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

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

prec partial_lon int_dlon int_dlon int_dlon int_dlon ∂_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)

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

CE

null

CS

null

datatype

doublearraytype

file_missing_value

-999.0

missing_value

NaN

units

1036.8 meter second year^-1

history

$integral dtime$ mean mean $integral dlon$ $partialdiff sub lon$ [ UY prec ]

Averaged over lon[59.14474W, 53.35526W] minimum 0.0% data present
Averaged over lat[36.1413S, 29.3587S] 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

	time
grid: /time (months since 1960-01-01) ordered (Jan 1925) to (Dec 2009) by 1.0 N= 1020 pts :grid	D
$integral dtime$ mean mean $integral dlon$ $partialdiff sub lon$ [ UY prec ] [ time \|]	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
Differentiate along time
Take differences along time