From a basinwide perspective, the dominant mode of Indian Ocean tropical cyclone genesis (TCG) in September–November (SON) shows an equatorially symmetric east–west zonal dipole pattern, which can explain approximately 13% of the SON TCG variance. This zonal dipole TCG pattern is significantly related to the tripole pattern of the sea surface temperature anomalies (SSTAs) in the tropical Indo-Pacific Ocean (IPT). The IPT, which is a combined interbasin mode and presents a dipole pattern of SSTAs in the tropical Indian Ocean and El Niño–like SSTAs in the tropical Pacific Ocean, can influence the local Walker circulation and zonal dipole TCG pattern over the tropical Indian Ocean. Associated with a positive IPT phase, abnormal ascending (descending) motions are induced and favorable for more (less) water vapor transport to the lower–middle level in the western (eastern) tropical Indian Ocean; significant anticyclonic vorticity anomalies are evoked in the lower level over the eastern tropical Indian Ocean, and weak easterly vertical wind shear appears over the tropical Indian Ocean. Thus, abnormally strong upward motion, abundant water vapor in the lower–middle level, and weak vertical wind shear are favorable for more TCG in the western tropical Indian Ocean, while the combined negative contributions of the vertical motion, lower-level vorticity, and humidity terms result in less TCG in the eastern tropical Indian Ocean.
We use a case study to show that a continuous heavy rainfall process in southern China was closely related to tropical cyclone activity in the Bay of Bengal. The continuous heavy rainfall that occurred in southern China on 11–13 May 2002 can be considered as two different processes. The first process, referred to as a predecessor rain event, occurred over southwestern China before landfall of the tropical cyclone. The second process occurred after dissipation of the tropical cyclone when its remnant caused heavy rainfall that expanded from southwestern China to the middle to lower reaches of the Yangtze–Huaihe river basin. Both of the heavy rainfall processes were closely related to the transport of warm, moist air associated with a tropical cyclone originating over the Bay of Bengal, but the mechanisms in the two processes were quite different. Low-level orographic forcing was the main contributor to the predecessor rain event, whereas baroclinic frontogenesis induced by thermal advection was the main contributor to the tropical cyclone remnant event. Both heavy rainfall events occurred beneath the equatorial entrance of the upper level East Asian subtropical jet.
Postprocess correction is essential to improving the model forecasting result, in which machine learning methods play more and more important roles. In this study, three machine learning (ML) methods of Linear Regression, LSTM-FCN and LightGBM were used to carry out the correction of temperature forecasting of an operational high-resolution model GRAPES-3km. The input parameters include 2 m temperature, relative humidity, local pressure and wind speed forecasting and observation data in Shaanxi province of China from 1 January 2019 to 31 December 2020. The dataset from September 2018 was used for model evaluation using the metrics of root mean square error (RMSE), average absolute error (MAE) and coefficient of determination (R2). All three machine learning methods perform very well in correcting the temperature forecast of GRAPES-3km model. The RMSE decreased by 33%, 32% and 40%, respectively, the MAE decreased by 33%, 34% and 41%, respectively, the R2 increased by 21.4%, 21.5% and 25.2%, respectively. Among the three methods, LightGBM performed the best with the forecast accuracy rate reaching above 84%.
Using tropical cyclone data along with sea surface temperature data (SST) and atmospheric circulation reanalysis data during the period of 1980–2019, the seasonal variation of tropical cyclone genesis (TCG), and the related oceanic and atmospheric environments over the Arabian Sea (AS) and Bay of Bengal (BOB) are compared and analyzed in detail. The results show that TCG in both the BOB and AS present bimodal seasonal variations, with two peak periods in the pre-monsoon and post-monsoon season, respectively. The frequencies of TCG in the BOB and AS are comparatively similar in the pre-monsoon season but significantly different in the post-monsoon season. During the post-monsoon season of October–November, the TCG frequency in the BOB is approximately 2.3 times higher than that of the AS. The vertical wind shear and relative humidity in the low- and middle-level troposphere are the two major contributing factors for TCG, and the combination of these two factors determines the bimodal seasonal cycle of TCG in both the AS and BOB. In the pre-monsoon season, an increase in the positive contribution of vertical wind shear and a decrease in the negative contribution of relative humidity are collaboratively favorable for TCG in the AS and BOB. During the monsoon season, the relative humidity factor shows a significant and positive contribution to TCG, but its positive effect is offset by the strong negative effect of vertical wind shear and potential intensity, thus resulting in very low TCG in the AS and BOB. However, the specific relative contributions of each environmental factor to the TCG variations in the AS and BOB basins are quite different, especially in the post-monsoon season. In the post-monsoon season, the primary positive contributor to TCG in the AS basin is vertical wind shear, while the combined effect of vertical wind shear and relative humidity dominates in the BOB TCG. From the analysis of environmental factors, atmospheric circulations, and genesis potential index (GPI), the BOB is found to have more favorable TCG conditions than the AS, especially in the post-monsoon season.
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