weatherbench2.derived_variables.PrecipitationAccumulation

class weatherbench2.derived_variables.PrecipitationAccumulation(total_precipitation_name, accumulation_hours, lead_time_name='prediction_timedelta', set_negative_to_zero=True)

Compute precipitation accumulation from hourly accumulations.

Accumulation is computed for the time period leading up to and including the lead_time. E.g. 24h accumulation at lead_time=24h indicates accumulation from lead_time=0 to lead_time=24. This is equal to the values of total_precipitation_name at 24, minus the value at 0.

Caution: Small negative values sometimes appear in model output. Here, we set them to zero.

Parameters:

total_precipitation_name (str) –
accumulation_hours (int) –
lead_time_name (str) –
set_negative_to_zero (bool) –

total_precipitation_name

Name of hourly total_precipitation input.

Type:: str

accumulation_hours

Hours to accumulate precipitation over

Type:: int

lead_time_name

Name of lead_time dimension.

Type:: str

set_negative_to_zero

Specify whether to set negative temporal differences to zero.

Type:: bool

__init__(total_precipitation_name, accumulation_hours, lead_time_name='prediction_timedelta', set_negative_to_zero=True)

Parameters:

total_precipitation_name (str) –
accumulation_hours (int) –
lead_time_name (str) –
set_negative_to_zero (bool) –

Return type:

None

Methods

`__init__`(total_precipitation_name, ...[, ...])
`compute`(dataset)	Compute derived variable, returning it in a new DataArray.

Attributes

`all_input_core_dims`	The set of all input core dimensions.
`base_variables`	Return a list of base variables.
`core_dims`	Return core dimensions needed for computing this variable.
`lead_time_name`
`set_negative_to_zero`
`total_precipitation_name`
`accumulation_hours`