Shulei Li

Continuous observations of the central Arctic boundary layer (ABL) remain scarce, and ABL structure is strongly governed by stability, clouds, and wind shear. Using the year-round MOSAiC record, a condition-adaptive, multi-source neural network is developed to retrieve atmospheric boundary layer height (ABLH) from microwave radiometer, infrared hyperspectral radiances, ceilometer backscatter, wind profiles. Overall RMSE of 42.4 m is achieved, with best performance under clear skies and weakest under cloudy conditions. Feature-attribution and ablation analyses indicate wind-field information provides the strongest ABLH constraint. Clear-sky retrievals are improved by infrared observations, but cloud-related interference is introduced under cloudy conditions, whereas cloudy-scene robustness is enhanced by microwave observations. The ABLH annual cycle is found to peak in May and reach minima in August and December, mainly controlled by boundary-layer thermal evolution, cloud frequency, and low-level jets. Daily mean ABLH is closely linked to surface temperature, especially in spring and autumn. Stable and neutral boundary layers dominate: stable cases occur more often when cloud base lies above the ABL, while neutral cases are more frequent when cloud base lies below it. ABL development is promoted by wind shear beneath low-level jets, which increases with jet-core wind speed; wind shear stress, friction velocity, and downward longwave radiation also contribute. Model generalizability is confirmed by extrapolation to other Arctic sites and representative case studies. Overall, complementary active and passive remote-sensing observations are effectively integrated to generate continuous, condition-aware ABLH retrievals in complex Arctic environments, supporting characterization of boundary-layer variability and Arctic atmosphere–surface interactions.

Continuous observations of the central Arctic boundary layer (ABL) remain scarce, and ABL structure is strongly governed by stability, clouds, and wind shear. Using the year-round MOSAiC record, a condition-adaptive, multi-source neural network is developed to retrieve atmospheric boundary layer height (ABLH) from microwave radiometer, infrared hyperspectral radiances, ceilometer backscatter, wind profiles. Overall RMSE of 42.4 m is achieved, with best performance under clear skies and weakest under cloudy conditions. Feature-attribution and ablation analyses indicate wind-field information provides the strongest ABLH constraint. Clear-sky retrievals are improved by infrared observations, but cloud-related interference is introduced under cloudy conditions, whereas cloudy-scene robustness is enhanced by microwave observations. The ABLH annual cycle is found to peak in May and reach minima in August and December, mainly controlled by boundary-layer thermal evolution, cloud frequency, and low-level jets. Daily mean ABLH is closely linked to surface temperature, especially in spring and autumn. Stable and neutral boundary layers dominate: stable cases occur more often when cloud base lies above the ABL, while neutral cases are more frequent when cloud base lies below it. ABL development is promoted by wind shear beneath low-level jets, which increases with jet-core wind speed; wind shear stress, friction velocity, and downward longwave radiation also contribute. Model generalizability is confirmed by extrapolation to other Arctic sites and representative case studies. Overall, complementary active and passive remote-sensing observations are effectively integrated to generate continuous, condition-aware ABLH retrievals in complex Arctic environments, supporting characterization of boundary-layer variability and Arctic atmosphere–surface interactions.

Continuous observations of the central Arctic boundary layer (ABL) remain scarce, and ABL structure is strongly governed by stability, clouds, and wind shear. Using the year-round MOSAiC record, a condition-adaptive, multi-source neural network is developed to retrieve atmospheric boundary layer height (ABLH) from microwave radiometer, infrared hyperspectral radiances, ceilometer backscatter, wind profiles. Overall RMSE of 42.4 m is achieved, with best performance under clear skies and weakest under cloudy conditions. Feature-attribution and ablation analyses indicate wind-field information provides the strongest ABLH constraint. Clear-sky retrievals are improved by infrared observations, but cloud-related interference is introduced under cloudy conditions, whereas cloudy-scene robustness is enhanced by microwave observations. The ABLH annual cycle is found to peak in May and reach minima in August and December, mainly controlled by boundary-layer thermal evolution, cloud frequency, and low-level jets. Daily mean ABLH is closely linked to surface temperature, especially in spring and autumn. Stable and neutral boundary layers dominate: stable cases occur more often when cloud base lies above the ABL, while neutral cases are more frequent when cloud base lies below it. ABL development is promoted by wind shear beneath low-level jets, which increases with jet-core wind speed; wind shear stress, friction velocity, and downward longwave radiation also contribute. Model generalizability is confirmed by extrapolation to other Arctic sites and representative case studies. Overall, complementary active and passive remote-sensing observations are effectively integrated to generate continuous, condition-aware ABLH retrievals in complex Arctic environments, supporting characterization of boundary-layer variability and Arctic atmosphere–surface interactions.

Continuous observations of the central Arctic boundary layer (ABL) remain scarce, and ABL structure is strongly governed by stability, clouds, and wind shear. Using the year-round MOSAiC record, a condition-adaptive, multi-source neural network is developed to retrieve atmospheric boundary layer height (ABLH) from microwave radiometer, infrared hyperspectral radiances, ceilometer backscatter, wind profiles. Overall RMSE of 42.4 m is achieved, with best performance under clear skies and weakest under cloudy conditions. Feature-attribution and ablation analyses indicate wind-field information provides the strongest ABLH constraint. Clear-sky retrievals are improved by infrared observations, but cloud-related interference is introduced under cloudy conditions, whereas cloudy-scene robustness is enhanced by microwave observations. The ABLH annual cycle is found to peak in May and reach minima in August and December, mainly controlled by boundary-layer thermal evolution, cloud frequency, and low-level jets. Daily mean ABLH is closely linked to surface temperature, especially in spring and autumn. Stable and neutral boundary layers dominate: stable cases occur more often when cloud base lies above the ABL, while neutral cases are more frequent when cloud base lies below it. ABL development is promoted by wind shear beneath low-level jets, which increases with jet-core wind speed; wind shear stress, friction velocity, and downward longwave radiation also contribute. Model generalizability is confirmed by extrapolation to other Arctic sites and representative case studies. Overall, complementary active and passive remote-sensing observations are effectively integrated to generate continuous, condition-aware ABLH retrievals in complex Arctic environments, supporting characterization of boundary-layer variability and Arctic atmosphere–surface interactions.

Continuous observations of the central Arctic boundary layer (ABL) remain scarce, and ABL structure is strongly governed by stability, clouds, and wind shear. Using the year-round MOSAiC record, a condition-adaptive, multi-source neural network is developed to retrieve atmospheric boundary layer height (ABLH) from microwave radiometer, infrared hyperspectral radiances, ceilometer backscatter, wind profiles. Overall RMSE of 42.4 m is achieved, with best performance under clear skies and weakest under cloudy conditions. Feature-attribution and ablation analyses indicate wind-field information provides the strongest ABLH constraint. Clear-sky retrievals are improved by infrared observations, but cloud-related interference is introduced under cloudy conditions, whereas cloudy-scene robustness is enhanced by microwave observations. The ABLH annual cycle is found to peak in May and reach minima in August and December, mainly controlled by boundary-layer thermal evolution, cloud frequency, and low-level jets. Daily mean ABLH is closely linked to surface temperature, especially in spring and autumn. Stable and neutral boundary layers dominate: stable cases occur more often when cloud base lies above the ABL, while neutral cases are more frequent when cloud base lies below it. ABL development is promoted by wind shear beneath low-level jets, which increases with jet-core wind speed; wind shear stress, friction velocity, and downward longwave radiation also contribute. Model generalizability is confirmed by extrapolation to other Arctic sites and representative case studies. Overall, complementary active and passive remote-sensing observations are effectively integrated to generate continuous, condition-aware ABLH retrievals in complex Arctic environments, supporting characterization of boundary-layer variability and Arctic atmosphere–surface interactions.

Continuous observations of the central Arctic boundary layer (ABL) remain scarce, and ABL structure is strongly governed by stability, clouds, and wind shear. Using the year-round MOSAiC record, a condition-adaptive, multi-source neural network is developed to retrieve atmospheric boundary layer height (ABLH) from microwave radiometer, infrared hyperspectral radiances, ceilometer backscatter, wind profiles. Overall RMSE of 42.4 m is achieved, with best performance under clear skies and weakest under cloudy conditions. Feature-attribution and ablation analyses indicate wind-field information provides the strongest ABLH constraint. Clear-sky retrievals are improved by infrared observations, but cloud-related interference is introduced under cloudy conditions, whereas cloudy-scene robustness is enhanced by microwave observations. The ABLH annual cycle is found to peak in May and reach minima in August and December, mainly controlled by boundary-layer thermal evolution, cloud frequency, and low-level jets. Daily mean ABLH is closely linked to surface temperature, especially in spring and autumn. Stable and neutral boundary layers dominate: stable cases occur more often when cloud base lies above the ABL, while neutral cases are more frequent when cloud base lies below it. ABL development is promoted by wind shear beneath low-level jets, which increases with jet-core wind speed; wind shear stress, friction velocity, and downward longwave radiation also contribute. Model generalizability is confirmed by extrapolation to other Arctic sites and representative case studies. Overall, complementary active and passive remote-sensing observations are effectively integrated to generate continuous, condition-aware ABLH retrievals in complex Arctic environments, supporting characterization of boundary-layer variability and Arctic atmosphere–surface interactions.

Cloud horizontal heterogeneity within instrument footprints constitutes a major error source in ice cloud retrievals. We present a novel algorithm considering cloud horizontal heterogeneity based on lightweight convolutional neural network (LCNN) designed for Ice Cloud Imagers (ICI), which retrieves high‐resolution ice water path (IWP) by leveraging spatial correlations in low‐resolution satellite observations. Our approach employs a prior database generated through three‐dimensional radiative transfer simulations, containing collocated high‐resolution atmospheric profiles and cloud parameters, as well as the corresponding brightness temperature differences (BTDs). Then, the LCNN is trained and tested by the simulated prior database. Compared to Bayesian Monte Carlo integration (BMCI), the LCNN reduces mean absolute error (MAE) by 12.05 g/m ² while tripling spatial resolution (from 16 to 5.3 km). This resolution enhancement mitigates errors induced by ice cloud horizontal heterogeneity, achieving a 23.6% reduction in MAE attributing to plane‐parallel cloud assumptions within footprints. The framework demonstrates the viability of deep learning for submillimeter‐wave cloud sensing, providing a pathway to resolve sub‐footprint cloud variability in future satellite missions.

The eastern Tibetan Plateau has a high elevation, with a cold and dry atmospheric background. The features of the raindrop size distributions (DSD) in this region have notable differences from those in the plains. The general empirical relationships for retrieving parameters of precipitation from radar observations are not applicable in the eastern Tibetan Plateau. In this study, we developed a new method based on optimal estimation theory to retrieve the vertical profiles of N₀ and D_m from a Ka-band zenith-pointing Doppler radar. Validation by a field campaign during the summer of 2024 indicates that the mean bias in the log ₁₀(N₀) and D_m derived from the PARSIVEL² disdrometer and the retrieved values are 0.12 and −0.1 mm respectively, demonstrating the effectiveness of the retrieved DSD parameters in this region. Based on the retrieved vertical profiles of DSD parameters, some unique characteristics are found. The heavy precipitation (the maximum value in the reflectivity profile exceeding 30 dBZ) exhibits a higher particle number concentration above 2 km and larger raindrop size in the bottom of the rainfall on average. The mean values of D_m above 2 km are approximately 0.5 mm, for heavy precipitation, the value increase as the raindrops fall, reaching a peak at around 0.5 km. Precipitation that occurs after the nighttime cooling usually has higher particle concentrations and smaller particle sizes. Based on the above research, empirical relationships for the quantitative precipitation estimates (QPE) and attenuation correction using Ka-band radar in the eastern Tibetan Plateau are established.

The eastern Tibetan Plateau has a high elevation, with a cold and dry atmospheric background. The features of the raindrop size distributions (DSD) in this region have notable differences from those in the plains. The general empirical relationships for retrieving parameters of precipitation from radar observations are not applicable in the eastern Tibetan Plateau. In this study, we developed a new method based on optimal estimation theory to retrieve the vertical profiles of N₀ and D_m from a Ka-band zenith-pointing Doppler radar. Validation by a field campaign during the summer of 2024 indicate that the average bias in the log₁₀(N₀) and D_m derived from the PARSIVEL² disdrometer and the retrieved values is 0.12 and -0.1 mm, demonstrating the effectiveness of the retrieved DSD parameters in this region. Based on the retrieved vertical profiles of DSD parameters, some unique characteristics are found. The heavy precipitation (the maximum value in the reflectivity profile exceeding 30 dBZ) exhibits a higher particle number concentration above 2 km and larger raindrop size in the bottom of the rainfall on average. The mean values of D_m above 2 km are approximately 0.5 mm, for heavy precipitation, the value increase as the raindrops fall, reaching a peak at around 0.5 km. Precipitation that occurs after the nighttime cooling usually has higher particle concentrations and smaller particle sizes. Based on the above research, empirical relationships for the quantitative precipitation estimates (QPE) and attenuation correction using Ka-band radar in the eastern Tibetan Plateau are established.

The eastern Tibetan Plateau has a high elevation, with a cold and dry atmospheric background. The features of the raindrop size distributions (DSD) in this region have notable differences from those in the plains. The general empirical relationships for retrieving parameters of precipitation from radar observations are not applicable in the eastern Tibetan Plateau. In this study, we developed a new method based on optimal estimation theory to retrieve the vertical profiles of N₀ and D_m from a Ka-band zenith-pointing Doppler radar. Validation by a field campaign during the summer of 2024 indicate that the average bias in the log₁₀(N₀) and D_m derived from the PARSIVEL² disdrometer and the retrieved values is 0.12 and -0.1 mm, demonstrating the effectiveness of the retrieved DSD parameters in this region. Based on the retrieved vertical profiles of DSD parameters, some unique characteristics are found. The heavy precipitation (the maximum value in the reflectivity profile exceeding 30 dBZ) exhibits a higher particle number concentration above 2 km and larger raindrop size in the bottom of the rainfall on average. The mean values of D_m above 2 km are approximately 0.5 mm, for heavy precipitation, the value increase as the raindrops fall, reaching a peak at around 0.5 km. Precipitation that occurs after the nighttime cooling usually has higher particle concentrations and smaller particle sizes. Based on the above research, empirical relationships for the quantitative precipitation estimates (QPE) and attenuation correction using Ka-band radar in the eastern Tibetan Plateau are established.