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root mean sq ∂Y [ ENACTS version0 monthly temperature tmean ] : ∂Y Mean Temperature data
monthly temperature tmean partial_Y partial_Y partial_Y ∂Y Mean Temperature from ENACTS version0: version 0: CHIRPS-BLENDED and CRU.Independent Variables (Grids)
- Time (time)
- grid: /T (days since 1960-01-01) ordered [ (Jan 1981) (Feb 1981) (Mar 1981) ... (Dec 2012)] N= 384 pts :grid
- Latitude
- grid: /Y (degree_north) ordered (11.95S) to (22.15N) by 0.05 N= 683 pts :grid
Other Info
- CE
- null
- correlation_decay_distance
- 1200.0
- CS
- null
- datatype
- realarraytype
- file_missing_value
- 9.96920997E36
- missing_value
- NaN
- pointwidth
- 1
- standard_name
- air_temperature
- units
- 57.2957795130823 degree_Kelvin radian-1 north
- history
- $partialdiff sub Y$ [ ENACTS version0 monthly temperature tmean ]
- Wed 10 Jul 2013 18:28:10 BST : User ianharris : Program makegridsauto.for called by update.for
X regridded on UEA CRU TS3p21 monthly tmp
Y regridded on UEA CRU TS3p21 monthly tmp
root mean sq $partialdiff sub Y$ [ ENACTS version0 monthly temperature tmean ]- Averaged over X[20.975E, 52.025E] minimum 0.0% data present
- Wed 10 Jul 2013 18:28:10 BST : User ianharris : Program makegridsauto.for called by update.for
Last updated: Fri, 26 Jul 2024 12:21:16 GMT
Data Views
Views
data as colors
data as contours
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