<div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div>My opinion:</div><div><br></div><div>[1] You should remove the climatological annual cycle from each dataset.</div><div><br></div><div><a href="http://www.ncl.ucar.edu/Document/Functions/Contributed/clmMonTLL.shtml"><b>http://www.ncl.ucar.edu/Document/Functions/Contributed/clmMonTLL.shtml</b></a></div><div><a href="http://www.ncl.ucar.edu/Document/Functions/Contributed/calcMonAnomTLL.shtml"><b>http://www.ncl.ucar.edu/Document/Functions/Contributed/calcMonAnomTLL.shtml</b></a></div><br><div><br></div><div>[2] Typically, the 'swe' file contains a _FillValue (-999.0) when there is no snow cover. I suggest setting this to 0.0.</div><div><br></div><div> sn= in1->swe(iStrt:iLast,{50:70},{20:140}) ;only specific region averaged over total Eurasia<br> sneu r=<b> wgt_areaave_Wrap</b>(sn,1.0,1.0,0);;;create area averaged snow over Eurasia<br> sneur = <b>where</b>(<b>ismissing</b>(sneur), 0.0, sneur)</div><div> <b>printVarSummary</b>(sneur)<br> p<b>rintMinMax(</b>sneur,0)<br> print("---")<br> ymd := <b>cd_calendar</b>(sneur&time, -2) ; yyyymmdd<br> print(ymd+" "+sneur)<br> print("---")<br></div><div><br></div><div><div>[3] You *know* the climatology. You want to know the feedback of sst/sneur anomalies on each other.</div><div>Use the anomalies in the correlation calculations.</div><div><br></div></div><div>[4] There are statistical issues. EG: the number of independent values to be used in the testing.</div><div><br></div><div>Typically, successive monthly values of SST or SNE are not independent. If (say) a January SNEUR is very large, then the February SNE will likely be large also. So estimating the number of independent values is an issue. <br></div><div><br></div><div> <b> <a href="http://www.ncl.ucar.edu/Document/Functions/Built-in/equiv_sample_size.shtml">http://www.ncl.ucar.edu/Document/Functions/Built-in/equiv_sample_size.shtml</a></b><br></div><div><br></div><div>Estimate for the SNEUR/SST anomalies separately. Use the smaller value.</div><div><br></div><div>[5] I am sure the IITM has staff members who know more about statistical testing/inference than ncl-talk. I suggest talking with them.</div><div><br></div><div>Good luck<br></div></div></div></div></div></div></div><br><div class="gmail_quote"><div dir="ltr">On Tue, Nov 20, 2018 at 6:13 AM Sujata Mandke <<a href="mailto:amin@tropmet.res.in">amin@tropmet.res.in</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Dear NCL community,<br>
Greetings!<br>
I have ploted lag/lead correlation (cc) between two area-averaged <br>
(Eurasian snow and East Pacific SST) monthly time series as xy plot (line). <br>
Further, I want to indicate which part of cc is statistically <br>
significant on this line using line markers. <br>
<br>
I had tested statistical significance of cc using “rtest”. <br>
In the ncl script(attached), I have plotted line for<br>
lead/lag correlation. How to mark part of this cc (line), <br>
which is statistically significant by using line markers. <br>
<br>
My questions are:<br>
(i) Is my statistical significance testing correct?<br>
<br>
(ii) how to indicate those points (by marker or any other way),<br>
that are statistically significant on the lead/lag cc line plot.<br>
<br>
I had extensively searched NCL-talk archives but <br>
did not find solution to my problem. My guess is that,<br>
i had to use “where” function and then gsn_add_polymareker,<br>
but do not know how to implement this in the ncl script.<br>
I am using NCL version 6.4.0 on linux machine. <br>
<br>
Any suggestion would be of great help.<br>
Many thanks in advance.<br>
With best regards<br>
Dr. Sujata Mandke<br>
Scientist, Indian Institute of Tropical Meteorology<br>
PUNE, INDIA_______________________________________________<br>
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