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root mean sq min min detrended-bfl ∫dL ∫dL ∂L [ IRI Forecast_old SST_ECHAM-PRECIP_GPCC Forecast ProbForecast ] : ∂L Tercile Probability data
ProbForecast partial_L int_dL int_dL adif min min ∂L Tercile Probability from IRI Forecast_old SST_ECHAM-PRECIP_GPCC Forecast: Forecast and Error.Independent Variables (Grids)
- Latitude (latitude)
- grid: /Y (degree_north) ordered (29.5S) to (35.5S) by 1.0 N= 7 pts :grid
Other Info
- bufferwordsize
- 8
- CE
- null
- colorscalename
- tercileclassesscale
- CS
- null
- datatype
- doublearraytype
- file_missing_value
- -1.0
- maxncolor
- 254
- missing_value
- NaN
- pointwidth
- 3.0
- units
- 0.000833333333333333 year
- history
- min min detrended-bfl $integral dL$ $integral dL$ $partialdiff sub L$ [ IRI Forecast_old SST_ECHAM-PRECIP_GPCC Forecast ProbForecast ]
- min over C[Debajo de lo Normal, Superior a lo Normal]
min over L[1.0 months, 4.0 months]
root mean sq min min detrended-bfl $integral dL$ $integral dL$ $partialdiff sub L$ [ IRI Forecast_old SST_ECHAM-PRECIP_GPCC Forecast ProbForecast ]- Averaged over X[60W, 52W] S[0000 1 Jan 2015, 0000 1 Mar 2017] minimum 0.0% data present
- min over C[Debajo de lo Normal, Superior a lo Normal]
Last updated: Wed, 08 Mar 2017 14:24:39 GMT
Data Views
Views
line plots
color bars
scatter| Y | |
 grid: /X (degree_east) ordered (59.5W) to (52.5W) by 1.0 N= 8 pts :grid | |
| grid: /Y (degree_north) ordered (29.5S) to (35.5S) by 1.0 N= 7 pts :grid | M |
| grid: /C (ids) unordered [ (Debajo de lo Normal) (Normal) (Superior a lo Normal)] :grid | |
| grid: /S (months since 1960-01-01) ordered (0000 1 Jan 2015) to (0000 1 Mar 2017) by 1.0 N= 27 pts :grid | |
| grid: /L (months) ordered (2.0 months) to (3.0 months) N= 2 pts :grid | |
 root mean sq min min detrended-bfl $integral dL$ $integral dL$ $partialdiff sub L$ [ IRI Forecast_old SST_ECHAM-PRECIP_GPCC Forecast ProbForecast ] [ Y |] | 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 Y
- Differentiate along Y
- Take differences along Y
RMS (root mean square with mean *not* removed) over Y |
RMSA (root mean square with mean removed) over Y |
Maximum over Y |
Minimum over Y |
Detrend (best-fit-line) over Y |
Note on units