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?!