This study attempts dynamical downscaling to improve north Indian ocean (NIO) tropical cyclone prediction from a global multimodel ensemble prediction system using weather research and forecasting (WRF) model. A total of 16 ensembles are used in the WRF simulations, these ensembles are biascorrected prior to downscaling for model climatological errors. The ensemble mean constructed from the output of all downscaled ensembles is analyzed for added value to global predictions. This mean is also compared against observation as well as high-resolution (12 km) deterministic forecast from global forecast system (GFS). Two devastating NIO tropical cyclone cases of year 2017 which were not reliably predicted by global systems have been selected for this study. The results show that downscaled predictions well simulate the intensity and spatial distribution of the rainfall and relative vorticity associated with these cyclonic storms. The wind and temperature vertical profiles during cyclone mature stage are also captured more accurately than raw prediction and high-resolution global deterministic forecast. The study affirms the adequacy of dynamical downscaling in predicting the cyclonic storms over global real-time weather forecasting system.

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In an endeavor to design better forecasting tools for real-time prediction, the present work highlights the strength of the multi-model multi-physics ensemble over its operational predecessor version. The exiting operational extended range prediction system (ERPv1) combines the coupled, and its bias-corrected sea-surface temperature forced atmospheric model running at two resolutions with perturbed initial condition ensemble. This system had accomplished important goals on the sub-seasonal scale skillful forecast; however, the skill of the system is limited only up to 2 weeks. The next version of this ERP system is seamless in resolution and based on a multi-physics multi-model ensemble (MPMME). Similar to the earlier version, this system includes coupled climate forecast system version 2 (CFSv2) and atmospheric global forecast system forced with real-time bias-corrected sea-surface temperature from CFSv2. In the newer version, model integrations are performed six times in a month for real-time prediction, selecting the combination of convective and microphysics parameterization schemes. Additionally, more than 15 years hindcast are also generated for these initial conditions. The preliminary results from this system demonstrate appreciable improvements over its predecessor in predicting the large-scale low variability signal and weekly mean rainfall up to 3 weeks lead. The subdivision-wise skill analysis shows that MPMME performs better, especially in the northwest and central parts of India.

The intra-seasonal uctuations of Indian summer monsoon rainfall (ISMR) are mainly controlled by northward propagating Monsoon Intra-seasonal Oscillation (MISO) and eastward propagating Madden Julian Oscillation (MJO). In the current study, we examine the relationship between the intra-seasonal uctuations (active and break spells) of ISMR with the phase propagation and amplitude of MISO and MJO. We notice that active spells generally occur during MISO phase 2-5 (MJO phase 3-6), and break spells mainly occur during MISO phase 6-8 (MJO phase 6-8 and 1). The association of active/break spells with MISO phases is more prominent than with MJO phases. We show the phase composite of un ltered and regression based reconstructed rainfall for eight MISO and MJO phases, and the same is consistent with the earlier ndings. We notice that the reconstructed eld shows a systematic and wellorganised northward propagation compared to the un ltered eld. Phase composite also indicates that there is a lead-lag relationship between MISO and MJO phases. MISO phase composite shows more robust northward propagation than the MJO phase composite. MISO reconstructed rainfall explained more percentage variance than MJO reconstructed rainfall with reference to 20-90 days ltered rainfall.It is found that long active (> 7 days) predominantly occurs when either MISO or MJO, or both of them are active, and the associated signal is somewhere in between phase 2-5. A long break occurs when either one or both of them are feeble, or even though associated signals are strong, they are primarily located in phases 1, 6, 7 and 8.

The seamless forecast approach of subseasonal to seasonal scale variability has been succeeding in the forecast of multiple meteorological scales in a uniform framework. In this paradigm, it is hypothesized that reduction in initial error in dynamical forecast would help to reduce forecast error in extended lead-time up to 2-3 weeks. This is tested in a version of operational extended range forecasts based on Climate Forecast System version 2 (CFSv2) developed at Indian Institute of Tropical Meteorology (IITM), Pune. Forecast skills are assessed to understand the role of initial errors on the prediction skill for MJO. A set of lowest and highest initial day error (LIDE & HIDE) cases are defined and the error-growth for these categories are analysed for the strong MJO events during May to September (MJJAS). The MJO forecast initial errors are categorized and defined using the well-known multivariate MJO index introduced by Wheeler & Hendon (2004). The probability distribution of bivariate RMSE and error growth evolution (first order difference of index error for each successive lead days) with respect to extended range lead-time are used as metrics in this analysis. The result showed that initial error is not showing any influence in the skill of model after a lead time of 7-10 days and the error growth remains the same for both set of errors. A rapid error growth evolution of same order is seen for both the classified cases. Further the physical attribution of these errors are studied and found that the errors originate from the events with initial phase in Western Pacific and Indian Ocean. The spatial distribution of OLR and the zonal winds also confirms the same. The study emphasise the importance of better representation of MJO phases especially over Indian ocean in the model to improve the MJO prediction rather than focusing primarily on the initial conditions.

Flawless subseasonal prediction of tropical cyclogenesis and evolution over the narrow basin of North Indian Ocean (NIO) demands accurate rendition of the crucial parameters that influence the development of cyclonic storms. While many genesis potential indices are used for climatological monitoring and prediction of cyclogenesis globally, their skill in subseasonal prediction of individual storm development, especially near coastlines are limited. Thus, an improved genesis potential parameter (IGPP) is introduced in this study which can capture both cyclogenesis and daily evolution of cyclonic systems over NIO. The IGPP is a revised version of Kotal-Genesis Potential Parameter (KGPP) implemented by India Meteorological Department (IMD) for short-range operational cyclogenesis prediction over NIO. Daily averaged ERA-5 and ERA-Interim data sets are used for analysis and comparison of selected cyclonic storms over NIO for the period 1989-2018. Results reveal that false alarms and overestimation of values present in KGPP are remarkably reduced by using IGPP for all the analyzed storms. Moreover, IGPP outperforms KGPP in distinguishing between developing and nondeveloping storms by accurately representing the storm genesis, evolution, rapid intensification and intensity variations. Thus IGPP can be implemented operationally for improving the real-time prediction of cyclogenesis and storm evolution over NIO. Plain Language Summary Accurate representation of weather parameters that are crucial for the formation and development of tropical cyclones over a small basin like North Indian Ocean (NIO) is critical for biweekly storm predictions. Most studies utilize different cyclogenesis parameters in genesis potential indices to capture annual/interannual frequency of cyclone formations around the globe. While these parameters are successful in studying the average storm genesis and frequencies at longer time scales, forecasts in real-time over a landlocked and narrow basin such as NIO often fail to capture rapid storm development. This study introduces an improved parameter for capturing the genesis and daily evolution of individual storms thus modifying the presently used parameter by India Meteorological Department thus making it more suitable for NIO. Comparison of old and improved parameters using daily averaged reanalysis data sets reveal that new parameter is better than the older one with accurate representation of storm evolution and noticeable reduction in false storm signals present in the old parameter for all the storm cases analyzed here.

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