served from 129.236.110.25
root mean sq ∫dX ∂Y [ Princeton wspd ] : ∂Y Wind speed at 10 m data
root mean sq ∫dX ∂Y [ Princeton wspd ] .Independent Variables (Grids)
- Time (time)
- grid: /T (days since 1960-01-01 00:00:00) ordered (0000 1 Jan 1948) to (0000 31 Dec 2008) by 1.0 N= 22281 pts :grid
- Latitude (latitude)
- grid: /Y (degree_north) ordered (89.5S) to (89.5N) by 0.5 N= 359 pts :grid
- Depth
- grid: /Z (m) ordered [ (0)] :grid
Other Info
- bufferwordsize
- 8
- CE
- null
- CS
- null
- datatype
- doublearraytype
- file_missing_value
- 2.00000004E20
- missing_value
- NaN
- pointwidth
- 0
- units
- m /s
- standard units*
- meter second-1
- history
- Averaged over X[0, 0] minimum 0.0% data present
Last updated: Fri, 27 Nov 2020 04:20:45 GMT
Data Views
Views
data as colors
data as contours| T | Y | Z | |
 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 | O=0.25 | ||
| grid: /T (days since 1960-01-01 00:00:00) ordered (0000 1 Jan 1948) to (0000 31 Dec 2008) by 1.0 N= 22281 pts :grid | M | ||
 root mean sq $integral dX$ $partialdiff sub Y$ [ Princeton wspd ] 0 m[ Y Z | T] | 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 Y T
- Differentiate along Y T
- Take differences along Y T
RMS (root mean square with mean *not* removed) over Y T | Y T |
RMSA (root mean square with mean removed) over Y T | Y T |
Maximum over Y T | Y T |
Minimum over Y T | Y T |
Detrend (best-fit-line) over Y T | Y T |
Note on units