Looking at temperature data

This is a thing:

The above is the NOAA GHCN Monthly data for the United States, turned into anomaly form.  Starting with the 12,355 stations recorded in the data and working with an Oracle database, I built a table of baseline averages for each calendar month over the 30-year period 1981 – 2010.  With 12 months in the year and a 30-year baseline, a full data set would have 12 X 30 records, or 360 separate monthly summaries.  I used 345 records as a low cutoff, and only used stations that could build a full 12-month baseline.  That left me with 1807 stations.

Once that was completed, I took each station from the main data table that matched a station in my baseline table, and subtracted that 30-year average for the calendar month from the matching station record.  This was done for the period Jan 1900 through Mar 2019.  The final result was a monthly, 120-year record of anomalies for that station.

Finally, I used Excel to plot the data, with the result above.  I looked at that result with not a little skepticism.  Looked awfully flat, especially that trend line.  That’s about a quarter-degree of warming over 120 years.  I did the numbers three more times, using slightly different methods, and checking intermediate data to make sure I was wandering off-track somewhere.  What I really needed was another record to check my results against.

I went over to the Berkeley Earth BEST webpage and looked around for something I could perhaps reproduce.  Their methods are way more sophisticated than mine, and most of them incorporated sea temps, which I don’t have.  Still, I found this:

I have that data, and I adjusted my baseline to the same 1951-1980 period in my database queries.  I pulled in data  using the same criteria and methods as I did with the other graph, and this was the result:

I don’t have data as far back as BEST does, so I superimposed my graph over the same range of the BEST chart.  I think it’s a pretty good fit, considering our no-doubt-vast difference in processing.

It’s hard to see, but BEST’s lines are a darker blue than mine, and my markers are blue and BEST’s are red.  It’s a really good fit, which gives me confidence that my US chart is valid, too.  I also did charts for Europe, Russia, and Australia.  I’ll leave those here with no comment; it’s late, and there’s not all that much to say, anyway — they all look more like what one would expect.

Have a pleasant evening.

Talking About Temperatures

I’ve had a conversation going at another blog regarding the global temperature anomaly, how it’s calculated, and how it should be reported.  I feel that I’ve taken up enough time and space on this discussion at the other blog, so I’ve set up this page to allow the dialogue to continue.

Briefly, my position is that the precision of the daily data, as reported by NOAA to the tenths of a degree C, limits the precision of any result from those measurements to the same level of precision.  This position is supported by multiple sources at various university physics and physical sciences departments’ web pages.

I welcome further conversation on this topic.  Please feel free to join in.

Calling for Boycotts Is Most Likely Futile

The latest outrage among so-called Progressives is the news that In-N-Out Burger (INOB) donated $25K to the GOP in California. Obviously this signifies IaOB’s total commitment to racism, homo-and-trans-phobia, and white supremacy.  In the minds of the SJWs of the state, the proper response is to call for a boycott.

The problem with this approach is that it’s probably a good bet that very few of the people offended by the donation to the Party of All Bad Things are INOB fans in the first place. More likely, this boycott will end up like the abortive effort against Chick-Fil-A, where the call for boycott only increased sales.

Progressives are famous for pointing out how the deplorables often “vote against their interests,” which is SJW-speak for “not how we think they should vote.” Now we have an entity clearly voting for its own interests, and being vilified for it. In a high-tax state like California, how could a business support the party that would tax them out of business in the name of “the right side of history”?

They couldn’t and they didn’t, and the Left’s response is as predictable as ever. Apparently, it’s only valid to “vote your own interests” if those interests coincide with the Left’s.

Where the Difference Lay

Out of curiosity, today I Googled for the county-by-county results of the 2016 Presidential campaign.  They were easy to find, here, and I indulged myself in a bit of investigation to find out just where the difference lay in the popular vote, so championed as “more fair” than the “obsolete” Electoral College.

I had heard multiple times from various sources that California and New York were the states that put Clinton over the top in the popular vote, and that without them, Trump had won by millions of votes.  That may be true, but you can’t just take her votes away in those states and keep his.  You have to remove both sets of votes, and see how the numbers fall.

That’s what I did.  I figured that it was probably the more populous counties in the two states that made the difference, but I was a bit surprised by the result after spending some time the an Excel spreadsheet and the vote tallies.

Six counties.

That’s all it took: six counties in California and New York.  They were Alameda County, Los Angeles County, and San Francisco County in California, and Bronx County, New York County, and Queens County in New York.  If no one in those counties had voted, and everything else had stayed the same.  Trump would have won the popular vote as well as the Electoral College.  In fact, this is the exact scenario that the Founding Fathers were trying to avoid by creating the College, though they were thinking of entire states, not single counties.  They didn’t want a small, highly populated area dictating to the rest of the country the results of  a Presidential election.  They wanted a President to have broad appeal across the entire country.

Myself, I’m just as glad that LA, San Fran, and the Big Apple didn’t have the final say as to who got to be President.

Normalizing Audio

Once you’ve harvested your audio or video off the internet using the freeware Screen Capturer and Recorder, you’re going to want to do something with it.  Another great freeware solution for this task is ffmpeg.  Ffmpeg is a command-line utility that does just about everything with video and audio files that you’d ever want to do, but it is a bit cryptic at times.

What I wanted to do most recently was to normalize the audio in my files so that I wasn’t constantly messing with the volume during playback.  I wasn’t always super careful when setting the audio level during capture, so being able to do it with ffmpeg was great.  The problem was I only knew how to do one file at a time.

Capturing Video

The amount of streaming video out there on the internet is fantastic, and like I did, you probably look at it flowing across your screen and wonder, “How can I capture that to watch later?” If you already know how to do it, congratulations. If you’re looking for a way to do it, here’s the best I’ve found. It’s called “Screen Capturer Recorder.” Not the most inspiring name, granted — but for sheer usability and quality and being-free-ness, I haven’t found better.