Direct wind observations used to be rare over the Arabian Sea and the Bay of Bengal. Since the launch of ESA's Aeolus Doppler Wind Lidar, profiles of horizontal wind are acquired every day and are perfectly co-located with profiles of cirrus and deep convective clouds. In this study, we first observe that while the Tropical Easterly Jet (TEJ) is primarily driven by the thermal contrast between warmer land and cooler Indian Ocean, the diverging-rotating outflow around deep convective clouds can strengthen or weaken the TEJ over the Arabian Sea with respect to the wind expected from thermal wind balance, explaining a 3 m s−1 amplitude variation during the South-Asian Summer Monsoon (SASM). We further focus on disentangling the role of this wind in the formation of cirrus clouds over the Arabian Sea during the SASM, from the roles of temperature and convective intensity variations. We show that from June to October 2020, during the SASM, high altitude clouds formed over the Bay of Bengal by deep convection in the afternoon are advected westward towards the Arabian Sea in the morning by the fast winds of the TEJ. Consequently, the cirrus cloud cover over the Arabian Sea is 14 % at 06:00 LT on days where westward winds are faster than 23.6 m s−1 between 14 and 17 km of altitude, more than twice as much than on days where westward winds are slower than 23.6 m s−1 (6 %). These results suggest that the cirrus cloud cover over the Arabian Sea may decrease in the next decades, as the TEJ and the deep convection over the eastern Indian Ocean are expected to decrease in intensity.
The atmospheric trace gas carbon monoxide (CO) significantly influences the atmosphere's oxidative capacity and indirectly affects climate. However, the net land-atmosphere exchange of CO remains highly uncertain due to the scarcity of long-term monitoring. This study presents and analyses a 10-year-long CO flux data series from a tall-tower eddy covariance (EC) system operated in a rural Central European region. The measured fluxes were evaluated separately for vegetation-covered, (quasi)natural areas and for populated areas. The estimated monthly mean emissions from the populated areas varied between 12 and 110 nmolm-2s-1, with the lowest value observed in July and the highest in January, respectively. The measured wintertime emissions from the populated areas significantly exceed the activity-based estimations, presumably due to underestimated emissions factors for residential heating. The vegetation-dominated sector (agricultural fields and forests) acted as a weak net CO source from April to September with an average emission of 0.58 nmolm-2s-1 (monthly range 0.31–0.91 nmolm-2s-1), with the highest value occurring in July. The diurnal peaks of the median hourly emissions can be observed around noon, and they range from 1.59 to 2.66 nmolm-2s-1. The nighttime (20:00–04:00 local standard time (LST)) hourly median values range from −1.01 to +1.17 nmolm-2s-1. Their deviations from zero are not statistically significant at a p<0.05 probability level. Solar radiation was identified as the primary driver of CO exchange. This study also demonstrates the advantages and challenges of tall-tower EC flux measurements.
New particle formation (NPF) is a major source of atmospheric aerosols and cloud condensation nuclei, influencing climate and air quality. In highly polluted regions such as the Indo-Gangetic Plain (IGP), where precursor concentrations and condensation sinks are among the highest globally, NPF remains poorly constrained due to limited observations of particles and ions in the sub–10 nm size range. Here, we investigated the occurrence and growth of NPF at a suburban IGP site (CAS-AO, Sonipat, Haryana), during May–December 2023 using ion (0.8–42 nm) and particle (2.5–42 nm) measurements together with meteorological data. NPF events were frequent during summer, occurring on 30 of 35 valid days in May–June (86 %), and were much less frequent during the winter/post-monsoon period, with 20 out of 73 valid days (28 %). The weak seasonal variability of ion concentrations relative to nucleation-mode particles suggests that particle survival and growth were strongly modulated by condensation sink, meteorology, and air-mass heterogeneity. The median daily condensation sink during 06:00–12:00 LT was lower on event days than on non-event days (0.0095 vs. 0.0292 s−1). Particle growth rates increased with size, from 14.7 nm h−1 in the 3–7 nm range to 19.0 nm h−1 in the 7–20 nm range, indicating size-dependent growth. In this high-background setting, pollution plumes and meteorological variability intermittently mask or distort NPF signals, limiting the direct applicability of both visual and automated classification methods and growth rate estimation methods. Our observations highlight that careful application and improvement of data analysis methods, along with precursor gas measurements, are required to better constrain nucleation processes by avoiding methodological sensitivity due to the complexity of aerosol dynamic processes in multi-source, high-condensation-sink environments, such as IGP.
Da Qi-1 (DQ-1) carries the Aerosol and Carbon Dioxide Lidar (ACDL), a spaceborne integrated path differential absorption (IPDA) lidar that retrieves the column-averaged dry-air mole fraction of carbon dioxide ( X CO 2 ) using active laser measurements. Unlike passive near-infrared/shortwave-infrared sensors that rely on reflected sunlight, ACDL can observe at night and high latitudes, but its sampling and effective column definition differ from passive X CO 2 products. Evaluation is therefore needed before active and passive observations can be interpreted jointly. We compare DQ-1 ACDL with the Orbiting Carbon Observatory (OCO-2 and OCO-3) and Greenhouse gases Observing SATellite (GOSAT and GOSAT-2) missions from June 2022–December 2024 using a unified framework that combines Copernicus Atmosphere Monitoring Service (CAMS)-based coherence assessment, sampling availability, daily 2° aggregation, spatiotemporal collocation, and correction for X CO 2 column-definition differences. Validation against the Total Carbon Column Observing Network (TCCON) gives R =0.92, a mean bias of 0.21 ppm, and a root-mean-square error (RMSE) of 1.49 ppm for daytime DQ-1, comparable to GOSAT. Over coherent and well-sampled regions, DQ-1 is broadly consistent with OCO-2 and OCO-3, with mean differences generally below 0.5 ppm and similar spatial and meridional structures. Over ocean, the DQ-1–OCO differences vary with surface reflectance and wind speed, with different reflectance relationships for OCO-2 and OCO-3. Nighttime DQ-1 is also broadly consistent with CAMS-derived X CO 2 . Regional analyses show complementary DQ-1 coverage where passive observations are limited. These results support DQ-1 ACDL as a quantitatively comparable and complementary component of the satellite X CO 2 observing system.
Da Qi-1 (DQ-1) carries the Aerosol and Carbon Dioxide Lidar (ACDL), a spaceborne integrated path differential absorption (IPDA) lidar that retrieves the column-averaged dry-air mole fraction of carbon dioxide (XCO2) using active laser measurements. Unlike passive near-infrared/shortwave-infrared sensors that rely on reflected sunlight, ACDL can observe at night and high latitudes, but its sampling and effective column definition differ from passive XCO2 products. Evaluation is therefore needed before active and passive observations can be interpreted jointly. We compare DQ-1 ACDL with the Orbiting Carbon Observatory (OCO-2 and OCO-3) and Greenhouse gases Observing SATellite (GOSAT and GOSAT-2) missions from June 2022–December 2024 using a unified framework that combines Copernicus Atmosphere Monitoring Service (CAMS)-based coherence assessment, sampling availability, daily 2° aggregation, spatiotemporal collocation, and correction for XCO2 column-definition differences. Validation against the Total Carbon Column Observing Network (TCCON) gives R=0.92, a mean bias of 0.21 ppm, and a root-mean-square error (RMSE) of 1.49 ppm for daytime DQ-1, comparable to GOSAT. Over coherent and well-sampled regions, DQ-1 is broadly consistent with OCO-2 and OCO-3, with mean differences generally below 0.5 ppm and similar spatial and meridional structures. Over ocean, the DQ-1–OCO differences vary with surface reflectance and wind speed, with different reflectance relationships for OCO-2 and OCO-3. Nighttime DQ-1 is also broadly consistent with CAMS-derived XCO2. Regional analyses show complementary DQ-1 coverage where passive observations are limited. These results support DQ-1 ACDL as a quantitatively comparable and complementary component of the satellite XCO2 observing system.
