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

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

prec prec prec partial_lon partial_lon int_dtime ∂_lon ∂_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

bufferwordsize

CE

null

CS

null

datatype

doublearraytype

file_missing_value

-999.0

missing_value

NaN

units

3.28280635001174 meter radian^-2 east

history

root mean sq anom $integral dtime$ $partialdiff sub lon$ $partialdiff sub lon$ root mean sq [ UY prec ]

Averaged over lat[36.1413S, 29.3587S] minimum 0.0% data present
Averaged over time[0000 1 Jan 1925, 0000 1 Jan 2010] lon[58.56579W, 53.93421W] 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

[ |]

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