Saturday, September 27, 2014

Databases Galore - 5

Latitude & Longitude lines
This series is about home-school, home-science, or science and school anywhere else where there are no rocket scientists (in the sense of how easy it is to calculate the realities of climate change - Databases Galore - 2).

Today's graphs will deal with the NASA data keyed on the two hemispheres of the Earth.

That would be the Northern and Southern hemispheres, plus sections or swaths of those hemispheres at various northern and southern latitudes, so as to give a comprehensive look at what the global trend is and how differing latitudes are being impacted by global warming.

First, remember that the Equator is the division line between the Northern and Southern hemispheres.

On today's graphs when you see a northern latitude you know it is north of the Equator, and conversely, a southern latitude is south of the Equator.

When one of the graph sections has both a N. and a S. latitude in it, you know that it is a band that begins on one side of the Equator, then goes past the Equator in a swath that has the Equator in its data used to graph that line.

Longitudes span both hemispheres, top to bottom, so don't worry about that for today because we are dealing with belt zones.

For example, the graph above right is a graph of both hemispheres, plus a combined detail (N. is red, S. is black, and the combined (avg. mean) is green).

The graph tells us that both the S. and N. Hemispheres are tracking in a similar direction, in sync with the general global trend.

Interestingly, the S. Hemisphere is on a warming trend greater than the norm and greater than the N. Hemisphere trend line (notice also that it did not begin that way).

The next graph to the right tracks various bands or swaths of the N. Hemisphere from the Equator up to 90 degrees N. latitude.

To get an idea of where that is, Seattle, WA is at 47°37′N latitude (47 degrees, 37 minutes) and Dallas, TX is at 32°47′N (32 degrees, 47 minutes).

Are we on to something like the global warming is a factor of where the population of civilization and its industries lie? (Latitudes of Cities is our homework it would seem).

Anyway, the next and final graph (to the right) for today is several swaths or bands of latitude zones in the S. Hemisphere.

Most of the bands follow a general warming trend, with the southern polar region wigging out all over the place.

Databases and graphs are not mysterious, no, what is mysterious is why fifth graders can figure this stuff out, but too many adults in congress can't admit to it for some reason:
Learning about temperature measurements is a concept that is introduced as early as elementary school. A mean is a mathematical average of a set of numbers. Many students are required to compute a mean as early as elementary school and later on in middle and even high school. In practical terms, you might want to find a mean temperature to determine what the weather is typically like in a certain area.
(How to Calculate the Mean Temperature). That page goes on to give simple instructions as to how to take multiple temperature readings over several days, then average for a 'mean' value (values above and below the 'mean' are called 'variations from the mean').

The next post in this series is here, the previous post in this series is here.

Friday, September 26, 2014

Databases Galore - 4

It is Friday and I have been busy the last few days on, among other things, the climate data which various entities provide.

You, I, or any congress member who wants to find out for themselves can get a free copy of the record of temperatures.

The data are available from various agencies such as NASA, NOAA, the World Bank, and various universities (Databases Galore, 2, 3).

I have been reporting on this and demonstrating the availability of the data to anyone who wants it, and who wants to cut down on faith and trust while increasing real knowledge (The Pillars of Knowledge: Faith and Trust?).

I am beginning to graph the data with another tool (sciDavis) freely given to those who want a very robust application.

The graph above is composed of data from NASA which shows temperature ups and downs from 1880-2013 ... but the big picture is the steep trend over the past thirty years with heat going into the ocean the previous few years.

I downloaded raw data, cleaned it up for insertion into a mySQL database managed by a mySQL Server, then wrote programs to extract the data based on queries, and now am going to do lots of graphs.

Have a good weekend.

The next post in this series is here, the previous post in this series is here.

Thursday, September 25, 2014

Databases Galore - 3

Yesterday I said "(I plan to do two today to make up for it" but I did not get around to it because of some unexpected disruptions.

The best laid plans of mice and men eh?

I did finish one additional program this morning.

It accepts a "country id code" composed of three digits, then contacts the mySQL Server which collects the data from all weather stations in that specified country, then the program prints out a report on each weather station for each year available at that location.

For example, I printed out the info for the U.S. and that one report ended up having 514,535 lines of text (1,921 stations).

Today's post will concern a smaller country with fewer weather stations (only 7) so it will fit in a single post, and will give the gist of what a report looks like:

[UPDATE: I updated the dizzying numbers(1000 lines plus) with the following graphs of the country and its 7 stations]

There are a couple of things about the graphs I should explain, now that the individual numbers that were here are no longer here.

The year 2014 only has data available through August.

So those figures may gyrate from the norm.
Click on any graph to get a larger view of it.Later today, I may add an option to the program so it will print in HTML table format.

Note that these 7 stations are in different locations in Burkina Faso, and that they don't begin with the same year because some are older.
Looking at the data is an education in geography as well as some fundamentals of climate science, which is collecting and storing accurate and usable data.

We citizens on our own can find out about and take interest in the things that matter.
Plus, it allows us to have less "faith / trust" due to having personal experience and knowledge on the issue (The Pillars of Knowledge: Faith and Trust?).

This data has not been smoothed or rounded so there are jumps where data are missing for several months (wars, disasters, etc. at a station).
Thanks to the scientists who have made it available to us.

It gives new meaning to "don't try this at home" because it does not take a rocket scientist to make databases and convert the data to graphs.

We can see it for ourselves.
Meanwhile, back in DC, some are dancing to the war drums on a "deth starr" while others are listening to the music of the Earth.

The music is alarming because making war while our environment dies is tempting destruction on two fronts.

The death stars are killing the Earth with little to no remorse as it turns out (Oil-Qaeda: The Indictment - 3).

The words at the beginning of the song "Deth Starr" (video below) tell of the dangers facing civilization, yet the most important thing of all to them is building weapons of mass destruction.

The next post in this series is here, the previous post in this series is here.

"Listen To The Music", by The Doobie Brothers



"Deth Starr", by Tenacious D


Wednesday, September 24, 2014

Databases Galore - 2

I really got into the data and programming, yesterday, so I neglected to do a post (I plan to do two today to make up for it).

The databases shared by NOAA, a university, World Bank, and NASA, involve 7,280 individual weather stations in 236 countries and/or on 14 ships (which basically means all around the globe and oceans).

So you see, it takes a bit of time to write C++ programs to parse all that flat-file data, clean it up, and prepare it for a mySQL database with several tables.

The data have now been stored in a database named "climate_change" with seven tables (countries, weather stations, monthly temperatures, CO2 PPM, etc.) and are ready for queries to be written.

I have classes and programs that I have written and others I am still writing.

They utilize mySql++, a C++ library for SQL functionality involving mySQL databases (see e.g. mySQL, mySQL++) which are managed by a mySQL server.

I am showing that we can do this stuff at home or in class, since the scientists have shared the data transparently, and it is not mysterious or unreachable as the deniers tend to whine about (see e.g. How Fifth Graders Calculate Ice Volume - 5).

Just below is a sample output from one of those 7,280 weather stations, displaying monthly data in tables, with calculated averages on the rightmost column.

This is in response to an SQL query, where the first column of each row is January, the last column is December [before the brackets].

There are two rows per year, showing the monthly temperatures in Celsius and Fahrenheit (asterisks indicate missing monthly data):

Country, Station: [COSTA RICA, PUERTO LIMON]:

**** 24.94° 24.94° 25.94° 25.14° 25.24° 25.14° 25.24° 24.74° 25.24° 24.54° 23.64° [1961 avg = 24.98°] C
**** 76.89° 76.89° 78.69° 77.25° 77.43° 77.25° 77.43° 76.53° 77.43° 76.17° 74.55° [1961 avg = 76.96°] F

23.34° 23.74° 24.54° 25.04° 25.84° 26.64° 25.74° 25.34° 25.74° 25.84° 24.24° 23.44° [1962 avg = 24.96°] C
74.01° 74.73° 76.17° 77.07° 78.51° 79.95° 78.33° 77.61° 78.33° 78.51° 75.63° 74.19° [1962 avg = 76.92°] F

24.14° 24.04° 24.64° 24.54° 25.04° 25.44° 25.14° 25.44° 26.24° 25.34° 24.74° 24.34° [1963 avg = 24.92°] C
75.45° 75.27° 76.35° 76.17° 77.07° 77.79° 77.25° 77.79° 79.23° 77.61° 76.53° 75.81° [1963 avg = 76.86°] F

23.74° 25.34° 25.44° 25.44° 25.34° 25.24° 25.04° 25.64° 25.74° 25.44° 25.34° 25.14° [1964 avg = 25.24°] C
74.73° 77.61° 77.79° 77.79° 77.61° 77.43° 77.07° 78.15° 78.33° 77.79° 77.61° 77.25° [1964 avg = 77.43°] F

23.44° 25.04° 24.84° 25.14° 25.74° 25.64° 24.74° 24.74° 25.74° 25.54° 25.44° 24.94° [1965 avg = 25.08°] C
74.19° 77.07° 76.71° 77.25° 78.33° 78.15° 76.53° 76.53° 78.33° 77.97° 77.79° 76.89° [1965 avg = 77.15°] F

25.54° 25.64° 25.44° 26.14° 25.84° 25.64° 25.34° 25.44° 25.74° 25.94° 24.64° 24.74° [1966 avg = 25.51°] C
77.97° 78.15° 77.79° 79.05° 78.51° 78.15° 77.61° 77.79° 78.33° 78.69° 76.35° 76.53° [1966 avg = 77.91°] F

24.24° 24.44° 23.99° 24.89° 24.79° 24.59° 23.89° 24.79° **** 25.59° 24.59° 25.69° [1967 avg = 24.68°] C
75.63° 75.99° 75.18° 76.80° 76.62° 76.26° 75.00° 76.62° **** 78.06° 76.26° 78.24° [1967 avg = 76.43°] F

24.69° 23.69° 24.09° 24.09° 24.99° 25.19° 24.49° 24.49° 25.19° 25.79° 24.39° 24.69° [1968 avg = 24.65°] C
76.44° 74.64° 75.36° 75.36° 76.98° 77.34° 76.08° 76.08° 77.34° 78.42° 75.90° 76.44° [1968 avg = 76.37°] F

24.79° 24.39° 25.69° 25.99° 26.69° **** **** **** **** **** 25.09° 24.99° [1969 avg = 25.38°] C
76.62° 75.90° 78.24° 78.78° 80.04° **** **** **** **** **** 77.16° 76.98° [1969 avg = 77.68°] F

24.99° 23.99° 25.69° 26.19° 25.59° 26.59° 26.19° 25.99° 25.99° 26.29° 24.29° 24.81° [1970 avg = 25.55°] C
76.98° 75.18° 78.24° 79.14° 78.06° 79.86° 79.14° 78.78° 78.78° 79.32° 75.72° 76.66° [1970 avg = 77.99°] F

24.11° 24.01° 24.61° 25.31° 25.81° 25.51° 25.01° 25.51° 25.71° 25.61° 24.91° 24.11° [1971 avg = 25.02°] C
75.40° 75.22° 76.30° 77.56° 78.46° 77.92° 77.02° 77.92° 78.28° 78.10° 76.84° 75.40° [1971 avg = 77.03°] F

23.71° 24.41° 25.11° 25.91° 26.61° 26.61° 25.91° 26.11° 26.01° 25.91° 26.01° 25.51° [1972 avg = 25.65°] C
74.68° 75.94° 77.20° 78.64° 79.90° 79.90° 78.64° 79.00° 78.82° 78.64° 78.82° 77.92° [1972 avg = 78.17°] F

25.31° 25.51° 26.61° 27.01° 26.81° 26.61° 25.81° 26.61° 26.41° 26.11° 25.41° 23.61° [1973 avg = 25.99°] C
77.56° 77.92° 79.90° 80.62° 80.26° 79.90° 78.46° 79.90° 79.54° 79.00° 77.74° 74.50° [1973 avg = 78.77°] F

24.31° 23.81° 25.11° 25.11° 26.31° 26.31° 25.01° 25.91° 26.11° 25.51° 24.81° 24.81° [1974 avg = 25.26°] C
75.76° 74.86° 77.20° 77.20° 79.36° 79.36° 77.02° 78.64° 79.00° 77.92° 76.66° 76.66° [1974 avg = 77.47°] F

24.81° 24.41° 25.41° 25.51° 26.21° 25.51° 25.31° 25.31° 26.11° 26.11° 25.21° 23.61° [1975 avg = 25.29°] C
76.66° 75.94° 77.74° 77.92° 79.18° 77.92° 77.56° 77.56° 79.00° 79.00° 77.38° 74.50° [1975 avg = 77.53°] F

23.61° 23.11° 24.61° 26.01° 26.21° 26.21° 25.81° 25.81° 25.91° 26.11° 25.71° 25.61° [1976 avg = 25.39°] C
74.50° 73.60° 76.30° 78.82° 79.18° 79.18° 78.46° 78.46° 78.64° 79.00° 78.28° 78.10° [1976 avg = 77.71°] F

24.11° 24.81° 25.21° 25.71° 26.81° 26.21° 25.71° 25.71° 25.71° 25.71° 25.51° 25.31° [1977 avg = 25.54°] C
75.40° 76.66° 77.38° 78.28° 80.26° 79.18° 78.28° 78.28° 78.28° 78.28° 77.92° 77.56° [1977 avg = 77.98°] F

24.21° 24.91° 25.61° 26.31° 27.01° 26.11° 25.61° 26.11° 26.11° 25.71° 26.11° 25.31° [1978 avg = 25.76°] C
75.58° 76.84° 78.10° 79.36° 80.62° 79.00° 78.10° 79.00° 79.00° 78.28° 79.00° 77.56° [1978 avg = 78.37°] F

24.81° 24.91° 25.71° 26.11° 26.71° 26.41° 26.31° 26.01° 26.31° 26.11° 25.61° 24.61° [1979 avg = 25.80°] C
76.66° 76.84° 78.28° 79.00° 80.08° 79.54° 79.36° 78.82° 79.36° 79.00° 78.10° 76.30° [1979 avg = 78.44°] F

25.01° 24.51° 25.81° 26.21° 26.91° 26.41° 26.41° 26.61° 26.31° 25.91° 25.41° 24.01° [1980 avg = 25.79°] C
77.02° 76.12° 78.46° 79.18° 80.44° 79.54° 79.54° 79.90° 79.36° 78.64° 77.74° 75.22° [1980 avg = 78.43°] F

23.81° 24.71° 25.51° 25.71° 27.11° 26.31° 26.01° 26.21° 26.21° 26.01° 24.91° 24.71° [1981 avg = 25.60°] C
74.86° 76.48° 77.92° 78.28° 80.80° 79.36° 78.82° 79.18° 79.18° 78.82° 76.84° 76.48° [1981 avg = 78.08°] F

25.01° 25.41° 25.41° 25.91° 26.91° 26.51° 25.31° 25.81° 26.01° 25.21° 25.01° 24.91° [1982 avg = 25.62°] C
77.02° 77.74° 77.74° 78.64° 80.44° 79.72° 77.56° 78.46° 78.82° 77.38° 77.02° 76.84° [1982 avg = 78.11°] F

24.81° **** 26.61° 27.01° 26.81° 27.21° 25.61° 26.41° 26.51° 25.61° 25.51° 24.41° [1983 avg = 26.05°] C
76.66° **** 79.90° 80.62° 80.26° 80.98° 78.10° 79.54° 79.72° 78.10° 77.92° 75.94° [1983 avg = 78.88°] F

23.71° 24.51° 25.71° 26.51° 25.61° 25.61° 25.31° 24.91° 25.71° 25.61° 24.71° 24.31° [1984 avg = 25.18°] C
74.68° 76.12° 78.28° 79.72° 78.10° 78.10° 77.56° 76.84° 78.28° 78.10° 76.48° 75.76° [1984 avg = 77.33°] F

23.81° 24.31° 25.61° 25.91° 26.61° 26.41° 25.41° 25.81° 26.01° 25.91° 25.31° 24.71° [1985 avg = 25.49°] C
74.86° 75.76° 78.10° 78.64° 79.90° 79.54° 77.74° 78.46° 78.82° 78.64° 77.56° 76.48° [1985 avg = 77.87°] F

23.81° 24.71° 25.11° 25.61° 26.31° 26.31° 25.51° 26.01° 25.91° 25.91° 25.71° 25.61° [1986 avg = 25.54°] C
74.86° 76.48° 77.20° 78.10° 79.36° 79.36° 77.92° 78.82° 78.64° 78.64° 78.28° 78.10° [1986 avg = 77.98°] F

25.01° 25.01° 26.41° 25.51° 26.21° 26.41° 26.11° 26.11° 26.61° 25.71° 26.11° 25.61° [1987 avg = 25.90°] C
77.02° 77.02° 79.54° 77.92° 79.18° 79.54° 79.00° 79.00° 79.90° 78.28° 79.00° 78.10° [1987 avg = 78.62°] F

25.01° 24.91° 25.01° 26.41° 26.71° 26.51° 25.91° 26.41° 26.01° 25.91° 25.51° 24.41° [1988 avg = 25.73°] C
77.02° 76.84° 77.02° 79.54° 80.08° 79.72° 78.64° 79.54° 78.82° 78.64° 77.92° 75.94° [1988 avg = 78.31°] F

24.61° 23.61° 24.21° 25.21° 25.91° 25.91° 25.51° 26.11° 25.91° 25.91° 25.91° 24.61° [1989 avg = 25.28°] C
76.30° 74.50° 75.58° 77.38° 78.64° 78.64° 77.92° 79.00° 78.64° 78.64° 78.64° 76.30° [1989 avg = 77.51°] F

24.81° 24.81° 25.01° 25.91° 26.21° 26.31° 25.81° 26.11° 26.51° 26.51° 25.91° 24.61° [1990 avg = 25.71°] C
76.66° 76.66° 77.02° 78.64° 79.18° 79.36° 78.46° 79.00° 79.72° 79.72° 78.64° 76.30° [1990 avg = 78.28°] F

24.81° 24.51° 25.91° 25.81° 25.91° 26.41° 25.91° 25.91° 26.31° 26.11° 24.91° 24.61° [1991 avg = 25.59°] C
76.66° 76.12° 78.64° 78.46° 78.64° 79.54° 78.64° 78.64° 79.36° 79.00° 76.84° 76.30° [1991 avg = 78.07°] F

24.71° 25.21° 25.51° 26.21° 26.11° 27.31° 25.81° 25.41° 25.81° 25.61° 25.51° 25.21° [1992 avg = 25.70°] C
76.48° 77.38° 77.92° 79.18° 79.00° 81.16° 78.46° 77.74° 78.46° 78.10° 77.92° 77.38° [1992 avg = 78.26°] F

24.71° 24.81° 25.21° 26.81° 27.01° 26.61° 26.01° 26.21° 25.81° 26.11° 25.51° 24.81° [1993 avg = 25.80°] C
76.48° 76.66° 77.38° 80.26° 80.62° 79.90° 78.82° 79.18° 78.46° 79.00° 77.92° 76.66° [1993 avg = 78.44°] F

24.31° 24.41° 25.71° 25.71° 26.61° 26.01° 25.51° 26.01° 26.21° 26.31° 25.61° 24.81° [1994 avg = 25.60°] C
75.76° 75.94° 78.28° 78.28° 79.90° 78.82° 77.92° 78.82° 79.18° 79.36° 78.10° 76.66° [1994 avg = 78.08°] F

24.81° 24.81° 25.81° 26.31° 26.81° 26.81° 26.51° 26.91° 26.71° 26.61° 25.31° 25.51° [1995 avg = 26.08°] C
76.66° 76.66° 78.46° 79.36° 80.26° 80.26° 79.72° 80.44° 80.08° 79.90° 77.56° 77.92° [1995 avg = 78.94°] F

24.51° 24.31° 25.21° 26.01° 26.61° 26.21° 26.21° 26.01° 26.71° 26.61° 25.71° 24.41° [1996 avg = 25.71°] C
76.12° 75.76° 77.38° 78.82° 79.90° 79.18° 79.18° 78.82° 80.08° 79.90° 78.28° 75.94° [1996 avg = 78.28°] F

24.41° 24.11° 25.21° 26.31° 26.11° 26.61° 26.51° 26.61° 26.81° 26.41° 26.41° 25.51° [1997 avg = 25.92°] C
75.94° 75.40° 77.38° 79.36° 79.00° 79.90° 79.72° 79.90° 80.26° 79.54° 79.54° 77.92° [1997 avg = 78.65°] F

25.61° 26.41° 26.01° 26.71° 27.31° 26.91° 26.31° 26.51° 27.21° 26.51° 25.91° 25.01° [1998 avg = 26.37°] C
78.10° 79.54° 78.82° 80.08° 81.16° 80.44° 79.36° 79.72° 80.98° 79.72° 78.64° 77.02° [1998 avg = 79.46°] F

24.91° 24.81° 25.11° 26.01° 26.91° 26.21° 25.71° 26.11° 26.11° 25.91° 25.41° 23.91° [1999 avg = 25.59°] C
76.84° 76.66° 77.20° 78.82° 80.44° 79.18° 78.28° 79.00° 79.00° 78.64° 77.74° 75.04° [1999 avg = 78.07°] F

24.11° 24.01° 25.21° 25.91° 26.21° 26.31° 26.11° 26.01° 26.51° 26.21° 25.81° 24.91° [2000 avg = 25.61°] C
75.40° 75.22° 77.38° 78.64° 79.18° 79.36° 79.00° 78.82° 79.72° 79.18° 78.46° 76.84° [2000 avg = 78.10°] F

24.71° 25.01° 25.41° 25.91° 26.81° 26.51° 26.31° 26.71° 26.51° 27.11° 25.91° 25.81° [2001 avg = 26.06°] C
76.48° 77.02° 77.74° 78.64° 80.26° 79.72° 79.36° 80.08° 79.72° 80.80° 78.64° 78.46° [2001 avg = 78.91°] F

25.41° 25.11° 25.51° 25.71° 25.91° 27.01° 25.71° 26.01° 27.11° 25.91° 25.31° 25.51° [2002 avg = 25.85°] C
77.74° 77.20° 77.92° 78.28° 78.64° 80.62° 78.28° 78.82° 80.80° 78.64° 77.56° 77.92° [2002 avg = 78.53°] F

25.21° 25.91° 26.61° 27.01° 27.21° 27.11° 26.91° 26.71° 27.31° 27.41° 26.81° 25.71° [2003 avg = 26.66°] C
77.38° 78.64° 79.90° 80.62° 80.98° 80.80° 80.44° 80.08° 81.16° 81.34° 80.26° 78.28° [2003 avg = 79.99°] F

25.41° 25.71° 26.01° **** **** **** **** **** **** **** **** **** [2004 avg = 25.71°] C
77.74° 78.28° 78.82° **** **** **** **** **** **** **** **** **** [2004 avg = 78.28°] F

**** 24.70° 25.00° 25.80° 26.30° 26.70° 26.50° 26.60° 26.60° 26.80° 26.90° 25.60° [2006 avg = 26.14°] C
**** 76.46° 77.00° 78.44° 79.34° 80.06° 79.70° 79.88° 79.88° 80.24° 80.42° 78.08° [2006 avg = 79.05°] F

25.20° 25.10° 25.60° 26.30° 26.80° 26.90° 26.30° 26.90° 26.70° 26.80° 25.60° 25.00° [2007 avg = 26.10°] C
77.36° 77.18° 78.08° 79.34° 80.24° 80.42° 79.34° 80.42° 80.06° 80.24° 78.08° 77.00° [2007 avg = 78.98°] F

24.60° 25.00° 25.30° 26.20° 26.50° 26.80° 26.50° 26.60° 27.20° 26.90° 25.30° 25.40° [2008 avg = 26.02°] C
76.28° 77.00° 77.54° 79.16° 79.70° 80.24° 79.70° 79.88° 80.96° 80.42° 77.54° 77.72° [2008 avg = 78.84°] F

25.00° 24.70° 25.00° 26.10° 26.80° 27.00° 26.70° 26.70° 27.20° 26.80° 26.00° 26.10° [2009 avg = 26.17°] C
77.00° 76.46° 77.00° 78.98° 80.24° 80.60° 80.06° 80.06° 80.96° 80.24° 78.80° 78.98° [2009 avg = 79.11°] F

25.40° 26.00° 26.10° 27.10° 27.10° 27.20° 26.80° 26.90° 26.80° 26.50° 24.10° **** [2010 avg = 26.36°] C
77.72° 78.80° 78.98° 80.78° 80.78° 80.96° 80.24° 80.42° 80.24° 79.70° 75.38° **** [2010 avg = 79.45°] F

24.70° 25.30° 25.10° 25.60° 26.40° 27.00° 26.10° 26.70° 26.70° 25.80° 25.10° **** [2011 avg = 25.86°] C
76.46° 77.54° 77.18° 78.08° 79.52° 80.60° 78.98° 80.06° 80.06° 78.44° 77.18° **** [2011 avg = 78.55°] F

24.10° **** 25.10° **** 26.90° **** **** 26.50° 26.50° 26.40° **** 25.40° [2012 avg = 25.84°] C
75.38° **** 77.18° **** 80.42° **** **** 79.70° 79.70° 79.52° **** 77.72° [2012 avg = 78.52°] F

25.30° 25.10° 25.30° 26.30° 26.40° 26.70° 25.30° 26.40° 27.00° 26.50° 25.80° 25.20° [2013 avg = 25.94°] C
77.54° 77.18° 77.54° 79.34° 79.52° 80.06° 77.54° 79.52° 80.60° 79.70° 78.44° 77.36° [2013 avg = 78.69°] F

25.30° 25.20° 25.40° 25.80° 27.50° 26.60° **** **** **** **** **** **** [2014 avg = 25.97°] C
77.54° 77.36° 77.72° 78.44° 81.50° 79.88° **** **** **** **** **** **** [2014 avg = 78.74°] F



This example is typical, in that, the earliest years tend to show a lower annual average temperature than the more recent years do (a warming trend).
A home-made graph of the above data

What I am working on now is a program that goes through each of the 7,280 individual weather stations in those 236 countries and/or on those 14 ships.

It will collect, calculate, and display only the oldest year averages together with the most recent year averages, for a comprehensive comparison (e.g. there would only be the first two rows, and the last two rows in the example above).

The thing is that it will be a display for every weather station on the globe that has official recognition in these databases (@some 7,280 locations).

Then we can compare the PPM of CO2 which shows a similar pattern.

The next post in this series is here, the previous post in this series is here.

Monday, September 22, 2014

Databases Galore

Ancient Data
Transparency is becoming a buzz word these days.

People want to take a look at the wizard behind the bells, whistles, smoke, mirrors, and the big curtain.

When scientific and political matters are being analyzed this can lead to databases of information.

We have looked at Pentagon 1033 Program databases (Will The Military Become The Police? - 10), NOAA data concerning tornadoes (On The Origin of Tornadoes - 3), and now I am looking at large volumes of NOAA, NASA, and other sources for climate databases.

One database I have processed via software I am developing has about 12,348,532 individual fields of data concerning temperatures around the globe from circa 1878 to quite recently.

That data is a digest, in the sense of it being average temperatures for each month in each year.

It would be about 30 times larger if it was daily temperatures instead of monthly average temperatures.

Since I am looking at trends, the monthly averages are fine since they span close to a hundred and fifty years (a less detailed database goes back another hundred years to the late 1700s).

The NASA and NOAA folks are quite transparent, even to the point of making the data available for downloading.

The software parses the data to unify and clarify it, then it is stored in a MYSQL database where SQL queries can then be made on tables of that data.

By "unify and clarify" I mean changing the names of the columns to make them easier for me to read.

For example, the data that comes from old COBOL or BASIC styled methods of data storage have a certain way about them.

This is how the data came down:
ID,YEAR,ELEMENT,VALUE1,DMFLAG1,QCFLAG1,DSFLAG1,
. . .
. . .
. . .
VALUE12,DMFLAG12,QCFLAG12,DSFLAG12
I renamed them to:
country, station, year, jan, feb, mar, apr, may, jun, jul, aug, sep, oct, nov, dec
The database builders have been collecting data for a long, long time, and the technicians working with it understand the archaic names.

Since I just laid eyes on the data the renaming helps me work with the data more easily.

The "station" value links to another database that has very useful information about the weather station where the monthly temperatures were recorded:
Variables:

ID, LATITUDE, LONGITUDE, STNELEV, NAME, GRELEV, POPCLS,
POPSIZ, TOPO, STVEG, STLOC, OCNDIS, AIRSTN, TOWNDIS, GRVEG, POPCSS

Variable Definitions:

ID: 11 digit identifier, digits 1-3=Country Code, digits 4-8 represent
the WMO id if the station is a WMO station. It is a WMO station if
digits 9-11="000".

LATITUDE: latitude of station in decimal degrees

LONGITUDE: longitude of station in decimal degrees

STELEV: is the station elevation in meters. -999.0 = missing.

NAME: station name

GRELEV: station elevation in meters estimated from gridded digital
terrain data

POPCLS: population class (U=Urban, S=Suburban, R=Rural)

POPSIZ: the population of the city or town the station is location in
(expressed in thousands of persons).

TOPO: type of topography in the environment surrounding the station,
(Flat-FL, Hilly-HI, Mountain Top-MT, Mountainous Valley-MV).

STVEG: type of vegetation in environment of station if station is Rural
and when it is indicated on the Operational Navigation Chart
(Desert-DE, Forested-FO, Ice-IC, Marsh-MA).

STLOC: indicates whether station is near lake or ocean.

OCNDIS: distance to nearest ocean/lake from station (km).

AIRSTN: airport station indicator (A=station at an airport).

TOWNDIS: distance from airport to center of associated city or town (km).

GRVEG: vegetation type at nearest 0.5 deg x 0.5 deg gridded data point of vegetation dataset (44 total classifications).
BOGS, BOG WOODS, COASTAL EDGES, COLD IRRIGATED, COOL CONIFER, COOL CROPS, COOL DESERT, COOL FIELD/WOODS, COOL FOR./FIELD, COOL GRASS/SHRUB, COOL IRRIGATED, COOL MIXED, EQ. EVERGREEN E. SOUTH TAIGA HEATHS, MOORS, HIGHLAND SHRUB, HOT DESERT, ICE, LOW SCRUB, MAIN TAIGA MARSH, SWAMP, MED. GRAZING NORTH. TAIGA, PADDYLANDS, POLAR DESERT, SAND DESERT, SEMIARID WOODS, SIBERIAN PARKS, SOUTH. TAIGA, SUCCULENT THORNS, TROPICAL DRY FOR, TROP. MONTANE, TROP. SAVANNA TROP. SEASONAL, TUNDRA, WARM CONIFER, WARM CROPS, WARM DECIDUOUS, WARM FIELD WOODS, WARM FOR./FIELD, WARM GRASS/SHRUB, WARM IRRIGATED, WARM MIXED, WATER, WOODED TUNDRA
POPCSS: population class as determined by Satellite night lights (U=Urban (greater than 50,000 persons);  (S=Suburban (greater than or equal to 10,000 and less than or equal to 50,000 persons);
(R=Rural (less than 10,000 persons) City and town boundaries are determined from location of station on Operational Navigation Charts with a scale of 1 to 1,000,000. For cities greater than 100,000 persons, population data were provided by the United Nations Demographic Yearbook. For smaller cities and towns several atlases were uses to determine population.
The linkage of the monthly temperatures database to the station database allows one to look at temperature changes then ponder the affects to the local flora, fauna, and community around the weather station.

Anyway, this week I will be generating reports from the data recorded at interesting locations around the globe (here are some interesting weather station names in the country of Burkina Faso: Dori, Ouahigouya, Ouagadougou, Fada N'Gourma, Bobo-Dioulass, Boromo, Gaoua).

I hope to share some of it, including links to the sources of the data.

Why do the deniers hide everything and shroud themselves in darkness?

The climate scientists are very open with their data.

Have a good Monday.

The next post in this series is here.