Pingyi Dong

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.

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.

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.

Improving the accuracy of measuring ice cloud properties is crucial for the study of atmospheric circulation and climate models, and understanding the radiative forcing effects of ice clouds. In this study, a novel approach to retrieve ice cloud microphysics involving synergistically analyzing the spectra of submillimeter and infrared is proposed, combining the complementary information regarding ice cloud properties from each spectrum. The sensitivity of the synergistic channel pairs to ice water paths and mean mass diameters is thoroughly investigated by the synthetic lookup tables and sensitivity parameter analysis. A synergistic retrieval algorithm based on Quantile Regression Neural Networks is constructed toward a better evaluation of the retrieval biases and uncertainties quantitatively. The simulated retrieval results reveal that the synergistic retrievals outperform the results from individual spectra across the full range of ice water path from 1 g/m2 to 1000 g/m2 and mean mass diameter from 1 μm to 500 μm. Specifically, the mean root-mean-square-error of the synergistic retrievals for ice water path is 68% (95%) lower than that of the submillimeter-only (infrared-only) retrievals, and a 10% (24%) lower root-mean-square-error for mean mass diameter respectively. Additionally, the synergy can improve the correlation for ice water path by 3.7% (5.6%) and yields an 12.5% (17.6%) higher correlation for mean mass diameter compared to the submillimeter-only (infrared-only) retrievals. With the upcoming spaceborne submillimeter and infrared passive sensors, the results demonstrate a pressing need and the potential of combining observations to better understand ice cloud properties in the future.

Submillimeter and microwave radiometers exhibit distinct sensitivities towards hydrometeor in different cloud phases. This work aimed to investigate the application of the synergistic observations of spaceborne submillimeter and microwave radiometers in cloud detection and thermodynamic phase classification. The spectral variances between the submillimeter and microwave bands under the scenarios of clear sky, ice-phase cloud, liquid-phase cloud, and mixed-phase cloud were highlighted by quantifying the disparities in the observed spectra. Cloud detection and phase classification models based on random forests were trained and tested by the simulated synergistic observation datasets. Results from assessment metrics revealed that the overall accuracy of the classification model incorporating all features was 88.75%. Feature importance analysis provided the key metrics for cloud phase classifications, classification models trained with these simplified combinations of channels and metrics achieved an accuracy of 86.25%. The results demonstrated the feasibility of combining submillimeter and microwave radiometer observations for detection and phase classification.

A retrieval methodology based on the Bayesian neural network (BNN) is presented that inverts the ice water path (IWP), mean mass-weighted diameter (D _me ), and cloud height of ice clouds from sub-millimeter radiometer observations. The training dataset was created using collecting cloud profiles from the DARDAR (raDAR/liDAR) database and running simulations by the Atmospheric Radiative Transfer Simulator (ARTS) model. Since the effective radius (r _e ) is the size descriptor of ice particles in the DARDAR database, a look-up table of ice water content (IWC), D _me , and r _e was constructed to convert r _e profiles into D _me profiles. In addition, random noises corresponding to the measurement uncertainties of the Compact Scanning Submillimeter-wave Imaging Radiometer (CoSSIR) during the TC4 experiment were added to the simulated brightness temperatures before training the BNN. The proposed retrieval method was first applied to the simulated testing database, and then to the observations of CoSSIR. Moreover, the retrieved IWP and D _me were compared to the retrievals of the Bayesian Monte Carlo Integration (BMCI) method. The retrieved cloud height was assessed by cloud height extracted from the reflectivity data of the Cloud Radar System (CPS) flow on the same aircraft with CoSSIR. The comparison showed that the correlation coefficients of the retrieved IWP and D _me from the two methods are above 0.8, and the retrieved cloud height also showed good agreement with that extracted from the CPS.