MPO 674 Lecture 28 4/23/15

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

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

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

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?!