global scale for the year 2005. The same methane sinks, emissions and initial conditions have been applied to produce the 10 synthetic observation datasets. The same inversion set-up (statistical errors, prior emissions, inverse procedure) is then applied to derive flux estimates by inverse modelling. Consequently, only differences in the modelling of atmospheric transport may cause differences in the estimated fluxes. In our framework, we show that transport model errors lead to a discrepancy of 27 Tg yr−1 at the global scale, representing 5% of total methane emissions. At continental and annual scales, transport model errors are proportionally larger than at the global scale, with errors ranging from 36 Tg yr−1 in North America to 7 Tg yr−1 in Boreal Eurasia (from 23 to 48%, respectively). At the model grid-scale, the spread of inverse estimates can reach 150% of the prior flux. Therefore, transport model errors contribute significantly to overall uncertainties in emission estimates by inverse modelling, especially when small spatial scales are examined. Sensitivity tests have been carried out to estimate the impact of the measurement network and the advantage of higher horizontal resolution in transport models. The large differences found between methane flux estimates inferred in these different configurations highly question the consistency of transport model errors in current inverse systems. Future inversions should include more accurately prescribed observation covariances matrices in order to limit the impact of transport model errors on estimated methane fluxes.

scholarly journals Freeboard, snow depth and sea-ice roughness in East Antarctica from in situ and multiple satellite data

2011 ◽  
Vol 52 (57) ◽  
pp. 242-248 ◽  
Author(s):  
Thorsten Markus ◽  
Robert Massom ◽  
Anthony Worby ◽  
Victoria Lytle ◽  
Nathan Kurtz ◽  
...  
Keyword(s):  
Sea Ice ◽  
Correlation Coefficient ◽  
Satellite Data ◽  
Snow Depth ◽  
Large Scale ◽  
Mean Difference ◽  
Laser Altimeter ◽  
In Situ Data ◽  
Ice Roughness

AbstractIn October 2003 a campaign on board the Australian icebreaker Aurora Australis had the objective to validate standard Aqua Advanced Microwave Scanning Radiometer (AMSR-E) sea-ice products. Additionally, the satellite laser altimeter on the Ice, Cloud and land Elevation Satellite (ICESat) was in operation. To capture the large-scale information on the sea-ice conditions necessary for satellite validation, the measurement strategy was to obtain large-scale sea-ice statistics using extensive sea-ice measurements in a Lagrangian approach. A drifting buoy array, spanning initially 50 km × 100 km, was surveyed during the campaign. In situ measurements consisted of 12 transects, 50–500 m, with detailed snow and ice measurements as well as random snow depth sampling of floes within the buoy array using helicopters. In order to increase the amount of coincident in situ and satellite data an approach has been developed to extrapolate measurements in time and in space. Assuming no change in snow depth and freeboard occurred during the period of the campaign on the floes surveyed, we use buoy ice-drift information as well as daily estimates of thin-ice fraction and rough-ice vs smooth-ice fractions from AMSR-E and QuikSCAT, respectively, to estimate kilometer-scale snow depth and freeboard for other days. the results show that ICESat freeboard estimates have a mean difference of 1.8 cm when compared with the in situ data and a correlation coefficient of 0.6. Furthermore, incorporating ICESat roughness information into the AMSR-E snow depth algorithm significantly improves snow depth retrievals. Snow depth retrievals using a combination of AMSR-E and ICESat data agree with in situ data with a mean difference of 2.3 cm and a correlation coefficient of 0.84 with a negligible bias.

scholarly journals A framework for comparing remotely sensed and in-situ CO<sub>2</sub> concentrations

2008 ◽  
Vol 8 (1) ◽  
pp. 1549-1588 ◽  
Author(s):  
R. Macatangay ◽  
T. Warneke ◽  
C. Gerbig ◽  
S. Körner ◽  
R. Ahmadov ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Transport Model ◽  
Remotely Sensed ◽  
Infrared Spectrometry ◽  
Remotely Sensed Data ◽  
Solar Absorption ◽  
Mixing Ratios ◽  
In Situ Data ◽  
Global Calibration

Abstract. A framework that allows validating CO2 column averaged volume mixing ratios (VMRs) retrieved from ground-based solar absorption measurements using Fourier transform infrared spectrometry (FTS) against measurements made in-situ (such as from aircrafts and tall towers) has been developed. Since in-situ measurements are done frequently and at high accuracy on the global calibration scale, linking this scale with FTS total column retrievals ultimately provides a calibration scale for remote sensing. FTS, tower and aircraft data were analyzed from measurements during the CarboEurope Regional Experiment Strategy (CERES) from May to June 2005 in Biscarrosse, France. Carbon dioxide VMRs from the MetAir Dimona aircraft, the TM3 global transport model and Observations of the Middle Stratosphere (OMS) balloon based experiments were combined and integrated to compare with FTS measurements. The comparison agrees fairly well with differences resulting from the spatial variability of CO2 around the FTS as measured by the aircraft. Additionally, the Stochastic Time Inverted Lagrangian Transport (STILT) model served as a "transfer standard" between the in-situ data measured at a co-located tower and the remotely sensed data from the FTS. The variability of carbon dioxide VMRs was modeled well by STILT with differences coming partly from uncertainties in the spatial variation of carbon dioxide.

scholarly journals Ozone pollution during the COVID-19 lockdown in the spring 2020 over Europe analysed from satellite observations, in situ measurements and models

2021 ◽  
Author(s):  
Juan Cuesta ◽  
Lorenzo Costantino ◽  
Matthias Beekmann ◽  
Guillaume Siour ◽  
Laurent Menut ◽  
...  
Keyword(s):  
Large Scale ◽  
Transport Model ◽  
Surface Ozone ◽  
In Situ Measurements ◽  
Satellite Observations ◽  
Emission Inventories ◽  
Ozone Pollution ◽  
Chemistry Transport Model ◽  
Near Surface

Abstract. We present a comprehensive study integrating satellite observations of ozone pollution, in situ measurements and chemistry transport model simulations for quantifying the role of anthropogenic emission reductions during the COVID-19 lockdown in spring 2020 over Europe. Satellite observations are derived from the IASI+GOME2 multispectral synergism, which provides particularly enhanced sensitivity to near-surface ozone pollution. These observations are first analysed in terms of differences between the average on 1–15 April 2020, when the strictest lockdown restrictions took place, and the same period in 2019. They show clear enhancements of near-surface ozone in Central Europe and Northern Italy, and some other hotspots, which are typically characterized by VOC-limited chemical regimes. An overall reduction of ozone is observed elsewhere, where ozone chemistry is limited by the abundance of NOx. The spatial distribution of positive and negative ozone concentration anomalies observed from space is in relatively good quantitative agreement with surface in situ measurements over the continent (a correlation coefficient of 0.55, a root-mean-squared difference of 11 ppb and the same standard deviation and range of variability). An average bias of ∼8 ppb between the two observational datasets is remarked, which can partly be explained by the fact the satellite approach retrieves partial columns of ozone with a peak sensitivity above the surface (near 2 km of altitude). For assessing the impact of the reduction of anthropogenic emissions during the lockdown, we adjust the satellite and in situ surface observations for withdrawing the influence of meteorological conditions in 2020 and 2019. This adjustment is derived from the chemistry transport model simulations using the meteorological fields of each year and identical emission inventories. This observational estimate of the influence of lockdown emission reduction is consistent for both datasets. They both show lockdown-associated ozone enhancements in hotspots over Central Europe and Northern Italy, with a reduced amplitude with respect to the total changes observed between the two years, and an overall reduction elsewhere over Europe and the ocean. Satellite observations additionally highlight the ozone anomalies in the regions remote from in situ sensors, an enhancement over the Mediterranean likely associated with maritime traffic emissions and a marked large-scale reduction of ozone elsewhere over ocean (particularly over the North Sea), in consistency with previous assessments done with ozonesondes measurements in the free troposphere. These observational assessments are compared with model-only estimations, using the CHIMERE chemistry transport model. For analysing the uncertainty of the model estimates, we perform two sets of simulations with different setups, differing in the emission inventories, their modifications to account for changes in anthropogenic activities during the lockdown and the meteorological fields. Whereas a general qualitative consistency of positive and negative ozone anomalies is remarked between all model and observational estimates, significant changes are seen in their amplitudes. Models underestimate the range of variability of the ozone changes by at least a factor 2 with respect to the two observational data sets, both for enhancements and decreases of ozone, while the large-scale ozone decrease is not simulated. With one of the setups, the model simulates ozone enhancements a factor 3 to 6 smaller than with the other configuration. This is partly linked to the emission inventories of ozone precursors (at least a 30 % difference), but mainly to differences in vertical mixing of atmospheric constituents depending on the choice of the meteorological model.

scholarly journals Use of absorption optical indices to assess seasonal variability of dissolved organic matter in amazon floodplain lakes

2019 ◽  
Author(s):  
Maria Paula da Silva ◽  
Lino A. Sander de Carvalho ◽  
Evlyn Novo ◽  
Daniel S. F. Jorge ◽  
Claudio C. F. Barbosa
Keyword(s):  
Remote Sensing ◽  
Organic Matter ◽  
Dissolved Organic Matter ◽  
Seasonal Variability ◽  
Large Scale ◽  
River Bank ◽  
In Situ Data ◽  
Amazon Floodplain ◽  
Significant Difference

Abstract. Given the importance of DOM in the carbon cycling of aquatic ecosystems, information on its seasonal variability is crucial. This study assesses the use of available absorption optical indices based on in situ data to both characterize the seasonal variability of the DOM dynamics in a highly complex environment and their viability of being used for satellite remote sensing on large scale studies. The study area comprises four lakes located at the Mamirauá Sustainable Development Reserve (MSDR). Samples for the determination of coloured dissolved organic matter (CDOM) and remote sensing reflectance (Rrs) were acquired in situ. The Rrs was applied to simulate MSI visible bands and used in the proposed models. Differences between lakes were tested regarding CDOM indices. Significant difference in the average of aCDOM (440), aCDOM spectra and S275–295 were found between lakes located inside the flood forest and those near the river bank. The proposed model showed that aCDOM can be used as proxy of S275–295 during rising water with good validation results, demonstrating the potential of Sentinel/MSI imagery data in large scale studies on the dynamics of DOM.

scholarly journals Impact of transport model errors on the global and regional methane emissions estimated by inverse modelling

2013 ◽  
Vol 13 (4) ◽  
pp. 10961-11021
Author(s):  
R. Locatelli ◽  
P. Bousquet ◽  
F. Chevallier ◽  
A. Fortems-Cheney ◽  
S. Szopa ◽  
...  
Keyword(s):  
Transport Model ◽  
Atmospheric Transport ◽  
Spatial Scales ◽  
Global Scale ◽  
Methane Emissions ◽  
Inverse Modelling ◽  
Model Errors ◽  
Inverse Systems ◽  
Set Up ◽  
The Impact

Abstract. A modelling experiment has been conceived to assess the impact of transport model errors on the methane emissions estimated by an atmospheric inversion system. Synthetic methane observations, given by 10 different model outputs from the international TransCom-CH4 model exercise, are combined with a prior scenario of methane emissions and sinks, and integrated into the PYVAR-LMDZ-SACS inverse system to produce 10 different methane emission estimates at the global scale for the year 2005. The same set-up has been used to produce the synthetic observations and to compute flux estimates by inverse modelling, which means that only differences in the modelling of atmospheric transport may cause differences in the estimated fluxes. In our framework, we show that transport model errors lead to a discrepancy of 27 Tg CH4 per year at the global scale, representing 5% of the total methane emissions. At continental and yearly scales, transport model errors have bigger impacts depending on the region, ranging from 36 Tg CH4 in north America to 7 Tg CH4 in Boreal Eurasian (from 23% to 48%). At the model gridbox scale, the spread of inverse estimates can even reach 150% of the prior flux. Thus, transport model errors contribute to significant uncertainties on the methane estimates by inverse modelling, especially when small spatial scales are invoked. Sensitivity tests have been carried out to estimate the impact of the measurement network and the advantage of higher resolution models. The analysis of methane estimated fluxes in these different configurations questions the consistency of transport model errors in current inverse systems. For future methane inversions, an improvement in the modelling of the atmospheric transport would make the estimations more accurate. Likewise, errors of the observation covariance matrix should be more consistently prescribed in future inversions in order to limit the impact of transport model errors on estimated methane fluxes.

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