served from IRI/LDEO Climate Data Library
DASILVA SMD94 anomalies slp: sea level air presure data
anomalies sea level air presure from DASILVA SMD94: a five-volume atlas series depicting the seasonal and yearly variations of the surface marine atmosphere over the global oceans.Independent Variables (Grids)
- Time
- grid: /T (months since 1960-01-01) ordered (Jan 1945) to (Dec 1993) by 1.0 N= 588 pts :grid
- longitude
- grid: /X (degree_east) periodic (0.5E) to (0.5W) by 1.0 N= 360 pts :grid
- latitude
- grid: /Y (degree_north) ordered (89.5S) to (89.5N) by 1.0 N= 180 pts :grid
Other Info
- add_offset
- -19.084999
- datatype
- shortarraytype
- missing_value
- 32767
- scale_factor
- 0.0026342
- units
- hPa
- standard units*
- 100 kilogram meter-1 second-2
- history
- da Silva et al. 1994: Atlas of Surface Marine Data
References
A. da Silva, A. C. Young, S. Levitus. Atlas of Surface Marine Data 1994, Volume 1: Algorithms and Procedures, number 6, 1994
Last updated: Wed, 13 Feb 2019 20:27:28 GMT
Data Views
Views
data as colors
data as contours
colors with land
contours with land
colors with coasts| T | X | Y | |
 DASILVA SMD94 anomalies slp[ X Y | 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 X Y T
- Differentiate along X Y T
- Take differences along X Y T
RMS (root mean square with mean *not* removed) over X Y T | X Y X T Y T | X Y T |
RMSA (root mean square with mean removed) over X Y T | X Y X T Y T | X Y T |
Maximum over X Y T | X Y X T Y T | X Y T |
Minimum over X Y T | X Y X T Y T | X Y T |
Detrend (best-fit-line) over X Y T | X Y X T Y T | X Y T |
Note on units