The aerial measurement of wind speed and direction is important for the development of the low-altitude economy, meteorology, climate research, and renewable energy systems. Existing UAV-based wind measurements, whether instrument-based or flight-dynamic-based, consistently exhibit bias and significant errors, limiting their reliability for precise wind estimation. This study introduces a machine learning (ML) approach to improve the accuracy of the wind speed and direction estimation using UAVs. The proposed method leverages data from sensors onboard UAV platforms, combined with advanced ML algorithms trained on ground-truth measurements obtained through high-resolution LiDAR systems. The experiments reveal that incorporating a 10 s smoothing window yields a root mean square error (RMSE) value of 0.39 m/s for the wind speed (horizontal) and an even lower bias (≤0.069 m/s) when using a 60 s smoothing window, representing a marked improvement over traditional techniques. These results are particularly promising at longer smoothing windows (>50 s), where the ML-based approach achieves superior accuracy compared to LiDAR measurements. The findings underscore the potential of integrating machine learning with UAV-based wind measurement systems to achieve higher precision and reliability in wind characterization.

Records and projections of increasing global average temperature call for improvements of global stocktake inputs, which are vital to achieving targets of intergovernmental agreements on climate change. Unmanned Aerial Vehicle (UAV)-based atmospheric observation of greenhouse gas (GHG) concentrations is an upcoming addition to the top-down measurement methods due to its advantageous spatial-temporal resolutions, greater coverage area and lower costs. Hence, we developed and tested a lightweight UAV payload enclosure integrating a non-dispersive diffusion infrared (NDIR) spectrometer and two electrochemical sensors for measurements of carbon dioxide (CO₂), carbon monoxide (CO) and nitrogen dioxide (NO₂). To achieve higher response times and maintain measurement qualities, we designed a custom air inlet on the rotor-facing side of the enclosure to reduce measurement fluctuations caused by rotor downwash airflow. To validate the payload design, we conducted a controlled test for comparing chambered and chamber-less NDIR spectrometer measurements. From the test we observed a reduction of 0.48 hPa in terms of standard deviation of pressure measurements and minimised downwash-flow-induced anomalous biases (+0.49 ppm and +0.08 hpa for chambered compared to −1.33 ppm and −1.05 hpa for chamber-less). We also conducted an outdoor in-situ measurement test with multiple flights reaching 500 m above ground level (ABGL). The test yielded high resolution results representing vertical distributions of mole fraction concentrations of three types of gases via two types of flight trajectory planning methods. Therefore, we provide an alternative UAV payload integration method for NDIR spectrometer CO₂ measurements that complement existing airborne GHG observation methodologies. Additionally, we also introduced an aerodynamic approach in reducing measurement noises and biases for a low response time sensor configuration.

Based on actual atmospheric observations of internal mixing of light-absorbing brown carbon (BrC)-coated black carbon (BC), the optical properties of mixed black and brown carbon aerosols (BBC) were calculated using four mixing models (external mixing, core–shell, Bruggeman, and Maxwell-Garnett models), and changes in their optical properties with wavelength were compared and analyzed. Under the assumption of different volumetric mixing ratios (VR=V_BC/V_BBC), there is little difference in volumetric absorptive coefficient (K_ab) of BBC in these models in the ultraviolet band where both BC and BrC have strong absorption, particularly in the ultraviolet A band. In visible and near-infrared bands, the three internal mixing models significantly reduce the single scattering albedo compared to the external mixing scenario. In addition, the widely used core–shell model was used to evaluate the effects of BrC shell thickness and environmental relative humidity (RH) on the optical properties of BBC. The impacts of these factors are mainly seen in ultraviolet and visible bands. The volumetric extinction coefficient (K_ex) decreases with BrC shell thickness under a fixed BC core radius (0.12 µm) in these bands. This is because the radiation reaching the surface of BC particles is reduced under the absorption of less efficient BrC shells (known as the blocking effect), which is different from the BC and light-scattering aerosols internal mixing scenario. Moreover, the K_ex and K_ab of BBC decrease with RH, which is mainly due to both the increasing thickness of the BrC shell and the change in the complex refractive index of the BrC shell. Based on the assumptions of unchanged BC core parameters and actual observations, the extinction ability increases when BrC particles are more light-scattering in the ultraviolet and visible bands. The increase in extinction is mainly from scattering, rather than absorption. However, the situation is the opposite in the near-infrared band.

To evaluate the potential of an upcoming large-swath satellite for estimating surface methane (CH₄) fluxes at a weekly scale, we report the results from a series of observing system simulation experiments (OSSEs) that use an established modeling framework that includes the GEOS-Chem 3D atmospheric transport model and an ensemble Kalman filter. These experiments focus on the sensitivity of CH₄ flux estimates to systematic errors ($\mu$) and random errors ($\sigma$) in the column average methane (XCH₄) measurements. Our control test (INV_CTL) demonstrates that with median errors ($\mu$ = 1.0 ± 0.9 ppb and $\sigma$ = 6.9 ± 1.6 ppb) in XCH₄ measurements over a 1000 km swath, global CH₄ fluxes can be estimated with an accuracy of 5.1 ± 1.7%, with regional accuracies ranging from 3.8% to 21.6% across TransCom sub-continental regions. The northern hemisphere mid-latitudes show greater reliability and consistency across varying $\mu$ and $\sigma$ levels, while tropical and boreal regions exhibit higher sensitivity due to limited high-quality observations. In $\sigma$-sensitive regions, such as the North American boreal zone, expanding the swath width from 1000 km to 3000 km significantly reduces discrepancies, while such adjustments provide limited improvements for $\mu$-sensitive regions like North Africa. For TanSat-2 mission, with its elliptical medium Earth orbit and 1500 km swath width, the global total estimates achieved an accuracy of 3.1 ± 2.2%. Enhancing the swath width or implementing a dual-satellite configuration is proposed to further improve TanSat-2 inversion performance.

Satellite-based monitoring of atmospheric column-averaged dry-air mole fraction (XCH₄) is essential for quantifying methane (CH₄) emissions, yet uncharacterized spatially varying biases in XCH₄ observations can cause misattribution in flux estimates. This study assesses the potential of the upcoming TanSat-2 satellite mission to estimate China’s CH₄ emission using a series of Observing System Simulation Experiments (OSSEs) based on an Ensemble Kalman Filter (EnKF) inversion framework coupled with GEOS-Chem on a 0.5° × 0.625° grid, alongside an evaluation of current TROPOMI-based products against Total Carbon Column Observing Network (TCCON) observations. Assuming a target precision of 8 ppb, TanSat-2 could achieve an annual national emission estimate accuracy of 2.9% ± 4.2%, reducing prior uncertainty by 84%, with regional deviations below 5.0% across Northeast, Central, East, and Southwest China. In contrast, limited coverage in South China due to persistent cloud cover leads to a 26.1% discrepancy—also evident in pseudo TROPOMI OSSEs—highlighting the need for complementary ground-based monitoring strategies. Sensitivity analyses show that satellite retrieval biases strongly affect inversion robustness, reducing the accuracy in China’s total emission estimates by 5.8% for every 1 ppb increase in bias level across scenarios, particularly in Northeast, Central and East China. We recommend expanding ground-based XCH₄ observations in these regions to support the correction of satellite-derived biases and improve the reliability of satellite-constrained inversion results.

Substantial methane (CH₄) emissions in Asia are efficiently transported to the upper troposphere through the monsoon dynamical system, which forms a remarkable seasonal CH₄ enhancement in the upper troposphere. Using a chemical transport model, GEOS-Chem, driven by surface optimized CH₄ flux, the CH₄ enhancement over the Asian monsoon region is explored as a combined effect of the monsoon dynamical system and regionally increased emissions during the late monsoon season. The spatial distributions of CH₄ at the upper troposphere show strong subseasonal variability, which is closely tied to the east–west oscillation of the Asian monsoon anticyclone (AMA). Besides, the AMA patterns influence the efficiency of methane-rich air transport to the upper troposphere and lower stratosphere. The AMA center around 80° E favors the upward transport from organized monsoon convective sources over the Indian subcontinent and Bay of Bengal, while the AMA center around 105° E favors the source from southwest China transported to the upper troposphere. When the AMA shifts over the Iranian Plateau, further vertical transport from the convective outflow level to the upper troposphere is weakened and the horizontal redistribution becomes dominant. According to our model sensitivity study, the differences in the upper-troposphere CH₄ anomalies caused by large-scale circulation are 1–2 times the difference caused by regional surface emissions. Our research highlights the complex interaction between monsoon dynamics and surface emissions to determine the upper-troposphere methane.