Predicting Major Dry Spells of the Monsoon a Week to Ten Days in Advance

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This project seeks to improve the understanding and prediction of dynamical mechanisms responsible for the formation and maintenance of dry spells of the Indian Summer Monsoon. The investigators will develop means to forecast dry spells of the Indian Summer Monsoon more accurately, on time scales of the order a few weeks, by utilizing a suite of multimodels and the superensemble strategy. This research includes (i) an analysis and calibration of TIGGE (Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble) models; (ii) review of algorithms for downscaling and ensemble methodology; (iii) construction of TIGGE model forecasts and (iv) verification of a scenario of antecedents for the dry spells of the Indian Summer Monsoon. The investigators have examined antecedents of the dry spells that include an eastward extension of the rain belt from west Africa (and the eastern Atlantic Intertropical Convergence Zone). The rains over near equatorial north Africa contribute to a stronger than normal local Hadley Cell, generation of supergradient winds along the westerly subtropical jet of the southern Mediterranean, dynamical conversions of anticyclonic shear vorticity into curvature vorticity. That results in the generation of a deep tropospheric blocking high that spans over the upper half of the troposphere. This high carries a descending lobe of very dry desert air that makes its way to central India during these dry spells. These will be the modeling components of this project.With the large population and agricultural demands on water, the prediction of the dry spells of the monsoon a week to ten days in advance can be of immense socio-economic benefits. In terms of broader impacts, this project has a potential to demonstrate such a capability from a combination of diverse observational and modeling components. It can lead to similar thrusts for other parts of the monsoon world that are affected by similar intraseasonal variability. In addition, this project will train and support one graduate student and one post-doc.