Chemical Data Assimilation Research Articles

CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model

Abstract. We present a versatile, powerful, and user-friendly chemical data assimilation toolkit for simultaneously optimizing emissions and concentrations of chemical species based on atmospheric observations from satellites or suborbital platforms. The CHemistry and Emissions REanalysis Interface with Observations (CHEEREIO) exploits the GEOS-Chem chemical transport model and a localized ensemble transform Kalman filter algorithm (LETKF) to determine the Bayesian optimal (posterior) emissions and/or concentrations of a set of species based on observations and prior information using an easy-to-modify configuration file with minimal changes to the GEOS-Chem or LETKF code base. The LETKF algorithm readily allows for nonlinear chemistry and produces flow-dependent posterior error covariances from the ensemble simulation spread. The object-oriented Python-based design of CHEEREIO allows users to easily add new observation operators such as for satellites. CHEEREIO takes advantage of the Harmonized Emissions Component (HEMCO) modular structure of input data management in GEOS-Chem to update emissions from the assimilation process independently from the GEOS-Chem code. It can seamlessly support GEOS-Chem version updates and is adaptable to other chemical transport models with similar modular input data structure. A post-processing suite combines ensemble output into consolidated NetCDF files and supports a wide variety of diagnostic data and visualizations. We demonstrate CHEEREIO's capabilities with an out-of-the-box application, assimilating global methane emissions and concentrations at weekly temporal resolution and 2∘ × 2.5∘ spatial resolution for 2019 using TROPOspheric Monitoring Instrument (TROPOMI) satellite observations. CHEEREIO achieves a 50-fold improvement in computational performance compared to the equivalent analytical inversion of TROPOMI observations.

Air pollution is associated with abnormal left ventricular diastolic function: a nationwide population-based study

BackgroundAir pollution is a growing public health concern of global significance. Till date, few studies have explored the associations between air pollutants and cardiac imaging phenotypes. In this study, we aim to explore the association of ambient air pollution and abnormal left ventricular diastolic function (ALVDF) among a large-scale free-living population.MethodsThe participants were from a national representative large-scale cross-sectional study, i.e., the China Hypertension Survey (CHS), 2012–15. After exclusion, 25,983 participants from 14 provinces and 30 districts in China were included for the final analysis. The annual average ambient PM2.5, PM10 and NO2 concentrations were obtained from the chemical data assimilation system (ChemDAS). The clinical evaluation of left ventricular function was conducted in the survey field which was based on echocardiography. Grading diastolic dysfunction was based on Recommendations for the evaluation of left ventricular diastolic function by echocardiography (2009).ResultsThe mean age of 25,983 participants was 56.8 years, 46.5% were male, and the crude prevalence of GradeI-III ALVDF were 48.1%, 1.6% and 1.1%, respectively. The ORs (95% CI) for ALVDF in the fully adjusted model were 1.31 (1.11–1.56), 1.11 (1.01–1.21) and 1.18 (0.90–1.54) for an increase of 10 μg/m3 of PM2.5, PM10 and NO2, respectively. And for different grades of ALVDF, elevated concentration of PM2.5 and PM10 exposures significantly increased the risk of gradeIinstead of gradeII ~ III ALVDF. There was a positive linear and “J” shape concentration–response association between annual average ambient PM2.5 and NO2 and the ALVDF risk assessed by the restricted cubic spline. The exposure level of most participants to PM10 was less than 130 μg/m3, and the risk of ALVDF increased significantly with the concentration rise.ConclusionsThis large-scale nationwide population study demonstrated a significantly positive association between ambient PM2.5, PM10 and NO2 with ALVDF, especially for mild ALVDF. The functional abnormality may partially explain the enhanced cardiovascular morbidity and mortality associated with air pollution, which highlights the importance of appropriate interventions to reduce ambient air pollution in China.

Forecasting of an unusual dust event over western India by the Air Quality Early Warning System

This study presents the successful implementation of an operational Air Quality Early Warning System (AQEWS) in forecasting an unprecedented dust storm event that occurred in the western part of India from 21–24 January 2022. The AQEWS generates a daily 72-h forecast using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) at the core, which is initialized with assimilated data from the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals and in-situ measurements of fine particulate matter (PM2.5). The AQEWS was able to forecast the outbreak of this dust storm reasonably well over the north of the Arabian Sea on 21 January, as well as its movement and dispersal towards the Indian landmass in the subsequent 72 h, resulting in a significant amount of transported dust that engulfed the west Indian region on 23–24 January. We utilize satellite observations and in situ measurements to quantify the ability of the forecasting system to predict the spatiotemporal characteristics of the dust storm. Notably, the AQEWS aerosol data assimilation process played a critical role in forecasting the anomalous increase of PM10 in several west Indian cities, as it significantly improved the initial conditions of PM10 by approximately 24–40 μg/m3 and PM2.5 concentration by approximately 30–38 μg/m3. Without assimilation, the system fails to capture the intensity, and pattern of variability of the dust storm. The mean bias of the 72-h prediction of PM10 was substantially reduced with assimilation, from −44% to 0.35% in Mumbai and from −57% to 13% in Gujarat. In selected regions, in situ measurements confirmed that the AQEWS adequately captured the peak intensity and pattern of the dust storm event. The AQEWS thus demonstrated improved forecasting skills in predicting an extreme dust event during winter and the adoption of chemical data assimilation enhanced the accuracy of the real-time air quality forecasting system.

Performance comparisons of the three data assimilation methods for improved predictability of PM2·5: Ensemble Kalman filter, ensemble square root filter, and three-dimensional variational methods

To improve the predictability of concentrations of atmospheric particulate matter, a data assimilation (DA) system using ensemble square root filter (EnSRF) has been developed for the Community Multiscale Air Quality (CMAQ) model. The EnSRF DA method is a deterministic variant of the ensemble Kalman filter (EnKF) method, which means that unlike the EnKF method, it does not add random noise to the observations. To compare the performances of the EnSRF with those of other DA methods, such as EnKF and 3DVAR (three-dimensional variational), these three methods were applied to the same CMAQ model simulations with identical experimental settings. This is the first attempt in the field of chemical DA to compare the EnKF and EnSRF methods. An identical set of surface fine particulate matter (PM2.5) were assimilated every 6 h by all the DA methods over a CMAQ domain of East Asia, during the period from 01 May to 11 June 2016. In parallel with ‘reanalysis experiments’, we also carried out ‘48 h prediction experiments’ using the optimized initial conditions produced by the three DA methods. Detailed analyses among the three DA methods were then carried out by comparing both the reanalysis and the prediction outputs with the observed surface PM2.5 over four regions (i.e., South Korea, the Beijing–Tianjin–Hebei (BTH) region, Shandong province, and Liaoning province). The comparison results revealed that the EnSRF produced the best reanalysis and prediction fields in terms of several statistical metrics. For example, when the 3DVAR, EnKF, and EnSRF methods were used, averaged normalized mean biases (NMBs) decreased by (57.6, 85.6, and 91.8) % in reanalyses and (39.7, 87.6, and 91.5) % in first-day predictions, compared to the CMAQ control experiment (i.e., without DA) over South Korea, respectively. Also, over the three Chinese regions, the EnSRF method outperformed the EnKF and 3DVAR methods.

Inferring and evaluating satellite-based constraints on NOx emissions estimates in air quality simulations

Abstract. Satellite observations of tropospheric NO2 columns can provide top-down observational constraints on emissions estimates of nitrogen oxides (NOx). Mass-balance-based methods are often applied for this purpose but do not isolate near-surface emissions from those aloft, such as lightning emissions. Here, we introduce an inverse modeling framework that couples satellite chemical data assimilation to a chemical transport model. In the framework, satellite-constrained emissions totals are inferred using model simulations with and without data assimilation in the iterative finite-difference mass-balance method. The approach improves the finite-difference mass-balance inversion by isolating the near-surface emissions increment. We apply the framework to separately estimate lightning and anthropogenic NOx emissions over the Northern Hemisphere for 2019. Using overlapping observations from the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI), we compare separate NOx emissions inferences from these satellite instruments, as well as the impacts of emissions changes on modeled NO2 and O3. OMI inferences of anthropogenic emissions consistently lead to larger emissions than TROPOMI inferences, attributed to a low bias in TROPOMI NO2 retrievals. Updated lightning NOx emissions from either satellite improve the chemical transport model's low tropospheric O3 bias. The combined lighting and anthropogenic emissions updates improve the model's ability to reproduce measured ozone by adjusting natural, long-range, and local pollution contributions. Thus, the framework informs and supports the design of domestic and international control strategies.

Changes in US background ozone associated with the 2011 turnaround in Chinese NOx emissions

Assessing the effectiveness of surface ozone air quality regulations is complicated by non-local sources and inhibited by sparse in situ observational networks. Here, we harness satellite measurements of ozone and its precursors over Asia to identify the Asian contribution to background ozone over the United States (US) from 2006-2016 using a state-of-the-art chemical data assimilation system. Our results indicate that Chinese NOx emissions reached an apex in 2011, increasing daily average ozone concentrations over Los Angeles by up to 3.8 ppb at 750 hPa and 0.7 ppb at the surface in 2012 relative to 2006, with somewhat larger impacts in the high altitude regions of the Western US (assuming constant VOC emissions). Decreasing Chinese NOx emissions after 2011 contributed to a reduction of similar magnitude in Western US background ozone from 2012-2016. We also assess the impact of direct assimilation of satellite-based tropospheric ozone profile measurements over Asia and find that it substantially modifies global ozone, including US surface concentrations. These results suggest that measurements of both ozone and its precursors over Asia play a substantial role in evaluating not only local but also global air quality. The simultaneous assimilation of ozone and its precursors proffers a powerful way to constrain the vertical profile of ozone and improve understanding of ozone variations. In the future, a more extensive satellite observing system has great potential to better constrain both local pollution and background ozone globally when employed in conjunction with chemical data assimilation.

Left ventricular diastolic dysfunction and cardiovascular disease in different ambient air pollution conditions: A prospective cohort study

Although previous studies indicated that the left ventricular diastolic dysfunction (LVDD) is associated with cardiovascular disease (CVD), it remains unclear whether effects would be enhanced or accelerated by long-term air pollution exposure. During 4.65 years (107,726 person-years) of follow-up, 942 cases of CVD events incident were identified among 23,143 participants from the China Hypertension Survey (CHS). Grading diastolic dysfunction was based on Recommendations for the evaluation of left ventricular diastolic function by echocardiography (2009). The annual average PM2.5, PM10 and NO2 concentrations were obtained by the chemical data assimilation system. Cox proportional hazards models were employed to estimate hazard ratios (HRs) for CVD in relation to LVDD. At baseline, the participants' mean age was 56.7 years, 46.8% were male. Compared to normal group, the HR (95% CI) of LVDD was 1.27 (1.07–1.50) after adjusting for all covariates. When stratified by ambient air pollution, we found that in middle and worst third PM2.5 areas, increased CVD risk was associated with increasing LVDD grade, both P for trend <0.05; The HRs (95% CI) of the CVD incidence were 1.52 (0.68–3.44), 4.97 (1.76–14.03) and 4.07 (1.44–11.49) for severe LVDD in the best, middle and worst third PM2.5 areas, respectively. Similar results were also presented for PM10 and NO2. In conclusion, our study highlights a stronger detectable adverse association between LVDD with CVD in worse ambient air quality assessed by any of the three primary ambient air pollutants (PM2.5, PM10 and NO2). Our study calls for appropriate interventions to reduce air pollution, which may promote great benefits to public health potentially by providing protection against the adverse CVD events.

A comparison of the impact of TROPOMI and OMI tropospheric NO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; on global chemical data assimilation

Abstract. This study gives a systematic comparison of the Tropospheric Monitoring Instrument (TROPOMI) version 1.2 and Ozone Monitoring Instrument (OMI) QA4ECV tropospheric NO2 column through global chemical data assimilation (DA) integration for the period April–May 2018. DA performance is controlled by measurement sensitivities, retrieval errors, and coverage. The smaller mean relative observation errors by 16 % in TROPOMI than OMI over 60∘ N–60∘ S during April–May 2018 led to larger reductions in the global root-mean-square error (RMSE) against the assimilated NO2 measurements in TROPOMI DA (by 54 %) than in OMI DA (by 38 %). Agreements against the independent surface, aircraft-campaign, and ozonesonde observation data were also improved by TROPOMI DA compared to the control model simulation (by 12 %–84 % for NO2 and by 7 %–40 % for ozone), which were more obvious than those by OMI DA for many cases (by 2 %–70 % for NO2 and by 1 %–22 % for ozone) due to better capturing spatial and temporal variability by TROPOMI DA. The estimated global total NOx emissions were 15 % lower in TROPOMI DA, with 2 %–23 % smaller regional total emissions, in line with the observed negative bias of the TROPOMI version 1.2 product compared to the OMI QA4ECV product. TROPOMI DA can provide city-scale emission estimates, which were within 10 % differences with other high-resolution analyses for several limited areas, while providing a globally consistent analysis. These results demonstrate that TROPOMI DA improves global analyses of NO2 and ozone, which would also benefit studies on detailed spatial and temporal variations in ozone and nitrate aerosols and the evaluation of bottom-up NOx emission inventories.

Optimal Planning of Air Quality-Monitoring Sites for Better Depiction of PM2.5 Pollution across China.

A myriad of studies have attempted to use ground-level observations to obtain gap-free spatiotemporal variations of PM2.5, in support of air quality management and impact studies. Statistical methods (machine learning, etc.) or numerical methods by combining chemical transport modeling and observations with data assimilation techniques have been typically applied, yet the significance of site placement has not been well recognized. In this study, we apply five proper orthogonal decomposition (POD)-based sensor placement algorithms to identify optimal site locations and systematically evaluate their reconstruction ability. We demonstrate that the QR pivot is relatively more reliable in deciding optimal monitoring site locations. When the number of planned sites (sensors) is limited, using a lower number of modes would yield lower estimation errors. However, the dimension of POD modes has little impact on reconstruction quality when sufficient sensors are available. The locations of sites guided by the QR pivot algorithm are mainly located in regions where PM2.5 pollution is severe. We compare reconstructed PM2.5 pollution based on QR pivot-guided sites and existing China National Environmental Monitoring Center (CNEMC) sites and find that the QR pivot-guided sites are superior to existing sites with respect to reconstruction accuracy. The current planning of monitoring stations is likely to miss sources of pollution in less-populated regions, while our QR pivot-guided sites are planned based on the severity of PM2.5 pollution. This planning methodology has additional potentials in chemical data assimilation studies as duplicate information from current CNEMC-concentrated stations is not likely to boost performance.

Improvement of PM2.5 forecast over China by the joint adjustment of initial conditions and emissions with the NLS-4DVar method

Particulate pollution is a serious environmental problem that affects regional air quality and the global climate. We developed an advanced joint chemical data assimilation system to improve atmospheric aerosol forecasting based on the nonlinear least squares four-dimensional variational (NLS-4DVar) method. The chemical initial conditions (ICs) and emission fluxes were optimized jointly every 24 h in the system by assimilating multi-time observations. The NLS-4DVar approach allows us to perform joint assimilation with a large state vector benefited from its high computational efficiency. Observed hourly surface fine particulate matter (PM2.5) concentrations were assimilated into the Weather Research and Forecasting model coupled with the Community Multiscale Air Quality (WRF-CMAQ) model with 32-km spatial resolution. According to the results of sensitivity experiments, simultaneous adjustment of ICs and emissions brings more significant improvement of PM2.5 48-h forecasting than optimizing only the ICs. The impact of joint assimilation on PM2.5 mass concentration forecasting over China from 10 to November 24, 2018 was evaluated. In the Yangtze River Delta region, joint assimilation reduced the root mean square error by 48.4% for estimations of the initial concentration fields, 21.9% for 24-h forecasts, and 13.4% for 48-h forecasts. We also assessed the differences between optimized and prior emissions. These results indicate that the joint data assimilation system can effectively reduce the uncertainty in PM2.5 predictions during pollution episodes by simultaneously optimizing ICs mass concentrations and emissions.

Improving predictability of high-ozone episodes through dynamic boundary conditions, emission refresh and chemical data assimilation during the Long Island Sound Tropospheric Ozone Study (LISTOS) field campaign

Abstract. Although air quality in the United States has improved remarkably in the past decades, ground-level ozone (O3) often rises in exceedance of the national ambient air quality standard in nonattainment areas, including the Long Island Sound (LIS) and its surrounding areas. Accurate prediction of high-ozone episodes is needed to assist government agencies and the public in mitigating harmful effects of air pollution. In this study, we have developed a suite of potential forecast improvements, including dynamic boundary conditions, rapid emission refresh and chemical data assimilation, in a 3 km resolution Community Multiscale Air Quality (CMAQ) modeling system. The purpose is to evaluate and assess the effectiveness of these forecasting techniques, individually or in combination, in improving forecast guidance for two major air pollutants: surface O3 and nitrogen dioxide (NO2). Experiments were conducted for a high-O3 episode (28–29 August 2018) during the Long Island Sound Tropospheric Ozone Study (LISTOS) field campaign, which provides abundant observations for evaluating model performance. The results show that these forecast system updates are useful in enhancing the capability of this 3 km forecasting model with varying effectiveness for different pollutants. For O3 prediction, the most significant improvement comes from the dynamic boundary conditions derived from the NOAA operational forecast system, National Air Quality Forecast Capability (NAQFC), which increases the correlation coefficient (R) from 0.81 to 0.93 and reduces the root mean square error (RMSE) from 14.97 to 8.22 ppbv, compared to that with the static boundary conditions (BCs). The NO2 from all high-resolution simulations outperforms that from the operational 12 km NAQFC simulation, regardless of the BCs used, highlighting the importance of spatially resolved emission and meteorology inputs for the prediction of short-lived pollutants. The effectiveness of improved initial concentrations through optimal interpolation (OI) is shown to be high in urban areas with high emission density. The influence of OI adjustment, however, is maintained for a longer period in rural areas, where emissions and chemical transformation make a smaller contribution to the O3 budget than that in high-emission areas. Following the assessment of individual updates, the forecasting system is configured with dynamic boundary conditions, optimal interpolation of initial concentrations and emission adjustment, to simulate a high-ozone episode during the 2018 LISTOS field campaign. The newly developed forecasting system significantly reduces the bias of surface NO2 prediction. When compared with the NASA Langley GeoCAPE Airborne Simulator (GCAS) vertical column density (VCD), this system is able to reproduce the NO2 VCD with a higher correlation (0.74), lower normalized mean bias (40 %) and normalized mean error (61 %) than NAQFC (0.57, 45 % and 76 %, respectively). The 3 km system captures magnitude and timing of surface O3 peaks and valleys better. In comparison with lidar, O3 profile variability of the vertical O3 is captured better by the new system (correlation coefficient of 0.71) than by NAQFC (correlation coefficient of 0.54). Although the experiments are limited to one pollution episode over the Long Island Sound, this study demonstrates feasible approaches to improve the predictability of high-O3 episodes in contemporary urban environments.

Numerical discretization causing error variance loss and the need for inflation

AbstractThe effects of model discretization errors on the propagation of error covariance have a more complex nature than the effect on the state variable. The analysis is carried out for the advection transport equation, where the continuous (in space and time) propagation of the related error covariance function can be written, solved and compared with the discrete model applied to the covariance matrix. The numerical analysis of the problem is carried out with a 1D‐problem, but is also illustrated with a 3D chemical transport model (CTM) used for chemical data assimilation. It is shown that variance loss (compared to the continuous propagation solution) depends on the covariance function itself as well as the numerical discretization scheme. The variance loss is particularly sensitive to the correlation length. In a simple first‐order discretization, an analytical expression is obtained and is used to derive an analytical expression for inflation. Experiments show, for example, that following an analysis over a dense network of observations (with spatially uncorrelated errors) a significant variance loss occurs in the propagation step. With the (variance) inflation scheme, we are able to restore the variance lost at each grid point, and at each timestep, during the entire integration. The variance inflation scheme applied to an EnKF can be formulated to change the variance spread of the ensemble or to act directly on the state.

Impacts of Horizontal Resolution on Global Data Assimilation of Satellite Measurements for Tropospheric Chemistry Analysis

AbstractWe present the results from a global 0.56°‐resolution chemical data assimilation that integrates satellite observations of ozone, NO2, CO, HNO3, and SO2 from OMI, GOME‐2, SCIAMACHY, TES, MOPITT, and MLS. The assimilation is based on an ensemble Kalman filter technique and simultaneously optimizes ozone precursor emissions and concentrations of various species. The data assimilation at 0.56° resolution reduced model errors against independent surface, aircraft, and ozonesonde observations, which was larger than at coarser resolutions for many cases. By the data assimilation, surface model errors over major polluted regions were reduced by 33%–75% for NO2 and by 15%–18% for ozone. Agreements against assimilated observations for NO2 were improved using the data assimilation at 0.56° resolution by a factor of 1.5–3 compared to 2.8° resolution over major polluted regions. The estimated global total NOx emission over medium and strong source areas were smaller by 15% at 0.56° resolution than at 2.8° resolution associated with resolving small‐scale transport and chemistry processes, while 2%–26% smaller emissions were found for regional total emissions over Europe, the United States, China, India, and South Africa, with larger differences over megacities such as Los Angeles (−41%). The estimated ship emissions were 5%–7% smaller at 0.56° resolution over the Pacific and Atlantic. The 0.56°‐resolution data assimilation provides globally consistent analyses of the emissions and concentrations on a megacity scale, which benefit studies on air quality and its impact on human health at various spatial scales over different regions of the world.

Aerosol optics model for black carbon applicable to remote sensing, chemical data assimilation, and climate modelling.

Aerosol optics models are an integral part of of climate models and of retrieval methods for global remote sensing observations. Such large-scale environmental applications place tight constraints on the affordable model complexity, which are difficult to reconcile with the considerable level of detail that is needed to capture the sensitivity of optical properties to morphological aerosol characteristics. Here, we develop a novel core-grey-shell dimer model and demonstrate its potential for reproducing radiometric and polarimetric properties of black carbon aerosols. The depolarisation is mainly sensitive to the relative size of the monomers, while the optical cross sections depend on the core-shell partitioning of black carbon. The optimum choice of these parameters is fairly stable across particle sizes and soot volume fraction, as is demonstrated by comparison with a more realistic coated aggregate model.

Enhancing Accuracy of Air Quality and Temperature Forecasts During Paddy Crop Residue Burning Season in Delhi Via Chemical Data Assimilation

AbstractThis paper examines the accuracy of Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem) generated 72 hr fine particulate matter (PM2.5) forecasts in Delhi during the crop residue burning season of October‐November 2017 with respect to assimilation of the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals, persistent fire emission assumption, and aerosol‐radiation interactions. The assimilation significantly pushes the model AOD and PM2.5 toward the observations with the largest changes below 5 km altitude in the fire source regions (northeastern Pakistan, Punjab, and Haryana) as well as the receptor New Delhi. WRF‐Chem forecast with MODIS AOD assimilation, aerosol‐radiation feedback turned on, and real‐time fire emissions reduce the mean bias by 88–195 μg/m3 (70–86%) with the largest improvement during the peak air pollution episode of 6–13 November 2017. Aerosol‐radiation feedback contributes ~21%, ~25%, and ~24% to reduction in mean bias of the first, second, and third days of PM2.5 forecast. Persistence fire emission assumption is found to work really well, as the accuracy of PM2.5 forecasts driven by persistent fire emissions was only 6% lower compared to those driven by real fire emissions. Aerosol‐radiation feedback extends the benefits of assimilating satellite AOD beyond PM2.5 forecasts to surface temperature forecast with a reduction in the mean bias of 0.9–1.5°C (17–30%). These results demonstrate that air quality forecasting can benefit substantially from satellite AOD observations particularly in developing countries that lack resources to rapidly build dense air quality monitoring networks.

Evaluation of PM&lt;sub&gt;2.5&lt;/sub&gt; Forecast using Chemical Data Assimilation in the WRF-Chem Model: A Novel Initiative Under the Ministry of Earth Sciences Air Quality Early Warning System for Delhi, India

Evaluation of PM&lt;sub&gt;2.5&lt;/sub&gt; Forecast using Chemical Data Assimilation in the WRF-Chem Model: A Novel Initiative Under the Ministry of Earth Sciences Air Quality Early Warning System for Delhi, India

An intercomparison of tropospheric ozone reanalysis products from CAMS, CAMS interim, TCR-1, and TCR-2

Abstract. Global tropospheric ozone reanalyses constructed using different state-of-the-art satellite data assimilation systems, prepared as part of the Copernicus Atmosphere Monitoring Service (CAMS-iRean and CAMS-Rean) as well as two fully independent reanalyses (TCR-1 and TCR-2, Tropospheric Chemistry Reanalysis), have been intercompared and evaluated for the past decade. The updated reanalyses (CAMS-Rean and TCR-2) generally show substantially improved agreements with independent ground and ozone-sonde observations over their predecessor versions (CAMS-iRean and TCR-1) for diurnal, synoptical, seasonal, and interannual variabilities. For instance, for the Northern Hemisphere (NH) mid-latitudes the tropospheric ozone columns (surface to 300 hPa) from the updated reanalyses show mean biases to within 0.8 DU (Dobson units, 3 % relative to the observed column) with respect to the ozone-sonde observations. The improved performance can likely be attributed to a mixture of various upgrades, such as revisions in the chemical data assimilation, including the assimilated measurements, and the forecast model performance. The updated chemical reanalyses agree well with each other for most cases, which highlights the usefulness of the current chemical reanalyses in a variety of studies. Meanwhile, significant temporal changes in the reanalysis quality in all the systems can be attributed to discontinuities in the observing systems. To improve the temporal consistency, a careful assessment of changes in the assimilation configuration, such as a detailed assessment of biases between various retrieval products, is needed. Our comparison suggests that improving the observational constraints, including the continued development of satellite observing systems, together with the optimization of model parameterizations such as deposition and chemical reactions, will lead to increasingly consistent long-term reanalyses in the future.

Evaluation of a multi-model, multi-constituent assimilation framework for tropospheric chemical reanalysis

Abstract. We introduce a Multi-mOdel Multi-cOnstituent Chemical data assimilation (MOMO-Chem) framework that directly accounts for model error in transport and chemistry, and we integrate a portfolio of data assimilation analyses obtained using multiple forward chemical transport models in a state-of-the-art ensemble Kalman filter data assimilation system. The data assimilation simultaneously optimizes both concentrations and emissions of multiple species through ingestion of a suite of measurements (ozone, NO2, CO, HNO3) from multiple satellite sensors. In spite of substantial model differences, the observational density and accuracy was sufficient for the assimilation to reduce the multi-model spread by 20 %–85 % for ozone and annual mean bias by 39 %–97 % for ozone in the middle troposphere, while simultaneously reducing the tropospheric NO2 column biases by more than 40 % and the negative biases of surface CO in the Northern Hemisphere by 41 %–94 %. For tropospheric mean OH, the multi-model mean meridional hemispheric gradient was reduced from 1.32±0.03 to 1.19±0.03, while the multi-model spread was reduced by 24 %–58 % over polluted areas. The uncertainty ranges in the a posteriori emissions due to model errors were quantified in 4 %–31 % for NOx and 13 %–35 % for CO regional emissions. Harnessing assimilation increments in both NOx and ozone, we show that the sensitivity of ozone and NO2 surface concentrations to NOx emissions varied by a factor of 2 for end-member models, revealing fundamental differences in the representation of fast chemical and dynamical processes. A systematic investigation of model ozone response and analysis increment in MOMO-Chem could benefit evaluation of future prediction of the chemistry–climate system as a hierarchical emergent constraint.

Impact of Aerosols From Urban and Shipping Emission Sources on Terrestrial Carbon Uptake and Evapotranspiration: A Case Study in East Asia

AbstractThis study quantifies the immediate influences of aerosols from urban anthropogenic and shipping emission sources on carbon and water fluxes in East Asia on a cloudy day in spring 2016 when strong regional pollution transport occurred and intensive field campaign measurements are available. Within National Aeronautics and Space Administration (NASA)'s Land Information System (LIS), a long‐term offline Noah‐multiparameterization (MP) simulation with dynamic vegetation is performed. Modeled soil moisture and leaf area index are evaluated with satellite observations to ensure that land surface conditions are moderately well reproduced. The LIS output is then used to initialize several coupled NASA‐Unified Weather Research and Forecasting model simulations with online chemistry in which urban anthropogenic and shipping emissions are (1) largely based on the Hemispheric Transport of Air Pollution Phase 2 inventory for 2010, (2) reduced by 20% and 50% for all chemical species, and (3) adjusted only for nitrogen oxides (NOx) using satellite observations. Overall, modeled gross primary productivity and evapotranspiration almost linearly increase with the all‐species emission reductions, but their responses to emission‐induced aerosol optical depth (AOD) changes show strong spatial variability resulting from combined radiation and temperature impacts. Using satellite‐observation‐constrained NOx emissions, modeled nitrogen species and AOD better match various measurements at some locations/times. All‐species and NOx‐only emission adjustments lead to different gross primary productivity and evapotranspiration changes with AOD, especially over South Korea. This study demonstrates the importance of accurately quantifying emission impacts on atmosphere‐biosphere interactions. Improving more species' emission inputs for Earth system models, including applying effective chemical data assimilation methods, is strongly encouraged.

A study on harmonizing total ozone assimilation with multiple sensors

Abstract. Bias estimations and corrections of total column measurements are applied and evaluated with ozone data from satellite instruments providing near-real-time products during summer 2014 and 2015 and winter 2015. The developed standalone bias-correction system can be applied in near-real-time chemical data assimilation and long-term reanalysis. The instruments to which these bias corrections were applied include the Global Ozone Monitoring Experiment-2 instruments on the MetOp-A and MetOp-B satellites (GOME-2A and GOME-2B), the total column ozone mapping instrument of the Ozone Mapping Profiler Suite (OMPS-NM) on the Suomi National Polar-orbiting Partnership (S-NPP) satellite, and the Ozone Monitoring Instrument (OMI) instrument on the Aura research satellite. The OMI data set based on the TOMS version 8.5 retrieval algorithm was chosen as the reference used in the bias correction of the other satellite-based total column ozone data sets. OMI data were chosen for this purpose instead of ground-based observations due to OMI's significantly better spatial and temporal coverage, as well as interest in near-real-time assimilation. Ground-based Brewer and Dobson spectrophotometers, and filter ozonometers, as well as the Solar Backscatter Ultraviolet satellite instrument (SBUV/2), served as independent validation sources of total column ozone data. Regional and global mean differences of the OMI-TOMS data with measurements from the three ground-based instrument types for the three evaluated 2-month periods were found to be within 1 %, except for the polar regions, where the largest differences from the comparatively small data set in Antarctica exceeded 3 %. Values from SBUV/2 summed partial columns were typically larger than OMI-TOMS on average by 0.6 % to 1.2 %, with smaller differences than with ground-based observations over Antarctica. Bias corrections as a function of latitude and solar zenith angle were performed for GOME-2A/B and OMPS-NM using colocation with OMI-TOMS and three variants of differences with short-term model forecasts. These approaches were shown to yield residual biases of less than 1 %, with the rare exceptions associated with bins with less data. These results were compared to a time-independent bias-correction estimation that used colocations as a function of ozone effective temperature and solar zenith angle which, for the time period examined, resulted in larger residual biases for bins whose bias varies more in time. The impact of assimilating total column ozone data from single and multiple satellite data sources with and without bias correction was examined with a version of the Environment and Climate Change Canada variational assimilation and forecasting system. Assimilation experiments for July–August 2014 show a reduction of global mean biases for short-term forecasts relative to ground-based Brewer and Dobson observations from a maximum of about 2.3 % in the absence of bias correction to less than 0.3 % in size when bias correction is included. Both temporally averaged and time-varying mean differences of forecasts with OMI-TOMS were reduced to within 1 % for nearly all cases when bias-corrected observations are assimilated for the latitudes where satellite data are present.