data: root mean sq anom $partialdiff sub lon$ $integral dtime$ [ UY precipitacion ]

root mean sq anom ∂_lon ∫dtime [ UY precipitacion ] : ∂_lon Precipitacion data

precipitacion precipitacion int_dtime int_dtime partial_lon ∂_lon Precipitacion from UY: PRECM_UY_v1p1: Gridded precipitation dataset at 30 km. It corresponds to Experiment No. 3 in the documentation.

Independent Variables (Grids)

lat: grid: /lat (degree_north) ordered (29.5S) to (36S) by 0.2826087 N= 24 pts :grid
lon: grid: /lon (degree_east) ordered (58.85526W) to (53.64474W) by 0.2894737 N= 19 pts :grid

Other Info

bufferwordsize: 8
CE: null
CS: null
datatype: doublearraytype
file_missing_value: -999.0
missing_value: NaN
units: 59404.2641991638 meter radian^-1 east second year^-1
history: Averaged over time[Jan 1925, Dec 2009] minimum 0.0% data present

References

Muñoz, ?~A.G., González, P., Baethgen, W.,: Gridded precipitation dataset ff or Uruguay. Version 1.1

Last updated: Sun, 02 Jun 2024 17:51:42 GMT

Data Views

	lat	lon
grid: /time (days since 1960-01-01) ordered [ (Jan 1925) (Feb 1925) (Mar 1925) ... (Dec 2009)] N= 1020 pts :grid
grid: /lon (degree_east) ordered (58.85526W) to (53.64474W) by 0.2894737 N= 19 pts :grid		M
grid: /lat (degree_north) ordered (29.5S) to (36S) by 0.2826087 N= 24 pts :grid	M
root mean sq anom $partialdiff sub lon$ $integral dtime$ [ UY precipitacion ] [ lon \| lat]	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 lon lat
Differentiate along lon lat
Take differences along lon lat