Prediction of Monsoon Intra-Seasonal Oscillations using high-resolution coupled modeling and data-assimilation
Abstract
The active/break periods of the Indian Summer Monsoon rain manifests as strong north-ward propagating convective rainbands and is the main driver of intra-seasonal variability in the precipitation over the south Asian monsoon region. The monsoon intra-seasonal oscillation [MISO] influences many different weather and climate phenomenon both locally in the Indian Ocean region as well as the remotely via atmospheric teleconnections into the mid-latitudes and other ocean basins. The processes controlling MISO characteristics a"re still not well understood. Improvements in weather and climate modeling, such as improved physics parameterization, higher model"" resolutions, over the past few decades have also improved the simulation and predictions of MISO significantly. Yet, the current we"ather prediction models are still limited in their prediction skill of the MISO compared to the potential predictability of the MISO" events. Processes relevant to MISO simulations such as the coupled feedbacks in the northern Indian Ocean, interactions with the Fi""ndlater jet, and the large-scale Indian monsoon circulation are inadequately represented in regional and global coupled models. Thes"e inaccuracies could be related to the either poor parameterization of model physics or insufficient model resolution to resolve the" critical processes. New efforts in observations, process understanding and translation into weather and climate models are necessar"y for improvements in simulation and prediction of the MISO. There is also need for identify-cation of sources of predictability in" the region for MISO events on the subseasonal timescale. Hence, targeted experiments to help identify these sources will help impro"ve capabilities to predict MISO events further. We propose to address the key goals of the MISO-BOB DRI relevant to improving the understanding and prediction of the MISO events using a regional coupled ocean-atmosphere model and data assimilation tools. We will" design a regional coupled ocean-atmosphere forecasting tool with data assimilation for improved process understanding, identify sou"rces of predictability as well as better design of observation networks in the region. We will also collaborated with other scientists in the DRI to optimally use observations as well as global modeling studies for better understanding and prediction of the MISO events.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Sep 29, 2017
- Source ID
- N000141712865
Entities
People
- Arthur J. Miller
Organizations
- Office of Naval Research
- United States Navy
- University of California, San Diego