### Distributed Solar: The Democratizaton of Energy

### Blogroll

- The Alliance for Securing Democracy dashboard
- "Impacts of Green New Deal energy plans on grid stability, costs, jobs, health, and climate in 143 countries" (Jacobson, Delucchi, Cameron, et al) Global warming, air pollution, and energy insecurity are three of the greatest problems facing humanity. To address these problems, we develop Green New Deal energy roadmaps for 143 countries.
- Beautiful Weeds of New York City
- Earth Family Alpha Michael Osborne’s blog (former Executive at Austin Energy, now Chairman of the Electric Utility Commission for Austin, Texas)
- GeoEnergy Math Prof Paul Pukite’s Web site devoted to energy derived from geological and geophysical processes and categorized according to its originating source.
- BioPython A collection of Python tools for quantitative Biology
- American Statistical Association
- distributed solar and matching location to need
- Carl Safina's blog One of the wisest on Earth
- Mike Bloomberg, 2020 He can get progress on climate done, has the means and experts to counter the Trump and Republican digital disinformation machine, and has the experience, knowledge, and depth of experience to achieve and unify.
- Rasmus Bååth's Research Blog Bayesian statistics and data analysis
- Gabriel's staircase
- Lenny Smith's CHAOS: A VERY SHORT INTRODUCTION This is a PDF version of Lenny Smith’s book of the same title, also available from Amazon.com
- Mertonian norms
- Tony Seba Solar energy, electric vehicle, energy storage, and business disruption professor and visionary
- Flettner Rotor Bruce Yeany introduces the Flettner Rotor and related science
- Busting Myths About Heat Pumps Heat pumps are perhaps the most efficient heating and cooling systems available. Recent literature distributed by utilities hawking natural gas and other sources use performance figures from heat pumps as they were available 15 years ago. See today’s.
- The Keeling Curve: its history History of the Keeling Curve and Charles David Keeling
- Earle Wilson
- Mark Berliner's video lecture "Bayesian mechanistic-statistical modeling with examples in geophysical settings"
- Ives and Dakos techniques for regime changes in series
- Healthy Home Healthy Planet
- Prediction vs Forecasting: Knaub “Unfortunately, ‘prediction,’ such as used in model-based survey estimation, is a term that is often subsumed under the term ‘forecasting,’ but here we show why it is important not to confuse these two terms.”
- Awkward Botany
- James' Empty Blog
- The Mermaid's Tale A conversation about biological complexity and evolution, and the societal aspects of science
- Dr James Spall's SPSA
- SASB Sustainability Accounting Standards Board
- Patagonia founder Yvon Chouinard on how businesses can help our collective environmental mess Patagonia’s Yvon Chouinard set the standard for how a business can mitigate the ravages of capitalism on earth’s environment. At 81 years old, he’s just getting started.
- Subsidies for wind and solar versus subsidies for fossil fuels
- Woods Hole Oceanographic Institution (WHOI)
- Team Andrew Weinberg Walking September 8th for the Jimmy Fund!
- Gavin Simpson
- Professor David Draper
- Brendon Brewer on Overfitting Important and insightful presentation by Brendon Brewer on overfitting
- Label Noise
- Higgs from AIR describing NAO and EA Stephanie Higgs from AIR Worldwide gives a nice description of NAO and EA in the context of discussing “The Geographic Impact of Climate Signals on European Winter Storms”
- Dominic Cummings blog Chief advisor to the PM, United Kingdom
- The Plastic Pick-Up: Discovering new sources of marine plastic pollution
- Why It’s So Freaking Hard To Make A Good COVID-19 Model Five Thirty Eight’s take on why pandemic modeling is so difficult
- Hermann Scheer Hermann Scheer was a visionary, a major guy, who thought deep thoughts about energy, and its implications for humanity’s relationship with physical reality
- Slice Sampling
- Ted Dunning
- American Association for the Advancement of Science (AAAS)
- Tim Harford's “More or Less'' Tim Harford explains – and sometimes debunks – the numbers and statistics used in political debate, the news and everyday life
- John Cook's reasons to use Bayesian inference
- Dollars per BBL: Energy in Transition
- Simon Wood's must-read paper on dynamic modeling of complex systems I highlighted Professor Wood’s paper in https://hypergeometric.wordpress.com/2014/12/26/struggling-with-problems-already-attacked/
- ggplot2 and ggfortify Plotting State Space Time Series with ggplot2 and ggfortify
- WEAPONS OF MATH DESTRUCTION Cathy O’Neil’s WEAPONS OF MATH DESTRUCTION,

### climate change

- James Hansen and granddaughter Sophie on moving forward with progress on climate
- The HUMAN-caused greenhouse effect, in under 5 minutes, by Bill Nye
- Interview with Wally Broecker Interview with Wally Broecker
- Ellenbogen: There is no Such Thing as Wind Turbine Syndrome
- Bloomberg interactive graph on “What's warming the world''
- Isaac Held's blog In the spirit of Ray Pierrehumbert’s “big ideas come from small models” in his textbook, PRINCIPLES OF PLANETARY CLIMATE, Dr Held presents quantitative essays regarding one feature or another of the Earth’s climate and weather system.
- MIT's Climate Primer
- weather blocking patterns
- Paul Beckwith Professor Beckwith is, in my book, one of the most insightful and analytical observers on climate I know. I highly recommend his blog, and his other informational products.
- Équiterre Equiterre helps build a social movement by encouraging individuals, organizations and governments to make ecological and equitable choices, in a spirit of solidarity.
- Jacobson WWS literature index
- "Climate science is setttled enough"
- Climate change: Evidence and causes A project of the UK Royal Society: (1) Answers to key questions, (2) evidence and causes, and (3) a short guide to climate science
- Sea Change Boston
- Skeptical Science
- Exxon-Mobil statement on UNFCCC COP21
- Nick Bower's "Scared Scientists"
- “Ways to [try to] slow the Solar Century''
- "Warming Slowdown?" (part 1 of 2) The idea of a global warming slowdown or hiatus is critically examined, emphasizing the literature, the datasets, and means and methods for telling such. In two parts.
- Simple box models and climate forcing IMO one of Tamino’s best posts illustrating climate forcing using simple box models
- Model state level energy policy for New Englad Bob Massie’s proposed energy policy for Massachusetts, an admirable model for energy policy anywhere in New England
- Anti—Anti-#ClimateEmergency Whether to declare a climate emergency is debatable. But some critics have gone way overboard.
- Rabett Run Incisive analysis of climate science versus deliberate distraction
- HotWhopper: It's excellent. Global warming and climate change. Eavesdropping on the deniosphere, its weird pseudo-science and crazy conspiracy whoppers.
- The Scientific Case for Modern Human-caused Global Warming
- Jacobson WWS literature index
- "Getting to the Energy Future We Want," Dr Steven Chu
- The great Michael Osborne's latest opinions Michael Osborne is a genius operative and champion of solar energy. I have learned never to disregard ANYTHING he says. He is mentor of Karl Ragabo, and the genius instigator of the Texas renewable energy miracle.
- "Warming Slowdown?" (part 2 of 2) The idea of a global warming slowdown or hiatus is critically examined, emphasizing the literature, the datasets, and means and methods for telling such. The second part.
- Earth System Models
- Spectra Energy exposed
- Climate model projections versus observations
- Andy Zucker's "Climate Change and Psychology"
- Climate Change: A health emergency … New England Journal of Medicine Caren G. Solomon, M.D., M.P.H., and Regina C. LaRocque, M.D., M.P.H., January 17, 2019 N Engl J Med 2019; 380:209-211 DOI: 10.1056/NEJMp1817067
- Tamino's Open Mind Open Mind: A statistical look at climate, its science, and at science denial
- The beach boondoggle Prof Rob Young on how owners of beach property are socializing their risks at costs to all of us, not the least being it seems coastal damage is less than it actually is
- History of discovering Global Warming From the American Institute of Physics.
- Wally Broecker on climate realism
- The net average effect of a warming climate is increased aridity (Professor Steven Sherwood)
- Climate Change Denying Organizations
- Mrooijer's Global Temperature Explorer
- The Keeling Curve The first, and one of the best programs for creating a spatially significant long term time series of atmospheric concentrations of CO2. Started amongst great obstacles by one, smart determined guy, Charles David Keeling.
- `Who to believe on climate change': Simple checks By Bart Verheggen
- World Weather Attribution
- "Impacts of Green New Deal energy plans on grid stability, costs, jobs, health, and climate in 143 countries" (Jacobson, Delucchi, Cameron, et al) Global warming, air pollution, and energy insecurity are three of the greatest problems facing humanity. To address these problems, we develop Green New Deal energy roadmaps for 143 countries.
- James Powell on sampling the climate consensus
- Agendaists Eli Rabett’s coining of a phrase
- An open letter to Steve Levitt
- AIP's history of global warming science: impacts The American Institute of Physics has a fine history of the science of climate change. This link summarizes the history of impacts of climate change.
- All Models Are Wrong Dr Tamsin Edwards blog about uncertainty in science, and climate science

### Archives

### Jan Galkowski

# Category Archives: R

## Phase Plane plots of COVID-19 deaths *with uncertainties*

I. Introduction. It’s time to fulfill the promise made in “Phase plane plots of COVID-19 deaths“, a blog post from 2nd May 2020, and produce the same with uncertainty clouds about the functional trajectories(*). To begin, here are some assumptions … Continue reading

Posted in American Statistical Association, Andrew Harvey, anomaly detection, count data regression, COVID-19, dependent data, dlm package, Durbin and Koopman, dynamic linear models, epidemiology, filtering, forecasting, Kalman filter, LaTeX, model-free forecasting, Monte Carlo Statistical Methods, numerical algorithms, numerical linear algebra, population biology, population dynamics, prediction, R, R statistical programming language, regression, statistical learning, stochastic algorithms
Tagged prediction intervals
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## Reanalysis of business visits from deployments of a mobile phone app

Updated, 20th October 2020 This reports a reanalysis of data from the deployment of a mobile phone app, as reported in: M. Yauck, L.-P. Rivest, G. Rothman, “Capture-recapture methods for data on the activation of applications on mobile phones“, Journal … Continue reading

Posted in Bayesian computational methods, biology, capture-mark-recapture, capture-recapture, Christian Robert, count data regression, cumulants, diffusion, diffusion processes, Ecological Society of America, ecology, epidemiology, experimental science, field research, Gibbs Sampling, Internet measurement, Jean-Michel Marin, linear regression, mark-recapture, mathematics, maximum likelihood, Monte Carlo Statistical Methods, multilist methods, multivariate statistics, non-mechanistic modeling, non-parametric statistics, numerics, open source scientific software, Pierre-Simon Laplace, population biology, population dynamics, quantitative biology, quantitative ecology, R, R statistical programming language, sampling, sampling algorithms, segmented package in R, statistical ecology, statistical models, statistical regression, statistical series, statistics, stepwise approximation, stochastic algorithms, surveys, V. M. R. Muggeo
1 Comment

## On odds of storms, and extreme precipitation

People talk about “thousand year storms”. Rather than being a storm having a recurrence time of once in a thousand years, these are storms which have a 0.001 chance per year of occurring. Storms aren’t the only weather events of … Continue reading

Posted in American Meteorological Association, American Statistical Association, AMETSOC, catastrophe modeling, climate disruption, climate economics, climate education, ecopragmatism, evidence, extreme events, extreme value distribution, flooding, floods, games of chance, global warming, global weirding, insurance, meteorological models, meteorology, R, R statistical programming language, real estate values, risk, Risky Business, riverine flooding, science, Significance
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## Macros in R

via Macros in R See also: The gtools package of R which enables these. There’s a description and motivation beginninng on page 11 of an (old: 2001) R News issue. They have been around a long time, but I haven’t … Continue reading

Posted in macros, R, R statistical programming language
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## On bag bans and sampling plans

Plastic bag bans are all the rage. It’s not the purpose of this post to take a position on the matter. Before you do, however, I’d recommend checking out this: and especially this: (Note: My lovely wife, Claire, presents this … Continue reading

Posted in bag bans, citizen data, citizen science, Commonwealth of Massachusetts, Ecology Action, evidence, Google, Google Earth, Google Maps, goverance, lifestyle changes, microplastics, municipal solid waste, oceans, open data, planning, plastics, politics, pollution, public health, quantitative ecology, R, R statistical programming language, reasonableness, recycling, rhetorical statistics, sampling, sampling networks, statistics, surveys, sustainability
2 Comments

## Bayesian blocks via PELT in R

The Bayesian blocks algorithm of Scargle, Jackson, Norris, and Chiang has an enthusiastic user community in astrostatistics, in data mining, and among some in machine learning. It is a dynamic programming algorithm (see VanderPlas referenced below) and, so, exhibits optimality … Continue reading

Posted in American Statistical Association, AMETSOC, anomaly detection, astrophysics, Cauchy distribution, changepoint detection, engineering, geophysics, multivariate statistics, numerical analysis, numerical software, numerics, oceanography, population biology, population dynamics, Python 3, quantitative biology, quantitative ecology, R, Scargle, spatial statistics, square wave approximation, statistics, stepwise approximation, time series, Woods Hole Oceanographic Institution
3 Comments

## Rushing the +2 degree Celsius boundary

I made a comment on Google+ pertaining to a report of a recent NOAA finding. Enjoy. But remember that COP21 boundary is equivalent to 450 ppm CO2.

Posted in adaptation, AMETSOC, Anthropocene, atmosphere, Bill Nye, bridge to nowhere, carbon dioxide, Carbon Tax, Carbon Worshipers, citizenship, civilization, clean disruption, climate, climate disruption, COP21, corporate litigation on damage from fossil fuel emissions, differential equations, disruption, distributed generation, Donald Trump, ecology, El Nina, El Nino, energy, energy reduction, engineering, environment, environmental law, Epcot, explosive methane, forecasting, fossil fuel divestment, fossil fuels, geophysics, global warming, greenhouse gases, greenwashing, Hyper Anthropocene, investment in wind and solar energy, IPCC, local generation, Mark Jacobson, Martyn Plummer, microgrids, Miguel Altieri, philosophy, physical materialism, R, resiliency, Ricky Rood, risk, Sankey diagram
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## data.table

R provides a helpful data structure called the “data frame” that gives the user an intuitive way to organize, view, and access data. Many of the functions that you would us… Source: Intro to The data.table Package

## Gavin Simpson updates his temperature analysis

See the very interesting discussion at his blog, From the bottom of the heap. It would be nice to see some information theoretic measures on these results, though.

Posted in AMETSOC, Anthropocene, astrophysics, Berkeley Earth Surface Temperature project, carbon dioxide, changepoint detection, climate, climate change, climate data, climate disruption, climate models, ecology, environment, evidence, Gavin Simpson, Generalize Additive Models, geophysics, global warming, HadCRUT4, hiatus, Hyper Anthropocene, information theoretic statistics, Kalman filter, maths, meteorology, numerical analysis, R, rationality, reasonableness, splines, time series
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## R and “big data”

On 2nd November 2015, Wes McKinney, the developer of the highly useful Python pandas module (and other things, including books), wrote an amusing blog post, “The problem with the data science language wars“. I by no means disagree with him. … Continue reading

## STUFF IN PROGRESS

It’s a good time to reconnoiter and review the things I have in progress and are planned, both as a teaser, and as a promise. I am currently working the following technical projects, entirely on my personal time outside of … Continue reading

Posted in numerical analysis, planning, R, rationality, reasonableness, state-space models, statistics
2 Comments

## Comprehensive and compact tutorial on Petris’ DLM package in R; with an update about Helske’s KFAS

A blogger named Lalas produced on Quantitative Thoughts a very comprehensive and compact tutorial on the R package dlm by Petris. I use dlm a lot. Unfortunately, Lalas does not give details on how the SVD is used. They do … Continue reading

Posted in Bayes, Bayesian, dynamic linear models, dynamical systems, forecasting, Kalman filter, mathematics, maths, multivariate statistics, numerical software, open source scientific software, prediction, R, Rauch-Tung-Striebel, state-space models, statistics, stochastic algorithms, SVD, time series
1 Comment

## Earth Day, my hope

Posted in carbon dioxide, Carl Sagan, Charles Darwin, citizen science, citizenship, civilization, clean disruption, climate, climate change, climate education, compassion, conservation, Darwin Day, demand-side solutions, ecology, economics, education, efficiency, energy reduction, environment, ethics, forecasting, fossil fuel divestment, geophysics, history, humanism, investing, investment in wind and solar energy, IPCC, mathematics, maths, meteorology, NCAR, NOAA, oceanography, open data, open source scientific software, physics, politics, population biology, Principles of Planetary Climate, privacy, probit regression, R, rationality, Ray Pierrehumbert, reasonableness, reproducible research, risk, science, science education, scientific publishing, Scripps Institution of Oceanography, sociology, the right to know, Unitarian Universalism, UU Humanists, WHOI, wind power
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## We are trying. And the bitterest result is to have so-called colleagues align themselves with the Koch brothers

I attended a 350.org meeting tonight. One group A group presenting there called “Fighting Against Natural Gas” applauded themselves for assailing Senator Whitehouse of Rhode Island for his supportive position on natural gas pipelines. Now, I am no friend of … Continue reading

Posted in Anthropocene, astrophysics, Boston Ethical Society, bridge to nowhere, carbon dioxide, carbon dioxide sequestration, Carbon Tax, chemistry, citizenship, climate, climate change, climate education, consumption, decentralized electric power generation, demand-side solutions, ecology, economics, energy reduction, engineering, forecasting, fossil fuel divestment, investment in wind and solar energy, IPCC, JAGS, meteorology, methane, model comparison, NASA, natural gas, NCAR, Neill deGrasse Tyson, oceanography, open data, physics, politics, population biology, Principles of Planetary Climate, Python 3, R, rationality, reasonableness, reproducible research, risk, science, science education, Scripps Institution of Oceanography
4 Comments

## Dynamic Linear Models package, dlmodeler

I’m checking out the dlmodeler package in R for a work project. It is accompanied by textbooks, G. Petris, S. Petrone, P. Campagnoli, Dynamic Linear Models with R, Springer, 2009 and J. Durbin, S. J. Koopman, Time Series Analysis by … Continue reading

## Markov Chain Monte Carlo methods and logistic regression

This post could also be subtitled “Residual deviance isn’t the whole story.” My favorite book on logistic regression is by Dr Joseph Hilbe, Logistic Regression Models, CRC Press, 2009, Chapman & Hill. It is a solidly frequentist text, but its … Continue reading

Posted in Bayes, Bayesian, logistic regression, MCMC, notes, R, statistics, stochastic algorithms, stochastic search
3 Comments

## R vs Python: Practical Data Analysis

R vs Python: Practical Data Analysis (Nonlinear Regression).

Posted in Bayes, Bayesian, biology, climate change, ecology, environment, Python 3, R, statistics, Wordpress
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## The designers of our climate

Originally posted on …and Then There's Physics:

Okay, I finally succumbed and actually waded through some of the new paper by Monckton, Soon, Legates & Briggs called Why models run hot: results from an irreducibly simple climate model. I…

Posted in astrophysics, bridge to nowhere, carbon dioxide, carbon dioxide capture, carbon dioxide sequestration, Carbon Tax, Carl Sagan, citizenship, civilization, climate, climate change, climate education, differential equations, ecology, economics, engineering, environment, ethics, forecasting, fossil fuel divestment, geoengineering, geophysics, humanism, IPCC, mathematics, mathematics education, maths, meteorology, methane, NASA, NCAR, Neill deGrasse Tyson, NOAA, oceanography, open data, open source scientific software, physics, politics, population biology, Principles of Planetary Climate, probabilistic programming, R, rationality, reasonableness, reproducible research, risk, science, science education, scientific publishing, sociology, solar power, statistics, testing, the right to know
1 Comment

## On nested equivalence classes of climate models, ordered by computational complexity

I’m digging into the internals of ABC, for professional and scientific reasons. I’ve linked a great tutorial elsewhere, and argued that this framework, advanced by Wood, and Wilkinson (Robert), and Wilkinson (Darren), and Hartig and colleagues, and Robert and colleagues, … Continue reading

Posted in approximate Bayesian computation, Bayes, Bayesian, biology, ecology, environment, forecasting, geophysics, IPCC, mathematics, maths, MCMC, meteorology, NCAR, NOAA, oceanography, optimization, population biology, Principles of Planetary Climate, probabilistic programming, R, science, stochastic algorithms, stochastic search
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## illustrating particle filters and Bayesian fusion using successive location estimates on the unit circle

Introduction Modern treatments of Bayesian integration to obtain posterior densities often use some form of Markov Chain Monte Carlo (“MCMC”), typically Gibbs sampling. Gibbs works well with many Bayesian hierarchical models. The standard problem-solving situation with these is that a … Continue reading

## Bayesian deconvolution of stick lengths

Consider trying to determine the length of a straight stick. Instead of the measurement errors being clustered about zero, suppose the errors are known to be always positive, that is, no measurement ever underestimates the length of the stick. Such … Continue reading

## The dp-means algorithm of Kulis and Jordan in R and Python

dp-means algorithm. Think k-means but with the number of clusters calculated. By John Myles White, in R. (Github link off that page.) By Scott Hendrickson, in Python. (Github link off that page.)

Posted in Bayesian, Gibbs Sampling, JAGS, mathematics, maths, R, statistics, stochastic algorithms, stochastic search
Tagged dp-means
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## Blind Bayesian recovery of components of residential solid waste tonnage from totals data

This is a sketch of how maths and statistics can do something called blind source separation, meaning to estimate the components of data given only their totals. Here, I use Bayesian techniques for the purpose, sometimes called Bayesian inversion, using … Continue reading

## “The joy and martyrdom of trying to be a Bayesian”

Bayesians have all been there. Some of us don’t depend upon producing publications to assure our pay, so we less have the pressure of pleasing peer reviewers. Nonetheless, it’s all reacting to “What the hell are you doing? I don’t … Continue reading

## How fast is JAGS?

How fast is JAGS?.

## The zero-crossings trick for JAGS: Finding roots stochastically

BUGS has a “zeros trick” (Lund, Jackson, Best, Thomas, Spiegelhalter, 2013, pages 204-206; see also an online illustration) for specifying a new distribution which is not in the standard set. The idea is to couple an invented-for-the-moment Poisson density to … Continue reading

Posted in Bayesian, BUGS, education, forecasting, Gibbs Sampling, JAGS, mathematics, MCMC, probabilistic programming, R, statistics, stochastic search
Tagged error-in-variables problem, optimization, zeros trick
4 Comments

## HadCRUT4 version “HadCRUT.4.2.0.0” available as .RData R workspace or image

I’m happy to announce that I have made available the HadCRUT4 observational ensemble data as an .RData image for use with R. These were downloaded from the MetOffice Hadley Observations Web site. Detailed documentation is available on this page, with the … Continue reading