Conference Agenda

Aerosols and clouds are key factors influencing climate change and radiative forcing, and improving the accuracy of their vertical profile observations helps reduce uncertainties in climate model simulations and forecasting systems. The development of spaceborne high-spectral-resolution lidar has provided important technical support for high-precision three-dimensional global observations of aerosols and clouds. The Aerosol and Carbon Detection Lidar (ACDL) onboard the Chinese atmospheric environment monitoring satellite DQ-1 is the world’s first spaceborne lidar employing an iodine molecular filter for high-spectral-resolution detection. By integrating polarization detection, multi-wavelength detection, and high-spectral-resolution techniques, ACDL enables the direct retrieval of key optical properties of aerosols and clouds and supports high-accuracy layer detection and classification. The EarthCARE satellite was launched in May 2024. Its Atmospheric Lidar (ATLID), operating at 355 nm, is equipped with a high-spectral-resolution channel and a depolarization measurement channel.

This study is mainly based on the continuous research conducted during the three and a half years of stable on-orbit operation of ACDL. During this period, the research team completed the algorithm evolution from the early processing versions to Baseline05 and established a relatively systematic ground-processing framework, including data preprocessing, retrieval of key optical properties, layer identification, and multi-source validation. The Baseline05 processing chain covers the workflow from Level 1A to Level 2C, with major improvements in denoising and averaging strategies for both daytime and nighttime observations, calibration coefficient updating, layer detection, and iterative retrieval of weak signals. Compared with the earlier L2C processing results, Baseline05 has significantly improved the distribution characteristics and physical consistency of optical-property products under both daytime and nighttime conditions, increased the effective data yield, and enhanced the capability to detect stratospheric aerosols, thin clouds, and other weakly scattering layers.

On this basis, the study further addresses data quality assessment and application expansion for spaceborne high-spectral-resolution lidar by carrying out comparison studies between ACDL/DQ-1 and EarthCARE/ATLID. Through spatiotemporal collocation, quality control, and the selection of representative scenes, joint analyses are performed on backscatter and depolarization observations from the two instruments for dust, cirrus, and stratospheric targets. These analyses are used to evaluate the stability, consistency, and applicability of the ACDL Baseline05 products and to provide guidance for further optimization of calibration strategies and data processing algorithms. In addition, through the joint use of multiple spaceborne lidars, including ACDL/DQ-1, EarthCARE/ATLID, CALIPSO/CALIOP, and Aeolus/ALADIN, together with OSSE experiments, this study investigates the impacts of different observing configurations and coordinated observation strategies on the capability to detect wind fields, aerosol profiles, and cloud profiles, and explores the information gain achieved through synergistic use of ACDL, ATLID, and other observing systems. The results are expected to support future calibration and validation, synergistic retrieval, and application development of spaceborne high-spectral-resolution lidar products.

A pillar activity of the DRAGON-6 project #95437 is the validation of plasma density and magnetic field observations from several low-Earth-orbit (LEO) satellites.

In this regard, we compared and validated Swarm plasma density datasets (which now include multiple official, empirical, physics-based, and neural-network products) against incoherent scatter radar (ISR) observations from Jicamarca, Arecibo, and Millstone Hill using both climatological comparisons and strict satellite-radar conjunctions. The analysis, covering different solar activity levels, seasons, local times, and orbital altitudes, shows that the baseline Langmuir Probe product generally underestimates plasma density by about 10-20%, with biases depending on satellite, local time, and solar conditions. Empirical calibrations reduce the mean bias but do not always minimize the dispersion, while the neural-network and physics-based corrections provide the closest agreement with the ISR reference. These results establish a robust benchmark for selecting the most suitable Swarm plasma density products for future research in the project and, moreover, provide the physical and methodological basis for the implementation of a new Swarm plasma density calibration model in the next stage of the project.

Another important validation activity of the project concerns the vector magnetic field observations from the Macau Science Satellite (MSS-1A). A first assessment based on conjunctions with Swarm A and comparisons with CHAOS-model residuals shows a high level of consistency between the two missions, with more than one thousand conjunction events and average magnetic residuals of only a few nT. The MSS-1A data also clearly reproduce key natural magnetic signatures, including those associated with the equatorial electrojet and ring current, confirming the scientific quality of the measurements. In addition, first applications demonstrate that MSS-1A magnetic observations can be exploited to detect equatorial plasma bubbles through machine-learning approaches trained with Swarm-based labels. At the same time, some artificial disturbances of instrumental or platform origin have been identified and are being further investigated to consolidate the use of MSS-1A data in multi-satellite studies.

Plasma density and magnetic field validated and calibrated observations are key to the monitoring and modelling of ionospheric irregularities like plasma depletions and plasma bubbles. Based on 9 years of CHAMP and 11 years of Swarm magnetic observations, together with 12 years of Swarm plasma density data, we developed statistical and machine-learning models of equatorial plasma depletion occurrence. These models estimate the probability of irregularities as a function of local time, longitude, season, and solar flux, and reveal both pre-midnight and post-midnight structures. The plasma-density-based machine-learning approach is particularly promising because it is sensitive not only to deep depletions but also to shallower structures that are often missed by magnetic-only diagnostics. This modelling activity is complemented by conjunction analyses involving Swarm, the MSS-1a, and COSMIC-2 satellites, aimed at extending multi-platform validation to new magnetic and plasma measurements acquired by recent LEO missions.

Within the same framework, dedicated analyses of CSES-01 plasma density and electric field observations provide new insight into post-midnight plasma bubbles. New automatic detection methods based on Langmuir Probe and Electric Field Detector data show good mutual agreement and strong complementarity. Their application demonstrates that post-midnight plasma depletions increase with solar activity, are confined mainly within ±20° magnetic latitude, and display marked seasonal and longitudinal dependencies, with occurrence maxima during the June solstice and over preferred longitude sectors. Comparisons with Swarm B plasma density observations confirm the robustness of the detection strategy and further support the use of cross-mission analyses for characterizing irregular structures in the nighttime ionosphere.

Overall, the project demonstrates that the combined validation, calibration, and coordinated exploitation of observations from Swarm, CSES-01, and MSS-1a satellites, along with other LEO satellites and ground-based facilities, can significantly improve our capability to monitor the Earth’s magnetic and plasma environment, which is fundamental to the study of ionospheric irregularities with relevance for space-weather-oriented monitoring and modelling.