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November 2015

Novel data science approaches could drive advances in seasonal to sub-seasonal predictions of precipitation

Predictions at the seasonal to sub-seasonal scale are important for planning and decision-making in a variety of disciplines, and improving understanding and model skill at this timescale is a key research priority. An as yet underexplored approach to sub-seasonal prediction using data science and graph theory methods that are increasingly common to other fields outside of meteorology and climate science shows potential to improve predictions at this challenging timescale.

Early Dynamics of Deep Blue XBT Probes

A new study funded by CPO’s Climate Observations Division was recently published in the Journal of Atmospheric and Oceanic Technology. The paper, “Early Dynamics of Deep Blue XBT Probes,” focuses on the use of  expendable bathythermographs (XBTs)to monitor global ocean heat content, variability of ocean currents, and meridional heat transports.

Objective Diagnostics and the Madden–Julian Oscillation: Application to Moist Static Energy and Moisture Budgets.

Process understanding of the Madden-Julian Oscillation (MJO) has increased dramatically over the past decade, but many observed features of the MJO are not well explained by physical mechanisms believed to underlie the phenomenon. New CVP-supported research published in the Journal of Climate examines Moist Static Energy (MSE) and moisture budgets to understand MJO moisture variations.

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