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mean ∂C ∂S [ ANACIM CPT ENACTSv1 cfs2 sst precip Forecast TercProb ] : ∂C ∂S Probabilités des Terciles data
TercProb partial_S partial_C ∂C ∂S Probabilités des Terciles from ANACIM CPT ENACTSv1 cfs2 sst precip Forecast: Prédictions.Independent Variables (Grids)
- C
- grid: /C (ids) unordered [ (ÍÌeA33eA™™dA) (™™gA)] :grid
- Horizon
- grid: /L (months) ordered (2.5 months) to (4.5 months) N= 2 pts :grid
- S (forecast_reference_time)
- grid: /S (months since 1960-01-01) ordered [ (Apr 2018)] :grid
- Longitude (longitude)
- grid: /X (degree_east) ordered (17.5875W) to (11.2125W) by 0.0375 N= 171 pts :grid
Other Info
- bufferwordsize
- 8
- CE
- null
- colorscalename
- tercileclassesscale
- CS
- null
- datatype
- doublearraytype
- file_missing_value
- -1
- maxncolor
- 254
- missing_value
- NaN
- pointwidth
- 0
- units
- 0.12 year-1 /ids
- history
- Averaged over Y[12.20625N, 16.81875N] minimum 0.0% data present
Last updated: Thu, 26 Oct 2023 14:58:08 GMT
Data Views
Views
data as colors
data as contours
line plots
color bars| C | L | S | X | |
 mean $partialdiff sub C$ $partialdiff sub S$ [ ANACIM CPT ENACTSv1 cfs2 sst precip Forecast TercProb ] Apr 2018[ X C | S L] | M | M | M | M |
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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 C L
- Differentiate along X C L
- Take differences along X C L
RMS (root mean square with mean *not* removed) over X C L | X C X L C L | X C L |
RMSA (root mean square with mean removed) over X C L | X C X L C L | X C L |
Maximum over X C L | X C X L C L | X C L |
Minimum over X C L | X C X L C L | X C L |
Detrend (best-fit-line) over X C L | X C X L C L | X C L |
Note on units