There are probably other ways, but this is how I'd do it:
t = [1/24:1/24:365]'; % Define Time
% GENERATE DATA
y = 1 + (30-1).*rand(1,length(t));
ya = 10 + 20*sin(2*pi*t/max(t)); % Annual variation °C
yd = (1.1 + sin(2*pi*t/max(t))) .* (2*sin(2*pi*t + 4)); % Circadian variation °C
y = ya + yd; % Water Temperature °C
% PLOT ANNUAL TEMPERATURE DATA
figure(1)
plot(t,y)
title('Annual Temperatures')
xlabel('Time (Days)')
ylabel('H_{2}O Temperature (°C)')
grid
% GENERATE ENSEMBLE MATRIX OF DAILY TEMPERATURE RECORDS BY HOUR
for k1 = 1:365
DayT(:,k1) = y([1:24]+24*(k1-1));
end
% FIND TEMPERATURE MAXIMA AND MINIMA AND THE TIMES THEY OCCUR FOR EACH DAY
for k1 = 1:365
[TempMax TimeMax] = max(DayT(:,k1));
DayMax(k1,:) = [TempMax TimeMax];
[TempMin TimeMin] = min(DayT(:,k1));
DayMin(k1,:) = [TempMin TimeMin];
end
% PLOT SELECTED DAILY TEMPERATURES
figure(2)
plot([1:24]', DayT(:,1:19:end))
title('Daily Temperatures')
xlabel('Time (Hours)')
ylabel('H_{2}O Temperature (°C)')
grid
The variable DayT is your [24 x 365] matrix of hourly temperature data by day. [In the figure(2) plot, I limited the display to provide clarity.]
NOTE that I created all data as column vectors or column-major matrices for convenience.
For techniques to analyse your data, I suggest you explore DETECTING TREND AND OTHER CHANGES IN HYDROLOGICAL DATA. Hydrology and climatology aren't my areas of expertise, so I can't advise you further.
