<div dir="ltr"><div>I forgot. Please see:<b><br></b></div><div><b> </b><a href="https://www.ncl.ucar.edu/Document/Graphics/Interfaces/gsn_histogram.shtml"><b>gsn_histogram</b></a></div><div><br></div><div><b><a href="https://www.ncl.ucar.edu/Applications/histo.shtm">https://www.ncl.ucar.edu/Applications/histo.shtm</a>l</b></div><div><br></div><div><a href="https://www.ncl.ucar.edu/Document/Graphics/Interfaces/gsn_histogram.shtml"><b></b> </a><br></div><div><br></div><div><br></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Mon, Feb 24, 2020 at 7:38 PM Dennis Shea <<a href="mailto:shea@ucar.edu">shea@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 dir="ltr"><div>Good questions. I'm not a statistician so I'm not sure if I have good answers. I think it is best to consult a statistician. A few random thoughts:<br></div><div><br></div><div>[1.1] ave1, ave2: averages of the 2002 JJAS season [122 days]<br></div><div>[1.2] var1, var2: variances " ...."<br></div><div>[1.3] s1, s2: Typically, in<b> mid-latitudes</b>, the time between independent estimates [aka: the characteristic time: T0] for 'continuous' quantities like temperature and (say) sea-level-pressure is 4-to-7 days. These are derived by assuming an autoregressive process. Regions of frequent cyclogenesis have the smaller values. These estimates could be derived for every grid-point. However, for simplicity and convenience and being somewhat conservative [T0=6]: s1=N/T0=122/6 = 20 is a good estimate for <b>each</b> seasonal time series. <br></div><div>====</div><div>PDF: If you have a region: latS:latN, lonE:lonE</div><div><br></div><div>I guess it depends on what you are looking for.</div><div><br></div><div>PDF_all = <a href="https://www.ncl.ucar.edu/Document/Functions/Contributed/pdfx.shtml" target="_blank"><b>pdfx</b></a>(bias(:,{latS:latN},{lonW:lowE}), nbin, opt) ; over all time and region<br></div><div>PDF_specific_time = <a href="https://www.ncl.ucar.edu/Document/Functions/Contributed/pdfx.shtml" target="_blank"><b>pdfx</b></a>(bias(nt,{latS:latN},{lonW:lowE}), nbin, opt) ; specific time [nt] for a region</div><div>PDF_specific_grid_point: <a href="https://www.ncl.ucar.edu/Document/Functions/Contributed/pdfx.shtml" target="_blank"><b>pdfx</b></a>(bias(:,{LAT},{LON}), nbin, opt) ; all time for a specific location</div><div><br></div><div>===</div><div>Good luck<br></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Sun, Feb 23, 2020 at 6:59 AM Dr. Suman Maity via ncl-talk <<a href="mailto:ncl-talk@ucar.edu" target="_blank">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 dir="ltr"><div>Dear all</div><div>
<div>I am new for ncl and recently started using it. However, I have the following queries:<br></div><div>1. I have temperature data from the model (time,lat,lon) and observation
(time,lat,lon) with time=122 days (JJAS), lon=692 grid-points and lat=466 grid-points.
I want to plot JJAS bias
(model-observation) over-layed
with 95% significant bias gridpoints as dots. I took the help of "ttest" function in ncl. My script is attached herewith. I would like to request you to check the script and modify if needed. However, general syntax of ttest is like the following:</div><div>prob = ttest(ave1,var1,s1,ave2,var2,s2,iflag,tval_opt)</div><div>What is this "s1" and "s2" ? it is mentioned the they gives no. of statistically independent observation. Am I need to calculate that using "equiv_sample_size" function? or any constant value will be fine? if yes what will be the approximate values?</div><div><br></div><div>2. I want to generate probability density function of temperature bias over a region. I have bias data(time,lat,lon). My query is: <br></div><div>1) pdfx function is generally applied on which dimension? I mean time or space?</div><div>2) Is the rand_normal is required in case of my data?</div><div>3)
In my case bias (model-obs) is a time(122 days) X lon X lat dimension
data. How pdfx function will be applied on that? Will it directly go to
the pdfx function like:</div><div>ap = pdfx(bias,0,True)</div><div>or</div><div>I need to average the bias w.r.t time and then apply
ap = pdfx(bias,0,True) <br></div><div>Any sort of suggestion will be highly appreciated.</div><div>Best</div><div>Suman</div></div><div><div><div dir="ltr"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div><div>++++++++++++++++++++++++++++++++++++++++++</div><div><font size="2">Dr. Suman Maity</font></div><div><font size="2">Research Associate</font></div><div><font size="2">School of Atmospheric Science</font></div><div><font size="2">Sun Yat-sen University, Zhuhai campus,Tangjiawan Town<br></font></div><div><font size="2"> Zhuhai, Guangdong, Postcode-<span>519082,</span>China.</font></div><div><font size="2">Phone:+86-13543861045 (China), +91-9732636778 (India)<br></font></div><div><font size="2">skype: reach2suman</font></div><div><font size="2"><a href="https://www.researchgate.net/profile/Suman_Maity" target="_blank">https://www.researchgate.net/profile/Suman_Maity</a></font></div><span>++++++++++++++++++++++++++++++++++++++++++</span></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>
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