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

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

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

Independent Variables (Grids)

Other Info

CE

null

CS

null

datatype

realarraytype

file_missing_value

-999.0

missing_value

NaN

units

188.090948814419 meter radian^-3 east

history

max $partialdiff sub lat$ mean $integral dtime$ $partialdiff sub lat$ $partialdiff sub lon$ [ UY prec ]

Averaged over lon[58.85526W, 53.64474W] minimum 0.0% data present
max over time[0000 1 Jan 1925, 0000 1 Jan 2010] lat[29.78261S, 35.71739S]

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

[ |]

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
Differentiate along
Take differences along

Average over
RMS (root mean square with mean *not* removed) over
RMSA (root mean square with mean removed) over
Maximum over
Minimum over
Detrend (best-fit-line) over

Note on units