mpo 674 lecture 28 4/23/15. the course on one slide 1. intro: numerical models, ensembles, the...
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MPO 674 Lecture 28
4/23/15
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The course on one slide
• 1. Intro: numerical models, ensembles, the science of prediction
• 2. Lorenz 1963, 1965, 1969, Error Growth, TLMs, Adjoints, SVs, EOFs, Ensemble Methods
• 3. State Estimation: Bayes, old DA, objective analysis, OI, 3d-Var, 4d-Var, EnKFs, Hybrids
• 4. Applications: polynomial chaos, targeted observations, observation sensitivity and impact, mesoscale and tropical predictability
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What we didn’t cover
• Linear Inverse Modeling– Extraction of dynamical properties of a system based on
observed statistics– Split model into non-linear part and a linear, stochastic
component predicted statistics• Theoretically superior (but practically cumbersome) non-
linear DA schemes– Particle filters, direct implementation of Bayes
• Information theory– Entropy; transmission of information over noisy channel
• Parameter estimation• Lagrangian predictability and DA
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Predictability: Future Scientific Directions (Hacker et al., BAMS 2005)
• Initial-condition error and model error– Synergy between their error sources– How to quantify it statistically?
• Importance of the norm– Traditionally global 500 hPa Z– Focus more on subspace and user needs– Norm-insensitive results?
• Towards generalization across disciplines– Hierarchical approach has mostly worked for basic
geophysical systems– Coupled atm-ocean; ecological; biological, other?– Seek different bases for system classification
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Future directions
• Uncertainty using full PDFs• Quantifying predictability on convective-scale
and mesoscale• Timescales beyond 2 weeks: coupled atm-
ocean, seasonal, climate … also coastal ocean• Very short time scales – assimilation of smart
phone data, (very) rapid state estimation• Impact-based studies – what is the
predictability of your road flooding?!