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In August 2022, China successfully launched the Terrestrial Ecosystem Carbon Inventory Satellite (TECIS). The primary payload of this satellite is an onboard multi-beam lidar system, which is capable of observing aerosol optical parameters on a global scale. This pioneering study used the Fernald forward integration method to retrieve aerosol optical parameters based on the Level 2 data of the TECIS, including the aerosol depolarization ratio, aerosol backscatter coefficient, aerosol extinction coefficient, and aerosol optical depth (AOD). The validation of the TECIS-retrieved aerosol optical parameters was conducted using CALIPSO Level 1 and Level 2 data, with relative errors within 30%. A comparison of the AOD retrieved from the TECIS with the AERONET and MODIS AOD products yielded correlation coefficients greater than 0.7 and 0.6, respectively. The relative error of aerosol optical parameter profiles compared with ground-based measurements for CALIPSO was within 40%. Additionally, the correlation coefficients R2 with MODIS and AERONET AOD were approximately between 0.5 and 0.7, indicating the high accuracy of TECIS retrievals. Utilizing the TECIS retrieval results, combined with ground air quality monitoring data and HYSPLIT outcomes, a typical dust transport event was analyzed from 2 to 7 April 2023. The results indicate that dust was transported from the Taklamakan Desert in Xinjiang, China, to Henan and Anhui provinces, with a gradual decrease in the aerosol depolarization ratio and backscatter coefficient during the transport process, causing varying degrees of pollution in the downstream regions. This research verifies the accuracy of the retrieval algorithm through multi-source data comparison and demonstrates the potential application of the TECIS in the field of aerosol science for the first time. It enables the fine-scale regional monitoring of atmospheric aerosols and provides reliable data support for the three-dimensional distribution of global aerosols and related scientific applications. [ABSTRACT FROM AUTHOR]

The variation trends and characteristics of polar mesospheric clouds (PMCs) are important for studying the evolution of atmospheric systems and understanding various atmospheric dynamic processes. Through observation and analysis of PMCs, we can gain a comprehensive understanding of the mechanisms driving atmospheric processes, providing a scientific basis and support for addressing climate change. Ultraviolet (UV) imaging technology, adopted by the Cloud Imaging and Particle Size (CIPS) instrument on board the Aeronomy of Ice in the Mesosphere (AIM) satellite, has significantly advanced the research on PMCs. Due to the retirement of the AIM satellite, there is currently no concrete plan for next-generation instruments based on the CIPS model, resulting in a discontinuity in the observation data sequence. In this study, we propose a compact and cost-effective wide-field-of-view ultraviolet imager (WFUI) that can be integrated into various satellite platforms for future PMC observation missions. A forward model was built to evaluate the detection capability and efficiency of the WFUI. CIPS and Solar Occultation for Ice Experiment (SOFIE) data were fused to reconstruct a three-dimensional PMC scene as the input background. Based on the scattering and extinction characteristics of ice particles and atmospheric molecules, the radiative transfer was calculated using the solar radiation path through the atmosphere and PMCs. The optical system and satellite platform parameters of the WFUI were selected according to CIPS, enabling the calculation of the number of photons received by the WFUI. The actual detection signal is then simulated by photoelectric conversion, and the PMC information can be obtained by removing detector noise. Subsequently, a comparison with the input background field was conducted to compute and analyze the detection efficiency. Additionally, a sensitivity analysis of the instrument and platform parameters was conducted. Simulations were performed for both individual orbits and for the entire PMC seasons. The research results demonstrate that the WFUI performs well in PMC detection and has high detection efficiency. Statistical analysis of the detection efficiency using data from 2008 to 2012 revealed an exponential relationship between the ice water content (IWC) of PMCs and detection efficiency. During the initial and final durations of the PMC season, when the IWC was relatively low, the detection efficiency remained limited. However, as the season progressed and the IWC increased, the detection efficiency significantly improved. We note that regions at lower latitudes exhibited a lower IWC and, consequently, lower detection efficiency. In contrast, regions at higher latitudes, with a greater IWC, demonstrated better detection efficiency. Additionally, the sensitivity analysis results suggest that increasing the satellite orbit altitude and expanding the field of view (FOV) of the WFUI both contribute to improving the detection efficiency. [ABSTRACT FROM AUTHOR]

The Atmospheric Environment Monitoring Satellite (AEMS), also called Daqi-1 or DQ-1, was launched in April 2022; one of its main payloads is a high-spectral-resolution lidar (HSRL) system. This new system enables the accurate measurements of global aerosol optical properties, which can be used in the geoscientific community after the retirement of the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. Developing a suitable retrieval algorithm and validating retrieved results are necessary. This research demonstrates a retrieval algorithm for aerosol optical properties using the DQ-1 HSRL system. This method has retrieved the aerosol linear depolarization ratio, backscatter coefficient, extinction coefficient, and optical depth. For validation purposes, we compared retrieved results with those obtained through CALIPSO. The results indicate that the profiles of the two datasets are in good agreement, with DQ-1 showing an improved signal-to-noise ratio (SNR). Optical property profiles from National Aeronautics and Space Administration (NASA) Micro-Pulse Lidar Network (MPLNET) stations were selected for validation with the DQ-1 measurements, resulting in a relative error of 25 %. Between June 2022 and December 2022, aerosol optical depth measurements using the DQ-1 satellite and the AErosol RObotic NETwork (AERONET) were correlated and yielded a value of R2 equal to 0.803. We use the DQ-1 dataset to initially investigate the transport processes of the Saharan dust and the South Atlantic volcanic aerosols. These validations and applications show that the DQ-1 HSRL system can accurately measure global aerosols and has significant potential for Earth scientific applications. [ABSTRACT FROM AUTHOR]

The variation trends and characteristics of polar mesospheric clouds (PMCs) are important for studying the evolution of atmospheric systems and understanding various atmospheric dynamic processes. Through observation and analysis of PMCs, we can gain a comprehensive understanding of the mechanisms driving atmospheric processes, providing a scientific basis and support for addressing climate change. Ultraviolet (UV) imaging technology, adopted by the Cloud Imaging and Particle Size (CIPS) instrument onboard the Aeronomy of Ice in the Mesosphere (AIM) satellite, has significantly advanced the research on PMCs. Due to the retirement of the AIM satellite, there is currently no concrete plan for next-generation instruments based on the CIPS model, resulting in a discontinuity in the observation data sequence. In this study, we propose a compact and cost-effective wide field-of-view ultraviolet imager (WFUI) that can be integrated into various satellite platforms for future PMCs observation missions. A forward model was built to evaluate the detection capability and efficiency of the WFUI. The fusion of CIPS and solar occultation for ice experiment (SOFIE) data was employed to reconstruct a three-dimensional PMCs scene as the input background. Based on the scattering and extinction characteristics of ice particles and atmospheric molecules, the radiative transfer was calculated using the solar radiation path through the atmosphere and PMCs. The optical system and satellite platform parameters of the WFUI were selected according to the CIPS, enabling the calculation of the number of photons received by the WFUI. The actual detection signal is then simulated by photoelectric conversion, and the PMCs information can be obtained by removing detector noise. Subsequently, a comparison with the input background field was conducted to compute and analyze the detection efficiency. Additionally, a sensitivity analysis of the instrument and platform parameters was conducted. Simulations were performed for both individual orbits and for the entire PMCs seasons. The research results demonstrate that using data from 2008 to 2012 revealed an exponential relationship between the ice water content (IWC) of PMCs and detection efficiency. During the initial and final durations of the PMCs season, when the IWC was relatively low, the detection efficiency the WFUI performs well in PMCs detection and has high detection efficiency. Statistical analysis of the detection efficiency remained limited. However, as the season progressed and the IWC increased, the detection efficiency significantly improved. We note that regions at lower latitudes exhibited a lower IWC and, consequently, lower detection efficiency. In contrast, regions at higher latitudes, with a greater IWC, demonstrated better detection efficiency. Additionally, the sensitivity analysis results suggest that increasing the satellite orbit altitude and expanding the field of view (FOV) of the WFUI both contribute to improving the detection efficiency. [ABSTRACT FROM AUTHOR]

Methane (CH4) is recognized as the second most important greenhouse gas. An accurate and precise monitoring of methane gas globally has a vital role in studying the carbon cycle and global warming. The spaceborne integrated path differential absorption (IPDA) lidar is one of the most effective payload for methane detection. The simulation and optimization of the lidar system parameters can create an important base for the development of spaceborne payloads. However, previous IPDA lidar simulations have mostly used standard atmospheric models at simulation conditions, and to the best of our knowledge, there is no literature yet which applies a wavelength optimization to the IPDA system. In this study, we have investigated the relationship between the IPDA lidar system, based on wavelength optimization, and error measurement for CH4 column-averaged concentration. By selecting the wavelengths with the lowest comprehensive error as on-line and off-line, the error has been minimized by 10 ppb approximately relative to before optimization. We have proposed an IPDA simulation model at real atmospheric conditions, combining with ERA-5 reanalysis data, to simulate methane concentration globally, and present the distribution of errors. Finally, after the optimization of the lidar system parameters, we have ensured that the maximum inversion error for CH4 measurement is less than 10 ppb, to provide a reference for designing spaceborne IPDA lidar systems for high-precision CH4 column-averaged concentration detection. [ABSTRACT FROM AUTHOR]

In this work, we extracted the near-surface CO2 concentration from the Greenhouse gases Observing SATellite (GOSAT) and the National Oceanic and Atmospheric Administration (NOAA) CarbonTracker model datasets for a temporal period of eight years from 2010 to 2017 to study the spatiotemporal distribution of near-surface CO2 and the factors affecting it over five regions of Asia, including Central Asia, East Asia, South Asia, Southeast Asia, and West Asia. The near-surface CO2 datasets from both satellite and model were first validated against the ground-based CO2 observations obtained from the World Data Center for Greenhouse Gases (WDCGG) stations located in Asia to confirm their applicability, and the results showed a good agreement between the datasets with significant correlations. The results from the time-series analyses showed a gradual increase in the near-surface CO2 with significant monthly and seasonal variations over all the regions. To study the factors affecting the spatial distribution of near-surface CO2, we investigated the relationship of near-surface CO2 with the anthropogenic CO2 emissions, terrestrial ecosystem, and winds. The results showed that, over Asia, the anthropogenic CO2 emissions and winds primarily controlled the spatial distribution of near-surface CO2. However, in the areas where anthropogenic emissions were lower, the terrestrial ecosystem and winds affected the near-surface CO2 distribution. To study the factors controlling the temporal distribution of near-surface CO2, the relationship of near-surface CO2 with vegetation, precipitation, and relative humidity was investigated. The results showed an inverse relationship between near-surface CO2 and normalized difference vegetation index (NDVI), precipitation, and relative humidity over monsoon-influenced regions, i.e., East Asia, South Asia, and Southeast Asia. However, a positive relation of near-surface CO2 was observed with precipitation and relative humidity over arid and semiarid regions, i.e., Central Asia and West Asia. The results were also verified by determining the correlations among these variables. [ABSTRACT FROM AUTHOR]