Mean for particular seasons over 32 yrs..say January to March(1:3),4:6,7:9,10:12

I want to take the mean season wise..say for Winter, Spring, Summer and Autumn.. and then need to show over a period of 32 years ..how the seasonal mean is changing.

datacoord = reshape(data, 361,361,4);
lat = squeeze(datacoord(:,:,3));
long = squeeze(datacoord(:,:,4));

%m_proj('Azimuthal Equal-area');
m_proj('stereographic','lat',90,'long',30,'radius',22);
m_grid('xtick',12,'tickdir','out','ytick',[65 70 75 80 85 90],'linest','-');
m_coast('patch',[.7 .7 .7],'edgecolor','r');
m_elev('contour',[ ],'edgecolor',' ');

for y=1979:2012,
    % open binary file for
    % Polar Pathfinder Daily 25 km EASE-Grid Sea Ice Motion Vectors, Version 2

      folderpath = 'C:\Users\Documents\MATLAB\Mean_months\';

      stryear = num2str(y);
      filepath = fullfile(folderpath,stryear);

      files = dir(fullfile(filepath,'\icemotion.mean.*.n.v02.bin') );
      files = {files.name};

      Umean = [];
      Vmean = [];

      for i=1:numel(files),
          disp(files{i});
          filename = fullfile(filepath,files{i});

          fid = fopen(filename);
          rawdata = fread(fid,[361, inf],'int16');
          fclose(fid);

          % reshape it into a [3x361x361] matrix
          dd = reshape(rawdata,[3, 361, 361]);

          % change 3d matrix to simple matrix, and divide by 10 to get cm/second
          u = squeeze(dd(1,:,:)) ./ 10;
          v = squeeze(dd(2,:,:)) ./ 10;

          % Change the data values from cm/s to m/second by multiplying it with 1/100
          u = u*1/100;
          v = v*1/100;
      end
      find(lat < 1 | lat ~= fix(lat))
      find(long < 1 | long ~= fix(long))
      i = lat;
      j = long;
      k = 1:length(1979:2012);
      u = u(i,j,k);
      v = v(i,j,k);
      yrlst = 1979:2012;
      wintlst = 1:3;
      for i = 1:length(yrlst)
          tempwint = wintlst +(i-1)*12;
          tmpu = squeeze(nanmean(u(:,:,tempwint),3));
          tmpv = squeeze(nanmean(v(:,:,tempwint),3));
          for j = 1:size(tmpu,1)
              k = 1:size(tmpu,2)
              tuwint(j,k,i) = tmpu(j,k);
              tvwint(j,k,i) = tmpv(j,k);
          end
      end
  end

Answer 1

[Chad Greene]

It's hard to decipher your nested loops, but here's how I'd tackle it. I'll assume you have a big 3D matrix U whose dimensions correspond to (lon x lat x time) or (gridx x gridy x time) or something of the like. You probably also have a 3D matrix of the same size V for zonal motion, and I'll assume you have an array t and length(t) = size(U,3).

We're gonna trick downsample_ts into computing seasonal means like this:

1. Get t into components of year, month, and day:

[year,month,day] =  datevec(t);

2. The downsample_ts function computes monthly means, so we'll lie to downsample_ts and tell it that all December and February data happened in January.

month(month==12) = 1; 
month(month==2) = 1;

Do the same for the other seasons, i.e.,

month(month==3) = 4; 
month(month==5) = 5;

And when you do the same for the other seasons, you should get

unique(month) 
    = [1, 4, 7, 10];

Create a new fake t2 vector:

t2 = datenum([year,month,day]);

Then a 32 year record of seasonal means can be obtained by

[Umean,tmean] = downsample_ts(U,t,'monthly','mean'); 
Vmean = downsample_ts(V,t,'monthly','mean');

Dimensions of Umean and Vmean will be size(U,1) x size(U,2) x 128 because 4 seasons times 32 years is 128. All DJF data will be given by

Umean(:,:,tmean==1);

And if you want to get a linear trend map of DJF seasonal averages over 32 years you could do

U_djf_trend = trend(Umean(:,:,tmean==1),1,3);

using the trend function.

[Sophia]

Thanks Green for such a nice explanation, but i am still facing the same problem.. Some Details about dataset- I have a dataset for 1979-2012.. I have two components u and v.. U is the value of ice motion in horizontal direction , say east to west and v is the ice motion in vertical direction say north to south. these values correspond to a two dimensional matrix with lat-long to show the values of ice motion in a particular spatial location(say lat-long).

But this data set for a period of 32 years. I hope it helps.

[Chad Greene]

Hi Sophia,

Yes, my assumption was that for each velocity component you have a grid for each month, and if you stack them together into a 3D matrix the first dimension is east-west, the second dimension is north-south, and the third dimension is time.

[Max Reinicke]

Dear Chad,

How can I go back from the cryptic t2=datenum values to f.e. the year of the winter season?

so to have two columns of: year;season;Umean

Thanks for the great function and help!

[Walter Roberson]

Dwaipayan Chatterjee comments to Chad Greene:

[Umean,tmean] = downsample_ts(U,t,'monthly','mean');
Vmean = downsample_ts(V,t,'monthly','mean');

At this stage I suppose instead of 't', 't2' should be used