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

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

prec partial_lon 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
time: grid: /time (days since 1960-01-01) ordered [ (1200 16 Dec 1924 - 1200 16 Jan 1925) (1200 16 Jan 1925 - 2400 14 Feb 1925) (0000 15 Feb 1925 - 1200 16 Mar 1925) ... (1200 16 Dec 2009 - 1200 16 Jan 2010)] N= 1021 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 lon[58.85526W, 53.64474W] 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	time
UY[precipitacion prec ]
$integral dtime$ mean $partialdiff sub lon$ [ UY prec ] [ time \| 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 time lat
Differentiate along time lat
Take differences along time lat