MATLAB: How to take out extreme event annually from the data.

sir i have program who calculate extreme event for whole 95 years but i want to calculate extreme event annually (it mean 365 then 365 ….each separate years) then what will be change in this program for doing this? my data size is (prec_all_years) 20x22x34675(lon,lat,time).this is daily data.

prec_all_years = prec_all_years*86400; % rainfall in mm/day

xx = []

for ii= 1:95;

for i = 1:length(X1);

for j = 1:length(Y1);

xx = [xx (i-1)*[1:365]];

prec1 = prec_all_years(i,j,xx);

inds1 = find(prec1 >=64.5 & prec1 <= 124.5); inds2 = find(prec1 > 124.5 & prec1 <=244.5); inds3 = find(prec1 > 244.5);

hr(i,j) = length(inds1); vhr(i,j)= length(inds2); ehr(i,j)= length(inds3);

end end end % now find frequency of rainfall in different regions

% western region

xe = [13:20]; ye = [11:16]; xw = [1:8]; yw = [11:16]; xn = [5:9]; yn = [15:20]; xs = [5:9]; ys = [3:7];

% eastern region

freq_east_hr = hr(xe,ye);

count_hr_east = sum(freq_east_hr(:))

freq_east_vhr = vhr(xe,ye);

count_vhr_east = sum(freq_east_vhr(:))

freq_east_ehr = ehr(xe,ye);

count_ehr_east = sum(freq_east_ehr(:))

Best Answer

Slightly new approach, given your comment on the previous answer. This one creates hr, vhr, and ehr, which are 20-by-22-by-95 (lat/lon/year) arrays holding the counts over each year, for each lat/lon location. These are then summed over all locations to give count_X_east, which are 95-element vectors (one total for each year).

prec_all_years = prec_all_years*86400; % rainfall in mm/day
bins = [64.5 124.5 244.5 Inf];
% Reshape into lat/lon/day/year, and bin over 3rd dimension (days)
counts = histc(reshape(prec_all_years,20,22,365,95),bins,3);
% Extract the counts for each of the 3 categories
hr = squeeze(counts(:,:,1,:));
vhr = squeeze(counts(:,:,2,:));
ehr = squeeze(counts(:,:,3,:));
% Extract a region
xe = 13:20; ye = 11:16;% xe is x for east,ye is y for east
% Extract counts for each category
count_hr_east = sum(reshape(hr(xe,ye,:),[],95));
count_vhr_east = sum(reshape(vhr(xe,ye,:),[],95));
count_ehr_east = sum(reshape(ehr(xe,ye,:),[],95));

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MATLAB: How to take out extreme event annually from the data.

I really, Really, REALLY wish you'd quit this incessant creation of new threads and just carry on a linear conversation...I'm thinking this is about the last shot you've got with me, any way.

OK, Ravi, let's try this an entirely different way -- instead of the complexity of the reshape over multiple dimensions that you just don't seem to be grasping the basics of, let's just go back to brute force and stubborness and maybe you can at least then get to an answer, letting code refinement come later...

IIUC, you've a 3D data set of precipitation predictions on a daily basis for a 2D matrix of geographical locations with the 3rd dimension representing each day over a period of years. Your present objective is to return the maximum predicted rainfall for each year by year over the entire time period.

In our previous thread on data selection, we agreed that there is a corollary vector of time data that can be generated as

dn = datenum([2006 1 1 ;2100 12 31]);    % Matlab datenum corresponding to planes
Y = datevec(dn);                         % the year of each of the above

ERRATUM:

Y = datevec(dn);                         % the year of each of the above

should be

[Y,~] = datevec([dn(1):dn(2)]);
--dpb

So, given that, to find the maximum for each year by year is easy enough...

max_precip=zeros(size(precip,1),size(precip,2),Y(end)-Y(1)+1,1); % preallocate 
iyr=0;                                    % counter for the array
for yr=Y(1):Y(end)                        % loop over all the years in range
  iyr=iyr+1;                              % increment the counter
  max_precip(:,:,iyr)=max(precip(:,:,yr==Y),3); % max over the years
end

Now you'll have a lat x lon x 95 array of the yearly maxima each plane being the max for each year in the dataset at each point. You can simply modify the two lat/lon indices to select various regions rather than globally.

ADDENDUM:

I did think about the dataset option a little -- it's possible but would require duplicating a lot of data to convert the 3D storage to 2D as the dataset object isn't quite as flexible as I was hoping perhaps it might be.

MATLAB: How to extract seasonal means

For those just coming here, you can see additional details and explanation here.

You seem to be grouping Jan, Feb and Dec from the same year together. I would be more inclined to group the winters as Dec yr1 with Jan yr2, Feb yr2. In doing so, I see the challenge being.

Grouping Dec from one year with Jan, Feb of the next
The first year will only have Jan, Feb while the last year will only have Dec.
If no data is removed, this means your will have data covereing 38 winters (Jan, Feb in last year as well as Dec in last year).

You asked what you were doing wrong. I don't think cumsum is doing what you want. The first value is itself. The second is the first plus the second. This pattern continues until the last value is the sum of all previous values plus itself. Instead, you want to sum each group separately.

Here's an approach that uses findgroups/splitapply

% Load data from nc file
lon=ncread('sstarc.nc','lon');
lat=ncread('sstarc.nc','lat');
sst=ncread('sstarc.nc','sst');
% Create vector of dates to use for extracting winter months
startDate = datetime(1982,1,1,"Format","MMM-uuuu");
endDate = datetime(2018,12,31,"Format","MMM-uuuu");
dates = startDate:calmonths(1):endDate;
winterMonth = month(dates) == 1 | month(dates) == 2 | month(dates) == 12;
% Extract winter data from sst
sstWinter = sst(:,:,winterMonth);
%% Create grouping variable for winter year 
% (treating consecutive Dec, Jan Feb as winter that year)
% Extract winter year as the year corresponding to January
winterYr = year(dates(winterMonth));
winterYr(month(dates(winterMonth))==12) = winterYr(month(dates(winterMonth))==12)+1;
% Find groups
G = findgroups(winterYr);
% Splitapply can't be used on 3D matrices. Reshaped to 2D to take the mean
sstTemp = reshape(sstWinter,size(sstWinter,1)*size(sstWinter,2),size(sstWinter,3));
f = @(x) mean(x,2,"omitnan");
tempYrMean = splitapply(f,sstTemp,G);
% reshape back to original number of rows, columns
sstYrMean = reshape(tempYrMean,size(sstWinter,1),size(sstWinter,2),[]);
%% Visualizing data
% Create variables for visualizing
[LAT, LON] = meshgrid(lat,lon);
winter1 = sstYrMean(:,:,1); % Change the index of the 3rd dimension to change the year
% Visualize the data for Winter 1982
geoscatter(LAT(:),LON(:),[],winter1(:))
colorbar(gca,"eastoutside")
title("Winter " + num2str(winterYr(1)))

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MATLAB: How to take out extreme event annually from the data.

MATLAB: How to extract seasonal means

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