Aerosol particles affect the Earth's radiative balance and represent one of the largest uncertainties in climate research. The removal of clouds is the first step and critical in the aerosol retrievals. However, the cloud detection is still challenging. Here, a novel simplified cloud detection algorithm (SCDA) is proposed to identify the cloud and clear-sky over land and based on a simplified radiative transfer model (RTM). The fewer input bands, dynamic thresholds, and only one parameter to be modified are the main advantages of the algorithm, which can be applied to different satellite sensors. In this article, we apply SCDA to the Himawari-8 data in 2016 for preliminary analysis. The detection results are validated using Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) vertical feature mask (VFM) data and the National Centers for Environmental Information (NCEI) ground-based observation data. We also compare the results with the Himawari-8 cloud products from the Japan Aerospace Exploration Agency (JAXA). Compared with CALIPSO VFM data and NCEI ground-based observation data, the correct rate of SCDA cloud detection result is 86.08% and 79.86%, which are higher than that of Himawari-8 cloud products (85.71% and 78.89%). The correct rate of SCDA clear-sky detection result is 88.33% and 87.85%, which are close to the correct rate of Himawari-8 clear-sky products (90.54% and 88.63%). The overall performance of the SCDA is comparable to that of the threshold method for JAXA Himawari-8 cloud products. Therefore, the SCDA can provide accurate cloud mask with only one threshold to be modified and few input parameters.

The strong winds and heavy rainfall of tropical cyclones (TCs) pose a serious threat to human life and property. Accurate and rapid TC positioning is crucial for the precise prediction of TC landfall locations, facilitating timely disaster warnings and strategic responses. Here, a novel cross correlation based on particle image velocimetry (PIVC) method is proposed to determine TC center using Himawari-8. The rotation and divergence characteristics of the entire TC lifecycle for TC positioning can be obtained using infrared brightness temperature data at a 10-min interval over Western North Pacific in 2016-2022. The positioning results are validated against best-track (BT) datasets of China Meteorological Administration (CMA), U.S. Department of Defense Joint Typhoon Warning Center (JTWC), and Regional Specialized Meteorological Centre-Tokyo (RSMC). The mean median error of PIVC method validated against CMA, JTWC, and RSMC BT datasets is 32.36 km, 39.19 km, and 36.53 km, respectively. The PIVC method results are also compared with Advanced Dvorak Technique (ADT) track product and Automated Rotational Center Hurricane Eye Retrieval used in ADT (ARCHER-ADT) track product. The results show that the accuracy of PIVC positioning center is better than ADT and ARCHER-ADT validated against CMA and RSMC BT datasets. Utilizing only the infrared cloud imagery from geostationary satellites, the PIVC method can achieve positioning performance that are comparable to those achieved by methods integrating microwave data. The PIVC method utilizes temporal information to quantify novel TC characteristics and can also be used to locate other vortex systems.

Land surface temperature (LST) is fundamental for monitoring surface energy balance and environmental dynamics, with remote sensing providing the primary means of acquisition. However, directional anisotropy (DA) introduces systematic bias in satellite-derived LST products, particularly over complex landscapes. This study examines the impact of angular effects on LST retrievals from three leading East Asian geostationary satellites (FengYun 4A, FengYun 4B, and Himawari 9) across Hunan Province, China, using integrated comparison with in situ measurements and reanalysis data. Results show that all products exhibit a systematic cold bias, with FY4B achieving the highest accuracy. Diurnal retrieval precision increases with higher solar zenith angles (SZA), while no consistent relationship is observed between viewing zenith angle (VZA) and retrieval accuracy. Notably, the retrieval bias of the FY4 series increases significantly when the sun and sensor are aligned in azimuth, particularly when the relative azimuth angle (RAA) is less than or equal to 30°. Parametric modeling reveals that emissivity kernel-induced anisotropy is the principal driver of significant LST deviations in central Hunan, while solar kernel effects result in LST overestimation in mountainous regions and underestimation in plains. Increases in elevation or vegetation density reduce emissivity-induced errors but amplify errors caused by shadowing and sunlit effects. Emissivity anisotropy is thus identified as the primary source of LST DA. These findings deepen the understanding of LST DA in remote sensing and provide essential guidance for refining retrieval algorithms and improving the applicability of LST products in complex terrains.

The aim of this work was to reveal the differences in the macro- and microphysical characteristics and precipitation mechanisms of tropical precipitation systems (TPSs) in different regions. Based on the GPM satellite observation from 2014 to 2022, global TPSs were identified, and eight high-frequency areas were defined. Subsequently, their horizontal and vertical development, precipitation characteristics, and microphysical vertical structure were systematically analyzed. The results show that the horizontal development scale of TPSs is mostly between 10⁴ and 10⁵ km², with vertical development exceeding 10 km. The convective area fraction (CAF) ranges from 20% to 60%, and TPSs have a higher CAF and lower vertical development over the ocean than over land. Continental TPSs exhibit significantly stronger vertical development and more intense precipitation in convective cores than oceanic TPSs. The stronger vertical development over land is mainly attributed to stronger updrafts associated with topographic lifting, which further enhances ice-phase microphysical processes and increases ice particle size. Meanwhile, the intensified updrafts also lead to higher collision–coalescence efficiency in the liquid layer, and temperature perturbations over land further enhance turbulent collision efficiency. Together, these processes result in stronger precipitation intensity in the convective cores of continental TPSs. Stratiform regions are characterized by weak precipitation dominated by raindrop breakup with small regional differences. These findings clarify the key land–ocean disparities in TPSs and provide critical observational evidence for optimizing cloud microphysical parameterization schemes in numerical models.

Approaching precipitation forecast refers to the prediction of precipitation within a short time scale, which is usually regarded as a spatiotemporal sequence prediction problem based on radar echo maps. However, due to its reliance on single-image prediction, it lacks good capture of sudden severe convective events and physical constraints, which may lead to prediction ambiguities and issues such as false alarms and missed alarms. Therefore, this study dynamically combines meteorological elements from surface observations with upper-air reanalysis data to establish complex nonlinear relationships among meteorological variables based on multisource data. We design a Residual Spatiotemporal Convolutional Network (ResSTConvNet) specifically for this purpose. In this model, data fusion is achieved through the channel attention mechanism, which assigns weights to different channels. Feature extraction is conducted through simultaneous three-dimensional and two-dimensional convolution operations using a pure convolutional structure, allowing the learning of spatiotemporal feature information. Finally, feature fitting is accomplished through residual connections, enhancing the model’s predictive capability. Furthermore, we evaluate the performance of our model in 0–3 h forecasting. The results show that compared with baseline methods, this network exhibits significantly better performance in predicting heavy rainfall. Moreover, as the forecast lead time increases, the spatial features of the forecast results from our network are richer than those of other baseline models, leading to more accurate predictions of precipitation intensity and coverage area.