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Urban areas are responsible for more than 40 % of global energy-related carbon dioxide (CO.sub.2) emissions. The Tokyo metropolitan area (TMA), Japan, one of the most populated regions in the world, includes various emission sources, such as thermal power plants, automobile traffic, and residential facilities. In order to infer a top-down emission estimate, we conducted an intensive field campaign in the TMA from February to April 2016 to measure column-averaged dry-air mole fractions of CO.sub.2 (XCO.sub.2) with three ground-based Fourier transform spectrometers (one IFS 125HR and two EM27/SUN spectrometers). At two urban sites (Saitama and Sodegaura), measured XCO.sub.2 values were generally larger than those at a rural site (Tsukuba) by up to 9.5 ppm, and average diurnal variations increased toward evening. To simulate the XCO.sub.2 enhancement (ÎXCO.sub.2) resulting from emissions at each observation site, we used the Stochastic Time-Inverted Lagrangian Transport (STILT) model driven by meteorological fields at a horizontal resolution of â¼1 km from the Weather Research and Forecasting (WRF) model, which was coupled with anthropogenic (large point source and area source) CO.sub.2 emissions and biogenic fluxes. Although some of the diurnal variation of ÎXCO.sub.2 was not reproduced and plumes from nearby large point sources were not captured, primarily because of a transport modeling error, the WRF-STILT simulations using prior fluxes were generally in good agreement with the observations (mean bias, 0.30 ppm; standard deviation, 1.31 ppm). By combining observations with high-resolution modeling, we developed an urban-scale inversion system in which spatially resolved CO.sub.2 emission fluxes at 3 km resolution and a scaling factor of large point source emissions were estimated on a monthly basis by using Bayesian inference. The XCO.sub.2 simulation results from the posterior CO.sub.2 fluxes were improved (mean bias, -0.03 ppm; standard deviation, 1.21 ppm). The prior and posterior total CO.sub.2 emissions in the TMA are 1.026 ± 0.116 and 1.037 ± 0.054 Mt-CO.sub.2 d.sup.-1 at the 95 % confidence level, respectively. The posterior total CO.sub.2 emissions agreed with emission inventories within the posterior uncertainty, demonstrating that the EM27/SUN spectrometer data can constrain urban-scale monthly CO.sub.2 emissions.

[This corrects the article DOI: 10.1371/journal.pone.0220782.].

This manuscript presents a novel scheme to achieve high-resolution laser-radar ranging with a small sample number under low signal-to-noise ratio (SNR) conditions. To reduce the sample number, the Restricted Isometry Property-based optimal multi-channel coprime-sampling (RIP-OMCS) strategy is established. In the RIP-OMCS strategy, the data collected across multiple channels with very low coprime-sampling rates can record accurate range information on each target. Further, the asynchronous problem caused by channel sampling-time errors is considered. The sampling-time errors are estimated using the cross-correlation function. After canceling the asynchronous problem, the data collected by multiple channels are then merged into non-uniform sampled signals. Using data combination, target-range estimation is converted into an optimization problem of sparse representation consisting of a non-uniform Fourier dictionary. This optimization problem is solved using adaptive hybrid re-weighted constraint (AHRC) l[sub.1] minimization. Two constraints are formed from statistical attributes of the targets and clutter. Moreover, as the detailed characteristics of the target, clutter, and noise are unknown before the solution, the two constraints can be adaptively modified, which guarantees that l[sub.1] minimization obtains the high-resolution range profile and accurate distance of all targets under a low SNR. Our experiments confirmed the effectiveness of the proposed method.

Accurate estimates of net ecosystem CO.sub.2 exchange (NEE) would improve the understanding of natural carbon sources and sinks and their role in the regulation of global atmospheric carbon. In this work, we use and compare the random forest (RF) and the gradient boosting (GB) machine learning (ML) methods for predicting year-round 6 h NEE over 1996-2018 in a pine-dominated boreal forest in southern Finland and analyze the predictability of NEE. Additionally, aggregation to weekly NEE values was applied to get information about longer term behavior of the method. The meteorological ERA5 reanalysis variables were used as predictors. Spatial and temporal neighborhood (predictor lagging) was used to provide the models more data to learn from, which was found to improve considerably the accuracy of both ML approaches compared to using only the nearest grid cell and time step. Both ML methods can explain temporal variability of NEE in the observational site of this study with meteorological predictors, but the GB method was more accurate. Only minor signs of overfitting could be detected for the GB algorithm when redundant variables were included. The accuracy of the approaches, measured mainly using cross-validated R.sup.2 score between the model result and the observed NEE, was high, reaching a best estimate value of 0.92 for GB and 0.88 for RF. In addition to the standard RF approach, we recommend using GB for modeling the CO.sub.2 fluxes of the ecosystems due to its potential for better performance.

Open-path measurements of atmospheric composition provide spatial averages of trace gases that are less sensitive to small-scale variations and the effects of meteorology. In this study we introduce improvements to open-path near-infrared (OP-NIR) Fourier transform spectrometer measurements of CO.sub.2 and CH.sub.4 . In an extended field trial, the OP-NIR achieved measurement repeatability 6 times better for CO.sub.2 (0.28 ppm) and 10 times better for CH.sub.4 (2.1 ppb) over a 1.55 km one-way path than its predecessor. The measurement repeatability was independent of path length up to 1.55 km, the longest distance tested. Comparisons to co-located in situ measurements under well-mixed conditions characterise biases of 1.41 % for CO.sub.2 and 1.61 % for CH.sub.4 relative to in situ measurements calibrated to World Meteorological Organisation - Global Atmosphere Watch (WMO-GAW) scales. The OP-NIR measurements can detect signals due to local photosynthesis and respiration, and local point sources of CH.sub.4 . The OP-NIR is well-suited for deployment in urban or rural settings to quantify atmospheric composition on kilometre scales.

Improving the estimates of CO.sub.2 sources and sinks over India through inverse methods calls for a comprehensive atmospheric monitoring system involving atmospheric transport models that make a realistic accounting of atmospheric CO.sub.2 variability along with a good coverage of ground-based monitoring stations. This study investigates the importance of representing fine-scale variability in atmospheric CO.sub.2 in models for the optimal use of observations through inverse modelling. The unresolved variability in atmospheric CO.sub.2 in coarse models is quantified by using WRF-Chem (Weather Research and Forecasting model coupled with Chemistry) simulations at a spatial resolution of 10 km x 10 km. We show that the representation errors due to unresolved variability in the coarse model with a horizontal resolution of 1.sup.\" (â¼ 100 km) are considerable (median values of 1.5 and 0.4 ppm, parts per million, for the surface and column CO.sub.2, respectively) compared to the measurement errors. The monthly averaged surface representation error reaches up to â¼ 5 ppm, which is even comparable to half of the magnitude of the seasonal variability or concentration enhancement due to hotspot emissions. Representation error shows a strong dependence on multiple factors such as time of the day, season, terrain heterogeneity, and changes in meteorology and surface fluxes. By employing a first-order inverse modelling scheme using pseudo-observations from nine tall-tower sites over India, we show that the net ecosystem exchange (NEE) flux uncertainty solely due to unresolved variability is in the range of 3.1 % to 10.3 % of the total NEE of the region. By estimating the representation error and its impact on flux estimations during different seasons, we emphasize the need to take account of fine-scale CO.sub.2 variability in models over the Indian subcontinent to better understand processes regulating CO.sub.2 sources and sinks. The efficacy of a simple parameterization scheme is further demonstrated to capture these unresolved variations in coarse models.

A traditional metric used in hydrology to summarize model performance is the Nash–Sutcliffe efficiency (NSE). Increasingly an alternative metric, the Kling–Gupta efficiency (KGE), is used instead. When NSE is used, NSE = 0 corresponds to using the mean flow as a benchmark predictor. The same reasoning is applied in various studies that use KGE as a metric: negative KGE values are viewed as bad model performance, and only positive values are seen as good model performance. Here we show that using the mean flow as a predictor does not result in KGE = 0, but instead KGE =1-√2≈-0.41. Thus, KGE values greater than −0.41 indicate that a model improves upon the mean flow benchmark – even if the model's KGE value is negative. NSE and KGE values cannot be directly compared, because their relationship is non-unique and depends in part on the coefficient of variation of the observed time series. Therefore, modellers who use the KGE metric should not let their understanding of NSE values guide them in interpreting KGE values and instead develop new understanding based on the constitutive parts of the KGE metric and the explicit use of benchmark values to compare KGE scores against. More generally, a strong case can be made for moving away from ad hoc use of aggregated efficiency metrics and towards a framework based on purpose-dependent evaluation metrics and benchmarks that allows for more robust model adequacy assessment.