data: $integral dS$ $integral dS$ [ Hydromet Seasonal_Forecast Precipitation target

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∫dS ∫dS [ Hydromet Seasonal_Forecast Precipitation target_date ] : forecast time data

Precipitation target_date target_date int_dS 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 2 Mar 2019) (0000 1 Apr 2019) (0600 1 May 2019) (1800 31 May 2019) (0600 1 Jul 2019) (0000 1 Aug 2019) (1800 31 Aug 2019) (0600 1 Oct 2019) (1800 31 Oct 2019) (0600 1 Dec 2019) (0000 1 Jan 2020) (1200 31 Jan 2020) (1200 1 Mar 2020) (1800 31 Mar 2020) (0600 1 May 2020) (1800 31 May 2020) (0600 1 Jul 2020) (0000 1 Aug 2020) (1800 31 Aug 2020) (0600 1 Oct 2020) (1800 31 Oct 2020) (0600 1 Dec 2020) (0000 1 Jan 2021) (0600 31 Jan 2021) (1800 1 Mar 2021) (1800 31 Mar 2021) (0600 1 May 2021) (1800 31 May 2021) (0600 1 Jul 2021) (0000 1 Aug 2021) (0000 1 Sep 2021)] :grid

Other Info

bufferwordsize: 8
calendar: 360
CE: null
CS: null
datatype: doublearraytype
gridid: 191771464
pointwidth: 3
scale_max: null
scale_min: null
standard_name: forecast_reference_time
units: 622080000 ( year above 2000 ) second²

Last updated: Fri, 12 Jan 2024 14:16:35 GMT
Expires: Fri, 09 Feb 2024 00:00:00 GMT

Data Views

	L	S
$integral dS$ $integral dS$ [ Hydromet Seasonal_Forecast Precipitation target_date ] 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