Coastal flooding is a natural disaster that often occurs in coastal areas. Jakarta is an example of a location that is highly vulnerable to coastal flooding. Coastal flooding can result in economic and human life losses. Thus, there is a need for a coastal flooding early warning system in vulnerable locations to reduce the threat to the community and strengthen its resilience to coastal flooding disasters. This study aimed to measure the level of public acceptance toward the development of a coastal flooding early warning system of people who live in a coastal region in Jakarta. This knowledge is essential to ensure that the early warning system can be implemented successfully. A survey was conducted by distributing questionnaires to people in the coastal areas of Jakarta. The questionnaire results were analyzed using cross-tabulation and path analysis based on the variables of knowledge, perceptions, and community attitudes towards the development of a coastal flooding early warning system. The survey result shows that the level of public acceptance is excellent, as proven by the average score of the respondents’ attitude by 4.15 in agreeing with the establishment of an early warning system to manage coastal flooding. Thus, path analysis shows that knowledge and perception have a weak relationship with community attitudes when responding to the coastal flooding early warning model. The results show that only 23% of the community’s responses toward the coastal flooding early warning model can be explained by the community’s knowledge and perceptions. This research is expected to be useful in implementing a coastal flooding early warning system by considering the level of public acceptance.

In this study, we discuss the problem in measuring the risk in a portfolio based on value at risk (VaR) using asymmetric GJR-GARCH Copula. The approach based on the consideration that the assumption of normality over time for the return can not be fulfilled, and there is non-linear correlation for dependent model structure among the variables that lead to the estimated VaR be inaccurate. Moreover, the leverage effect also causes the asymmetric effect of dynamic variance and shows the weakness of the GARCH models due to its symmetrical effect on conditional variance. Asymmetric GJR-GARCH models are used to filter the margins while the Copulas are used to link them together into a multivariate distribution. Then, we use copulas to construct flexible multivariate distributions with different marginal and dependence structure, which is led to portfolio joint distribution does not depend on the assumptions of normality and linear correlation. VaR obtained by the analysis with confidence level 95% is 0.005586. This VaR derived from the best Copula model, t-student Copula with marginal distribution of t distribution.

The Maritime Continent (MC) is the rainiest region on Earth, where extreme precipitation constitutes a major hazard. Convectively coupled Kelvin waves (CCKWs) are weather systems that travel eastwards across the equatorial waveguide and can trigger convection in their convergent phase. CCKWs are linked to up to a fourfold increase in precipitation rates across the equatorial Maritime Continent (Ferrett et al., 2020). However, not all CCKWs produce precipitation extremes. Recent studies reveal that CCKWs arriving in phase with the local diurnal cycle of convection may be more likely to cause high impact weather events or when part of a multiscale interaction with the MJO, organising the large-scale precipitation on a more localised scale (Baranowski et al., 2016; Baranowski et al., 2020; Latos et al., 2021; Senior et al., 2023).Current methods for studying these mechanisms have some limitations. For example, composite studies of CCKWs are useful for revealing statistical links but smooth out key interactions. Case studies are useful for identifying mechanisms in particular high-impact weather events but are difficult to generalise. Modelling such high-impact weather events provides additional insights; however, lacks the capability for fine-tuning.Hence, we have developed a methodology for introducing synthetic CCKWs into convection-permitting Met Office Unified Model (MetUM) forecasts. This involves generating 3D CCKW structures on key dynamical fields using ERA5 data and adding these to the model’s initial conditions. The methodology will be presented, and a comparison of diagnostics from control and perturbation experiments will be provided. We will then discuss how the methodology will be applied to studying the mechanisms through which CCKWs cause precipitation extremes across various locations in the MC. Since CCKWs are an important dynamical predictor of extreme precipitation, understanding these mechanisms is crucial for providing accurate forecasts of hazardous weather in the MC.Baranowski, D.B. et al., (2016) Phase locking between atmospheric convectively coupled equatorial Kelvin waves and the diurnal cycle of precipitation over the Maritime Continent. Geophysical Research Letters, 43(15), 8269–8276. https://doi.org/10.1002/2016GL069602.Baranowski, D.B. et al., (2020) Social-media and newspaper reports reveal large-scale meteorological drivers of floods on Sumatra. Nature Communications, 11, 2503. https://doi.org/10.1038/s41467-020-16171-2.Ferrett, S. et al., (2020) Linking extreme precipitation in Southeast Asia to equatorial waves. Quarterly Journal of the Royal Meteorological Society, 146(727), 665–684. https://doi.org/10.1002/qj.3699.Latos, B. et al., (2021) Equatorial waves triggering extreme rainfall and floods in Southwest Sulawesi, Indonesia. Monthly Weather Review, 149(5), 1381–1401. https://doi.org/10.1175/MWR-D-20-0262.1.Senior, N.V. et al., (2023) Extreme precipitation at Padang, Sumatra triggered by convectively coupled Kelvin waves. Quarterly Journal of the Royal Meteorological Society, 149(755), 2281–2300. https://doi.org/10.1002/qj.45