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root mean sq ∂X [ IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 ] : ∂X Probabilidades - R0 - Categorías data
root mean sq ∂X [ IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 ] .Independent Variables (Grids)
- Categorias
- grid: /C (ids) unordered [ (Below) (Normal) (Above)] :grid
- Forecast Lead Time in Months
- grid: /L (months) ordered [ (2.5)] :grid
- Longitude (longitude)
- grid: /X (degree_east) ordered (125W) to (66.5W) by 0.5 N= 118 pts :grid
Other Info
- bufferwordsize
- 8
- CE
- null
- CS
- null
- datatype
- doublearraytype
- file_missing_value
- -1.0
- missing_value
- NaN
- pointwidth
- 0
- units
- 0.572957795130823 radian-1 east
- history
- Averaged over Y[23.5N, 50.5N] S[0000 1 Nov 2018, 0000 1 Dec 2018] minimum 0.0% data present
Last updated: Thu, 24 Jan 2019 03:12:42 GMT
Expires: Tue, 05 Feb 2019 00:00:00 GMT
Data Views
Views
data as colors
data as contours
line plots
color bars| C | L | S | X | Y | |
 IRI NOAA-VECTOR R0FCST[R0FCST_DJF20182019
target_date
PROBABILIDADES
] | |||||
| IRI NOAA-VECTOR R0FCST PROBABILIDADES 2.5 months[ X Y | L S] | M | M | O=0.25 | M | |
|---|---|---|---|---|---|
 root mean sq $partialdiff sub X$ [ IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 ] 2.5 months[ X C | L] | 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 C
- Differentiate along X C
- Take differences along X C
RMS (root mean square with mean *not* removed) over X C | X C |
RMSA (root mean square with mean removed) over X C | X C |
Maximum over X C | X C |
Minimum over X C | X C |
Detrend (best-fit-line) over X C | X C |
Note on units