data: $integral dtime$ $partialdiff sub lon$ [ UY prec ]

∫dtime ∂_lon [ UY prec ] : ∂_lon Precipitacion data

prec prec partial_lon partial_lon 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 (58.71053W) to (53.78947W) by 0.2894736 N= 18 pts :grid
time: grid: /time (days since 1960-01-01) ordered [ (1200 31 Dec 1924 - 1200 31 Jan 1925) (1200 31 Jan 1925 - 2400 1 Mar 1925) (0000 2 Mar 1925 - 1200 31 Mar 1925) ... (1-30 Dec 2009)] N= 1020 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

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	time
UY[precipitacion prec ]
grid: /time (months since 1960-01-01) ordered (Jan 1925) to (Dec 2009) by 1.0 N= 1020 pts :grid			D
grid: /lon (degree_east) ordered (58.71053W) to (53.78947W) by 0.2894736 N= 18 pts :grid		M
grid: /lat (degree_north) ordered (29.5S) to (36S) by 0.2826087 N= 24 pts :grid	M
$integral dtime$ $partialdiff sub lon$ [ UY prec ] [ time lon \| lat]	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 time lon lat
Differentiate along time lon lat
Take differences along time lon lat