Despite significant advances in seasonal climate forecasts, the reliability of both dynamical and empirical models for the Indian Ocean Dipole (IOD) prediction is still limited to a lead time of one season or less. In this study, the skill of the NCEP Climate Forecast System version 2 (CFSv2) for the IOD prediction during the period 1982-2014 is evaluated. The results indicate that the model performance for the IOD prediction is the worst in spring among the four seasons, which is manifested in the fact that a skilful prediction of spring IOD event is limited to a lead time of only about 1-2 months. To improve the forecast of spring IOD events, a physical-empirical (PE) model and a convolutional neural network (CNN) model are established in the present study. The IOD in April-May-June (AMJ) is taken as the predictand, and the CFSv2-predicted sea surface height (SSH) in AMJ and the observed Laptev sea ice in the preceding December are used as the two predictors. The original CFSv2-predicted IOD time series has an insignificant correlation with the observed IOD time series with a temporal correlation coefficient (TCC) of 0.03; the PE model (CNN model) can largely improve the IOD prediction with a TCC of 0.74 (0.77) between the PE-model-predicted (CNN-model-predicted) IOD and the observed IOD during AMJ. Thus, the PE model and the CNN model developed in the present study can be applied to improve the IOD predictions from numerical models in the future.
In this study, the grounded‐based Ka‐band millimetre‐wave cloud radar (MMCR) is used to analyse the cloud characteristics over the western Tianshan mountains. The cloud top height (CTH) obtained by MMCR is verified by comparing it with the Fengyun‐4A (FY‐4A) observations. Overall, the MMCR‐obtained CTHs are attenuated and lower than the FY‐4A‐obtained CTHs under precipitation conditions. Thus, the FY‐4A data is used to complement MMCR data when there is rainfall. The diurnal, seasonal variation and vertical structures of the cloud are further examined using the combination of MMCR and FY‐4A. The result indicates that the CTH and cloud base height (CBH) are highest in summer and lowest in winter. Also, clouds tend to form frequently at night in spring, summer, and winter. Although in autumn, clouds tend to form most frequently in the morning. This may be related to the diurnal variation of temperature, humidity, wind speed, and wind direction. Moreover, the CTHs occur most frequently at heights of 8–9 km in spring and autumn, 9–10 km in summer, and 7–8 km in winter. The high CTHs caused by the strong convective activities in summer may be related to sufficient water vapour transport. These corrected CTH data are also used to classify cloud types, the results indicate that the proportion of high clouds is highest in summer, while the proportion of medium clouds is lower than in the other three seasons. Also, the average CTH of the low, medium, and high clouds is highest in summer and lowest in winter.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.