RSS satellite temperature for Australia/Pacific

Below are RSS satellite temperatures for various regions in Australia/Pacific.  In a previous post (here) I derived lower tropospheric temp for Australia in a rectangle shown in the map below.

In this post I define a smaller rectangle (red) to avoid any ocean areas, between longitudes 123.75 E to 146.25 E and latitudes 31.25 South to 21.25 South.

All area inside & including the rectangle is summed & averaged. The data starts Dec 1978 and ends Feb 2016. The actual measured area can extend a little outside the rectangle to a maximum of 2.5/2 degrees, which is why I've drawn the rectangles smaller than the target area to avoid overlap.  The resulting temperature graph is below:

It shows a very slight uptrend of 0.07C per century. Nothing like the official (specially homogenised) graph provided by Bureau of Meteorology which shows a lot more warming the last few years (in line with their global warming theory).

For comparison here is my graph again with a 16-point moving average and the Steven Goddard graph of Australia temps (from here) which ends a couple of years earlier:

I think I get a slightly better match to Steven Goddard's graph with the larger rectangle of the previous post. 

The .nc file is from here:

http://www.remss.com/measurements/upper-air-temperature
↳
ftp://ftp.remss.com/msu/data/netcdf

↳

uat4_tb_v03r03_anom_chtlt_197812_201602.nc3.nc

You have to register since Dec 2016 to download stuff from RSS. If you just want the above file I've uploaded it here:

https://drive.google.com/open?id=0B-wD9lfH3doOZ0hiZUhjRmpVeVU

This sort of .nc (NetCDF) file can be opened by mathematics programs like Matlab. The code has changed a bit between revisions 2015a and 2016a. In the last post using r2015a I used function nansum to sum values for a given area such as Australia.  In r2016a nansum is replaced by tsnansum which stands for time-series not-a-number sum.

Here is the code to open the file in Matlab 2016a (more details here). Let us define a matrix a1.

a1 = ncread('uat4_tb_v03r03_anom_chtlt_197812_201602.nc3.nc','brightness_temperature_anomaly')

Now you have a 144 x 72 x 458 matrix which is long x lat x months. Let us find the time series for one location, Sydney, Australia. Sydney's longitude & latitude is 151E 33.8S.  Unfortunately the grid resolution is quite course so individual cities can only be approximated. Using my conversion table (see bottom of post here) the nearest grid point is long = 133, lat = 23.

Sydney. Syd = squeeze(a1(133,23,:)

Temp anomaly (C):

Melbourne (this grid square is displaced quite a bit).  Mel = squeeze(a1(130,22,:))

Tasmania.  Tas = squeeze(a1(131,19,:))

Queenland:

Qld = squeeze(tsnansum(a1(128:132,:,:)))

Qld2 = squeeze(tsnansum(Qld(25:29,:)))'

Qld3 = Qld2/(5*5)

Northern Territory:

NT = squeeze(tsnansum(a1(124:128,:,:)))

NT2 = (squeeze(tsnansum(NT(25:29,:))))'

NT3 = NT2/(5*6)

Perth.  Perth = squeeze(a1(119,24,:))

Mawson Station Antarctica. Maw = squeeze(a1(98,9,:))

New Zealand. Area centred on Taupo, North Island. NZ = squeeze(a1(143,21,:))

Nauru Pacific island near equator. Grid point is just east of it. Nauru = squeeze(a1(140,36,:))

Notice how much lower the temperature variation at this equatorial Pacific location is compared to the others. Equatorial ocean regions, especially the Pacific, act like air conditioners for the earth perhaps moreso than the frigid poles, by emitting EMR to space and losing heat by evaporation.

Canterbury South Island New Zealand.  Cant = squeeze(a1(141,19,:))

Western Australia:

WA = squeeze(tsnansum(a1(119:124,:,:)))
WA2 = squeeze(tsnansum(WA(24:28,:)))'
WA3 = WA2/(5*6)

=================

Sundry points:

Title of data file is:

'Monthly anomalies of brightness temperatures in the Lower Troposphere on a 2.5 degree grid'

Main variable description:

"Global monthly anomaly of Lower Tropospheric temperatures derived from microwave radiometers on NOAA and NASA polar orbiting satellites. The anomalies are deviations from 1979-1998 mean."

There are 144 points of longitude & 72 of latitude. The points bunch up as they get closer to the poles. There is no data within ~ 10 - 20 degrees of the poles due to the orbits of the satellites.

The complete header of the RSS TLT .nc file is in a text file here.