- Generated by
1.8.18
|
NCEPLIBS-nemsio
2.5.3
|
January, 2014
The NCEP general interpolation library (iplib) contains Fortran 90 subprograms to be used for interpolating between nearly all grids used at NCEP. The library is particularly efficient when interpolating many fields at one time. The library has been extensively tested with AIX and Intel Fortran compilers.
There are currently six interpolation methods available in the library: bilinear, bicubic, neighbor, budget, spectral and neighbor-budget.
Some of the methods have interpolation sub-options. A few methods have restrictions on the type of input or output grids. Also, several methods can perform interpolation on fields with bitmaps (i.e. some points on the input grid may be undefined). In this case, the bitmap is interpolated to the output grid. Only valid input points are used to interpolate to valid output points. An output bitmap will also be created to locate invalid data where the output grid extends outside the domain of the input grid.
The driver routine for interpolating scalars is ipolates, while the routine for interpolating vectors is ipolatv. The interpolation method is chosen via the first argument of these routines (variable IP). Sub-options are set via the IPOPT array.
Bilinear interpolation is chosen by setting IP=0. This method has two sub-options. (1) The percent of valid input data required to make output data (the default is 50%). (2) If valid input data is not found near an a spiral search may be performed. The spiral search is only an option for scalar data. The bilinear method also has no restrictions and can interpolate with bitmaps.
Bicubic interpolation is chosen by setting IP=1. This method has two sub-options, (1) A monotonic constraint option for straight bicubic or for constraining the output value to be within the range of the four surrounding input values. (2) The percent of valid input data required to make output data, which defaults to 50%. Note: the bicubic method cannot interpolate data with bitmaps.
Neighbor interpolation is chosen by setting IP=2. Neighbor interpolation means that the output value is set to the nearest input value. It would be appropriate for interpolating integer fields such as vegetation index. This method has one sub-option: If valid input data is not found near an an output point, a spiral search is optionally performed. The neighbor method has no restrictions and can interpolate with bitmaps.
Budget interpolation is chosen by setting IP=3. Budget interpolation means a low-order interpolation method that quasi-conserves area averages. It would be appropriate for interpolating budget fields such as precipitation. This method assumes that the field really represents box averages where each box extends halfway to its neighboring grid point in each direction. The method actually averages bilinearly interpolated values in a square array of points distributed within each output grid box. This method can interpolate data with bitmaps. There are several sub-options: (1) The number of points in the radius of the square array may be set. The default is 2, meaning that 25 sample points will be averaged for each output value.
(2) The respective averaging weights for the radius points are adjustable. The default is for all weights equal to 1, giving an unweighted average. (3) Optionally, one may assume the boxes stretch nearly all the way to each of the neighboring grid points and the weights are the adjoint of the bilinear interpolation weights. (4) The percent of valid input data required to make output data is adjustable. The default is 50%. (5) In cases where there is no or insufficient valid input data, a spiral search may be invoked to search for the nearest valid data. search squre (scalar interpolation only).
Spectral interpolation is chosen by setting IP=4. This method has two sub-options, to (1) set the spectral shape (triangular or rhomboidal) and (2) set the spectral truncation. The input grid must be a global cylindrical grid (either Gaussian or equidistant). This method cannot interpolate data with bitmaps. Unless the output grid is a global cylindrical grid, a polar stereographic grid centered at the pole, or a Mercator grid, this method can be quite expensive.
Neighbor-budget interpolation is chosen by setting IP=6. This method computes weighted averages of neighbor points arranged in a square box centered around each output grid point and stretching nearly halfway to each of the neighboring grid points. The main difference with the budget interpolation (IP=3) is neighbor vs bilinear interpolation of the square box of points. There are the following sub-options: (1) The number of points in the radius of the square array may be set. The default is 2, meaning that 25 sample points will be averaged for each output value.
(2) The respective averaging weights for the radius points are adjustable. The default is for all weights equal to 1, giving an unweighted average. (3) The percent of valid input data required to make output data is adjustable. The default is 50%.
The library can handle two-dimensional vector fields as well as scalar fields. The input and output vectors are rotated if necessary so that they are either resolved relative to their defined grid in the direction of increasing x and y coordinates or resolved relative to eastward and northward directions on the earth. The rotation is determined by the grid definitions. Vectors are generally interpolated (by all methods but spectral interpolation) by moving the relevant input vectors along a great circle to the output point, keeping their orientations with respect to the great circle constant, before independently interpolating the respective components. This ensures that vector interpolation will be consistent over the whole globe including the poles.
The input and output grids are defined by their respective GRIB grid description sections (GDS) passed in integer form KGDS as decoded by subprogram w3fi63 in w3ncolib or by subprogram makgds in this library. That is, the interpolation subprograms can readily interpolate from a GRIB field that is unpacked by subprogram w3fi63; the interpolation subprograms can also readily interpolate to an NCEP pre-defined grid that is expanded into KGDS form by subprogram makgds (which in turn calls w3fi71). There are currently seven grid projections recognized in the library. The projections are respectively equidistant cylindrical (KGDS(1)=000), Mercator cylindrical (KGDS(1)=001), Lambert conformal conical (KGDS(1)=003), Gaussian cylindrical (KGDS(1)=004), polar stereographic azimuthal (KGDS(1)=005), semi-staggered "E" grid on rotated equidistant cyclindrical (KGDS(1)=203), and non-staggered grid on rotated equidistant cyclindrical grid (KGDS(1)=205).
If the output data representation type is negative (KGDSO(1)<0), then the output data may be just a set of station points. In this case, the user must pass the number of points to be output along with their latitudes and longitudes. For vector interpolation, the vector rotations parameters must also be passed. On the other hand, for non-negative output data representation types, the number of output grid points and their latitudes and longitudes (and the vector rotation parameters for vector interpolation) are all returned by the interpolation subprograms.
If an output equidistant cylindrical grid contains multiple pole points, then the pole points are forced to be self-consistent. That is, scalar fields are obliged to be constant at the pole and vector components are obliged to exhibit a wavenumber one variation at the pole.
Generally, only regular grids can be interpolated in this library. However, the thinned WAFS grids and the staggered eta grids can be interpolated by using transform subprograms (ipxwafs and ipxetas, respectively) in this library that will either expand the irregular grid to a regular grid or contract a regular grid to an irregular grid as necessary.
The return code issued by an interpolation subprogram determines whether it ran successfully or how it failed. Check nonzero return codes against the docblock of the respective subprogram.
Developers are encouraged to create additional interpolation methods or to create additional map projection "wizards" for iplib.
Questions may be directed to: NCEP.List.EMC.nceplibs.Developers@noaa.gov
Scalar field interpolation subprograms
| Name | Function |
|---|---|
| IPOLATES | IREDELL'S POLATE FOR SCALAR FIELDS |
| POLATES0 | INTERPOLATE SCALAR FIELDS (BILINEAR) |
| POLATES1 | INTERPOLATE SCALAR FIELDS (BICUBIC) |
| POLATES2 | INTERPOLATE SCALAR FIELDS (NEIGHBOR) |
| POLATES3 | INTERPOLATE SCALAR FIELDS (BUDGET) |
| POLATES4 | INTERPOLATE SCALAR FIELDS (SPECTRAL) |
| POLATES6 | INTERPOLATE SCALAR FIELDS (NEIGHBOR-BUDGET) |
| POLFIXS | MAKE MULTIPLE POLE SCALAR VALUES CONSISTENT |
Vector field interpolation subprograms
| Name | Function |
|---|---|
| IPOLATEV | IREDELL'S POLATE FOR VECTOR FIELDS |
| POLATEV0 | INTERPOLATE VECTOR FIELDS (BILINEAR) |
| POLATEV1 | INTERPOLATE VECTOR FIELDS (BICUBIC) |
| POLATEV2 | INTERPOLATE VECTOR FIELDS (NEIGHBOR) |
| POLATEV3 | INTERPOLATE VECTOR FIELDS (BUDGET) |
| POLATEV4 | INTERPOLATE VECTOR FIELDS (SPECTRAL) |
| POLATEV6 | INTERPOLATE VECTOR FIELDS (NEIGHBOR-BUDGET) |
| MOVECT | MOVE A VECTOR ALONG A GREAT CIRCLE |
| POLFIXV | MAKE MULTIPLE POLE VECTOR VALUES CONSISTENT |
Grid description section decoders
| Name | Function |
|---|---|
| GDSWZD | GRID DESCRIPTION SECTION WIZARD |
| GDSWZD_C | 'C' WRAPPER FOR CALLING GDSWZD |
| GDSWZD00 | GDS WIZARD FOR EQUIDISTANT CYCLINDRICAL |
| GDSWZD01 | GDS WIZARD FOR MERCATOR CYCLINDRICAL |
| GDSWZD03 | GDS WIZARD FOR LAMBERT CONFORMAL CONICAL |
| GDSWZD04 | GDS WIZARD FOR GAUSSIAN CYCLINDRICAL |
| GDSWZD05 | GDS WIZARD FOR POLAR STEREOGRAPHIC |
| GDSWZDCB | GDS WIZARD FOR ROTATED EQUIDISTANT CYCLINDRICAL "E" STAGGER. |
| GDSWZDCD | GDS WIZARD FOR ROTATED EQUIDISTANT CYCLINDRICAL NON "E" STAGGER. |
| GAUSSLAT | COMPUTE GAUSSIAN LATITUDES |
| IJKGDS0/1 | RETURN FIELD POSITION FOR A GIVEN GRID POINT |
| MAKGDS | MAKE OR BREAK A GRID DESCRIPTION SECTION |
Transform subprograms for special irregular grids
| Name | Function |
|---|---|
| IPXWAFS/2/3 | EXPAND OR CONTRACT WAFS GRIDS |
Interpolate from an arbitrary input grid (probably 1x1) to NCEP grid 27 (65x65 northern polar stereographic). Interpolate heights bilinearly and winds bicubically. Interpolate soil moisture and precipitation using bitmaps with the budget method. Encode the soil moisture bitmap in GRIB. Subprograms GETGB and PUTGB from w3ncolib are referenced.
c example of using ipolate package.
c see documentation of ipolates and ipolatev
c for further possible options.
integer ipopt(20)
integer jpds(25),jgds(22),kpdsi(25),kgdsi(22),kpdso(25),kgdso(22)
parameter(ji=360*181,ig=27,jo=65*65,km=4)
real rlat(jo),rlon(jo),crot(jo),srot(jo)
integer ibi(km),ibo(km)
logical li(ji,km),lo(jo,km)
real hi(ji,km),ri(ji),ui(ji),vi(ji)
real ho(jo,km),ro(jo),uo(jo),vo(jo)
character gdso(42)
integer lev(km)
data lev/1000,500,250,100/
c define 65x65 grid
call makgds(ig,kgdso,gdso,lengds,iret)
if(iret.ne.0) call exit(iret)
kgdso(4)=-20826! fix w3fi71 error
print *,'kgdso=',kgdso
ipopt=0
c interpolate 4 levels of height to 65x65 bilinearly
do k=1,km
jpds=-1
jpds(5)=7
jpds(6)=100
jpds(7)=lev(k)
call getgb(11,31,ji,0,jpds,jgds,ki,kr,kpdsi,kgdsi,
& li(1,k),hi(1,k),iret)
if(iret.ne.0) call exit(iret)
call putgb(50,ki,kpdsi,kgdsi,li(1,k),hi(1,k),iret)
if(iret.ne.0) call exit(iret)
ibi(k)=mod(kpdsi(4)/64,2)
print *,'ibi(k)=',ibi(k)
enddo
call ipolates(0,ipopt,kgdsi,kgdso,ji,jo,km,ibi,li,hi,
& ko,rlat,rlon,ibo,lo,ho,iret)
if(iret.ne.0) call exit(iret)
kpdso=kpdsi
kpdso(3)=ig
do k=1,km
kpdso(7)=lev(k)
call putgb(51,ko,kpdso,kgdso,lo(1,k),ho(1,k),iret)
if(iret.ne.0) call exit(iret)
enddo
c interpolate precipitation to 65x65 with budget method
c (zero rain is masked out)
jpds=-1
jpds(5)=61
call getgb(11,31,ji,0,jpds,jgds,ki,kr,kpdsi,kgdsi,
& li,ri,iret)
if(iret.ne.0) call exit(iret)
call putgb(50,ki,kpdsi,kgdsi,li,ri,iret)
if(iret.ne.0) call exit(iret)
ipopt(1)=-1
ipopt(2)=-1
li(1:ki,1)=ri(1:ki).gt.0.
call ipolates(3,ipopt,kgdsi,kgdso,ji,jo,1,1,li,ri,
& ko,rlat,rlon,ibo,lo,ro,iret)
if(iret.ne.0) call exit(iret)
kpdso=kpdsi
kpdso(3)=ig
call putgb(51,ko,kpdso,kgdso,lo,ro,iret)
if(iret.ne.0) call exit(iret)
c interpolate soil moisture to 65x65 with budget method
jpds=-1
jpds(5)=144
jpds(6)=112
jpds(7)=10
call getgb(11,31,ji,0,jpds,jgds,ki,kr,kpdsi,kgdsi,
& li,ri,iret)
if(iret.ne.0) call exit(iret)
call putgb(50,ki,kpdsi,kgdsi,li,ri,iret)
if(iret.ne.0) call exit(iret)
ibi(1)=mod(kpdsi(4)/64,2)
ipopt(1)=-1
ipopt(2)=-1
call ipolates(3,ipopt,kgdsi,kgdso,ji,jo,1,ibi,li,ri,
& ko,rlat,rlon,ibo,lo,ro,iret)
if(iret.ne.0) call exit(iret)
kpdso=kpdsi
kpdso(3)=ig
kpdso(4)=128+64*ibo(1)
call putgb(51,ko,kpdso,kgdso,lo,ro,iret)
if(iret.ne.0) call exit(iret)
c interpolate 200 mb winds to 65x65 bicubically
jpds=-1
jpds(5)=33
jpds(6)=100
jpds(7)=200
call getgb(11,31,ji,0,jpds,jgds,ki,kr,kpdsi,kgdsi,
& li,ui,iret)
if(iret.ne.0) call exit(iret)
call putgb(50,ki,kpdsi,kgdsi,li,ui,iret)
if(iret.ne.0) call exit(iret)
jpds(5)=34
call getgb(11,31,ji,0,jpds,jgds,ki,kr,kpdsi,kgdsi,
& li,vi,iret)
if(iret.ne.0) call exit(iret)
call putgb(50,ki,kpdsi,kgdsi,li,vi,iret)
if(iret.ne.0) call exit(iret)
ipopt(1)=0
call ipolatev(1,ipopt,kgdsi,kgdso,ji,jo,1,0,li,ui,vi,
& ko,rlat,rlon,crot,srot,ibo,lo,uo,vo,iret)
if(iret.ne.0) call exit(iret)
kpdso=kpdsi
kpdso(3)=ig
kpdso(5)=33
call putgb(51,ko,kpdso,kgdso,lo,uo,iret)
if(iret.ne.0) call exit(iret)
kpdso(5)=34
call putgb(51,ko,kpdso,kgdso,lo,vo,iret)
if(iret.ne.0) call exit(iret)
stop
end
#
Interpolate winds from an arbitrary input grid (probably 1x1) to 4 station points while truncating spectrally to R30.
c read and unpack the 500 mb winds, truncate to R30,
c and interpolate to 4 corners of a box
integer ipopt(20)
integer jpds(25),jgds(22),kpdsi(25),kgdsi(22),kgdso(22)
parameter(jf=360*181,kp=4)
real rlat(kp),rlon(kp),crot(kp),srot(kp)
logical lgi(jf),lgo(kp)
real ui(jf),vi(jf),uo(kp),vo(kp)
jpds=-1
jpds(5)=33
jpds(6)=100
jpds(7)=500
call getgb(11,31,jf,0,jpds,jgds,kf,kr,kpdsi,kgdsi,
& lgi,ui,iret)
jpds(5)=34
call getgb(11,31,jf,0,jpds,jgds,kf,kr,kpdsi,kgdsi,
& lgi,vi,iret)
kgdso=-1
rlat(1)=20.
rlat(2)=20.
rlat(3)=10.
rlat(4)=10.
rlon(1)=-50.
rlon(2)=-40.
rlon(3)=-50.
rlon(4)=-40.
crot=1.
srot=0.
ipopt(1)=1
ipopt(2)=30
uo=-999
vo=-999
call ipolatev(4,ipopt,kgdsi,kgdso,jf,kp,1,0,lgi,ui,vi,
& kp,rlat,rlon,crot,srot,ibo,lgo,uo,vo,iret)
print '(2(2x,2f8.2))',(uo(k),vo(k),k=1,kp)
end
#
Interpolate winds from an arbitrary input grid (probably 1x1) bilinearly to 3 station points.
c read and unpack 4 levels of heights and winds
c and interpolate to 3 sonde sites.
integer ipopt(20)
integer jpds(25),jgds(22),kpdsi(25),kgdsi(22),kgdso(22)
parameter(ji=360*181,km=4,jo=3)
real rlat(jo),rlon(jo),crot(jo),srot(jo)
integer ibi(km),ibo(km)
logical li(ji,km),lo(jo,km)
real hi(ji,km),ui(ji,km),vi(ji,km),ho(jo,km),uo(jo,km),vo(jo,km)
integer lev(km)
data lev/1000,500,250,100/
c define output locations
kgdso=-1
rlat(1)=22.2
rlat(2)=33.3
rlat(3)=44.4
rlon(1)=-50.
rlon(2)=-40.
rlon(3)=-30.
crot=1.
srot=0.
c heights
do k=1,km
jpds=-1
jpds(5)=7
jpds(6)=100
jpds(7)=lev(k)
call getgb(11,31,ji,0,jpds,jgds,ki,kr,kpdsi,kgdsi,
& li(1,k),hi(1,k),iret)
if(iret.ne.0) call exit(iret)
ibi(k)=mod(kpdsi(4)/64,2)
enddo
call ipolates(0,ipopt,kgdsi,kgdso,ji,jo,km,ibi,li,hi,
& jo,rlat,rlon,ibo,lo,ho,iret)
if(iret.ne.0) call exit(iret)
c winds
do k=1,km
jpds=-1
jpds(5)=33
jpds(6)=100
jpds(7)=lev(k)
call getgb(11,31,ji,0,jpds,jgds,ki,kr,kpdsi,kgdsi,
& li(1,k),ui(1,k),iret)
if(iret.ne.0) call exit(iret)
jpds=-1
jpds(5)=34
jpds(6)=100
jpds(7)=lev(k)
call getgb(11,31,ji,0,jpds,jgds,ki,kr,kpdsi,kgdsi,
& li(1,k),vi(1,k),iret)
if(iret.ne.0) call exit(iret)
ibi(k)=mod(kpdsi(4)/64,2)
enddo
call ipolatev(0,ipopt,kgdsi,kgdso,ji,jo,km,ibi,li,ui,vi,
& jo,rlat,rlon,crot,srot,ibo,lo,uo,vo,iret)
if(iret.ne.0) call exit(iret)
print '((i8,3(2x,3f8.2)))',
& (lev(k),(ho(j,k),uo(j,k),vo(j,k),j=1,jo),k=1,km)
end
Example 4. Interpolate 850 mb height and winds from the staggered meso-eta to a regional 0.25 degree grid.
integer ipopt(20)
integer jpds(25),jgds(22),kpdsi(25),kgdsi(22),kpdso(25),kgdso(22)
integer kgdsi2(22)
parameter(ji=361*271,ig=255,jo=121*81)
real rlat(jo),rlon(jo),crot(jo),srot(jo)
logical li(ji),lo(jo)
real fi(ji),fi2(ji),fo(jo)
real vi(ji),vi2(ji),vo(jo)
character gdso(400)
kgdso=0
kgdso(1)=0
kgdso(2)=121
kgdso(3)=81
kgdso(4)=30000
kgdso(5)=-90000
kgdso(6)=128
kgdso(7)=50000
kgdso(8)=-60000
kgdso(9)=250
kgdso(10)=250
kgdso(11)=64
kgdso(19)=0
kgdso(20)=255
if(iret.ne.0) call exit(iret)
ipopt=0
jpds=-1
jpds(6)=100
jpds(7)=850
jpds(5)=7
call getgb(11,31,ji,0,jpds,jgds,ki,kr,kpdsi,kgdsi,
& li,fi,iret)
if(iret.ne.0) call exit(iret)
call ipxetas(1,ji,ji,1,kgdsi,fi,kgdsi2,fi2,iret)
if(iret.ne.0) call exit(iret)
call ipolates(0,ipopt,kgdsi2,kgdso,ji,jo,1,0,li,fi2,
& ko,rlat,rlon,ibo,lo,fo,iret)
if(iret.ne.0) call exit(iret)
kpdso=kpdsi
kpdso(3)=ig
kpdso(4)=128+64*ibo
kpdso(22)=1
call putgb(51,ko,kpdso,kgdso,lo,fo,iret)
if(iret.ne.0) call exit(iret)
jpds(5)=33
call getgb(11,31,ji,0,jpds,jgds,ki,kr,kpdsi,kgdsi,
& li,fi,iret)
if(iret.ne.0) call exit(iret)
call ipxetas(2,ji,ji,1,kgdsi,fi,kgdsi2,fi2,iret)
if(iret.ne.0) call exit(iret)
jpds(5)=34
call getgb(11,31,ji,0,jpds,jgds,ki,kr,kpdsi,kgdsi,
& li,vi,iret)
if(iret.ne.0) call exit(iret)
call ipxetas(2,ji,ji,1,kgdsi,vi,kgdsi2,vi2,iret)
if(iret.ne.0) call exit(iret)
call ipolatev(0,ipopt,kgdsi2,kgdso,ji,jo,1,0,li,fi2,vi2,
& ko,rlat,rlon,crot,srot,ibo,lo,fo,vo,iret)
if(iret.ne.0) call exit(iret)
kpdso=kpdsi
kpdso(3)=ig
kpdso(4)=128+64*ibo
kpdso(22)=1
kpdso(5)=33
call putgb(51,ko,kpdso,kgdso,lo,fo,iret)
if(iret.ne.0) call exit(iret)
kpdso(5)=34
call putgb(51,ko,kpdso,kgdso,lo,vo,iret)
if(iret.ne.0) call exit(iret)
end
1.8.18