How to extract time series averaged over a region?

Hi All,

I've been working on this monthly chlorophyll dataset where the "chl variable" has the dimensions 489x221x1X24 (lon x lat x depth x time). So basically the 4th dimensions has 24 chlorophyll values corresponding to 24 months from Jan'20 to Dec'21. Now I wanted to extract the chlorophyll values for 24 months of the entire region into a table; in that way I have a time series for further analysis.

lon=ncread("chl2020.nc","longitude");lat=ncread("chl2020.nc","latitude");
chl=ncread("chl2020.nc","chl");
this=squeeze(chl);

% removes the 3rd dimension
latrange= -25:0.25:30;
lonrange= 32:0.25:120;

% Indexing the region
for i=1:length(latrange)
latindex(i)= find(lat==latrange(i));
end
for i=1:length(lonrange)
longindex(i)= find(lon==lonrange(i));
end

%Finding the values for January - 2020
newchl = this(longindex,latindex,1);
%Finding the values for February - 2020
newchl2 = this(longindex,latindex,2);

Now, the last two lines basically extract for each month. However, this is time consuming and I wanted to know how to code a more efficient and quicker way to get all the monthly values averaged over the entire region from Jan '20 - Dec '21. The dataset is below:

https://drive.google.com/file/d/16B68a4j2EfwC0FoSiINg3-qmQTK97dCD/view?usp=sharing

My end goal is a 2-D result with Months and Chlorophyll values. I'd be grateful for any assistance in this. Thanks!

Answer 1

[Peter Perkins]

Keegan, there's a bunch of ways to do this, here's one that uses a timetable:

>> lat = ncread("chl2020.nc","latitude");
>> lon = ncread("chl2020.nc","longitude");
>> chlor = squeeze(ncread("chl2020.nc","chl"));
>> whos
  Name         Size                   Bytes  Class     Attributes

  chlor      489x241x24            22627008  double              
  lat        241x1                     1928  double              
  lon        489x1                     3912  double              

>> j = (-25 <= lat) & (lat <= 30);
>> i = (32 <= lon) & (lon <= 120);
>> lat = lat(j);
>> lon = lon(i);
>> chlor = chlor(i,j,:);
>> whos
  Name         Size                   Bytes  Class      Attributes

  chlor      353x221x24            14978496  double               
  i          489x1                      489  logical              
  j          241x1                      241  logical              
  lat        221x1                     1768  double               
  lon        353x1                     2824  double               

>> Date = datetime(2020,(1:24)',1,Format="MMMyy");
>> Chlor = permute(chlor,[3 2 1]);
>> tt = timetable(Date,Chlor)
tt =
  24×1 timetable
    Date          Chlor      
    _____    ________________
    Jan20    1×221×353 double
    Feb20    1×221×353 double
    Mar20    1×221×353 double
    Apr20    1×221×353 double
    May20    1×221×353 double
    Jun20    1×221×353 double
    Jul20    1×221×353 double
    Aug20    1×221×353 double
    Sep20    1×221×353 double
    Oct20    1×221×353 double
    Nov20    1×221×353 double
    Dec20    1×221×353 double
    Jan21    1×221×353 double
    Feb21    1×221×353 double
    Mar21    1×221×353 double
    Apr21    1×221×353 double
    May21    1×221×353 double
    Jun21    1×221×353 double
    Jul21    1×221×353 double
    Aug21    1×221×353 double
    Sep21    1×221×353 double
    Oct21    1×221×353 double
    Nov21    1×221×353 double
    Dec21    1×221×353 double
>> tt.MeanChlor = mean(tt.Chlor,2:3,"omitnan")
tt =
  24×2 timetable
    Date          Chlor          MeanChlor
    _____    ________________    _________
    Jan20    1×221×353 double     0.21169 
    Feb20    1×221×353 double     0.21149 
    Mar20    1×221×353 double     0.15771 
    Apr20    1×221×353 double     0.13574 
    May20    1×221×353 double      0.1589 
    Jun20    1×221×353 double     0.22037 
    Jul20    1×221×353 double     0.29464 
    Aug20    1×221×353 double     0.32263 
    Sep20    1×221×353 double     0.26623 
    Oct20    1×221×353 double     0.21402 
    Nov20    1×221×353 double     0.19245 
    Dec20    1×221×353 double     0.22103 
    Jan21    1×221×353 double     0.26072 
    Feb21    1×221×353 double     0.20912 
    Mar21    1×221×353 double     0.15732 
    Apr21    1×221×353 double     0.13859 
    May21    1×221×353 double     0.16133 
    Jun21    1×221×353 double     0.23466 
    Jul21    1×221×353 double     0.30558 
    Aug21    1×221×353 double     0.32634 
    Sep21    1×221×353 double     0.27826 
    Oct21    1×221×353 double     0.22286 
    Nov21    1×221×353 double     0.19408 
    Dec21    1×221×353 double     0.21508 

[Keegan Carvalho]

Hi Peter, thanks a ton for this. I've understood where I've missed out; seems the indexing is key here to get things right.

Much appreciated :)

Cheers!