ICTS

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I will discuss two fundamental systems in moist geophysical fluid dynamics: (1) The moist shallow water equations, giving rise to a theory of the MJO, and (2) A idealised system of moist convection, the 'Rainy-Benard Model'.

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Interannual variability in Asian Summer Monsoon rainfall features two modes that are correlated with concurrent and preceding winter ENSO, respectively. The latter post-ENSO effect is especially puzzling. The lecture explains how coupled ocean-atmospheric modes affect the Asian Summer Monsoon.

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This talk will summarize our current thinking on the role of ocean-atmosphere coupling to intraseasonal oscillations including the Madden-Julian oscillation and its boreal summer counterpart. The importance of surface flux and SST variability for maintenance and propagation of intraseasonal oscillations will be examined, including insights derived from models. The issue of how coupling affects models simulations of intraseasonal variability will be examined, including whether it is mean state changes caused by the act of coupling versus the active role of coupling for improving the simulation of intraseasonal oscillations. Cross scale interactions between intraseasonal oscillations and other modes of coupled tropical variability will also be examined, including the Indian Ocean Dipole and El Nino-Southern Oscillation. An upcoming field program in the tropical Pacific to better understand some of these cross-scale interactions will also be highlighted.

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In this talk, the moist physical processes associated with extreme precipitation will be discussed and the lack of representation of moist dynamics in numerical model will be emphasized. It will be shown with specific example the current status of forecasting extreme by NWP models and also by data driven models. The talk will throw insights on the gaps that can be addressed for possible improvement of moist dynamics vis-a-vis the fidelity of models in capturing extreme precipitation

In this plenary lecture, I will discuss various aspects of seasonal and decadal monsoon forecasting for the Indian monsoon. In the initial years, the seasonal forecasts for the Indian summer monsoon rainfall (ISMR) are based on statistical methods using predictors representing the monsoon teleconnections. In 2017, the India Meteorological Department (IMD) introduced the statistical ensemble forecasting system, which was used by IMD for operational forecasts. With the rapid advancements in coupled modelling strategies and tools, the seasonal forecasts for monsoon rainfall are now produced using coupled ocean-atmosphere models. In 2021, IMD introduced multi-model ensemble forecasts based on different dynamical models. These models have shown more skill compared to the current statistical models. However, the current seasonal forecast skill is still far below the potential predictability. The ways in which the seasonal forecasts can be further improved will be discussed. Since the monsoon precipitation exhibits significant multi-decadal variability, attempts are being made to predict the decadal variability of the monsoon precipitation. However, recent results suggest that the decadal prediction for south Asian monsoon region is currently not very skilful.

Climate Scientists are increasingly turning to Machine Learning to answer various questions in their domain. In this talk, we will discuss a few typical problems related to Indian Monsoon, and discuss ML-based approaches for them. Specifically, we will focus on multi-scale forecasting, downscaling, and attribution to large-scale drivers.

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