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NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast PROBABILITIES: Probabilidades data
Probabilidades from NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast: Forecast and Error.Independent Variables (Grids)
- Forecast Lead Time in Months
- grid: /L (months) ordered [ (2.5)] :grid
- Hecho en (forecast_reference_time)
- grid: /S (months since 1960-01-01) ordered (0000 1 Jan 2019) to (0000 1 May 2021) by 1.0 N= 29 pts :grid
- Longitud (longitude)
- grid: /X (degree_east) ordered (119.9W) to (70W) by 0.1 N= 500 pts :grid
- Latitud (latitude)
- grid: /Y (degree_north) ordered (39.95N) to (4.95S) by 0.1 N= 450 pts :grid
Other Info
| Key | |
|---|---|
| 1.0 | ≤35 Bajo |
| 2.0 | 35-40 Bajo |
| 3.0 | 40-45 Bajo |
| 4.0 | 45-50 Bajo |
| 5.0 | 50-55 Bajo |
| 6.0 | 55-60 Bajo |
| 7.0 | 60-65 Bajo |
| 8.0 | 65-70 Bajo |
| 9.0 | 70-75 Bajo |
| 10.0 | 75-80 Bajo |
| 11.0 | ≥80 Bajo |
| 12.0 | ≤35 Normal |
| 13.0 | 35-40 Normal |
| 14.0 | 40-45 Normal |
| 15.0 | 45-50 Normal |
| 16.0 | 50-55 Normal |
| 17.0 | 55-60 Normal |
| 18.0 | 60-65 Normal |
| 19.0 | 65-70 Normal |
| 20.0 | 70-75 Normal |
| 21.0 | 75-80 Normal |
| 22.0 | ≥80 Normal |
| 23.0 | ≤35 Sobre |
| 24.0 | 35-40 Sobre |
| 25.0 | 40-45 Sobre |
| 26.0 | 45-50 Sobre |
| 27.0 | 50-55 Sobre |
| 28.0 | 55-60 Sobre |
| 29.0 | 60-65 Sobre |
| 30.0 | 65-70 Sobre |
| 31.0 | 70-75 Sobre |
| 32.0 | 75-80 Sobre |
| 33.0 | ≥80 Sobre |
- bufferwordsize
- 4
- CE
- 33
- colorscalename
- halfgreyscale
- CS
- 1
- datatype
- realarraytype
- file_missing_value
- 0
- fnname
- maskle
- maxncolor
- 254
- missing_value
- NaN
- scale_max
- 33.0
- scale_min
- 1.0
- units
- ids
- history
- [ dominant_class ( NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast PROB_FCST Sobre ) + masklt ( { [ dominant_class ( NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast PROB_FCST ) - 1. ] * 11. } , 22 ) ] + [ dominant_class ( NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast PROB_FCST Normal ) + masknotrange ( { [ dominant_class ( NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast PROB_FCST ) - 1. ] * 11. } , 10 , 12 ) ]
- dominant_class [ NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast PROB_FCST Sobre ] + masklt [ ( { dominant_class [ NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast PROB_FCST ] - 1. } * 11. ) , 22 ]
- dominant_class [ NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast PROB_FCST Sobre ]
dominant_class over PROB_FCST[≤35, ≥80]
- masklt [ ( { dominant_class [ NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast PROB_FCST ] - 1. } * 11. ) , 22 ]
dominant_class over C[Bajo, Sobre]
- dominant_class [ NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast PROB_FCST Sobre ]
- dominant_class [ NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast PROB_FCST Normal ] + masknotrange [ ( { dominant_class [ NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast PROB_FCST ] - 1. } * 11. ) , 10 , 12 ]
- dominant_class [ NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast PROB_FCST Normal ]
dominant_class over PROB_FCST[≤35, ≥80]
- masknotrange [ ( { dominant_class [ NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast PROB_FCST ] - 1. } * 11. ) , 10 , 12 ]
dominant_class over C[Bajo, Sobre]
- dominant_class [ NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast PROB_FCST Normal ]
- dominant_class [ NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast PROB_FCST Sobre ] + masklt [ ( { dominant_class [ NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast PROB_FCST ] - 1. } * 11. ) , 22 ]
- dominant_class [ NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast PROB_FCST Bajo ] + maskgt [ ( { dominant_class [ NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast PROB_FCST ] - 1. } * 11. ) , 0 ]
- dominant_class [ NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast PROB_FCST Bajo ]
dominant_class over PROB_FCST[≤35, ≥80]
- maskgt [ ( { dominant_class [ NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast PROB_FCST ] - 1. } * 11. ) , 0 ]
dominant_class over C[Bajo, Sobre]
- dominant_class [ NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast PROB_FCST Bajo ]
- [ dominant_class ( NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast PROB_FCST Sobre ) + masklt ( { [ dominant_class ( NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast PROB_FCST ) - 1. ] * 11. } , 22 ) ] + [ dominant_class ( NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast PROB_FCST Normal ) + masknotrange ( { [ dominant_class ( NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast PROB_FCST ) - 1. ] * 11. } , 10 , 12 ) ]
- colorscale
Last updated: Thu, 17 Feb 2022 19:19:01 GMT
Data Views
Views
data as colors
data as contours
colors with land
contours with land
colors with coasts| L | S | X | Y | |
 NextGen CPT 0p1 Guatemala V1[Models
NextGen
] | ||||
| grid: /X (degree_east) ordered (119.9W) to (70W) by 0.1 N= 500 pts :grid | M | |||
|---|---|---|---|---|
| grid: /Y (degree_north) ordered (39.95N) to (4.95S) by 0.1 N= 450 pts :grid | M | |||
| grid: /S (months since 1960-01-01) ordered (0000 1 Jan 2019) to (0000 1 May 2021) by 1.0 N= 29 pts :grid | M | |||
 NextGen CPT 0p1 Guatemala V1 Models CanSIPSv2 Forecast PROBABILITIES 2.5 months[ X Y | L S] | M | M | 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 X Y S
- Differentiate along X Y S
- Take differences along X Y S
RMS (root mean square with mean *not* removed) over X Y S | X Y X S Y S | X Y S |
RMSA (root mean square with mean removed) over X Y S | X Y X S Y S | X Y S |
Maximum over X Y S | X Y X S Y S | X Y S |
Minimum over X Y S | X Y X S Y S | X Y S |
Detrend (best-fit-line) over X Y S | X Y X S Y S | X Y S |
Note on units