Accurate quantification of the distribution and variability of atmospheric CO₂ is crucial for a better understanding of global carbon cycle characteristics and climate change. Model simulation and observations are only two ways to globally estimate CO₂ concentrations and fluxes. However, large uncertainties still exist. Therefore, quantifying the differences between model and observations is rather helpful for reducing their uncertainties and further improving model estimations of global CO₂ sources and sinks. In this paper, the GEOS-Chem model was selected to simulate CO₂ concentration and then compared with the Greenhouse Gases Observing Satellite (GOSAT) observations, CarbonTracker (CT) and the Total Carbon Column Observing Network (TCCON) measurements during 2009–2011 for quantitatively evaluating the uncertainties of CO₂ simulation. The results revealed that the CO₂ simulated from GEOS-Chem is in good agreement with other CO₂ data sources, but some discrepancies exist including: (1) compared with GOSAT retrievals, modeled XCO₂ from GEOS-Chem is somewhat overestimated, with 0.78 ppm on average; (2) compared with CT, the simulated XCO₂ from GEOS-Chem is slightly underestimated at most regions, although their time series and correlation show pretty good consistency; (3) compared with the TCCON sites, modeled XCO₂ is also underestimated within 1 ppm at most sites, except at Garmisch, Karlsruhe, Sodankylä and Ny-Ålesund. Overall, the results demonstrate that the modeled XCO₂ is underestimated on average, however, obviously overestimated XCO₂ from GEOS-Chem were found at high latitudes of the Northern Hemisphere in summer. These results are helpful for understanding the model uncertainties as well as to further improve the CO₂ estimation.

The international journal of Aerosol and Air Quality Research (AAQR) covers all aspects of aerosol science and technology, atmospheric science and air quality related issues.

Severe hailstorms frequently occurred in Beijing between May and August 2021, leading to extensive hail damage. These hailstorms were observed by radar and satellite data, and reported by surface observers. In this study, the spectral and cloud microphysical characteristics of typical Beijing events in 2021 were analyzed using Himawari-8 satellite products and ground-based S-band weather radar data obtained from the Beijing Meteorological Bureau. The relationship between Himawari-8 brightness temperature differences (BTD) and radar reflectivity was also investigated. The results revealed that the significant spectral depression of brightness temperatures (BTs) in hail clouds was observed by a satellite. Furthermore, the stronger the radar reflectivity was, the more rapidly BTD decreased, with a nonlinear relationship between them. The results of cloud physical characteristics show that, for cloud-top heights above 10 km, the cloud effective radius was about 25 μm, with a cloud-top temperature of 225 K during these hail events. By means of Gaussian fitting, the BT threshold value (11.2 μm) was determined by satellite at 230 K, with a BTD focused on 1.9 K when hailstorms occurred. These results will help us better understand the characteristics of hailstorms, while also providing information for future hail suppression in Beijing.

Estimating biomass burning emissions remains challenging due to both the substantial spatiotemporal variability of fires and the inherent uncertainties associated with the limited overpass frequency of polar-orbiting satellites. Integrating geostationary (5–10 min, 2 km) and polar-orbiting (twice daily, 375 m) satellite observations provides a detailed characterization of active fire diurnal cycles. However, the conventional unimodal Gaussian approximation (Pol/Geo-Uni), commonly used in fire emission models, fails to accurately reproduce the diurnal patterns. This study systematically analyzed the seasonal and diurnal variation in different types of active fires (AFs) and fire radiative power (FRP) across North America (GOES-16, ABI) and East Asia (Himawari-8, AHI). In North America, forest and savanna fires exhibited high FRP and a pronounced bimodal diurnal cycle lasting 4–8 h, whereas the corresponding fire types in East Asia exhibited a shorter, unimodal pattern of 2–4 h. Agricultural fires in East Asia were predominantly small in scale with low FRP, and frequently occurred at night. We used a modified Gaussian function to estimate dry matter burned (DMB), quantitating regional emission impacts for different fire types. The fused product (VIIRS/ABI-Bi) yielded amounts of DMB in North America that was 1.22 and 1.24 times higher than that from VIIRS/ABI-Uni and GFASv1.2, respectively. In East Asia, VIIRS/AHI-Bi DMB exceeded those from VIIRS/AHI-Uni and GFASv1.2 by 1.08 and 0.94 times, with agricultural fire estimates during the fire season being 1.18–1.62 times higher. This increase was notably pronounced in eastern China, where VIIRS/AHI-Bi DMB reached 1.76 to 9.77 times higher than estimates from VIIRS/AHI-Uni, GFED5, GFED4.1s, and GFASv1.2. Overall, integrating high spatiotemporal resolution satellite fire products with regionally diurnal models can substantially improve emission estimates, particularly for frequent, small-scale fire events.