Year-end Sale % OFF 
Abstract
Based on the CLoud, Albedo and RAdiation dataset, AVHRR-based, version 2 (CLARA-A2), Tropical Rainfall Measuring Mission 3B43 (TRMM-3B43), and European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) reanalysis data, the potential cloud precipitation capacity (PCPA) of typical regions in China is compared, and the relationship between impact factors and PCPA is discussed. Results have suggested that the Tarim Basin (TB) has scarce cloud water resources, while cloud water path (CWP) values are higher in South China (SC) and Sichuan Basin (SB) under the influence of the East Asian monsoon. Moreover, different typical regions of China exhibit varying dependencies on the ice water path (IWP) and liquid water path (LWP). There is a strong correlation between the IWP and precipitation in the Tibet Plateau (TP), Northeast China (NE), SC, and SB. The precipitation in TB demonstrates a more pronounced correlation with the LWP. Through a comparison of the correlation between PCPA and influencing factors in different typical regions of China, it is found that convective available potential energy (CAPE), surface latent heat flux (SLHF), surface sensible heat flux (SSHF), and 0–3 km relative humidity (RH) exhibit stronger correlation with PCPA than 2 m temperature (T2m) and 2–5 km vertical wind shear (SHEAR). Further investigation revealed that the joint effect of CAPE, RH, and SLHF has a pronounced effect on PCPA, particularly during spring and autumn. Additionally, the PCPA of TP exhibits significant dependency on the joint effect of these three influential factors. Furthermore, the ratio of LWP to IWP (RLI) also affects PCPA. In spring and autumn, the PCPA of TB and NC exhibits a positive correlation with RLI, whereas the PCPA of TP, SC, NE, and SB shows a negative correlation with RLI. In summer, the PCPA of TB and SC exhibits a notably negative correlation with RLI. This study deepens the understanding of the formation mechanism of cloud precipitation in typical regions of China, provides the basis for climate forecast and improves the accuracy of weather forecast.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
