results_uwnd_sst_Joni

==**1. Load in your data again, and extract 240-month time series ts1(t) and ts2(t) at longitude index 80 = 200E = 160W.** == ==** What is the fundamental (lowest) frequency possible in this time series? ** == f = 1/T = 1/240 months What is the//bandwidth//or spectral resolution (Δf) of the spectrum you will create from it? f = 1/T = 1/240 months What is the Nyquist (highest resolvable) frequency in this time series? 0.5fs (where fs is the samplng frequency) = 0.5(1/t) o= 0.5(1/1 month) Based on the above, make a 1D frequency array f to use as the x axis on your plots in part 2. f = [1:T/2]/T;

**2. Make the 1D power spectrum of your first field time series:**
**(Thanks Angela!)** 
 * Plot the spectrum** as a power spectral density PSD = Δ(variance)/Δf = Pow//Δf vs. frequency f.//





//**2. Plot the spectrum** as an indefinite integral (cumulative power) vs. period or log period.//





//**3. Plot the spectrum** as f*power vs. log(f). Area under the curve should still be proportional to total power (variance).//





//**4. Plot the spectrum** as f*power vs. log(period)//





//**5. Plot the spectrum** as log(Pow) vs. log(f)//





//**6. Plot the spectrum in your favorite format, after rebinning** f and Pow to coarser frequency bins//





//**7. Plot the spectrum in your favorite format,** **overplotting** the PSD you get// when you **pad the ends of the time series with zeroes**


 * (THANKS TO CHAKO!!!!!!)**//





==**3. Significance testing of peaks: Overplot a red noise spectrum and its 95% significance level (the F test).** ==


 * Estimate** your lag-1 autocorrelation value r1 for your field1.

Use r1 to **create and overplot** the power spectrum of an autoregressive (AR(1)) or "red noise" process with the same r1 and same variance as your series.

Use the [|F test] to **overplot** a line indicating the 99% significance level for spectral peaks.