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∫dT2 ∂T [ MGAP CPT CFSv2_AtlPac_sst hindcast_date ] : ∂T hindcast time data
MGAP CPT CFSv2_AtlPac_sst hindcast_date partial_T ∂T hindcast time from SOURCES: datos de MGAP SNIA prueba.Independent Variables (Grids)
- Forecast Lead Time in Months
- grid: /L (months) ordered (2.5 months) to (4.5 months) by 1.0 N= 3 pts :grid
- Time (time)
- grid: /T (months since 01-Jan) periodic (16 Dec - 15 Jan) to (16 Nov - 15 Dec) by 1.0 N= 12 pts :grid
- T2
- grid: /T2 (months since 1960-01-01) ordered (0000 1 Jan 1980) to (0000 1 Jan 2015) by 12.0 N= 36 pts :grid
Other Info
- bufferwordsize
- 8
- calendar
- 360
- CE
- null
- CS
- null
- datatype
- doublearraytype
- gridid
- 183242360
- pointwidth
- 3
- units
- months
- standard units*
- 0.0833333333333333 year
Last updated: Thu, 04 Feb 2016 15:18:08 GMT
Data Views
Views
data as colors
data as contours
line plots
color bars| L | T | T2 | |
 doc[] | |||
| MGAP CPT[Forecast
PredErrorVars
hindcast_date
InputObs
CFSv2_AtlPac_sst
CrossValHind
PearsonR
target_date
] | |||
 $integral dT2$ $partialdiff sub T$ [ MGAP CPT CFSv2_AtlPac_sst hindcast_date ] [ T2 T L |] | 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 T2 T L
- Differentiate along T2 T L
- Take differences along T2 T L
RMS (root mean square with mean *not* removed) over T2 T L | T2 T T2 L T L | T2 T L |
RMSA (root mean square with mean removed) over T2 T L | T2 T T2 L T L | T2 T L |
Maximum over T2 T L | T2 T T2 L T L | T2 T L |
Minimum over T2 T L | T2 T T2 L T L | T2 T L |
Detrend (best-fit-line) over T2 T L | T2 T T2 L T L | T2 T L |
Note on units