The southwest vortex (SWV) is a critical weather system in China, but our knowledge of this system remains incomplete. Here, we investigate the cloud properties in the SWV. First, we search for the SWVs with time steps and center locations that are consistent between the SWV yearbook and ERA-Interim reanalysis data. Second, we supplement these SWVs’ life spans and movement paths. Third, we relocate the Fengyun (FY) satellite FY-4A cloud retrievals in the 10° × 10° region centered on each SWV and analyze the cloud occurrence frequency (COF), cloud-top height (CTH), and cloud optical thickness (COT). A distribution mode of cloud types is summarized from the COFs, with water clouds, supercooled clouds, mixed clouds, ice clouds, cirrus clouds, and overlap clouds occurring sequentially from west to east. The CTH probability density (PD) distribution features a significant north–south difference. In addition, the COT PD distributions exhibit a common trend: with increasing COT, the PD increases rapidly and then slowly before peaking, whereupon the PD decreases abruptly. From spring to summer, the region with the highest convective COF shifts from the northeast to the northwest, and an east–west gradient of the convective COF appears in autumn and winter. Furthermore, we investigate the cloud properties during SWV-related heavy rainfall. Heavy rain occurs mainly in the west of the SWV, and convective clouds are mainly in the northwest, partly in the southwest and near the SWV center. The average CTH in heavy rainfall is generally higher than 6 km, and the average COT is greater than 20. Significance Statement The southwest vortex (SWV) is an important weather system in China. However, we do not yet comprehensively know this weather system. The cloud properties can indicate the structures of weather systems and are key parameters in numerical weather prediction (NWP) models. Thus, investigating cloud properties is necessary and meaningful to understand the SWV and accurately predict SWV-related precipitation in NWP models. In this paper, a typical distribution mode of six cloud types in the SWV is summarized from the cloud occurrence frequency, and the distribution features of convective clouds, cloud-top height, and cloud optical thickness in the SWV are analyzed. Furthermore, the cloud properties in SWV-related heavy rain are also studied.
Abstract. In order to study the on-board processing technology of meteorological satellites, a decision tree cloud detection algorithm is proposed by taking FY-4A satellite data as an example. According to the channel setting of the Advanced Geosynchronous Radiation Imager (AGRI) on FY-4A satellite, the 0.65 μm, 1.375 μm, 3.75 μm, and 10.7 μm bands are selected as the cloud detection channels, and the reflectance, brightness temperature or bright temperature difference of the four channels are used as the cloud detection indicators, the thresholds of the four cloud detection indicators are obtained through statistics. On this basis, the decision tree cloud detection model is constructed and validated using FY-4A satellite data. The results show that the algorithm is simple, convenient and efficient, and the overall effect of cloud detection is good. It is an effective way for meteorological satellite cloud detection on-board processing technology.
Cloud top height (CTH) indicates the vertical development of clouds. Intensely vertically developing clouds are usually accompanied by extreme weather systems and pose a threat to aviation safety. Therefore, nowcast for CTH is necessary and meaningful to guide aviation flights. In this study, we researched the nowcast for CTH (mainly within 0–2 h) based on deep learning algorithms. With Sichuan Province as the study area, we collected CTH data of Himawari‐8 satellite from 2018 to 2020. Convolutional‐long‐short‐term‐memory (ConvLSTM) and trajectory‐gated‐recurrent‐unit (TrajGRU) were used to build nowcast models in the encoder‐forecaster framework. The optical flow model and persistence were used as benchmarks. The results showed that the deep learning models did not have significant advantages over the benchmarks in the first 20 min. However, with increasing nowcast time, the nowcast skills of the deep learning models were gradually exhibited. For all four seasons, the TrajGRU‐based model showed superior performance over the ConvLSTM‐based model and the benchmarks. In spring, autumn and winter, the results yielded by the ConvLSTM‐based model were second only to those of the TrajGRU‐based model. However, in summer, the ConvLSTM‐based model did not outperform the persistence. The results of the optical flow model worsened significantly with increasing nowcast time. In contrast to the persistence, the optical flow model had almost no nowcast skills after 40 min.
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