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kriging 在国外的网站上下的！！

SIZE="-1"><STRONG>Purpose</STRONG></FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">2-D spatial filter for kriged
data.</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1"><STRONG></STRONG></FONT><FONT
FACE="Helvetica" SIZE="-1"><STRONG>Synopsis</STRONG></FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">F = barnes (xi, yi, zi, c,
g)</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1"><STRONG></STRONG></FONT><FONT
FACE="Helvetica" SIZE="-1"><STRONG>Description</STRONG></FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">The Barnes' filter is a low-pass
2-D filter whose mathematical description is:</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1"><img src="engli0{image10}.gif"
width="162" height="28" align=bottom ></FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1">where <img
src="engli0{image11}.gif" width="163" height="102" align=bottom > ,
<img src="engli0{image12}.gif" width="232" height="102" align=bottom
> , </FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"><img src="engli0{image13}.gif"
width="178" height="29" align=bottom > and <img
src="engli0{image14}.gif" width="186" height="29" align=bottom
>.</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1"><U>Input
variables:</U></FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">xi:  column grid
coordinates</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">yi:  row grid
coordinates</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">zi:  grid data</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">c, g:  filter
parameters</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1"><U>Output
variable:</U></FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">F:  filtered data </FONT></P>


<P><FONT FACE="Helvetica"
SIZE="-1"><STRONG>Example</STRONG></FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">The tintore function provides a
good example of spatial filters in physical oceanography.</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1"><STRONG></STRONG></FONT><FONT
FACE="Helvetica" SIZE="-1"><STRONG>References</STRONG></FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">Maddox, R.A., 1980.  An
Objective Technique for Separating Macroscale and Mesoscale Features
in Meteorological Data.  Monthly Weather Rev., 108:
1108-1121.<STRONG></STRONG></FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">Tintor&eacute; et al., 1991.
Mesoscale Dynamics and Vertical Motion in the Alboran Sea.  J.
Phys.  Oceanogr., 21:  811-823.</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1"><STRONG></STRONG></FONT><FONT
FACE="Helvetica" SIZE="-1"><STRONG>See also</STRONG></FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">tintore, filresp</FONT></P>



<P><FONT FACE="Helvetica"><STRONG>cokri / cokri2</STRONG></FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1"></FONT><FONT FACE="Helvetica"
SIZE="-1"><STRONG>Purpose</STRONG></FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">cokri:  Point or block cokriging
in D dimensions.</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">cokri2:  Cokriging function
called from cokri.</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1"><STRONG></STRONG></FONT><FONT
FACE="Helvetica" SIZE="-1"><STRONG>Synopsis</STRONG></FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">[x0s, s, sv, id, l] = cokri (x,
x0, model, c, itype, avg, block, nd, ival, nk, rad, ntok,
d)</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">[x0s, s, id, l, k0] = cokri2 (x,
x0, id, model, c, sv, itype, avg, ng, d)</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1"><STRONG></STRONG></FONT><FONT
FACE="Helvetica" SIZE="-1"><STRONG>Description</STRONG></FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">Cokriging means kriging with
more than one variable.  When cokri is called with only one variable
at a time, the results will be those of simple kriging, ordinary
kriging, universal kriging, point kriging or block kriging.  More
details can be found in the paper of Marcotte (1991).</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1">Available kriging options are:
</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1"> simple cokriging </FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> ordinary cokriging with one
nonbias condition (Isaaks and Srivastava)</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> ordinary cokriging with p
nonbias condition</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> universal cokriging with drift
of order 1</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> universal cokriging with drift
of order 2</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">99.cokriging is not performed,
only sv is computed</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1">Available semivariogram models
are:</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1"> nugget effect</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> exponential model</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> gaussian model</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> spherical model</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> linear model</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> quadratic model</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> power (h<SUP>d</SUP>)
model</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> logarithmic</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> sinc(h)</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> Bessel [ Jo (h) ]</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> exp(-h) * cos(dh)</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> exp(-h) * Jo(dh)</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> exp(-h<SUP>2</SUP>) *
cos(dh)</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> exp(-h<SUP>2</SUP>) *
Jo(dh)</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> exp(-h<SUP>2</SUP>) * (1 -
dh<SUP>2</SUP>)</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> 1 - 3*min(h,1) +
2*min(h,1)</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> h * log(max(h,eps))</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1">New models can be added quite
easily since models are calculated using the eval
function.</FONT></P>



<P><FONT FACE="Helvetica" SIZE="-1"><U>Input
variables:</U></FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">x:  data matrix [x y z var1 var2
...]</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">x0:  grid coordinates [xi yi
zi]</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">model:  [models, a (h=r/a),
rotation angles].</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">No rotation angle is required
for an isotropic distribution.</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">Example:  model = [1 10; 4 30]
means that the distribution is isotropic and that it is represented
by a nugget effect of range 10 plus a spherical model of range
30.</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">c:  amplitudes of the
models</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">itype:  kriging
option</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">block:  vector (1 x D), giving
the size of the block to estimate; for point cokriging:  block =
ones(1,D)</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">nd:  Vector (1 x D), giving the
discretization grid for block cokriging; for point cokriging:  nd =
ones(1,D)</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">ival:  Code for
cross-validation.</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> 0:  no
cross-validation</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> 1:  cross-validation is
performed by removing one variable at a</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> time at a given
location.</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> 2:  cross-validation is
performed by removing all variables at a</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> given location.</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">nk:  number of nearest neighbors
in x matrix to use in the cokriging.</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">rad:  search radius.</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">ntok:  points in x0 will be
kriged by groups of ntok grid points.  </FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">d:  model coefficients.  This
coefficient has been added to the original Marcotte's function.
Warning:  In cokri, models are defined in terms of h = r/a.  In
variogr, the dependent variable is r and hence d = b*a (see
variogr).</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1"><U>Output
variables</U></FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">x0s:  kriged data matrix at x0
locations.</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">s:  kriged data variance matrix
at x0 locations.</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">sv:  variance of each
variable.</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">id, l see Marcotte
(1991)</FONT></P>


<P><FONT FACE="Helvetica"
SIZE="-1"><STRONG>Reference</STRONG></FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">Marcotte, D.  1991.  Cokrigeage
with matlab.  Computers &amp; Geosciences.  17(9):
1265-1280.</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1"><STRONG></STRONG></FONT><FONT
FACE="Helvetica" SIZE="-1"><STRONG>See also</STRONG></FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">cokri2, variogr, trans,
means<STRONG></STRONG></FONT></P>



<P><FONT FACE="Helvetica"><STRONG>confint</STRONG></FONT><FONT
FACE="Helvetica" SIZE="-1"><STRONG></STRONG></FONT></P>


<P><FONT FACE="Helvetica"
SIZE="-1"><STRONG>Purpose</STRONG></FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">Confidence intervals.</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1"><STRONG></STRONG></FONT><FONT
FACE="Helvetica" SIZE="-1"><STRONG>Synopsis</STRONG></FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">[k2, k1] = confint (g, m,
S2)</FONT></P>


<P><FONT FACE="Helvetica"
SIZE="-1"><STRONG>Description</STRONG></FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">Confidence intervals for the
structure function</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1">CONF {k2 variance k1}
(1)</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1">The structure function is a
measure of the variance of a given variable as a function of
distance.  The estimation of the confidence intervals in such a case
is given by (1).</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1">k1 = (n-1) * S<SUP>2</SUP> /
c1</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">k2 = (n-1) * S<SUP>2</SUP> / c2
(2)</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> </FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">where n = sample size =
m+1</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">m = number of degrees of
freedom</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">S<SUP>2</SUP> = variance of the
sample</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">c1 and c2 are determined by the
solution to the equations</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1">F(c1) = (1-g) /2</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">F(c2) = (1+g) /2 (3)</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1">where g = confidence level (95%,
99% or the like)</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">F = <img
src="engli0{image15}.gif" width="15" height="20" align=bottom >
distribution</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1">Solutions are obtained by
function chitable (Chi Toolbox).</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1"><STRONG></STRONG></FONT><FONT
FACE="Helvetica" SIZE="-1"><STRONG>References</STRONG></FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">Denman, K.  L.  and H.  J.
Freeland (1985).  Correlation Scales, Objective Mapping and a
Statistical Test of Geostrophy over the Continental Shelf.  J.  of
Mar.  Res., 43:  517-539.</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">Kreyszig, E., 1988.
<EM>Advanced Engineering Mathematics</EM>, sixth ed., John Wiley
&amp; Sons, New York, p.1252</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1"><STRONG>See
also</STRONG></FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">chitable</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1"><STRONG></STRONG></FONT><FONT
FACE="Helvetica"><STRONG>davis</STRONG></FONT><FONT FACE="Helvetica"
SIZE="-1"><STRONG></STRONG></FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1"><STRONG>Purpose</STRONG>
</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">Point kriging using Davis' set
of equations A W = B</FONT></P>


<P><FONT FACE="Times New Roman" SIZE="-1"></FONT><FONT FACE="Times
New Roman" SIZE="-1"> </FONT><FONT FACE="Helvetica"
SIZE="-1">where</FONT><FONT FACE="Times New Roman" SIZE="-1"> <img
src="engli0{image16}.gif" width="245" height="108" align=bottom >,
<img src="engli0{image17}.gif" width="67" height="102" align=bottom
> </FONT><FONT FACE="Helvetica" SIZE="-1">and</FONT><FONT
FACE="Times New Roman" SIZE="-1"> <img src="engli0{image18}.gif"
width="83" height="105" align=bottom >.</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1">The g(h<SUB>ik</SUB>) is the
semivariance of sample pairs separated by distance h<SUB>ik</SUB>.
Non bias conditions require the sum of W<SUB>i</SUB> to be equal to
1.  In that case, one more degree of freedom must be introduced with
the use of a Lagrange multiplier<img src="engli0{image19}.gif"
width="14" height="21" align=bottom > in order to minimize the
estimation error.</FONT></P>


<P><FONT FACE="Helvetica"
SIZE="-1"><STRONG>Synopsis</STRONG></FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">[Zp, Sp] = davis (data, x0,
model, a, d, c, A)</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1"><STRONG></STRONG></FONT><FONT
FACE="Helvetica" SIZE="-1"><STRONG>Description</STRONG></FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1">Available models are:</FONT></P>


<P><FONT FACE="Helvetica" SIZE="-1"> <MULTICOL COLS="2" WIDTH="507"
GUTTER="46"> nugget effect</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> exponential model</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> gaussian model</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> spherical model</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> linear model</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> quadratic model</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> power model
(h<SUP>d</SUP>)</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> logarithmic model</FONT></P>

<P><FONT FACE="Helvetica" SIZE="-1"> sinc(h)</FONT></P>