mean detrended-bfl ∂T ∫dY [ Estation vgt-dmp v2p0 SPOTV-ECOWAS dmp ] : ∂T Productivité en matière sèche data
dmp int_dY partial_T adif
∂T Productivité en matière sèche from Estation vgt-dmp v2p0 SPOTV-ECOWAS: SPOT-ECOWAS dmp.
Independent Variables (Grids)
- Time
- grid: /T (days since 1960-01-01) ordered [ (6-15 Jan 2019) (0000 16 Jan 2019 - 1200 26 Jan 2019) (1200 26 Jan 2019 - 2400 5 Feb 2019) (6-15 Feb 2019) (16-24 Feb 2019) (25 Feb 2019 - 5 Mar 2019) (6-15 Mar 2019) (0000 16 Mar 2019 - 1200 26 Mar 2019) (1200 26 Mar 2019 - 2400 5 Apr 2019) (6-15 Apr 2019) (16-25 Apr 2019) (26 Apr 2019 - 5 May 2019) (6-15 May 2019) (0000 16 May 2019 - 1200 26 May 2019) (1200 26 May 2019 - 2400 5 Jun 2019) (6-15 Jun 2019) (16-25 Jun 2019) (26 Jun 2019 - 5 Jul 2019) (6-15 Jul 2019) (0000 16 Jul 2019 - 1200 26 Jul 2019) (1200 26 Jul 2019 - 2400 5 Aug 2019) (6-15 Aug 2019) (0000 16 Aug 2019 - 1200 26 Aug 2019)] :grid
- Longitude (longitude)
- grid: /X (degree_east) ordered (19.00446W) to (24.99765E) by 0.008929 N= 4929 pts :grid
Other Info
- bufferwordsize
- 8
- CE
- null
- CS
- null
- datatype
- doublearraytype
- missing_value
- NaN
- units
- 2.02005700462307×10-11 kilogram meter-2 radian north second-1
- history
- Averaged over Y[3.998847N, 28.00893N] minimum 0.0% data present
Last updated: Sat, 12 Jun 2021 03:19:49 GMT
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 X
T
- Differentiate along X
T
- Take differences along X
T
Average over
X
T
|
X T
|
RMS (root mean square with mean *not* removed) over
X
T
|
X T
|
RMSA (root mean square with mean removed) over
X
T
|
X T
|
Maximum over
X
T
|
X T
|
Minimum over
X
T
|
X T
|
Detrend (best-fit-line) over
X
T
|
X T
|
Note on units