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root mean sq ∂Y max [ T , max ( Princeton wspd ) ] : ∂Y Time, Wind speed at 10 m data
root mean sq ∂Y max [ T , max ( Princeton wspd ) ] .Independent Variables (Grids)
- Time (time)
- grid: /T (julian_day) ordered (0000 1 Jan 1948) to (0000 31 Dec 2008) by 1.0 N= 22281 pts :grid
Other Info
- bufferwordsize
- 8
- calendar
- standard
- CE
- null
- CS
- null
- datatype
- doublearraytype
- fnname
- max
- gridid
- 175570704
- missing_value
- NaN
- pointwidth
- 0
- scale_max
- null
- scale_min
- null
- standard_name
- time
- units
- 4950355.34993031 radian-1 north second
- history
- root mean sq $partialdiff sub Y$ max [ T , max ( Princeton wspd ) ]
- max over X[0.25E, 0.25W] T[0000 1 Jan 1948, 0000 31 Dec 2008]
Averaged over Y[89.5S, 89.5N] minimum 0.0% data present
- max over X[0.25E, 0.25W] T[0000 1 Jan 1948, 0000 31 Dec 2008]
Last updated: Fri, 27 Nov 2020 04:20:45 GMT
Data Views
Views
line plots
color bars
scatter| T | |
 Princeton[prcp
tmin
tmax
wspd
] | |
| grid: /X (degree_east) periodic (0.25E) to (0.25W) by 0.5 N= 720 pts :grid | |
| grid: /Y (degree_north) ordered (89.75S) to (89.75N) by 0.5 N= 360 pts :grid | |
 root mean sq $partialdiff sub Y$ max [ T , max ( Princeton wspd ) ] [ T |] | 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 T
- Differentiate along T
- Take differences along T
RMS (root mean square with mean *not* removed) over T |
RMSA (root mean square with mean removed) over T |
Maximum over T |
Minimum over T |
Detrend (best-fit-line) over T |
Note on units