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</o:shapelayout></xml><![endif]--></head><body lang=EN-ZA link=blue vlink=purple><div class=WordSection1><p class=MsoNormal>Dear All<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>I am trying to create a time series of anomalies from a seasonal climatology.<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>I am using CRU TS 3.1 data. The dimensions of the dataset are as follows:<o:p></o:p></p><p class=MsoNormal>File 1 : CRU TS 3.10 Precipitation<o:p></o:p></p><p class=MsoNormal> Descriptor: cru_ts_3_10.1901.2009.pre.dat.nc<o:p></o:p></p><p class=MsoNormal> Binary: cru_ts_3_10.1901.2009.pre.dat.nc<o:p></o:p></p><p class=MsoNormal> Type = Gridded<o:p></o:p></p><p class=MsoNormal> Xsize = 720 Ysize = 360 Zsize = 1 Tsize = 1308 Esize = 1<o:p></o:p></p><p class=MsoNormal> Number of Variables = 1<o:p></o:p></p><p class=MsoNormal> pre 0 t,y,x precipitation<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>I have managed to calculate long-run monthly averages and convert these to a climatology in order to calculate monthly anomalies from the long-run average, based on a response from Brian Doty to a similar post (attached below). Note he goes on to demonstrate a ‘manual’ approach to calculating long-run seasonal averages. What I’d like to do is to calculate seasonal averages in a similar fashion to the approach used below to calculate long-run monthly averages, and then build a time-series of anomalies from the long-run seasonal average, as is done below for monthly data.<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>Can anyone assist?<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>Thanks<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>Alec Joubert<o:p></o:p></p><p class=MsoNormal>_______________________________________________<o:p></o:p></p><p class=MsoNormal><u>Previous post showing calculation of monthly averages, anomalies from a monthly average and seasonal average calculations:<o:p></o:p></u></p><p class=MsoNormal><o:p> </o:p></p><pre>Have you tried using the "modify" command to create seasonal averages?<o:p></o:p></pre><pre><o:p> </o:p></pre><pre><a href="http://grads.iges.org/grads/gadoc/gradcomdmodify.html">http://grads.iges.org/grads/gadoc/gradcomdmodify.html</a><o:p></o:p></pre><pre><o:p> </o:p></pre><pre>from above web site:<o:p></o:p></pre><pre>---<o:p></o:p></pre><pre>modify /varname type/<o:p></o:p></pre><pre><o:p> </o:p></pre><pre>This command defines a climatological variable, which is<o:p></o:p></pre><pre>year-independent. /varname/ is a defined grid. There are two options for<o:p></o:p></pre><pre>/type/:<o:p></o:p></pre><pre><o:p> </o:p></pre><pre> |seasonal |- For creating monthly or multi-monthly climatologies<o:p></o:p></pre><pre> |diurnal |- For creating climatologies over a time period less<o:p></o:p></pre><pre> than a day<o:p></o:p></pre><pre><o:p> </o:p></pre><pre><o:p> </o:p></pre><pre> Usage Notes<o:p></o:p></pre><pre><o:p> </o:p></pre><pre><o:p> </o:p></pre><pre> Example<o:p></o:p></pre><pre><o:p> </o:p></pre><pre>Say you have a 50-year timeseries of monthly mean sea surface<o:p></o:p></pre><pre>temperatures (a variable named sst with 600 time steps) and you want to<o:p></o:p></pre><pre>create a climatology and then look at the monthly anomalies. First, set<o:p></o:p></pre><pre>the time range for 1 to 12, to span a complete year. Second, define the<o:p></o:p></pre><pre>variable "sstclim" which will contain the January mean in the first time<o:p></o:p></pre><pre>step, the February mean in the second time set, etc. Then use 'modify'<o:p></o:p></pre><pre>to turn 'sstclim' into a climatological variable. This means that the<o:p></o:p></pre><pre>calendar year associated with 'sstclim' (the first year in the original<o:p></o:p></pre><pre>sst data set) becomes a wild card. Then you can define the anomaly by<o:p></o:p></pre><pre>subtracting the climatology from the original time series. The commands<o:p></o:p></pre><pre>are as follows:<o:p></o:p></pre><pre><o:p> </o:p></pre><pre>'set t 1 12'<o:p></o:p></pre><pre>'define sstclim = ave(sst, t+0, t=600, 12)'<o:p></o:p></pre><pre>'modify sstclim seasonal'<o:p></o:p></pre><pre>'set t 1 last'<o:p></o:p></pre><pre>'define anom = sst - sstclim'<o:p></o:p></pre><pre>---<o:p></o:p></pre><pre><o:p> </o:p></pre><pre>and from a Brian Doty email to the GrADS Listserv:<o:p></o:p></pre><pre><o:p> </o:p></pre><pre>---<o:p></o:p></pre><pre>...<o:p></o:p></pre><pre> set lon 0 360<o:p></o:p></pre><pre> set lat -90 90<o:p></o:p></pre><pre> set z 1<o:p></o:p></pre><pre> set t 1 12<o:p></o:p></pre><pre> define ozave = ave(ozone,t+0,t=144,12)<o:p></o:p></pre><pre><o:p> </o:p></pre><pre>Now 'ozave' contains the seasonal mean for each month (the above assumes<o:p></o:p></pre><pre>the data starts with January).<o:p></o:p></pre><pre><o:p> </o:p></pre><pre>Then, to display a seasonal mean -- for example, DJF:<o:p></o:p></pre><pre><o:p> </o:p></pre><pre> display (ozave(t=12)+ozave(t=1)+ozave(t=2))/3<o:p></o:p></pre><pre><o:p> </o:p></pre><pre>or if you want to account for the lengths of the months (note that ave<o:p></o:p></pre><pre>does not do this by default when averaging monthly data):<o:p></o:p></pre><pre><o:p> </o:p></pre><pre> display (ozave(t=12)*31+ozave(t=1)*31+ozave(t=2)*28)/90<o:p></o:p></pre><pre><o:p> </o:p></pre><pre>...Brian<o:p></o:p></pre><p class=MsoNormal><o:p> </o:p></p></div></body></html>