Qinghui Li

Despite the recognized importance of turbulence in Arctic mixed‐phase cloud (AMPC) persistence, its effects on ice microphysics and size‐dependent sedimentation remain poorly understood. Using yearlong shipborne MOSAiC observations, we examine these processes across eddy dissipation rate (EDR) ranges. The mixed‐phase layer base (MLB) shows relatively high EDR, with the highest range (10 ^−3.5 –10 ⁻² m ² s ⁻³ ) associated with lower temperatures ( T _MLB ). At the MLB, the typical relationship between particle size and ice sedimentation weakens as EDR increases. Under stronger turbulence, sedimentation is enhanced for layers with smaller particles but weakened for those with larger particles, a contrast broadly consistent with preferential sweeping and loitering. These sedimentation changes are accompanied by smaller ice particles in the less cold regime ( T _MLB > −20°C) and higher ice particle concentrations in the colder regime ( T _MLB < −20°C). Such sedimentation changes may influence AMPC microphysics and potentially contribute to ice‐recycling processes.

As a typical cloud type in the Arctic, the microphysical characteristics of Arctic stratiform mixed‐phase clouds (MPCs) are essential for understanding interactions among Arctic clouds, aerosols, and radiation. This study uses 1‐year of shipborne observations from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the Arctic Ocean. It summarizes the typical distribution of microphysical parameters in Arctic MPCs and investigates how cloud‐top droplet microphysical parameters and environmental factors influence ice crystal formation and growth. The results show that Arctic stratiform MPCs exhibit a vertical structure characterized by a liquid phase at the top, a mixed phase in the middle, and an ice phase at the bottom. Ice crystals mainly originate from the liquid‐phase region near the cloud top and grow as they descend through the mixed‐phase layer. Higher liquid water content at the cloud top enhances both riming and depositional growth of ice crystals in the mixed‐phase layer. Small droplets are more readily converted into ice crystals, whereas large droplets favor ice crystal growth to larger sizes. Ice crystal concentration is exponentially negatively correlated with cloud‐top temperature, and ice crystal size reaches its maximum near −15°C due to the combined effects of depositional growth and aggregation. Additionally, significant radiative cooling occurs at the top of MPCs, stimulating turbulence and strong vertical mixing within the cloud, thereby facilitating the formation and maintenance of MPCs.

This study investigates the linkage between the radar reflectivity slope and raindrop size distribution (DSD) in precipitation with bright bands through coordinated C-band/Ka-band radar and disdrometer observations in southern China. Precipitation is classified into three types based on the reflectivity slope (K-value) below the freezing level, revealing distinct microphysical regimes: Type 1 (K = 0 to −0.9) shows coalescence-dominated growth; Type 2 (|K| > 0.9) shows the balance between coalescence and evaporation/size sorting; and Type 3 (K = 0.9 to 0) demonstrates evaporation/size-sorting effects. Surface DSD analysis demonstrates distinct precipitation characteristics across classification types. Type 3 has the highest frequency of occurrence. A gradual decrease in the mean rain rates is observed from Type 1 to Type 3, with Type 3 exhibiting significantly lower rainfall intensities compared to Type 1. At equivalent rainfall rates, Type 2 exhibits unique microphysical signatures with larger mass-weighted mean diameters (D_m) compared to other types. These differences are due to Type 2 maintaining a high relative humidity above the freezing level (influencing initial D_m at bottom of melting layer) but experiencing limited D_m growth due to a dry warm rain layer and downdrafts. Type 1 shows opposite characteristics—a low initial D_m from the dry upper layers but maximum growth through the moist warm rain layer and updrafts. Type 3 features intermediate humidity throughout the column with updrafts and downdrafts coexisting in the warm rain layer, producing moderate growth.

The thermodynamic phase of clouds is fundamental to rain initiation and development. Although it is well established that majority of rainfall from mid‐ to‐high latitudes originates from ice, there is a lack of consensus on the major thermodynamic phase of raining clouds over low latitudes. Previous CloudSat‐based studies have yet reached consistent results on the fraction of ice‐phase raining clouds over Southern China. In this work, we found that CloudSat‐based approaches are subject to metrics used for flagging rainfall events, which may lead to different estimates. Analysis of multi‐year surface radar observations suggests that has diurnal variation and increases with rain rate, while the surface rainfall originating from warm clouds does not exceed 50 . Using disdrometer observations to flag surface rainfall events, we found that about 71 and 95 of surface rainfall events and rainfall accumulation come from cold clouds, respectively. In addition, the observed rainfall accumulations over Longmen are consistent with the fifth generation atmospheric reanalysis (ERA5), whereas the frequency of warm rainfall over Longmen is significantly underestimated in ERA5.

The United States geostationary operational environmental satellites GOES-16 and GOES-17 provide cloud-top height (CTH) data. A fusion model combining their CTH is vital for global high-resolution weather forecasting and climate analysis. However, ensuring a high degree of consistency between CTH from GOES-16 and GOES-17 is essential for building an effective CTH fusion model. In this paper, a consistency analysis was conducted on the CTH in regions where GOES-16/ABI and GOES-17/ABI observations overlap. Results show that the CTH of GOES-16 and GOES-17 exhibits a small mean error (ME) and high correlation, but between 9:30 and 15:00 (UTC), the ME, standard deviation (Std), and root mean square error (RMSE) are significantly larger than those during other periods, with the maximum values occurring between approximately 12:00 and 14:00. The difference in Satellite viewing zenith angle (ΔVZA) at the same observation pixel had an impact on the CTH consistency. The larger ΔVZA is, the larger ME, Std, and RMSE are; The increase in ME of between the two CTH from 9:30 to 15:00 was related to the difference in radiance of the 12.3μm and 13.3μm channel, while the increase in Std was related to the difference in radiance of the 13.3μm. It is due to anomalies in the channel radiance observations caused by the degradation of the imager's performance by solar radiation exposure during this period. The fusion of GOES-16 and GOES-17 CTH data needs to take into consideration the differences in VZA and the data quality control between 9:30 and 15:00.

Different cloud-top heights (CTHs) have different degrees of atmospheric heating, which is an important factor for weather forecasting and aviation safety. AHIs (Advanced Himawari Imagers) on the Himawari-8 satellite are a new generation of visible and infrared imaging spectrometers characterized by a wide observation range and a high temporal resolution. In this paper, a cloud-top height retrieval algorithm based on XGBoost is proposed. The algorithm comprehensively utilizes AHI L1 multi-channel radiance data and calculates the input parameters of the generated model according to the characteristics of the cloud phase, texture, and the local brightness temperature change of the cloud. In addition, the latitude, longitude, solar zenith angle and satellite zenith angle are input into the model to further constrain the influence of the geographical and spatial factors such as the sea and land location, on CTH. Compared with the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) cloud-top height data (CTH_CAL), the results show that: the algorithm retrieved the cloud-top height (CTH_XGB) with a mean error (ME) of 0.3 km, a standard deviation (Std) of 1.72 km, and a root mean square error (RMSE) of 1.74 km. Additionally, it improves the problem of the large systematic deviation in the cloud-top height products released by the Japan Meteorological Agency (CTH_JMA), especially for ice clouds and multi-layer clouds with ice clouds on the top layer. For water clouds below 2 km and multi-layer clouds with water clouds at the top, the algorithm solves the systematically serious CTH_JMA problem. XGBoost can effectively distinguish between different cloud scenarios within the model, which is robust and suitable for CTH retrieval.

It is well known that the measurement of cloud top height (CTH) is important, and a geostationary satellite is an important measurement method. However, it is difficult for a single geostationary satellite to observe the global CTH, so joint observation by multiple satellites is imperative. We used both active and passive sensors to evaluate the reliability of joint observation of geostationary satellites, which includes consistency and accuracy. We analyzed the error of CTH of FY-4A and HIMAWARI-8 and the consistency between the two satellites and conducted research on the problem of missing measurement (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) has CTH data, but FY-4A/HIMAWARI-8 does not) of the two satellites. The results show that FY-4A and HIMAWARI-8 have good consistency and can be jointly observed, but the measurement of CTH of FY-4A and HIMAWARI-8 has large errors, and the error of FY-4A is greater than that of HIMAWIRI-8. The error of CTH is affected by the CTH, cloud optical thickness (COT) and cloud type, and the consistency between the two satellites is mainly affected by the cloud type. FY-4A and HIMAWARI-8 have the problem of missing measurement. The missing rate of HIMAWARI-8 is greater than that of FY-4A, and the missing rate is not affected by the CTH, COT and surface type. Therefore, although FY-4A and HIMAWARI-8 have good consistency, the error of CTH and the problem of missing measurement still limit the reliability of their joint observation.

To improve the measurement accuracy of the dew point humidity sensor (sensor), starting from the problem of water vapor release and adsorption during the temperature change and based on tunable diode laser absorption spectroscopy (TDLAS), the experimental environment is designed and built with an embedded water content tester (tester), low temperature dew point generator (dew point generator), MBW humidity standard device (standard device), variable temperature freezer refrigerator (variable temperature box) and other equipment. The water vapor release effect of the sensor when the ambient temperature rises and the water vapor adsorption effect when the ambient temperature decreases are verified, and the degree of water vapor release and adsorption effects under different temperature changes, different water vapor concentrations, and different flow rates are quantitatively measured. The results show that the greater the temperature change is, the greater the release and adsorption of water vapor; the greater the concentration of water vapor is under the same temperature change, the greater the release and adsorption of water vapor; and the greater the gas flow is, the less obvious the release and adsorption of water vapor. The water vapor release and adsorption under different water vapor concentrations, different starting temperature points, and different temperature changes in the gas path are calculated. Subsequently, a reference table of sensor variable temperature compensation coefficients is given, which provides a new way to further improve the accuracy of sensor measurement.