served from 129.236.110.25
root mean sq anom ∂S max [ S , max ( IRI NOAA-VECTOR R0FCST PROBABILIDADES ) ] : ∂S Hecho en, Probabilities data
root mean sq anom ∂S max [ S , max ( IRI NOAA-VECTOR R0FCST PROBABILIDADES ) ] . isIndependent Variables (Grids)
- S (forecast_reference_time)
- grid: /S (months since 1960-01-01) ordered [ (Nov 2018)] :grid
Other Info
- bufferwordsize
- 8
- calendar
- 360
- CE
- null
- CS
- null
- datatype
- doublearraytype
- fnname
- max
- gridid
- 175960336
- missing_value
- NaN
- pointwidth
- 0
- scale_max
- null
- scale_min
- null
- standard_name
- forecast_reference_time
- units
- unitless
- history
- [ dominant_class ( IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 Above ) + masklt ( { [ dominant_class ( IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 ) - 1. ] * 11. } , 22 ) ] + [ dominant_class ( IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 Normal ) + masknotrange ( { [ dominant_class ( IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 ) - 1. ] * 11. } , 10 , 12 ) ]
- dominant_class [ IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 Above ] + masklt [ ( { dominant_class [ IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 ] - 1. } * 11. ) , 22 ]
- dominant_class [ IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 Above ]
dominant_class over R0FCST_DJF20182019[<35, >80]
- masklt [ ( { dominant_class [ IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 ] - 1. } * 11. ) , 22 ]
dominant_class over C[Below, Above]
- dominant_class [ IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 Above ]
- dominant_class [ IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 Normal ] + masknotrange [ ( { dominant_class [ IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 ] - 1. } * 11. ) , 10 , 12 ]
- dominant_class [ IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 Normal ]
dominant_class over R0FCST_DJF20182019[<35, >80]
- masknotrange [ ( { dominant_class [ IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 ] - 1. } * 11. ) , 10 , 12 ]
dominant_class over C[Below, Above]
- dominant_class [ IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 Normal ]
- dominant_class [ IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 Above ] + masklt [ ( { dominant_class [ IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 ] - 1. } * 11. ) , 22 ]
- dominant_class [ IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 Below ] + maskgt [ ( { dominant_class [ IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 ] - 1. } * 11. ) , 0 ]
- dominant_class [ IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 Below ]
dominant_class over R0FCST_DJF20182019[<35, >80]
- maskgt [ ( { dominant_class [ IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 ] - 1. } * 11. ) , 0 ]
dominant_class over C[Below, Above]
- dominant_class [ IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 Below ]
- max over X[125.25W, 66.25W] S[0000 1 Nov 2018, 0000 1 Dec 2018]
root mean sq anom $partialdiff sub S$ max [ S , max ( IRI NOAA-VECTOR R0FCST PROBABILIDADES ) ]- Averaged over Y[50.25N, 23.75N] minimum 0.0% data present
- [ dominant_class ( IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 Above ) + masklt ( { [ dominant_class ( IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 ) - 1. ] * 11. } , 22 ) ] + [ dominant_class ( IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 Normal ) + masknotrange ( { [ dominant_class ( IRI NOAA-VECTOR R0FCST R0FCST_DJF20182019 ) - 1. ] * 11. } , 10 , 12 ) ]
Last updated: Thu, 24 Jan 2019 03:12:42 GMT
Expires: Tue, 05 Feb 2019 00:00:00 GMT
Data Views
| S | |
 IRI NOAA-VECTOR R0FCST[target_date
R0FCST_DJF20182019
PROBABILIDADES
] | |
| grid: /X (degree_east) ordered (125.25W) to (66.25W) by 0.5 N= 119 pts :grid | |
| grid: /Y (degree_north) ordered (50.25N) to (23.75N) by 0.5 N= 54 pts :grid | |
 root mean sq anom $partialdiff sub S$ max [ S , max ( IRI NOAA-VECTOR R0FCST PROBABILIDADES ) ] Nov 2018[ S |] | 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
- Differentiate along
- Take differences along
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