Project

General

Profile

Forums » Support »

Regression - ENSO 3.4 time series vs Spatial Field

Added by Sarah O'Keefe about 8 years ago

Hi - Does anyone know if cdo can do this? I want to look at the relationship between a time series and a 3D (lat,lon,time) field. Thanks! Sarah.


Replies (3)

RE: Regression - ENSO 3.4 time series vs Spatial Field - Added by Ralf Mueller about 8 years ago

hi Sarah!

Do you want to correlate the time series? you could use the enlarge operator. I uploaded some sample input and ouput files. you can check the commands in the history attribute of the files.

Download all files
timeSeries.nc (4.11 KB) timeSeries.nc input
enlargedTimeSeries.nc (52.3 KB) enlargedTimeSeries.nc output

RE: Regression - ENSO 3.4 time series vs Spatial Field - Added by Cary Lynch over 7 years ago

Hello!
Similar question. I want to perform a least square regression between a 1-D time series and a 3-D field. I want the regression coefficients and y-int from this function, if possible. Has the original question been answered?

RE: Regression - ENSO 3.4 time series vs Spatial Field - Added by Karin Meier-Fleischer over 7 years ago

Hi Cary,

yes, the original question was answered, see reply 1. ;)

The answer to you is the same as for the original question - use the enlarge operator to enlarge the 1D time series to the size of the data file to be compared or to compute the trend.

Using Ralf's test data:

cdo -C -enlarge,r10x10 timeSeries.nc enlargedTimeSeries.nc
cdo -trend enlargedTimeSeries.nc afile.nc bfile.nc

cdo info afile.nc 

    -1 :       Date     Time   Level Gridsize    Miss :     Minimum        Mean     Maximum : Parameter ID
     1 : 2124-12-26 04:00:00       0      100       0 :      1.4678      1.4678      1.4678 : 255          

cdo info: Processed 100 values from 1 variable over 1 timestep ( 0.00s )

[~/CDO/Support/enlarge] > cdo info bfile.nc
    -1 :       Date     Time   Level Gridsize    Miss :     Minimum        Mean     Maximum : Parameter ID
     1 : 2124-12-26 04:00:00       0      100       0 :   0.0028344   0.0028344   0.0028344 : 255           
cdo info: Processed 100 values from 1 variable over 1 timestep ( 0.00s )

ncdump -h afile.nc 

netcdf afile {
dimensions:
    lon = 10 ;
    lat = 10 ;
    lev = 1 ;
    time = UNLIMITED ; // (1 currently)
variables:
    float lon(lon) ;
        lon:standard_name = "longitude" ;
        lon:long_name = "longitude" ;
        lon:units = "degrees_east" ;
        lon:axis = "X" ;
    float lat(lat) ;
        lat:standard_name = "latitude" ;
        lat:long_name = "latitude" ;
        lat:units = "degrees_north" ;
        lat:axis = "Y" ;
    double lev(lev) ;
        lev:long_name = "generic" ;
        lev:units = "level" ;
        lev:axis = "Z" ;
    double time(time) ;
        time:standard_name = "time" ;
        time:units = "day as %Y%m%d.%f" ;
        time:calendar = "proleptic_gregorian" ;
        time:axis = "T" ;
    double hight(time, lev, lat, lon) ;
        hight:standard_name = "hi_acc" ;
        hight:long_name = "ice thickness" ;
        hight:units = "m" ;
        hight:code = 255 ;

ncdump -h bfile.nc

netcdf bfile {
dimensions:
    lon = 10 ;
    lat = 10 ;
    lev = 1 ;
    time = UNLIMITED ; // (1 currently)
variables:
    float lon(lon) ;
        lon:standard_name = "longitude" ;
        lon:long_name = "longitude" ;
        lon:units = "degrees_east" ;
        lon:axis = "X" ;
    float lat(lat) ;
        lat:standard_name = "latitude" ;
        lat:long_name = "latitude" ;
        lat:units = "degrees_north" ;
        lat:axis = "Y" ;
    double lev(lev) ;
        lev:long_name = "generic" ;
        lev:units = "level" ;
        lev:axis = "Z" ;
    double time(time) ;
        time:standard_name = "time" ;
        time:units = "day as %Y%m%d.%f" ;
        time:calendar = "proleptic_gregorian" ;
        time:axis = "T" ;
    double hight(time, lev, lat, lon) ;
        hight:standard_name = "hi_acc" ;
        hight:long_name = "ice thickness" ;
        hight:units = "m" ;
        hight:code = 255 ;

-Karin

    (1-3/3)