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We developed a high-resolution surface flux inversion system based on the global Lagrangian–Eulerian coupled tracer transport model composed of National Institute for Environmental Studies Transport Model (NIES-TM) and FLEXible PARTicle dispersion model (FLEXPART). The inversion system is named NTFVAR (NIES-TM-FLEXPART-variational) as it applies variational optimisation to estimate surface fluxes. We tested the system by estimating optimized corrections to natural surface CO2 fluxes to achieve best fit to atmospheric CO2 data collected by the global in-situ network, as a necessary step towards capability of estimating anthropogenic CO2 emissions. We employ the Lagrangian particle dispersion model (LPDM) FLEXPART to calculate the surface flux footprints of CO2 observations at a 0.1° × 0.1° spatial resolution. The LPDM is coupled to 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 is being 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) dataset. High-resolution prior flux fields were prepared using Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) for fossil fuel combustion, Global Fire Assimilation System (GFAS) for biomass burning, the Vegetation Integrative SImulator for Trace gases (VISIT) model for terrestrial biosphere exchange and Ocean Tracer Transport Model (OTTM) for oceanic exchange. The terrestrial biospheric flux field was constructed using a vegetation mosaic map and separate simulation of CO2 fluxes at daily time step by the VISIT model for each vegetation type. The prior flux uncertainty for 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 vegetation productivity gradients. The resulting flux estimates improve fit to the observations at continuous observations sites, reproducing both the seasonal variation and short-term concentration variability, including high CO2 concentration events associated with anthropogenic emissions. The use of high-resolution atmospheric transport in global CO2 flux inversion has the advantage of better resolving the transport from the mix of the anthropogenic and biospheric sources in densely populated continental regions and shows potential for better separation between fluxes from terrestrial ecosystems and strong localised sources such as anthropogenic emissions and forest fires. Further improvements in the modelling system are needed as the posterior fit is better than that by the National Oceanic and Atmospheric Administration (NOAA) CarbonTracker only for a fraction of the monitoring sites, mostly at coastal and island locations experiencing mix of background and local flux signals.

The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier transform spectrometers (FTSs) that record near-infrared (NIR) spectra of the sun. From these spectra, accurate and precise observations of CO2 column-averaged dry-air mole fractions (denoted XCO2) are retrieved. TCCON FTS observations have previously been used to validate satellite estimations of XCO2; however, our knowledge of the short-term spatial and temporal variations in XCO2 surrounding the TCCON sites is limited. In this work, we use the National Institute for Environmental Studies (NIES) Eulerian three-dimensional transport model and the FLEXPART (FLEXible PARTicle dispersion model) Lagrangian particle dispersion model (LPDM) to determine the footprints of short-term variations in XCO2 observed by operational, past, future and possible TCCON sites. We propose a footprint-based method for the collocation of satellite and TCCON XCO2 observations and estimate the performance of the method using the NIES model and five GOSAT (Greenhouse Gases Observing Satellite) XCO2 product data sets. Comparison of the proposed approach with a standard geographic method shows a higher number of collocation points and an average bias reduction up to 0.15 ppm for a subset of 16 stations for the period from January 2010 to January 2014. Case studies of the Darwin and Reunion Island sites reveal that when the footprint area is rather curved, non-uniform and significantly different from a geographical rectangular area, the differences between these approaches are more noticeable. This emphasises that the collocation is sensitive to local meteorological conditions and flux distributions.

Precipitation nowcasting is a short-range forecast of rain/snow (up to 2 hours), often displayed on top of the geographical map by the weather service. Modern precipitation nowcasting algorithms rely on the extrapolation of observations by ground-based radars via optical flow techniques or neural network models. Dependent on these radars, typical nowcasting is limited to the regions around their locations. We have developed a method for precipitation nowcasting based on geostationary satellite imagery and incorporated the resulting data into the Yandex.Weather precipitation map (including an alerting service with push notifications for products in the Yandex ecosystem), thus expanding its coverage and paving the way to a truly global nowcasting service., Final KDD 2019 version

We employed an atmospheric transport model to attribute column-averaged CO2 mixing ratios (XCO2) observed by Greenhouse gases Observing SATellite (GOSAT) to emissions due to large sources such as megacities and power plants. XCO2 enhancements estimated from observations were compared to model simulations implemented at the spatial resolution of the satellite observation footprint (0.1° × 0.1°). We found that the simulated XCO2 enhancements agree with the observed over several continental regions across the globe, for example, for North America with an observation to simulation ratio of 1.05 ± 0.38 (p

The Global Lagrangian Atmospheric Dispersion Model (GLADIM) is described. GLADIM is based on the global trajectory model, which had been developed earlier and uses fields of weather parameters from different atmospheric reanalysis centers for calculations of trajectories of air mass that include trace gases. GLADIM includes the parameterization of turbulent diffusion and allows the forward calculation of concentrations of atmospheric tracers at nodes of a global regular grid when a source is specified. Thus, GLADIM can be used for the forward simulation of pollutant propagation (volcanic ash, radionuclides, and so on). Working in the reverse direction, GLADIM allows the detection of remote sources that mainly contribute to the tracer concentration at an observation point. This property of Lagrangian models is widely used for data analysis and the reverse modeling of emission sources of a pollutant specified. In this work we describe the model and some results of its validation through a comparison with results of a similar model and observation data.

This study presents the outcome of an inverse modeling intercomparison experiment on the use of total column CO2 retrievals from Greenhouse Gas Observing Satellite (GOSAT) for quantifying global sources and sinks of CO2. Eight research groups submitted inverse modeling results for the first year of GOSAT measurements. Inversions were carried out using only GOSAT data, a combination of GOSAT and surface measurements, and using only surface measurements. As expected, the most robust flux estimates are obtained at large scales (e.g., within 20% of the annual flux at the global scale), and they quickly diverge toward the scale of the subcontinental TRANSCOM regions and beyond (to >100% of the annual flux). We focus our analysis on a shift in the CO2 uptake over land from the Tropics toward the Northern Hemisphere Extra tropics of ∼1 PgC/yr when GOSAT data are used in the inversions. This shift is largely driven by TRANSCOM regions Europe and Northern Africa, showing, respectively, an increased uptake and release of 0.7 and 0.9 PgC/yr. Inversions using GOSAT data show a reduced gradient between midlatitudes of the Northern Hemisphere and the Tropics, consistent with the latitudinal shift in carbon uptake. However, the reduced gradients degrade the agreement with background aircraft and surface measurements. To narrow the range of inversion-derived flux, estimates will require further efforts to understand the differences not only between the retrieval schemes but also between inverse models, as their contributions to the overall uncertainty are estimated to be of similar magnitude.

In situ observation of the atmospheric CO2, CH4, and CO mixing ratios at Hateruma Island (HAT, 24.05° N, 123.80° E) often show synoptic-scale variations with correlative elevations during winter, associated with air transport from the East Asian countries. We examine winter (November– March) trends in ΔCH4 / ΔCO2, ΔCO / ΔCO2, and ΔCO / ΔCH4 observed at Hateruma over the period 1999 to 2010. To investigate the relationship between the East Asian emissions and the short-term variations in the atmospheric mixing ratios, we use the FLEXPART Lagrangian particle dispersion model (LPDM). The observed ratios ΔCH4 / ΔCO2 and ΔCO / ΔCO2 both show an overall gradual decrease over the study period due to a recent rapid increase in fossil fuel consumption in China. We note, however, that the decreasing rates of ΔCH4 / ΔCO2 and ΔCO / ΔCO2 show gradual decrease and increase, respectively, during the entire observation periods used in this study. The ΔCO / ΔCH4 slope, on the other hand, shows an increasing trend during 1999–2004 but a decrease during 2005–2010. Calculation of the concentration footprint for the atmospheric observation at HAT by using the FLEXPART LPDM indicates that most of the short-term variations are caused by emission variations from northern and eastern China. Combined with a set of reported emission maps, we have estimated the temporal changes in the annual CH4 and CO emissions from China under the assumption that the estimate of the fossil-fuel-derived CO2 emissions based on the energy statistics are accurate. The estimated annual CH4 emissions, corresponding to nonseasonal sources or anthropogenic sources without rice fields, show a nearly constant value of 39 ± 7 TgCH4 yr−1 during 1998–2002, and then gradually increase to 46 ± 8 TgCH4 yr−1 in 2009/2010. The estimated annual CO emissions increase from 134 ± 32 TgCO yr−1 in 1998/1999 to 182 ± 42 TgCO yr−1 in 2004/2005, level off after 2005, and then slightly decrease to less than 160 TgCO yr−1 in 2008–2010.

Increasing atmospheric carbon dioxide (CO2) is the principal driver of anthropogenic climate change. Asia is an important region for the global carbon budget, with 4 of the world's 10 largest national emitters of CO2. Using an ensemble of seven atmospheric inverse systems, we estimated land biosphere fluxes (natural, land-use change and fires) based on atmospheric observations of CO2 concentration. The Asian land biosphere was a net sink of −0.46 (−0.70–0.24) PgC per year (median and range) for 1996–2012 and was mostly located in East Asia, while in South and Southeast Asia the land biosphere was close to carbon neutral. In East Asia, the annual CO2 sink increased between 1996–2001 and 2008–2012 by 0.56 (0.30–0.81) PgC, accounting for ∼35% of the increase in the global land biosphere sink. Uncertainty in the fossil fuel emissions contributes significantly (32%) to the uncertainty in land biosphere sink change., Land biosphere uptake of carbon is important in mitigating the anthropogenic increase in atmospheric CO2 and its climate forcing. Here, the authors show that land biosphere uptake of carbon in Asia has increased substantially since the mid 1990s, likely owing to reforestation and regional climate change.

We present the development of the Adjoint of the Global Eulerian–Lagrangian Coupled Atmospheric (A-GELCA) model that consists of the National Institute for Environmental Studies (NIES) model as an Eulerian three-dimensional transport model (TM), and FLEXPART (FLEXible PARTicle dispersion model) as the Lagrangian Particle Dispersion Model (LPDM). The forward tangent linear and adjoint components of the Eulerian model were constructed directly from the original NIES TM code using an automatic differentiation tool known as TAF (Transformation of Algorithms in Fortran; http://www.FastOpt.com), with additional manual pre- and post-processing aimed at improving transparency and clarity of the code and optimizing the performance of the computing, including MPI (Message Passing Interface). The Lagrangian component did not require any code modification, as LPDMs are self-adjoint and track a significant number of particles backward in time in order to calculate the sensitivity of the observations to the neighboring emission areas. The constructed Eulerian adjoint was coupled with the Lagrangian component at a time boundary in the global domain. The simulations presented in this work were performed using the A-GELCA model in forward and adjoint modes. The forward simulation shows that the coupled model improves reproduction of the seasonal cycle and short-term variability of CO2. Mean bias and standard deviation for five of the six Siberian sites considered decrease roughly by 1 ppm when using the coupled model. The adjoint of the Eulerian model was shown, through several numerical tests, to be very accurate (within machine epsilon with mismatch around to ±6 e−14) compared to direct forward sensitivity calculations. The developed adjoint of the coupled model combines the flux conservation and stability of an Eulerian discrete adjoint formulation with the flexibility, accuracy, and high resolution of a Lagrangian backward trajectory formulation. A-GELCA will be incorporated into a variational inversion system designed to optimize surface fluxes of greenhouse gases.

We designed a method to simulate atmospheric CO2 concentrations at several continuous observation sites around the globe using surface fluxes at a very high spatial resolution. The simulations presented in this study were performed using the Global Eulerian-Lagrangian Coupled Atmospheric model (GELCA), comprising a Lagrangian particle dispersion model coupled to a global atmospheric tracer transport model with prescribed global surface CO2 flux maps at a 1 × 1 km resolution. The surface fluxes used in the simulations were prepared by assembling the individual components of terrestrial, oceanic and fossil fuel CO2 fluxes. This experimental setup (i.e. a transport model running at a medium resolution, coupled to a high-resolution Lagrangian particle dispersion model together with global surface fluxes at a very high resolution), which was designed to represent high-frequency variations in atmospheric CO2 concentration, has not been reported at a global scale previously. Two sensitivity experiments were performed: (a) using the global transport model without coupling to the Lagrangian dispersion model, and (b) using the coupled model with a reduced resolution of surface fluxes, in order to evaluate the performance of Eulerian-Lagrangian coupling and the role of high-resolution fluxes in simulating high-frequency variations in atmospheric CO2 concentrations. A correlation analysis between observed and simulated atmospheric CO2 concentrations at selected locations revealed that the inclusion of both Eulerian-Lagrangian coupling and high-resolution fluxes improves the high-frequency simulations of the model. The results highlight the potential of a coupled Eulerian-Lagrangian model in simulating high-frequency atmospheric CO2 concentrations at many locations worldwide. The model performs well in representing observations of atmospheric CO2 concentrations at high spatial and temporal resolutions, especially for coastal sites and sites located close to sources of large anthropogenic emissions. While this study focused on simulations of CO2 concentrations, the model could be used for other atmospheric compounds with known estimated emissions.