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ENACTS version0 CIPH CSMT TempCondProb: Percent Occurrence of Temperature Condition in Historical Record data
Percent Occurrence of Temperature Condition in Historical Record from ENACTS version0 CIPH CSMT: Climatic Suitability for a Malaria Transmission.Independent Variables (Grids)
- Month (time)
- grid: /T (months since 01-Jan) periodic (Jan) to (Dec) by 1.0 N= 12 pts :grid
- Longitude
- grid: /X (degree_east) ordered (21E) to (52E) by 0.05 N= 621 pts :grid
- Latitude
- grid: /Y (degree_north) ordered (12S) to (22.2N) by 0.05 N= 685 pts :grid
Other Info
- CE
- 100.0
- CI
- 25.0
- correlation_decay_distance
- 1200.0
- CS
- 0.0
- datatype
- realarraytype
- file_missing_value
- 9.96920997E36
- fnname
- flagge
- maxncolor
- 254
- missing_value
- NaN
- pointwidth
- 1
- scale_max
- 100.0
- scale_min
- 0.0
- standard_name
- air_temperature
- units
- unitless
- history
- ENACTS version0 monthly temperature tmean
- Wed 10 Jul 2013 18:28:10 BST : User ianharris : Program makegridsauto.for called by update.for
X regridded on UEA CRU TS3p21 monthly tmp
Y regridded on UEA CRU TS3p21 monthly tmp
ENACTS version0 CIPH CSMT TempCondProb- Averaged over T2[1981, 2012] minimum 0.0% data present
- Wed 10 Jul 2013 18:28:10 BST : User ianharris : Program makegridsauto.for called by update.for
- colorscale
References
Grover-Kopec, E. K., M. B. Blumenthal, P. N. Ceccato, T. Dinku, J. Omumbo, S. J. Connor, 2006: Web-based climate information resources for malaria control in Africa. Malaria Journal, 5:38.
Last updated: Fri, 26 Jul 2024 12:21:16 GMT
Data Views
Views
data as colors
data as contours
colors with land
contours with land
colors with coasts| T | X | Y | |
 ENACTS version0 CIPH CSMT[Annual_Sum
PcpnCondProb
TempCondProb
RHCondProb
AllCondProb
] | |||
 ENACTS version0 CIPH CSMT TempCondProb[ X Y T |] | 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 T
- Differentiate along X Y T
- Take differences along X Y T
RMS (root mean square with mean *not* removed) over X Y T | X Y X T Y T | X Y T |
RMSA (root mean square with mean removed) over X Y T | X Y X T Y T | X Y T |
Maximum over X Y T | X Y X T Y T | X Y T |
Minimum over X Y T | X Y X T Y T | X Y T |
Detrend (best-fit-line) over X Y T | X Y X T Y T | X Y T |
Note on units