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.

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.

The accurate recognition of the cloud phase has a great influence on the retrieval of the cloud top height. In order to improve the accuracy of obtaining the cloud top height with OCO-2, we proposed a cloud phase recognition algorithm based on the threshold of parameter $\alpha$; $\alpha$ is defined as the reflectivity ratio of the region with weak continuous absorption of the oxygen A band to the region with weak continuous absorption of the CO₂ 1.6 µm band. The $\alpha$ under different solar zenith angles and different ground albedos was calculated. The results show the following: under the same surface albedo and solar zenith angle, $\alpha$ was large for ice clouds and small for water clouds. Under the same surface albedo, the greater the solar zenith angle, the smaller the $\alpha$ of the ice cloud, and the larger the $\alpha$ of the water cloud. Under the same solar zenith angle, the greater the surface albedo, the smaller the $\alpha$; when the solar zenith angle was less than 70°, $\alpha$ can be used to effectively distinguish between the ice cloud and water cloud. This study used OCO-2 data of a single orbit over ocean to verify the feasibility of the algorithm through comparison with the CALIOP cloud phase product, which provided a basis for OCO-2 cloud top height estimation.

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.

Microwave links as a novel method of rainfall monitoring have been extensively studied over the last two decades. Still, the applicability of γ (rain-induced attenuation rate) - R (rain rate) relationship in quantitative rainfall inversion with microwave link under tilted conditions has not been systematically investigated to date. With the establishment of rain-induced attenuation fields based on multiple sets of measured raindrop spectral profile data from micro-rain radar, microwave links under various tilted conditions are simulated and the performance of γ - R relationship in inversion of rain rate is theoretically analyzed. Afterwards, the effects of link height, tilt angle and rainfall reference point variations on the rainfall inversion performance are discussed in depth from the theoretical and data analysis perspectives, respectively. Furthermore, the connection between the study and the satellite-ground link-based rainfall measurement technique is mapped. Finally, the applicability analysis to different regions and the introduction of errors is also investigated.

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.