Most studies on validation of satellite trace gas retrievals or atmospheric chemical transport models assume that pointwise measurements, which roughly represent the element of space, should compare well with satellite (model) pixels (grid box). This assumption implies that the field of interest must possess a high degree of spatial homogeneity within the pixels (grid box), which may not hold true for species with short atmospheric lifetimes or in the proximity of plumes. Results of this assumption often lead to a perception of a nonphysical discrepancy between data, resulting from different spatial scales, potentially making the comparisons prone to overinterpretation. Semivariogram is a mathematical expression of spatial variability in discrete data. Modeling the semivariogram behavior permits carrying out spatial optimal linear prediction of a random process field using kriging. Kriging can extract the spatial information (variance) pertaining to a specific scale, which in turn translates pointwise data to a gridded space with quantified uncertainty such that a grid-to-grid comparison can be made. Here, using both theoretical and real-world experiments, we demonstrate that this classical geostatistical approach can be well adapted to solving problems in evaluating model-predicted or satellite-derived atmospheric trace gases. This study suggests that satellite validation procedures using the present method must take kriging variance and satellite spatial response functions into account. We present the comparison of Ozone Monitoring Instrument (OMI) tropospheric NO₂ columns against 11 Pandora spectrometer instrument (PSI) systems during the DISCOVER-AQ campaign over Houston. The least-squares fit to the paired data shows a low slope (OMI=0.76×PSI+1.18×1015 molecules cm⁻², r²=0.66), which is indicative of varying biases in OMI. This perceived slope, induced by the problem of spatial scale, disappears in the comparison of the convolved kriged PSI and OMI (0.96×PSI+0.66×1015 molecules cm⁻², r²=0.72), illustrating that OMI possibly has a constant systematic bias over the area. To avoid gross errors in comparisons made between gridded data vs. pointwise measurements, we argue that the concept of semivariogram (or spatial autocorrelation) should be taken into consideration, particularly if the field exhibits a strong degree of spatial heterogeneity at the scale of satellite and/or model footprints.

Questions about how emissions are changing during the COVID-19 lockdown periods cannot be answered by observations of atmospheric trace gas concentrations alone, in part due to simultaneous changes in atmospheric transport, emissions, dynamics, photochemistry, and chemical feedback. A chemical transport model simulation benefiting from a multi-species inversion framework using well-characterized observations should differentiate those influences enabling to closely examine changes in emissions. Accordingly, we jointly constrain NO_x and VOC emissions using well-characterized TROPOspheric Monitoring Instrument (TROPOMI) HCHO and NO₂ columns during the months of March, April, and May 2020 (lockdown) and 2019 (baseline). We observe a noticeable decline in the magnitude of NO_x emissions in March 2020 (14 %–31 %) in several major cities including Paris, London, Madrid, and Milan, expanding further to Rome, Brussels, Frankfurt, Warsaw, Belgrade, Kyiv, and Moscow (34 %–51 %) in April. However, NO_x emissions remain at somewhat similar values or even higher in some portions of the UK, Poland, and Moscow in March 2020 compared to the baseline, possibly due to the timeline of restrictions. Comparisons against surface monitoring stations indicate that the constrained model underrepresents the reduction in surface NO₂. This underrepresentation correlates with the TROPOMI frequency impacted by cloudiness. During the month of April, when ample TROPOMI samples are present, the surface NO₂ reductions occurring in polluted areas are described fairly well by the model (model: −21 ± 17 %, observation: −29 ± 21 %). The observational constraint on VOC emissions is found to be generally weak except for lower latitudes. Results support an increase in surface ozone during the lockdown. In April, the constrained model features a reasonable agreement with maximum daily 8 h average (MDA8) ozone changes observed at the surface (r=0.43), specifically over central Europe where ozone enhancements prevail (model: +3.73 ± 3.94 %, +1.79 ppbv, observation: +7.35 ± 11.27 %, +3.76 ppbv). The model suggests that physical processes (dry deposition, advection, and diffusion) decrease MDA8 surface ozone in the same month on average by −4.83 ppbv, while ozone production rates dampened by largely negative [NO₂]-[NO][O₃] become less negative, leading ozone to increase by +5.89 ppbv. Experiments involving fixed anthropogenic emissions suggest that meteorology contributes to 42 % enhancement in MDA8 surface ozone over the same region with the remaining part (58 %) coming from changes in anthropogenic emissions. Results illustrate the capability of satellite data of major ozone precursors to help atmospheric models capture ozone changes induced by abrupt emission anomalies.

Abstract To improve the representation of convective mixing of atmospheric pollutants in the presence of clouds, we developed a convection module based on Kain and Fritsch (KF) method and implemented it in the CMAQ (Community Multiscale Air Quality) model. The KF-convection method is a mass-flux-based model that accounts for updraft flux, downdraft flux, entrainment, detrainment, and the subsidence effect. The method is consistent with the convection parametrization of the meteorology model. We apply the KF-convection model to an idealized case and to a reference setup prepared for East Asia during the KORUS-AQ campaign period to investigate its impact on carbon monoxide (CO) concentration at various atmospheric altitudes. We investigate the impact of KF-convection on the horizontal distribution of CO concentrations by comparing it to aircraft measurements and the MOPITT CO column. We further discuss two types of impacts of KF-convection: the direct impact caused by vertical movement of CO concentrations by updraft or downdraft and the indirect impact caused by transport of lifted CO concentrations to another region. May 12 saw a high indirect impact originating from the Shanghai region at higher altitudes and a high direct impact of updraft fluxes at 1 km altitude. However, May 26 revealed an immense updraft increasing higher altitude concentrations (up to 40 ppbv) and diverse indirect impacts over the region of the study (±50 ppbv). The overall comparison shows a strong connection between differences in the amount of concentration caused by the direct impact at each altitude with the presence of an updraft at that altitude. The developed model can be employed in large domains (i.e., East Asia, Europe, North America, and Northern Hemisphere) with sub-grid scale cloud modeling to include the impacts of convection.

The absence of up-to-date emissions has been a major impediment to accurately simulating aspects of atmospheric chemistry and to precisely quantifying the impact of changes in emissions on air pollution. Hence, a nonlinear joint analytical inversion (Gauss–Newton method) of both volatile organic compounds (VOCs) and nitrogen oxide (NOx) emissions is made by exploiting the Smithsonian Astrophysical Observatory (SAO) Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM) formaldehyde (HCHO) and the National Aeronautics and Space Administration (NASA) Ozone Monitoring Instrument (OMI) tropospheric nitrogen dioxide (NO2) columns during the Korea–United States Air Quality (KORUS-AQ) campaign over East Asia in May–June 2016. Effects of the chemical feedback of NOx and VOCs on both NO2 and HCHO are implicitly included by iteratively optimizing the inversion. Emission uncertainties are greatly narrowed (averaging kernels > 0.8, which is the mathematical presentation of the partition of information gained from the satellite observations with respect to the prior knowledge) over medium- to high-emitting areas such as cities and dense vegetation. The prior amount of total NOx emissions is mainly dictated by values reported in the MIX-Asia 2010 inventory. After the inversion we conclude that there is a decline in emissions (before, after, change) for China (87.94±44.09 Gg d−1, 68.00±15.94 Gg d−1, −23 %), North China Plain (NCP) (27.96±13.49 Gg d−1, 19.05±2.50 Gg d−1, −32 %), Pearl River Delta (PRD) (4.23±1.78 Gg d−1, 2.70±0.32 Gg d−1, −36 %), Yangtze River Delta (YRD) (9.84±4.68 Gg d−1, 5.77±0.51 Gg d−1, −41 %), Taiwan (1.26±0.57 Gg d−1, 0.97±0.33 Gg d−1, −23 %), and Malaysia (2.89±2.77 Gg d−1, 2.25±1.34 Gg d−1, −22 %), all of which have effectively implemented various stringent regulations. In contrast, South Korea (2.71±1.34 Gg d−1, 2.95±0.58 Gg d−1, +9 %) and Japan (3.53±1.71 Gg d−1, 3.96±1.04 Gg d−1, +12 %) are experiencing an increase in NOx emissions, potentially due to an increased number of diesel vehicles and new thermal power plants. We revisit the well-documented positive bias (by a factor of 2 to 3) of MEGAN v2.1 (Model of Emissions of Gases and Aerosols from Nature) in terms of biogenic VOC emissions in the tropics. The inversion, however, suggests a larger growth of VOCs (mainly anthropogenic) over NCP (25 %) than previously reported (6 %) relative to 2010. The spatial variation in both the magnitude and sign of NOx and VOC emissions results in nonlinear responses of ozone production and loss. Due to a simultaneous decrease and increase in NOx∕VOC over NCP and YRD, we observe a ∼53 % reduction in the ratio of the chemical loss of NOx (LNOx) to the chemical loss of ROx (RO2+HO2) over the surface transitioning toward NOx-sensitive regimes, which in turn reduces and increases the afternoon chemical loss and production of ozone through NO2+OH (−0.42 ppbv h−1)∕HO2 (and RO2)+NO (+0.31 ppbv h−1). Conversely, a combined decrease in NOx and VOC emissions in Taiwan, Malaysia, and southern China suppresses the formation of ozone. Simulations using the updated emissions indicate increases in maximum daily 8 h average (MDA8) surface ozone over China (0.62 ppbv), NCP (4.56 ppbv), and YRD (5.25 ppbv), suggesting that emission control strategies on VOCs should be prioritized to curb ozone production rates in these regions. Taiwan, Malaysia, and PRD stand out as regions undergoing lower MDA8 ozone levels resulting from the NOx reductions occurring predominantly in NOx-sensitive regimes.

A number of human-induced elements contribute to influencing the intensity of tropical cyclones and prolonging their lifetime. Not only do ocean heat content, large-scale weather patterns, and surface properties affect the amount of release of energy, but the modulation from aerosol particles on cloud properties is also present. With Hurricane Harvey (2017) fairly isolated over Texas, there was a unique opportunity to study the indirect impact of aerosols on the amount of record-breaking rainfall over the greater Houston area. Due to the non-linear processes involved in clouds microstructure, aerosol properties and the variability associated with the atmospheric environment, the quantification of the response of storms to aerosols is complex. To this end, we first reproduce Harvey using the Weather Research and Forecasting (WRF) model coupled with a 3D-var assimilation framework that incorporates satellites, radio occultation, dropsondes, and surface measurements. We then study the aerosol indirect impacts using spectral bin microphysics in conjunction with aerosol properties simulated from the Goddard Earth Observing System (GEOS)-Chem TwO-Moment Aerosol Sectional (TOMAS) model leveraging online aerosol microphysics with anthropogenic emissions (SP) and without ones (SC). In the vicinity of Harvey’s landfall, the number concentration of cloud condensation nuclei at 1% supersaturation using the anthropogenic emissions is found to be one order of magnitude (855 cm-3) larger than those simulated with only natural emissions (83 cm-3). We observed that a narrow plume of anthropogenic aerosols from western Texas was transported over the area at the moment when deep convection initiated, accelerating updrafts through releasing more latent heat, which in turn, resulted in an average enhancement of precipitation by 25 mm (~ 8%) over the greater Houston area. We observed a second peak at the right tail of the distribution of differences between experiments, which is an indication of the presence of more extreme rainfall over the area. As such, studies on the impact of aerosol emissions controls on exacerbating severe weather should be more encouraged.

The primary focus of this study is to understand the contribution from excess moisture from crop transpiration to the severity of a heat wave episode that hit the Midwestern U.S from 16th to 20th July 2011. To elucidate this, we first provide an optimal estimate of the transpiration water vapor flux using satellite total column water vapor retrievals whose accuracy and precision are characterized using independent observations. The posterior transpiration flux is estimated using a local ensemble transform Kalman filter that employs a mesoscale weather model as the forward model. The new estimation suggests that the prior values of transpiration flux from crops are biased high by 15%. We further use the constrained flux to examine the sensitivity of meteorology to the contributions from crops. Over the agricultural areas during daytime, elevated moisture (up to 40%) from crops not only increases humidity (thus the heat index) but also provides a positive radiative forcing by increasing downward longwave radiation (13±4 W m-2) that results in even higher surface air temperature (+ 0.4oC). Consequently, we find that elevated moisture generally provides positive feedback to aggravate the heat wave, with daytime enhancements of heat index by as large as 3.3±0.8oC. Due to a strong diurnal cycle in the transpiration, the feedback tends to be stronger in the afternoon (up to 5oC), and weaker at night. Results offer a potential basis for designing mitigation strategies for the effect of transpiration from agriculture in the future, in addition to improving the estimation of canopy transpiration.

The nonlinear chemical processes involved in ozone production (P(O3)) have necessitated using proxy indicators to convey information about the primary dependence of P(O3) on volatile organic compounds (VOCs) or nitrogen oxides (NOx). In particular, the ratio of remotely sensed columns of formaldehyde (HCHO) to nitrogen dioxide (NO2) has been widely used for studying O3 sensitivity. Previous studies found that the errors in retrievals and the incoherent relationship between the column and the near-surface concentrations are a barrier in applying the ratio in a robust way. In addition to these obstacles, we provide calculational-observational evidence, using an ensemble of 0-D photochemical box models constrained by DC-8 aircraft measurements on an ozone event during the Korea-United States Air Quality (KORUS-AQ) campaign over Seoul, to demonstrate the chemical feedback of NO2 on the formation of HCHO is a controlling factor for the transition line between NOx-sensitive and NOx-saturated regimes. A fixed value (∼2.7) of the ratio of the chemical loss of NOx (LNOx) to the chemical loss of HO2+RO2 (LROx) perceptibly differentiates the regimes. Following this value, data points with a ratio of HCHO/NO2 less than 1 can be safely classified as NOx-saturated regime, whereas points with ratios between 1 and 4 fall into one or the other regime. We attribute this mainly to the HCHO-NO2 chemical relationship causing the transition line to occur at larger (smaller) HCHO/NO2 ratios in VOC-rich (VOC-poor) environments. We then redefine the transition line to LNOx/LROx∼2.7 that accounts for the HCHO-NO2 chemical relationship leading to HCHO = 3.7 × (NO2 – 1.14 × 1016 molec.cm-2). Although the revised formula is locally calibrated (i.e., requires for readjustment for other regions), its mathematical format removes the need for having a wide range of thresholds used in HCHO/NO2 ratios that is a result of the chemical feedback. Therefore, to be able to properly take the chemical feedback into consideration, the use of HCHO = a × (NO2 – b) formula should be preferred to the ratio in future works. We then use the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument to study O3 sensitivity in Seoul. The unprecedented spatial (250 × 250 m²) and temporal (∼every 2 h) resolutions of HCHO and NO2 observations form the sensor enhance our understanding of P(O3) in Seoul; rather than providing a crude label for the entire city, more in-depth variabilities in chemical regimes are observed that should be able to inform mitigation strategies correspondingly.

Over the last five decades, Earth’s atmosphere has been extensively monitored from space using different spectral ranges. Early efforts were directed at improving weather forecasts with the first meteorological satellites launched in the 1960s. Soon thereafter, the intersection between weather, climate and atmospheric chemistry led to the observation of atmospheric composition from space. During the 1970s the Nimbus satellite program started regular monitoring of ozone integrated columns and water vapor profiles using the Backscatter Ultraviolet Spectrometer, the Infrared Interferometer Spectrometer and the Satellite Infrared Spectrometer instruments. Five decades after these pioneer efforts, continuous progress in instrument design, and retrieval techniques allow researchers to monitor tropospheric concentrations of a wide range of species with implications for air quality, climate and weather. The time line of historic, present and future space-borne instruments measuring ultraviolet and visible backscattered solar radiation designed to quantify atmospheric trace gases is presented. We describe the instruments technological evolution and the basic concepts of retrieval theory. We include a review of algorithms developed for ozone, nitrogen dioxide, sulfur dioxide, formaldehyde, bromine monoxide, water vapor and glyoxal, a selection of studies using these algorithms, the challenges they face and how these challenges can be addressed. The paper ends by providing insights on the opportunities that new instruments will bring to the atmospheric chemistry, weather and air quality communities and how to address the pressing need for long-term, inter-calibrated data records necessary to monitor the response of the atmosphere to rapidly changing ecosystems.