W.O.O.D. 14 February 2019

Welcome to St. Valentines Day!

Needless to say I’m not focusing on the Blog today as the Spouse has other ideas. I’ll check in when I can.

Intro

This is another of the W.O.O.D. series of semi-regular
Weekly Occasional Open Discussions.
(i.e. if I forget and skip one, no big)

Immediate prior one here:

https://chiefio.wordpress.com/2019/02/05/w-o-o-d-5-february-2019/
and remains open for threads running there (at least until the ‘several month’ auto-close of comments on stale threads).

Canonical list of old ones here:
https://chiefio.wordpress.com/category/w-o-o-d/

So use “Tips” for “Oooh, look at the interesting ponder thing!”
and “W.O.O.D” for “Did you see what just happened?! What did you think about it?”

What’s Going On?

Venezuela has a US Aid food convoy on the border. We’ll see if the people are hungry enough to take down the barriers and let it in. Maduro is calling it a plot…

BREXIT is supposedly about 40ish days away. T. May continues to run out the clock. Nothing is changing in The Deal. I expect a last minuted minor change and then an ultimatum of “Take this, or it’s WTO Rules and the end of life…” and then there will an extension of 6 months to decide again. Hopefully The British People will inform their government to just get it over with now.

The Police in france are now beating up the Yellow Vest protestors. Something like 20 dead and a whole bunch of folks have lost an eye to exploding gas canisters. One wonders how long before The Government gets changed.

In Spain there’s a major upheaval going on. Not just the Catalonians wanting to leave, but the Spanish budget is busting EU rules. It looks like with the insane levels of unemployment (especially in the young generations) Spain is about to bubble with trouble.

It is shaping up to be a Germany + French Elite vs Europe + French Deplorables… On one side: Germany, French Government. On the other side: Spain, Italy, French People, Austria, Poland, Hungary, …. with Britain working to get the damn door open and run screaming from the room before the fighting breaks out… Don’t know where all the little Hanger On countries stand in all this. Holland, Denmark, Andora, Slovakia, etc. etc.

Then we have the spectacle of every Democrat with a pulse (so leaves out Pelosi & Shumer – the walking dead zombies from the past…) shouting they are running for President. Then in unison endorsing the Green Nutty Deal. Maybe given how horrible it is and how much it has blown up in their collective faces they will actually assign a staffer to read it next time… Slowly it is dawning on some of them that “No Oil, No Gas, No Coal” also means no airplanes, no limos, no Air conditioning, no ships, no winter heat, no trucks of food coming to the grocery store, no Snow Ploughs and no room heater in the blizzard.

In related news, Governor Newsance of California has canceled future sections of The Train To Nowhere; but is letting the part underway be finished. After all, can’t have those folks out of work… Even if you assure the finished thing will be absolutely worthless as it runs from Bakersfield to south of Sacramento. Maybe they can put tractor hauling cars on it for the farmers… He’s also partially killed the “River In A Tube”. Jerry Moonbeam wanted 2 giant bores to take the Sacramento River from the North side of the S.F. Delta to the South side so they could ship it to L.A. Never mind that they are requiring all that water to be dumped in the ocean to “Save the Delta Smelt”… so it isn’t available. So Gov. Newsom has killed one of the $10 Billion bore holes. Ought to have been both, but he seems to be big on 1/2 measures.

Why did he kill it? Well, for the train, the $10 Billion price tag had ballooned to $100 Billion. Even with the outrageous tax rates in California they didn’t have that kind of money. Something about bankrupting the State during his first term in office was enough to cause action.

Meanwhile, it’s been cold, nearly frigid here in the San Francisco Bay Area. We’ve had snow in the hills all around the Bay. Now we have an “Atmospheric River” taking tons of water and snow overhead. Tonight I got an “Emergency Alert” for flooding in San Jose on my cell phone. So I turned off emergency alerts ;-) But watch the news reports for the Sierra Nevada snowfall. It ought to be impressive.

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GHCN v3.3 Stations By Altitude By Years (or “Mountains? What Mountains?”)

I’ve mentioned a few times that the loss of thermometers after the “Baseline” period seemed to preferentially lose those in High Cold Places. Well, thought I, I think I can now graph that… So I did.

One thing I noticed was that the graphs strongly reflected the Hadley choice of baseline, extending to 1990. I thought of doing them all over again, but kind of like the way there’s a distinct slice of “Hadley Special” next to the blue NASA GIS baseline… (You’ll see what I mean).

Initially this was a challenge to do as there were some “limits on number or records that can be graphed being blown” and I was getting error branches without explanation. Adding “DISTINCT” to the stn_elev fixed that (and gave me what I really wanted anyway:
Is there a station at that altitude, or not?

IF 2 or more stations share exactly the same altitude, they will only show up as one dot, but that ought to be rare.

I also ran into some stations at -1000 meters elevation or so. WH? Turns out they are “missing data” flagged at -999 m (I’ll put a table at the end listing them).

First up will be a purple graph of All Stations. I start this in 1800 instead of 1850 (actual data goes back into the 1700s but it’s useless as there’s almost no thermometers). This lets you see the ramp up to “usable numbers” in 1850+ a bit better. I mostly left the altitude / years limits the same so the graphs can be directly compared, but Alldata, Asia, South America, and North America needed 5000 M limits (the rest are 4000 meters).

Change of Altitude over Years

GHCN v3.3 Distinct Altitude locations over years

Nice Big Fat lump of stations at altitudes above 2000 Meters in the baseline period. GIS temp starts in 1950 to 1980, Hadley runs 1960 to 1990. It looks like about 1950 to 1990 is at high altitude, the better to accommodate both I guess. Then the altitudes start to thin and things run off to the beaches and lower elevations.

Given the recent discussions of how temperature is a function of pressure / altitude:
“I think this matters”…

I’m now going to do the Regions (continents). Again, minus Region 8: as first off there’s no data outside the baseline and second off, no altitude data in those records (sea level being presumed?) or missing data flags of -999 m.

Region 1 – Africa

Altitude by Years Region 1 Africa GHCN v3.3

Africa is a lot shorter than I’d expected. Nothing over 2500 meters. I don’t see any obvious special treatment of the Baseline for altitude. Perhaps the changes in Africa are more “by latitude” (since we saw that in the area graph…)

Region 2 – Asia

Altitude by Years Asia Region 2 GHCN v3.3

In this one you can very clearly see the Hadley Basline where those stations at altitude have a ‘red tag’ on the side extending from the GIStemp baseline end of 1980 out to the Hadley baseline end of 1990. Then Oh Boy do they prune those mountains out of the data! Everything over about 1750 M getting thinned and stuff over about 2250 M getting massacred.

Region 3 – South America

Altitude by Years South America Region 3 GHCN v3.3

This one is a little different. We don’t lose so many very high altitude, mostly losing stations in that 1200 M to 2200 M band, BUT, look at the additions down low in the 200 M to 1000 M band. Loading up with lower altitude ought to be as effective as reducing high altitude. Then, those 1-2kM stations will be “infilled” with hypotheticals from down in the more tropical and coastal areas. Nice trick!

Region 4 – North America

Altitude by Years North America Region 4 GHCN v3.3

This was one where the volume of data was causing me grief. It still has the bottom of the graph very solidly colored. This likely needs breaking out by sub-regions to get a better visualization of the loss. It is still clear that “At Altitude” gets dropped just after the Baseline period, though they were nice enough to hold those top ones into the Hadley baseline (that red tag on the side of the blue).

Region 5 – Australia / Pacific

Altitude by Years Region 5 Australia / Pacific GHCN v3.3

Still a couple of stations at altitude (maybe in New Zealand?) but the rest pretty much toast. Huge bite out of the 750 m to 1250 m range. That sounds like Australian mountains to me.

Region 6 – Europe

Altitude by Year Region 6 Europe GHCN v3.3

A very distinct offset in the European data to the “Kept In The Baseline” being aligned with Hadley 1960-1990. Just after that the “High Cold Places” get heavily thinned.

Region 7 – Antarctica

Altitude by Years Region 7 Antarctica GHCN v3.3

Well, at least they kept one high altitude station. Probably a big name one so deleting it would cause notice…

In Conclusion

Here’s the report of stations where there is a -999 missing data flag:

mysql> SELECT name, stn_elev, stationID FROM invent3 WHERE stn_elev 

You would think, what with Google and all, they could look up the altitudes, but “whatever”. I note that 4 Antarctic stations made the list.

Here’s one example of the program. I didn’t save every copy this time as they differ only in the setting of the Region number:

# -*- coding: utf-8 -*-
import datetime
import pandas as pd
import numpy as np
import matplotlib.pylab as plt
import math
import MySQLdb

plt.title("Global Thermometer GHCN v3.3 Altitude")
plt.xlabel("year")
plt.ylabel("altitude")
plt.ylim(-200,5000)
plt.xlim(1800,2020)

try:
    db=MySQLdb.connect("localhost","root","OpenUp!",'temps')
    cursor=db.cursor()

    sql="SELECT DISTINCT I.stn_elev, T.year 
    FROM invent3 AS I INNER JOIN temps3 as T 
    ON I.stationID=T.stationID WHERE I.stn_elev>-100"
 
    cursor.execute(sql)
    stn=cursor.fetchall()
    data = np.array(list(stn))
    xs = data.transpose()[0]   # or xs = data.T[0] or  xs = data[:,0]
    ys = data.transpose()[1]

    plt.scatter(ys,xs,s=2,color='purple',alpha=1)

    plt.show()

    plt.title("Global Thermometer GHCN v3.3 Altitude")
    plt.xlabel("year")
    plt.ylabel("altitude")
    plt.ylim(-200,4000)
    plt.xlim(1800,2020)
    
    sql="SELECT DISTINCT I.stn_elev, T.year 
    FROM invent3 AS I INNER JOIN temps3 as T 
    ON I.stationID=T.stationID WHERE T.region='3' 
    AND I.stn_elev>-100 AND  year>1949 
    AND year<1981 AND I.stationID NOT IN 
    (SELECT I.stationID FROM invent3 AS I 
    INNER JOIN temps3 AS T ON I.stationID=T.stationID 
    WHERE year=2015);"
 
    cursor.execute(sql)
    stn=cursor.fetchall()
    data = np.array(list(stn))
    xs = data.transpose()[0]   # or xs = data.T[0] or  xs = data[:,0]
    ys = data.transpose()[1]

    plt.scatter(ys,xs,s=2,color='blue')
#    plt.show()

    sql="SELECT DISTINCT I.stn_elev, T.year 
    FROM invent3 AS I INNER JOIN temps3 AS T 
    ON I.stationID=T.stationID WHERE T.region='3' 
    AND I.stn_elev>-100 AND (year>1980 OR year<1950);"
 
    cursor.execute(sql)
    stn=cursor.fetchall()
    data = np.array(list(stn))
    xs = data.transpose()[0]   # or xs = data.T[0] or  xs = data[:,0]
    ys = data.transpose()[1]

    plt.scatter(ys,xs,s=2,color='red',alpha=1)

    plt.show()

except:
    print "This is the exception branch"

finally:
    print "All Done"
    if db:
        db.close()

After the first run I commented out the purple graph as I didn’t need to keep making it ;-)

I’m pretty sure this code is right, but it was a bit tricky to get past various odd errors, so I might have missed a trick in the disruption.

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