### Distributed Solar: The Democratizaton of Energy

### Blogroll

- Mark Berliner's video lecture "Bayesian mechanistic-statistical modeling with examples in geophysical settings"
- Tim Harford's “More or Less''
- Woods Hole Oceanographic Institution (WHOI)
- Earth Family Beta
- "Impacts of Green New Deal energy plans on grid stability, costs, jobs, health, and climate in 143 countries" (Jacobson, Delucchi, Cameron, et al)
- Label Noise
- Karl Broman
- Dominic Cummings blog
- Dr James Spall's SPSA
- John Cook's reasons to use Bayesian inference

### climate change

- The Carbon Cycle
- Simple box models and climate forcing
- David Appell's early climate science
- "Betting strategies on fluctuations in the transient response of greenhouse warming"
- The Scientific Case for Modern Human-caused Global Warming
- Interview with Wally Broecker
- Équiterre
- Berkeley Earth Surface Temperature
- Professor Robert Strom's compendium of resources on climate change
- US$165/tonne CO2: Sweden

### Archives

### Jan Galkowski

# Category Archives: Azimuth Project

## Complexity vs Simplicity in Geophysics

Originally posted on GeoEnergy Math:

In our book Mathematical GeoEnergy, several geophysical processes are modeled — from conventional tides to ENSO. Each model fits the data applying a concise physics-derived algorithm — the key being the algorithm’s conciseness but not…

Posted in abstraction, American Association for the Advancement of Science, American Meteorological Association, American Statistical Association, Azimuth Project, complex systems, control theory, differential equations, dynamical systems, eigenanalysis, information theoretic statistics, mathematics, Mathematics and Climate Research Network, mechanistic models, nonlinear systems, Paul Pukite, spectra, spectral methods, spectroscopy, theoretical physics, wave equations, WHT
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## “Applications of Deep Learning to ocean data inference and subgrid parameterization”

This is another nail in the coffin of the claim I heard at last year’s Lorenz-Charney Symposium at MIT that machine learning methods would not make a serious contribution to advancements in the geophysical sciences. T. Bolton, L. Zanna, “Applications … Continue reading

Posted in American Meteorological Association, American Statistical Association, artificial intelligence, Azimuth Project, deep learning, deep recurrent neural networks, dynamical systems, geophysics, machine learning, Mathematics and Climate Research Network, National Center for Atmospheric Research, oceanography, oceans, science, stochastic algorithms
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