How do I compute averages for NOAA SST data?

Hello,

I am new to Matlab and new to coding entirely. I need to compute NINO3.4 SST climatological values (averages for specific time stamps, say, average over all January, February, …, December) for each month and plot the climatological result (i.e., average values of NINO3.4 SST for the 12 months).

I have no idea how to do this. Can someone please help.

Answer 1

[Star Strider]

The easiest way to do this is to use the accumarray function —

T1 = readtable('ersst5.nino.mt...-20.ascii.csv', 'VariableNamingRule','preserve')

T1 = 889x10 table
     YR     MON    NINO1+2    ANOM     NINO3    ANOM_1    NINO4    ANOM_2    NINO3.4    ANOM_3
    ____    ___    _______    _____    _____    ______    _____    ______    _______    ______

    1950     1      23.01     -1.55    23.56     -2.1     26.94    -1.38      24.55     -1.99 
    1950     2      24.32     -1.78    24.89    -1.52     26.67    -1.53      25.06     -1.69 
    1950     3      25.11     -1.38    26.36    -0.84     26.52     -1.8      25.87     -1.42 
    1950     4      23.63      -1.9    26.44    -1.14      26.9    -1.73      26.28     -1.54 
    1950     5      22.68     -1.74    25.69    -1.57     27.73    -1.18      26.18     -1.75 
    1950     6      21.33     -1.79    25.51    -1.11     28.13    -0.84      26.46     -1.27 
    1950     7      20.31     -1.65    24.91     -0.9     27.64    -1.26      26.29     -1.01 
    1950     8      20.12     -0.88    24.47    -0.65     27.54    -1.25      25.88     -0.97 
    1950     9      19.56     -1.17    24.01    -0.89     27.27    -1.49      25.74     -0.98 
    1950    10      19.99     -1.02    24.17    -0.81     27.13    -1.63      25.69     -1.03 
    1950    11      19.85      -1.8    23.86    -1.24     26.99     -1.7      25.47     -1.23 
    1950    12      21.72     -1.09    24.15    -1.08     27.14     -1.4      25.29     -1.31 
    1951     1      24.11     -0.46    24.79    -0.87     27.21     -1.1      25.24      -1.3 
    1951     2      25.19     -0.91    25.65    -0.76     27.09    -1.11      25.71     -1.04 
    1951     3      25.74     -0.75    26.87    -0.33     27.74    -0.58       26.9     -0.38 
    1951     4      25.29     -0.25    27.37    -0.21     28.21    -0.42      27.58     -0.23 

MonthlyData = accumarray(T1.MON, (1:numel(T1.MON)).', [], @(x){T1.('NINO3.4')(x)})          % Returns All Years Of Data For Each Month

MonthlyData = 12x1 cell array
    {75x1 double}
    {74x1 double}
    {74x1 double}
    {74x1 double}
    {74x1 double}
    {74x1 double}
    {74x1 double}
    {74x1 double}
    {74x1 double}
    {74x1 double}
    {74x1 double}
    {74x1 double}

MonthlyMeans = accumarray(T1.MON, (1:numel(T1.MON)).', [], @(x)mean(T1.('NINO3.4')(x)))     % Returns Monthly Means For All 75 Years

MonthlyMeans = 12x1

26.4279 26.6104 27.1176 27.5719 27.6912 27.5139 27.0877 26.6761 26.5666 26.5354

figure
plot((1:12), MonthlyMeans)
grid
xlabel('Month Number')
ylabel('NINO3.4 Mean (°C)')
xlim([1 12])
set(gca,'XTick',(1:12))

Since you appear to be completely new to all of this, the approach here is to use accumarray (an efficient looping algorithm that aggregates data) to select the months of each year and then return the data from them. Here, the index vector are the months (the table format for these data makes this extremely convenent), and it then uses a vector for all the indices to aggregate them (the second argument). (The third argument is not necessary here, however since it needs to be supplied, common practics is to use the empty array [] in such instances.) The last argument is an anonymous function (see the documentation section on Anonymous Functions) that tells accumarray what to do with the data. In the first call to it, the purpose is to return all the data for all years for each month in a cell array. The second and completely separate call to it, calculates the monthly means over all the years and returns them as a single vector. I include both of them to illustrate different ways of using it, however for the present purpoose, only the second call to it is necessary.

EDIT — Corrected typographical errors. Code unchanged.

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