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∫dL ∫dS [ ANACIM CPT ENACTSv1 cfs2 sst precip Forecast DomTerProb ] : Probabilités du Tercile Dominant data
DomTerProb int_dS Probabilités du Tercile Dominant from ANACIM CPT ENACTSv1 cfs2 sst precip Forecast: Prédictions.Independent Variables (Grids)
- L
- grid: /L (months) ordered (1.5 months) to (5.5 months) by 2.0 N= 3 pts :grid
- S (forecast_reference_time)
- grid: /S (months since 1960-01-01) ordered (0000 16 Mar 2018) to (0000 16 May 2018) by 1.0 N= 3 pts :grid
- Longitude (longitude)
- grid: /X (degree_east) ordered (17.5875W) to (11.2125W) by 0.0375 N= 171 pts :grid
- Latitude (latitude)
- grid: /Y (degree_north) ordered (16.8N) to (12.225N) by 0.03749999 N= 123 pts :grid
Other Info
- bufferwordsize
- 8
- CE
- null
- colorscalename
- tercileclassesscale
- CS
- null
- datatype
- doublearraytype
- file_missing_value
- 0
- fnname
- maskle
- maxncolor
- 254
- missing_value
- NaN
- pointwidth
- 0
- units
- ids months months
- history
- [ masklt ( { [ dominant_class ( ANACIM CPT ENACTSv1 cfs2 sst precip Forecast TercProb ) - 1. ] * 11. } , 22 ) + dominant_class ( ANACIM CPT ENACTSv1 cfs2 sst precip Forecast TercProb Audessus_PNe ) ] + [ masknotrange ( { [ dominant_class ( ANACIM CPT ENACTSv1 cfs2 sst precip Forecast TercProb ) - 1. ] * 11. } , 10 , 12 ) + dominant_class ( ANACIM CPT ENACTSv1 cfs2 sst precip Forecast TercProb Proche_PNe ) ]
- masklt [ ( { dominant_class [ ANACIM CPT ENACTSv1 cfs2 sst precip Forecast TercProb ] - 1. } * 11. ) , 22 ] + dominant_class [ ANACIM CPT ENACTSv1 cfs2 sst precip Forecast TercProb Audessus_PNe ]
- masklt [ ( { dominant_class [ ANACIM CPT ENACTSv1 cfs2 sst precip Forecast TercProb ] - 1. } * 11. ) , 22 ]
dominant_class over C[Sous_PNe, Audessus_PNe]
- dominant_class [ ANACIM CPT ENACTSv1 cfs2 sst precip Forecast TercProb Audessus_PNe ]
dominant_class over TercProb[<35, >80]
- masklt [ ( { dominant_class [ ANACIM CPT ENACTSv1 cfs2 sst precip Forecast TercProb ] - 1. } * 11. ) , 22 ]
- masknotrange [ ( { dominant_class [ ANACIM CPT ENACTSv1 cfs2 sst precip Forecast TercProb ] - 1. } * 11. ) , 10 , 12 ] + dominant_class [ ANACIM CPT ENACTSv1 cfs2 sst precip Forecast TercProb Proche_PNe ]
- masknotrange [ ( { dominant_class [ ANACIM CPT ENACTSv1 cfs2 sst precip Forecast TercProb ] - 1. } * 11. ) , 10 , 12 ]
dominant_class over C[Sous_PNe, Audessus_PNe]
- dominant_class [ ANACIM CPT ENACTSv1 cfs2 sst precip Forecast TercProb Proche_PNe ]
dominant_class over TercProb[<35, >80]
- masknotrange [ ( { dominant_class [ ANACIM CPT ENACTSv1 cfs2 sst precip Forecast TercProb ] - 1. } * 11. ) , 10 , 12 ]
- masklt [ ( { dominant_class [ ANACIM CPT ENACTSv1 cfs2 sst precip Forecast TercProb ] - 1. } * 11. ) , 22 ] + dominant_class [ ANACIM CPT ENACTSv1 cfs2 sst precip Forecast TercProb Audessus_PNe ]
- maskgt [ ( { dominant_class [ ANACIM CPT ENACTSv1 cfs2 sst precip Forecast TercProb ] - 1. } * 11. ) , 0 ] + dominant_class [ ANACIM CPT ENACTSv1 cfs2 sst precip Forecast TercProb Sous_PNe ]
- maskgt [ ( { dominant_class [ ANACIM CPT ENACTSv1 cfs2 sst precip Forecast TercProb ] - 1. } * 11. ) , 0 ]
dominant_class over C[Sous_PNe, Audessus_PNe]
- dominant_class [ ANACIM CPT ENACTSv1 cfs2 sst precip Forecast TercProb Sous_PNe ]
dominant_class over TercProb[<35, >80]
- maskgt [ ( { dominant_class [ ANACIM CPT ENACTSv1 cfs2 sst precip Forecast TercProb ] - 1. } * 11. ) , 0 ]
- [ masklt ( { [ dominant_class ( ANACIM CPT ENACTSv1 cfs2 sst precip Forecast TercProb ) - 1. ] * 11. } , 22 ) + dominant_class ( ANACIM CPT ENACTSv1 cfs2 sst precip Forecast TercProb Audessus_PNe ) ] + [ masknotrange ( { [ dominant_class ( ANACIM CPT ENACTSv1 cfs2 sst precip Forecast TercProb ) - 1. ] * 11. } , 10 , 12 ) + dominant_class ( ANACIM CPT ENACTSv1 cfs2 sst precip Forecast TercProb Proche_PNe ) ]
Last updated: Thu, 26 Oct 2023 14:58:08 GMT
Data Views
Views
data as colors
data as contours
colors with land
contours with land
colors with coasts| L | S | X | Y | |
 $integral dL$ $integral dS$ [ ANACIM CPT ENACTSv1 cfs2 sst precip Forecast DomTerProb ] [ X Y | S L] | M | 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 X Y S L
- Differentiate along X Y S L
- Take differences along X Y S L
RMS (root mean square with mean *not* removed) over X Y S L | X Y X S X L Y S Y L S L | X Y S X Y L X S L Y S L | X Y S L |
RMSA (root mean square with mean removed) over X Y S L | X Y X S X L Y S Y L S L | X Y S X Y L X S L Y S L | X Y S L |
Maximum over X Y S L | X Y X S X L Y S Y L S L | X Y S X Y L X S L Y S L | X Y S L |
Minimum over X Y S L | X Y X S X L Y S Y L S L | X Y S X Y L X S L Y S L | X Y S L |
Detrend (best-fit-line) over X Y S L | X Y X S X L Y S Y L S L | X Y S X Y L X S L Y S L | X Y S L |
Note on units