Global climate change, especially extreme drought events, presents a complicated challenge to humanity and Earth’s system in the 21st century. As an extremely important carbon sink region in China, Southwest China has encountered frequent drought disasters in recent decades. It is critical to explore the frequency, duration, severity, and other associated characteristics of drought events as well as their spatial and temporal patterns in the region from a long-term perspective. In this study, we used the latest dataset from the Spanish National Research Council (CSIC) between 1901 and 2018 to extract all drought events by calculating the standardized anomaly of the Standardized Precipitation Evapotranspiration Index (SPEI). Theil–Sen median trend analysis, the Mann–Kendall test, and the moving t-test were used to reveal the spatial trend and mutation point of drought severity. The results showed that (1) The standardized anomaly of the 3-month SPEI can accurately identify drought events in Southwest China. In total, 72 drought events occurred during this period, of which the consecutive drought in autumn, winter, and spring from 2009 to 2010 lasted the longest, having the most substantial severity and the most extensive damage range. (2) Drought events mainly started in spring and early summer and ended in autumn and winter. The distribution of drought was the most expansive and the drought severity was the most serious in September. (3) In terms of spatial pattern, Guangxi has the highest frequency of drought events, with some areas experiencing up to 100 events. The average duration of drought events ranged between 3.5 and 5.5 months, with most lasting for 4–5 months. The most severe drought areas are mainly concentrated in southern Sichuan and western Yunnan. Overall, the severity of drought events in the west were generally higher compared to that in the east. (4) Over the past 120 years, most of the region (82.46%) showed an increasing trend in drought severity, with a slope of up to −0.01. About 15.12% of the areas exhibited a significant drying trend (p < 0.05), particularly in southern Sichuan, eastern Guizhou, and northern and southern Yunnan. Such analyses can serve as a scientific foundation for developing drought prevention and mitigation measures as well as exploring how drought events affect the structure and function of terrestrial ecosystems in Southwest China.

It remains challenging to accurately classify complicated clouds owing to the various types of clouds and their distribution on multiple layers. In this paper, multi-band radiation information from the geostationary satellite Himawari-8 and the cloud classification product of the polar orbit satellite CloudSat from June to September 2018 are investigated. Based on sample sets matched by two types of satellite data, a random forest (RF) algorithm was applied to train a model, and a retrieval method was developed for cloud classification. With the use of this method, the sample sets were inverted and classified as clear sky, low clouds, middle clouds, thin cirrus, thick cirrus, multi-layer clouds and deep convection (cumulonimbus) clouds. The results indicate that the average accuracy for all cloud types during the day is 88.4%, and misclassifications mainly occur between low and middle clouds, thick cirrus clouds and cumulonimbus clouds. The average accuracy is 79.1% at night, with more misclassifications occurring between middle clouds, multi-layer clouds and cumulonimbus clouds. Moreover, Typhoon Muifa from 2022 was selected as a sample case, and the cloud type (CLT) product of an FY-4A satellite was used to examine the classification method. In the cloud system of Typhoon Muifa, a cumulonimbus area classified using the method corresponded well with a mesoscale convective system (MCS). Compared to the FY-4A CLT product, the classifications of ice-type (thick cirrus) and multi-layer clouds are effective, and the location, shape and size of these two varieties of cloud are similar.

Typhoon Saola was the ninth typhoon that generated over the Western North Pacific (WNP) in 2023, and it caused severe storm impacts. However, its complex moving track and heavy intensity made it extremely difficult to forecast; therefore, detailed analysis is necessary. In this study, GPM, Himawari-9, and FY-4B satellite data were used to analyze the characteristics of the structure, brightness temperature, and precipitation of the typhoon cloud system. Our results showed that, in the 89 and 183 GHz channels of GPM-1CGMI, the brightness temperature of the typhoon eye was 80–90 K higher than that of the eye wall, and the strong convective areas below 200 K were clearer in these high-frequency channels. GPM-2ADPR estimated heavy rain (over 30 mm/h) area, storm height (5 km), and vertical precipitation rate (30–40 mm/h) more accurately than the GPM-2Aka and GPM-2Aku products. Himawari-9 satellite data showed that the brightness temperature of the eye wall and spiral cloud bands was 180–200 K, the typhoon eye was small and round, and strong convective activities were mostly located in the southwest side of the center. The FY-4B CLP and CLT products showed that, in the mature period of the typhoon, the percentage of supercooled and mixed clouds first stabilized and then rapidly decreased. The trends observed among the three types of ice-phase clouds were characterized by an initial increase, followed by a decrease, and then another increase, with percentages between 10% and 25%, 5% and 15%, and 15% and 30%, respectively.