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∫dY mean mean [ OLE2 CPT ESCENARIO1 TMIN ] : Probabilidades -Temperatura Mínima - Categorías data
∫dY mean mean [ OLE2 CPT ESCENARIO1 TMIN ] .Independent Variables (Grids)
- Categorias
- grid: /C (ids) unordered [ (Bajo la Normal) (Normal) (Sobre la Normal)] :grid
- Forecast Lead Time in Months
- grid: /L (months) ordered [ (2.5)] :grid
- Latitude (latitude)
- grid: /Y (degree_north) ordered (33N) to (63S) by 0.5 N= 193 pts :grid
Other Info
- bufferwordsize
- 8
- CE
- null
- CS
- null
- datatype
- doublearraytype
- file_missing_value
- -1.0
- missing_value
- NaN
- pointwidth
- 0
- units
- 0.000174532925199433 radian north
- history
- $integral dY$ mean mean [ OLE2 CPT ESCENARIO1 TMIN ]
- Averaged over S[0000 1 Jan 2000, 0000 1 Mar 2025] minimum 0.0% data present
Averaged over X[118W, 28W] minimum 0.0% data present
- Averaged over S[0000 1 Jan 2000, 0000 1 Mar 2025] minimum 0.0% data present
Last updated: Thu, 06 Mar 2025 07:30:01 GMT
Expires: Sat, 05 Apr 2025 00:00:00 GMT
Data Views
Views
data as colors
data as contours
line plots
color bars
OLE2 CPT ESCENARIO1[
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 C
- Differentiate along Y C
- Take differences along Y C
RMS (root mean square with mean *not* removed) over Y C | Y C |
RMSA (root mean square with mean removed) over Y C | Y C |
Maximum over Y C | Y C |
Minimum over Y C | Y C |
Detrend (best-fit-line) over Y C | Y C |
Note on units