served from dlibrary.snia.gub.uy
root mean sq anom max [ L , max ( MGAP CPT InputObs ) ] : L, input observations data
root mean sq anom max [ L , max ( MGAP CPT InputObs ) ] .Independent Variables (Grids)
- L
- grid: /L (months) ordered (3.0 months) to (4.0 months) N= 2 pts :grid
- Training period
- grid: /T2 (months since 1960-01-01) ordered (1980) to (2011) by 12.0 N= 32 pts :grid
- Latitude (latitude)
- grid: /Y (degree_north) ordered (29.5S) to (35.5S) by 0.5 N= 13 pts :grid
Other Info
- bufferwordsize
- 8
- CE
- null
- CS
- null
- datatype
- doublearraytype
- fnname
- max
- gridid
- 184889384
- missing_value
- NaN
- scale_max
- null
- scale_min
- null
- units
- months
- standard units*
- 0.0833333333333333 year
- history
- max [ MGAP CPT InputObs ]
- Output from CPT for 12 3-month running seasons for 1960-2009 between ECHAM4p5 GCM and CRU dataset, CPT recompiled on Mac 9.04 version
max over X[59.75W, 52.25W]
root mean sq anom max [ L , max ( MGAP CPT InputObs ) ]- Averaged over T[Sep, Nov] minimum 0.0% data present
- Output from CPT for 12 3-month running seasons for 1960-2009 between ECHAM4p5 GCM and CRU dataset, CPT recompiled on Mac 9.04 version
Last updated: Thu, 04 Feb 2016 15:18:08 GMT
Data Views
Views
data as colors
data as contours
line plots
color bars| L | T2 | Y | |
 MGAP CPT[PredErrorVars
CFSv2_AtlPac_sst
hindcast_date
Forecast
target_date
InputObs
CrossValHind
PearsonR
] | |||
| grid: /X (degree_east) ordered (59.75W) to (52.25W) by 0.5 N= 16 pts :grid | |||
| grid: /Y (degree_north) ordered (29.25S) to (35.75S) by 0.5 N= 14 pts :grid | O=-0.25 | ||
| grid: /T2 (months since 1960-01-01) ordered (1980) to (2011) by 12.0 N= 32 pts :grid | M | ||
| grid: /T (months since 01-Jan) ordered (Sep) to (Nov) by 1.0 N= 3 pts :grid | |||
 root mean sq anom max [ L , max ( MGAP CPT InputObs ) ] [ L Y T2 |] | M | M | M |
Filters
Here are some filters that are useful for manipulating data. There are actually many more available, but they have to be entered manually. See Ingrid Function Documentation for more information.- Monthly Climatology calculates a monthly climatology by averaging over all years.
- anomalies calculates the difference between the (above) monthly climatology and the original data.
- Integrate along L Y T2
- Differentiate along L Y T2
- Take differences along L Y T2
RMS (root mean square with mean *not* removed) over L Y T2 | L Y L T2 Y T2 | L Y T2 |
RMSA (root mean square with mean removed) over L Y T2 | L Y L T2 Y T2 | L Y T2 |
Maximum over L Y T2 | L Y L T2 Y T2 | L Y T2 |
Minimum over L Y T2 | L Y L T2 Y T2 | L Y T2 |
Detrend (best-fit-line) over L Y T2 | L Y L T2 Y T2 | L Y T2 |
Note on units