data: max $partialdiff sub Y$ $integral dY$ $integral dT$ [ NiMet CIPH CSMT TempCondProb ]

max ∂_Y ∫dY ∫dT [ NiMet CIPH CSMT TempCondProb ] : ∂_Y Percent Occurrence of Temperature Condition in Historical Record data

TempCondProb int_dT int_dY partial_Y ∂_Y Percent Occurrence of Temperature Condition in Historical Record from NiMet CIPH CSMT: Climatic Suitability for a Malaria Transmission.

Independent Variables (Grids)

Time: grid: /T (months since 1960-01-01) ordered (0000 1 Jan 1960) to (0000 1 Jan 1961) by 1.0 N= 13 pts :grid

Other Info

CE

100.0

CI

25.0

CS

0.0

datatype

realarraytype

file_missing_value

-99.0

fnname

flagge

maxncolor

254

missing_value

NaN

units

0.0833333333333334 year

history

$partialdiff sub Y$ $integral dY$ $integral dT$ [ NiMet CIPH CSMT TempCondProb ]

Boxes with less than 100.0% dropped
Averaged over T2[1981, 2024] minimum 0.0% data present

max $partialdiff sub Y$ $integral dY$ $integral dT$ [ NiMet CIPH CSMT TempCondProb ]

max over X[2.4E, 14.925E] Y[3.975N, 14.1N]

colorscale

References

Grover-Kopec_etal2006

Last updated: Mon, 17 Feb 2025 14:29:37 GMT

Data Views

	T
max $partialdiff sub Y$ $integral dY$ $integral dT$ [ NiMet CIPH CSMT TempCondProb ] [ T \|]	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 T
Differentiate along T
Take differences along T