max max [ ( OLE2 CPT ESCENARIO2 PROPENSIONTMED ) , max ( OLE2 CPT ESCENARIO2 SPEARMANTMIN ) ] : Propensión -Temperatura Media, Spearman Temperatura Mínima data
max max [ ( OLE2 CPT ESCENARIO2 PROPENSIONTMED ) , max ( OLE2 CPT ESCENARIO2 SPEARMANTMIN ) ] .
Independent Variables (Grids)
- Forecast Lead Time in Months
- grid: /L (months) ordered [ (2.5)] :grid
- Hecho en (forecast_reference_time)
- grid: /S (months since 1960-01-01) ordered (0000 1 Jan 2000) to (0000 1 Dec 2025) by 1.0 N= 312 pts :grid
- Latitud (latitude)
- grid: /Y (degree_north) ordered (32.75N) to (62.75S) by 0.5 N= 192 pts :grid
Other Info
| Key |
| 1.0 | 0 Bajo |
| 2.0 | 0-1 Bajo |
| 3.0 | 1-2 Bajo |
| 4.0 | 2-3 Bajo |
| 5.0 | 3-4 Bajo |
| 6.0 | 4-5 Bajo |
| 7.0 | 5-6 Bajo |
| 8.0 | 6-7 Bajo |
| 9.0 | 7-8 Bajo |
| 10.0 | 8-9 Bajo |
| 11.0 | >9 Bajo |
| 12.0 | 0 Nrormal |
| 13.0 | 0-1 Normal |
| 14.0 | 1-2 Normal |
| 15.0 | 2-3 Normal |
| 16.0 | 3-4 Normal |
| 17.0 | 4-5 Normal |
| 18.0 | 5-6 Normal |
| 19.0 | 6-7 Normal |
| 20.0 | 7-8 Normal |
| 21.0 | 8-9 Normal |
| 22.0 | >9 Normal |
| 23.0 | 0 Sobre |
| 24.0 | 0-1 Sobre |
| 25.0 | 1-2 Sobre |
| 26.0 | 2-3 Sobre |
| 27.0 | 3-4 Sobre |
| 28.0 | 4-5 Sobre |
| 29.0 | 5-6 Sobre |
| 30.0 | 6-7 Sobre |
| 31.0 | 7-8 Sobre |
| 32.0 | 8-9 Sobre |
| 33.0 | >9 Sobre |
- bufferwordsize
- 4
- CE
- 33
- colorscalename
- halfgreyscale
- CS
- 1
- datatype
- realarraytype
- file_missing_value
- 0
- fnname
- max
- maxncolor
- 254
- missing_value
- NaN
- pointwidth
- 0
- scale_max
- 33.0
- scale_min
- 1.0
- units
- ids
- history
- OLE2 CPT ESCENARIO2 PROPENSIONTMED
- [ dominant_class ( OLE2 CPT ESCENARIO2 ODDSTMED Sobre la Normal ) + masklt ( { [ dominant_class ( OLE2 CPT ESCENARIO2 ODDSTMED ) - 1. ] * 11. } , 22 ) ] + [ dominant_class ( OLE2 CPT ESCENARIO2 ODDSTMED Normal ) + masknotrange ( { [ dominant_class ( OLE2 CPT ESCENARIO2 ODDSTMED ) - 1. ] * 11. } , 10 , 12 ) ]
- dominant_class [ OLE2 CPT ESCENARIO2 ODDSTMED Sobre la Normal ] + masklt [ ( { dominant_class [ OLE2 CPT ESCENARIO2 ODDSTMED ] - 1. } * 11. ) , 22 ]
- dominant_class [ OLE2 CPT ESCENARIO2 ODDSTMED Sobre la Normal ]
dominant_class over ODDSTMED[0, >9.0]
- masklt [ ( { dominant_class [ OLE2 CPT ESCENARIO2 ODDSTMED ] - 1. } * 11. ) , 22 ]
dominant_class over C[Bajo la Normal, Sobre la Normal]
- dominant_class [ OLE2 CPT ESCENARIO2 ODDSTMED Normal ] + masknotrange [ ( { dominant_class [ OLE2 CPT ESCENARIO2 ODDSTMED ] - 1. } * 11. ) , 10 , 12 ]
- dominant_class [ OLE2 CPT ESCENARIO2 ODDSTMED Normal ]
dominant_class over ODDSTMED[0, >9.0]
- masknotrange [ ( { dominant_class [ OLE2 CPT ESCENARIO2 ODDSTMED ] - 1. } * 11. ) , 10 , 12 ]
dominant_class over C[Bajo la Normal, Sobre la Normal]
- dominant_class [ OLE2 CPT ESCENARIO2 ODDSTMED Bajo la Normal ] + maskgt [ ( { dominant_class [ OLE2 CPT ESCENARIO2 ODDSTMED ] - 1. } * 11. ) , 0 ]
- dominant_class [ OLE2 CPT ESCENARIO2 ODDSTMED Bajo la Normal ]
dominant_class over ODDSTMED[0, >9.0]
- maskgt [ ( { dominant_class [ OLE2 CPT ESCENARIO2 ODDSTMED ] - 1. } * 11. ) , 0 ]
dominant_class over C[Bajo la Normal, Sobre la Normal]
- max [ OLE2 CPT ESCENARIO2 SPEARMANTMIN ]
max over X[117.75W, 28.25W]
max max [ ( OLE2 CPT ESCENARIO2 PROPENSIONTMED ) , max ( OLE2 CPT ESCENARIO2 SPEARMANTMIN ) ] - max over X[117.75W, 28.25W]
- colorscale
Last updated: Sat, 06 Dec 2025 07:30:02 GMT
Expires: Mon, 05 Jan 2026 00:00:00 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 Y
S
- Differentiate along Y
S
- Take differences along Y
S
Average over
Y
S
|
Y S
|
RMS (root mean square with mean *not* removed) over
Y
S
|
Y S
|
RMSA (root mean square with mean removed) over
Y
S
|
Y S
|
Maximum over
Y
S
|
Y S
|
Minimum over
Y
S
|
Y S
|
Detrend (best-fit-line) over
Y
S
|
Y S
|
Note on units