∂X mean root mean sq ∂L detrended-bfl detrended-bfl detrended-bfl ∂S [ IRI Forecast_old SST_CFSV2-PRECIP_GPCC History InputObs ] : ∂X ∂L ∂S input observations data
InputObs partial_S adif adif adif partial_L partial_L partial_L
∂X ∂L ∂S input observations from IRI Forecast_old SST_CFSV2-PRECIP_GPCC History: Predicand, Hindcast and Skill.
Independent Variables (Grids)
- S (forecast_reference_time)
- grid: /S (months since 1960-01-01) ordered (Jan 2015) to (Feb 2017) by 1.0 N= 26 pts :grid
- Longitude (longitude)
- grid: /X (degree_east) ordered (59W) to (53W) by 1.0 N= 7 pts :grid
Other Info
- CE
- null
- colorscalename
- precip_colors
- CS
- null
- datatype
- realarraytype
- file_missing_value
- -999.0
- maxncolor
- 254
- missing_value
- NaN
- pointwidth
- 3.0
- units
- 99.0071069986062 meter radian-1 east year-3
- history
- $partialdiff sub X$ mean root mean sq $partialdiff sub L$ detrended-bfl detrended-bfl detrended-bfl $partialdiff sub S$ [ IRI Forecast_old SST_CFSV2-PRECIP_GPCC History InputObs ]
- Averaged over Y[36S, 29S] minimum 0.0% data present
Averaged over T2[1982, 2013] L[1.5 months, 3.5 months] minimum 0.0% data present
Last updated: Wed, 08 Mar 2017 14:24:39 GMT
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 X
S
- Differentiate along X
S
- Take differences along X
S
Average over
X
S
|
X S
|
RMS (root mean square with mean *not* removed) over
X
S
|
X S
|
RMSA (root mean square with mean removed) over
X
S
|
X S
|
Maximum over
X
S
|
X S
|
Minimum over
X
S
|
X S
|
Detrend (best-fit-line) over
X
S
|
X S
|
Note on units