https://orfeo.belnet.be/handle/internal/1 Tue, 16 Jun 2026 01:30:11 GMT 2026-06-16T01:30:11Z https://orfeo.belnet.be:443/bitstream/id/f5044b03-6ff9-4129-b6ca-ae71786a38eb/ https://orfeo.belnet.be/handle/internal/1 https://orfeo.belnet.be/handle/internal/14773 Geostationary observations of atmospheric ammonia over East Asia: spatio-temporal variations revealed by three years of FY-4B/GIIRS measurements Sheng, M.; Zhou, R.; Hua, J.; Han, S.; Liu, S.; Zhang, L.; Wang, W.; Dang, R.; Cao, H.; Chen, Z.; Gu, Y.; Liu, M.; Lee, L.; Qi, C.; Lu, F.; Han, C.; Shephard, M.W.; Guendouz, N.; Viatte, C.; Clarisse, L.; Van Damme, M.; Clerbaux, C.; Zeng, Z.-C. Satellite observations play a crucial role in quantifying ammonia sources by capturing large-scale variations of atmospheric NH3 concentrations. As the world's first geostationary hyperspectral infrared sounder, the Geostationary Interferometric Infrared Sounder (GIIRS) on board China's FengYun-4 satellite series provides a unique opportunity to monitor the diurnal cycle of NH3. Using NH3 retrievals from July 2022 to June 2025, this study investigates the spatio-temporal variability of NH3 columns over East Asia, with a focus on daytime variations (07:00–19:00 LT – local time) in major agricultural regions. Inter-comparison with polar-orbiting IASI and CrIS data shows that GIIRS NH3 retrievals are consistent in capturing spatial patterns and temporal dynamics. The NH3 peaks occur between March and July, with peak timing earlier in the south and later in the north, reflecting regional differences primarily driven by agricultural activities. Validation with ground-based FTIR measurements at Hefei in eastern China demonstrates the accuracy of GIIRS NH3, with a correlation coefficient of 0.77 and an RMSE of 9.67×1015 molec cm−2, while reproducing daytime variations observed by FTIR. For major agricultural areas, the NH3 columns generally increase from early morning to late afternoon, reaching 1.10–1.56 times morning levels in summer and spring. Compared with GEOS-CF model simulations, the results reveal pronounced discrepancies in spatial distributions over the Sichuan Basin in southwestern China and daytime variations over northern India. These findings highlight the valuable capability of FY-4B/GIIRS in identifying and tracking daytime dynamics of NH3 sources over East Asia, offering new insights beyond current low-Earth orbit (LEO) instruments. https://orfeo.belnet.be/handle/internal/14773 https://orfeo.belnet.be/handle/internal/14772 Introduction to multidisciplinary special collection: Solar Wind: Origin, acceleration, and Outflow Pierrard, V. On the occasion of the 50 years since the launch of Helios 1 and Helios 2, we proposed a special collection related to the solar wind, its origin, evolution, and space weather related effects. In addition to old missions like ULYSSES that explored the solar wind outside the ecliptic plane, recent missions like the Parker Solar Probe (PSP) and Solar Orbiter (SolO), as well as established ones such as the Solar Dynamics Observatory (SDO) and the Solar Terrestrial Relations Observatories (STEREOs), offer extensive new measurements that help to refine existing knowledge of slow and fast solar wind in the heliosphere and the development of new models. This collection addressed different solar wind topics, which included but were not limited to the mechanisms of solar wind acceleration and outflow, dynamics of stream interaction, the configuration of the magnetic field and plasma topology at the source surface and within the inner heliosphere. https://orfeo.belnet.be/handle/internal/14772 https://orfeo.belnet.be/handle/internal/14756 Updated global and regional trends of stratospheric ozone profiles Sofieva, V.F.; Szelag, M.E.; Kramarova, N.A.; Damadeo, R.; Steinbrecht, W.; Petropavlovskikh, I.; Vigouroux, C.; Maillard Barras, E.; Zawada, D.; Tourpali, K.; Frith, S.M.; Wild, J.D.; Davis, S.M.; Arosio, C.; Weber, M.; Rozanov, A.; Auffarth, B.; Froidevaux, L.; Fuller, R.; Degenstein, D.; Dube, K.; Effertz, P.; Leblanc, T.; Ancellet, G.; Godin-Beekmann, S.; McConville, G.; Querel, R.; Smale, D.; DeBacker, M.-R.; Mahieu, E.; Sussmann, R. We present updated evaluation of stratospheric ozone profile trends in the 60° S–60° N latitude range using long-term ground-based and satellite climate data records, as well as simulations by chemistry-climate models. The trends are evaluated using the LOTUS (Long-term Ozone Trends and Uncertainties in the Stratosphere) regression model. Analyses of satellite data confirm the statistically significant positive ozone trends in the period 2000–2024 in the upper stratosphere of ∼ 1–3 % per decade, with larger trends at mid-latitudes compared to the tropics. The trends are slightly positive or close to zero in the middle stratosphere, and mostly negative, −1 to −2 % per decade, in the lower stratosphere, but they are not statistically significant. The morphology and magnitude of ozone trends are similar to previous analyses (2000–2020 trends). Ozone trends in 2000–2024 predicted by chemistry-climate model simulations are in good agreement with combined satellite trends. In the upper stratosphere, models predict a slightly stronger ozone recovery than observations. In the lower stratosphere, both models and satellite observations report negative trends in the tropics, while modelled ozone trends are slightly positive at mid-latitudes. Ozone profile trends over several stations estimated from ground-based records capture the same overall vertical pattern of ozone trends as merged gridded satellite datasets. Analyses of regional ozone profile trends in 2003–2024 using merged satellite datasets confirmed the previous observations of a longitudinal structure in ozone trends in the NH mid-latitude stratosphere, with positive trends over Scandinavia and negative trends over Siberia. However, the magnitude of this dipole-like structure is reduced compared to previous analyses. https://orfeo.belnet.be/handle/internal/14756 https://orfeo.belnet.be/handle/internal/14757 Technical note: DACNO2 – a multi-constraint deep learning framework for high-resolution 3D NO2 field estimation Sun, W.; Tack, F.; Clarisse, L.; Van Roozendael, M. High-resolution 3D fields of nitrogen dioxide (NO2) are critical for air quality management and satellite retrievals, yet traditional chemistry-transport models (CTMs) face challenges in fine-scale modeling. Machine learning (ML) alternatives often struggle with generalization and transferability, inheriting biases from CTMs or being limited by sparse surface measurements. We present the Deep Atmospheric Chemistry NO2 model (DACNO2), a deep learning model that generates daily 2 km × 2 km 3D NO2 fields over Western Europe. The model's three-phase multi-constraint training strategy begins by pre-training on European Copernicus Atmosphere Monitoring Service (CAMS) reanalysis data to learn large-scale atmospheric patterns, then fine-tunes with CAMS and in-situ European Environmental Agency (EEA) surface data to correct biases and refine local detail, and completes with an adaptive fine-tuning to capture evolving trends. An evaluation for 2023 shows that DACNO2 reproduces broad-scale 3D CAMS fields (R2=0.90) and improves agreement with independent EEA stations over the CAMS reanalysis (R2 enhanced from 0.61 to 0.66; bias reduced from −1.15 to −0.38 µg m−3). The model resolves spatial details and exhibits physically plausible behavior. This hybrid training approach fuses the physical consistency of a process-based model with the real-world surface measurements, overcoming the limitations of using either constraint alone. Applying DACNO2 a-priori profiles to TROPOMI retrievals increases tropospheric NO2 columns by 3 % on average over those using European CAMS profiles, with enhanced contrast between low- and high-NO2 regions, primarily attributable to improved resolution. These results demonstrate the framework's potential to advance air quality monitoring and satellite remote sensing. https://orfeo.belnet.be/handle/internal/14757