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Re: GEOSTATS: variogram modeling

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  • Syed Abdul Rahman
    Vijay Sathya (Vijay.Sathya@dgr.epfl.ch) ... models ... It depends. What would your maps eventually be for? This might give you a clue as to whether you should
    Message 1 of 1 , Oct 1, 1997
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      Vijay Sathya (Vijay.Sathya@...)
      Fri, 19 Sep 1997 11:42:29 +0200 wrote:

      >air pollution modelling.A part of the uncertainity comes from the
      >interpolation process.I've just finished analysing data sets for
      >temperature in the domain and the variogram structure during the day is
      >very different from that during the night.
      >So if I intend to use kriging must I build and use different variogram
      >for the night and day periods.??

      It depends. What would your maps eventually be for? This might
      give you a clue as to whether you should combine day and night
      measurements. I would gather that temperature distributions
      for day and night periods are supposed to be very different anyway.
      Would the temperature maps be for a Stealth pilot? A meteorologist?
      A science exhibit? Would it be used for making far reaching decisions
      concering rainfall predictions or the like? Litigation?

      >Is second order stationarity a must in the variogram model used for

      Yes, strict (variogram and covariance exists) or not very strict (intrinsic

      hypotheses, or just the variogram). But of course, that does not
      necessarily mean that your data are actually stationary. Maybe you think
      there is a trend in there somewhere and would rather prefer to work with
      (hopefully) stationary residuals. Or maybe you can't live with the bias
      and would rather work with an IRF-k technique to derive a spatial
      model. Sometimes another meteorologist or geologist would come
      along and suggest that you break up the data into different
      "stationary" pockets and work on these separately, because combining
      them might introduce non-stationarity. There are a lot of factors to take
      into account. Maybe you alreay know that there is non-stationarity but
      intend to leave the variogram as it is by just using the early lag data.
      Perhaps you think you can find a more or less "stationary" area of the
      data and derive a variogram from this area. You can then krige with
      limited search neighborhoods. Follow your common sense, there is
      no hard or fast rule about this.

      >I would also like to have some information on softwares for variogram

      Refer the software FAQ at http://java.ei.jrc.it/rem/gregoire.

      Regards, Syed

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