data: $integral dS$ detrended-bfl [ Hydromet Seasonal_Forecast Precipitation target

∫dS detrended-bfl [ Hydromet Seasonal_Forecast Precipitation target_length ] : Length of forecast time data

Precipitation target_length target_length adif Length of forecast time from Hydromet Seasonal_Forecast: Seasonal Forecast.

Independent Variables (Grids)

Forecast Lead Time in Months: grid: /L (months) ordered [ (2.5)] :grid
S (forecast_reference_time): grid: /S (days since 1960-01-01) ordered [ (0000 17 Mar 2019) (0000 16 Apr 2019) (1200 16 May 2019) (0000 16 Jun 2019) (1200 16 Jul 2019) (1200 16 Aug 2019) (0000 16 Sep 2019) (1200 16 Oct 2019) (0000 16 Nov 2019) (1200 16 Dec 2019) (1200 16 Jan 2020) (1200 15 Feb 2020) (1200 16 Mar 2020) (0000 16 Apr 2020) (1200 16 May 2020) (0000 16 Jun 2020) (1200 16 Jul 2020) (1200 16 Aug 2020) (0000 16 Sep 2020) (1200 16 Oct 2020) (0000 16 Nov 2020) (1200 16 Dec 2020) (1200 16 Jan 2021) (0000 15 Feb 2021) (1200 16 Mar 2021) (0000 16 Apr 2021) (1200 16 May 2021) (0000 16 Jun 2021) (1200 16 Jul 2021) (1200 16 Aug 2021)] :grid

Other Info

bufferwordsize: 8
calendar: 360
CE: null
CS: null
datatype: doublearraytype
missing_value: NaN
pointwidth: 1.0
units: 7464960000 second²udunits(3): Invalid integer udunits(3): Invalid integer
standard units*
history: Averaged in T with overlapping interval 3

Last updated: Fri, 12 Jan 2024 14:16:35 GMT

Data Views

	L	S
$integral dS$ detrended-bfl [ Hydromet Seasonal_Forecast Precipitation target_length ] 2.5 months[ S L \|]	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 S
Differentiate along S
Take differences along S