mhw_efficiency.md

Comparison for the efficiency biases between detect and detectc

Here we compare the efficiency biases between detect and detectc.

Loading data

sst_full=NaN(32,32,datenum(2016,12,31)-datenum(1982,1,1)+1);
for i=1982:2016;
    file_here=['sst_' num2str(i)];
    load(file_here);
    eval(['data_here=sst_' num2str(i) ';'])
    sst_full(:,:,(datenum(i,1,1):datenum(i,12,31))-datenum(1982,1,1)+1)=data_here;
end

Comparing detect and detectc

Here we detect MHWs during 1982-2016 based on climatologies during 1982-2005.

tic
[MHW,mclim,m90,mhw_ts]=detect(sst_full,datenum(1982,1,1):datenum(2016,12,31),datenum(1982,1,1),datenum(2005,12,31),datenum(1993,1,1),datenum(2016,12,31));; %take about 30 seconds.
toc

It takes about ~10 seconds on my local machine.

tic
[MHWc,mclimc,m90c,mhw_tsc]=detectc(sst_full,datenum(1982,1,1):datenum(2016,12,31),datenum(1982,1,1),datenum(2005,12,31),datenum(1993,1,1),datenum(2016,12,31));; %take about 30 seconds.
toc

Using the function detectc, it takes only ~7 seconds and the biases will increase as the data size grows.

Examining detectc MHW outputs

Here we take a look at the MHW output from detectc. In this output, MHW events are stored in a cell, with each grid cell holding its corresponding MHWs.

>> size(MHWc{1,1})

ans =

    62    10

Here we can see 62 MHW events are detected in the grid (1,1).

Do we get the same number of events as what we got from detect?

>> num_mhw=cellfun(@(x)size(x,1),MHWc);
num_mhw=nansum(num_mhw(:))

num_mhw =

       70097

The total number of MHW events over the whole domain remains the same as the output from detect.