grads中modify函数的使用（已解决）

ziqiangbuxi

在计算月距平场，数据是10年的月均值monthly，假设是从2000-2010
line1：set lon -180 180
line2：set lat -90 90
line3：set lev 500
line4：set t 1 12
line5：define zave = ave(z,t+0,t=120,1yr)
line6：modify zave seasonal
line7：set t 120
line8：d z - zave
因为t的时间范围是1-120，zave的时间范围是1-12
那么如果不加modify这条命令，是不是结果只对于t=1到12的时间范围内有效，超过这个范围就要报错？
如果加了modify这个函数，就相当于是把zave变量的时间维度重复复制了9次，使得zave和z的时间维度一样，就可以运算了，是这个意思么？
请大家指点！

river

ave()

ave(expr, dim1, dim2 <,tinc> <,-b>)

Averages the result of expr over the specified dimension range. If the averaging dimension is time, an optional time increment tincr may be specified.

expr    - any valid GrADS expression
dim1    - the start point for the average
dim2    - the end point for the average
tinc    - optional increment for time averaging
-b      - use exact boundaries
dim1 and dim2 are standard GrADS dimension expressions whose dimensions must match.

Usage Notes

The limits and intervals of the averaging are set according to the grid coordinates of the default file. If dim1 and dim2 are specified in world coordinates, the coordinates are converted to the nearest integer grid coordinates based on the scaling of the default file. See the examples below for further illustration.
The end points are given normal weighting, unless the -b boundary flag is specified. The boundry flag indicates that the average should be taken to the exact boundaries specified by dim1 and dim2, rather than the nearest grid points.
The average is weighted by grid interval to account for non-linear grid spacing. Averages in the latitude dimension are weighted by the difference between the sines of the latitude at the northern and southern edges of the grid box. The edges of the grid box are always defined as being the midpoint between adjacent grid points. To calculate an average without using the latitude weighting, use the mean function.
Examples

For the following examples, the dimension environment is X-Y varying; Z-T are fixed.

Consider the following average, when the default file is file #1:
ave(z.2,t=1,t=10)
We are averaging a variable from file #2, but using the scaling from file #1. File #1 has a time interval of 6 hours, but file #2 has a time interval of 12 hours. The average will thus attempt to access data from file #2 for times that are not available, and an error will ocurr. To avoid this, the default file should be set to file #2: set dfile 2

The average:
ave(z,t=1,t=120,4)
will average only 00Z reports from file #1, since the time increment is 4, which for this file is 24 hours.

If you attempt to take a zonal average as follows:
ave(z,lon=0,lon=360)
the world coordinates will be converted to grid coordinates, here X varying from 1 to 181, and the grid point at longitude 0 (and 360) will be used twice in the average. To have the end points of this average weighted properly, use the -b flag:

ave(z,lon=0,lon=360,-b)
or average using the grid coordinates directly:

ave(z,x=1,x=180)
You can nest averaging operations:
ave(ave(z,x=1,x=180),y=1,y=46)
In this case, to take an areal average. Note that for areal averaging, the aave function is better. See the aave function description.

When nesting averages, the order of the nesting can have a dramatic affect on performance. Keep in mind the ordering of the data in a GrADS file: X varies the fastest, then Y, then Z, then T. When nesting averages, put the faster varying dimension within the inner average:

set lon -90
set lat -90 90
set lev 1000 100
d ave(ave(t,x=1,x=180),t=1,t=20)
This average would be more efficient than, for example:

ave(ave(t,t=1,t=20),x=1,x=180)
although the final numerical result would be the same.

The use of the define command can make certain operations much more efficient. If you want to calculate standard deviation, for example:
sqrt(ave(pow(ave(z,t=1,t=20)-z,2),t=1,t=20))
would be correct, but the inside average would be calculated 20 times. Defining the inside average in advance will be substantially faster:

define zave = ave(z,t=1,t=20)
d sqrt(ave(pow(zave-z,2),t=1,t=20))

river

modify

modify varname type
This command defines a climatological variable, which is year-independent. varname is a defined grid. There are two options for type:

seasonal    - For creating monthly or multi-monthly climatologies
diurnal     - For creating climatologies over a time period less than a day
Usage Notes

Example

Say you have a 50-year timeseries of monthly mean sea surface temperatures (a variable named sst with 600 time steps) and you want to create a climatology and then look at the monthly anomalies. First, set the time range for 1 to 12, to span a complete year. Second, define the variable "sstclim" which will contain the January mean in the first time step, the February mean in the second time set, etc. Then use 'modify' to turn 'sstclim' into a climatological variable. This means that the calendar year associated with 'sstclim' (the first year in the original sst data set) becomes a wild card. Then you can define the anomaly by subtracting the climatology from the original time series. The commands are as follows:

'set t 1 12'
'define sstclim = ave(sst, t+0, t=600, 12)'
'modify sstclim seasonal'
'set t 1 last'
'define anom = sst - sstclim'

ziqiangbuxi

river 发表于 2015-11-10 12:10
modify

modify varname type

谢谢您的解答，通过发的两个帖子，已经完全理解了。