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root mean sq anom ∂Y [ NextGen CPT 1p0 Guatemala CMC2-CanCM4 History InputObs ] : ∂Y input observations data
InputObs partial_Y ∂Y input observations from NextGen CPT 1p0 Guatemala CMC2-CanCM4 History: Predictand, Hindcast and Skill.Independent Variables (Grids)
- Forecast Lead Time in Months
- grid: /L (months) ordered [ (2.5)] :grid
- S
- grid: /S (months since 1960-01-01) ordered (0000 1 Jan 1983) to (0000 1 Dec 2009) by 1.0 N= 324 pts :grid
- Latitude (latitude)
- grid: /Y (degree_north) ordered (12.5N) to (18.5N) by 1.0 N= 7 pts :grid
Other Info
- bufferwordsize
- 8
- CE
- null
- colorscalename
- precip_colors
- CS
- null
- datatype
- doublearraytype
- file_missing_value
- -999.0
- maxncolor
- 254
- missing_value
- NaN
- pointwidth
- 0
- units
- mm /dia /degree_north
- history
- Averaged over X[94W, 87W] minimum 0.0% data present
Last updated: Fri, 10 Apr 2020 20:38:10 GMT
Data Views
Views
data as colors
data as contours
line plots
color bars| L | S | Y | |
 root mean sq anom $partialdiff sub Y$ [ NextGen CPT 1p0 Guatemala CMC2-CanCM4 History InputObs ] 2.5 months[ S | Y L] | 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 S Y
- Differentiate along S Y
- Take differences along S Y
RMS (root mean square with mean *not* removed) over S Y | S Y |
RMSA (root mean square with mean removed) over S Y | S Y |
Maximum over S Y | S Y |
Minimum over S Y | S Y |
Detrend (best-fit-line) over S Y | S Y |
Note on units