<div dir="ltr"><div>If the source array is 'x[*]' and the distribution in normally distributed with xMean and xStd, the.</div><div><br></div><div> x95 = xMean+2*xStd ; scalar<br></div><div> i95 = ind(x.ge.x95)</div><div> xHigh = x(i95) ; values <br></div><div> print(xHigh)<br></div><div>===</div><div>If 'x' is multidimensional [ 'X' ] then</div><div><br></div><div> x = ndtooned(X)</div><div><br></div><div><br></div><div><br></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Thu, Dec 5, 2019 at 6:53 AM Sri.durgesh Nandini-Weiss via ncl-talk <<a href="mailto:ncl-talk@ucar.edu">ncl-talk@ucar.edu</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">
<div>
<p>Hello everyone,</p>
<p>I am trying to extract the upper high end tail (95
percentiles-100percentiles) (shaded area below) of the probability
distribution.</p>
<p>Is one way: Area95-100percentiles= mean+2standard deviations? <br>
</p>
<p>Does anyone know how to extract (resampled) and plot this high
end/upper tail from a normal distribution?</p>
<p><br>
</p>
<p><img src="cid:16ed6ecd95bb684a6d21" alt=""></p>
<p><br>
</p>
<pre cols="72">--
Dr. Sri Nandini-Weiß
Research Scientist
Universität Hamburg
Center for Earth System Research and Sustainability (CEN)
Cluster of Excellence 'Climate, Climatic Change, and Society' (CLICCS)
Bundesstrasse 53, 20146 Hamburg
Tel: +49 (0) 40 42838 7472</pre>
</div>
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