pvlib.forecast.RAP

class pvlib.forecast.RAP(resolution='20', set_type='best')[source]

Subclass of the ForecastModel class representing RAP forecast model.

Model data corresponds to Rapid Refresh CONUS 20km resolution forecasts.

Parameters:
resolution: string or int, default ‘20’

The model resolution, either ‘20’ or ‘40’ (km)

set_type: string, default ‘best’

Type of model to pull data from.

Attributes:
dataframe_variables: list

Common variables present in the final set of data.

model: string

Name of the UNIDATA forecast model.

model_type: string

UNIDATA category in which the model is located.

variables: dict

Defines the variables to obtain from the weather model and how they should be renamed to common variable names.

units: dict

Dictionary containing the units of the standard variables and the model specific variables.

__init__(resolution='20', set_type='best')[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([resolution, set_type]) Initialize self.
cloud_cover_to_ghi_linear(cloud_cover, ghi_clear) Convert cloud cover to GHI using a linear relationship.
cloud_cover_to_irradiance(cloud_cover[, how]) Convert cloud cover to irradiance.
cloud_cover_to_irradiance_clearsky_scaling(…) Estimates irradiance from cloud cover in the following steps:
cloud_cover_to_irradiance_liujordan(…) Estimates irradiance from cloud cover in the following steps:
cloud_cover_to_transmittance_linear(cloud_cover) Convert cloud cover to atmospheric transmittance using a linear model.
connect_to_catalog()
get_data(latitude, longitude, start, end[, …]) Submits a query to the UNIDATA servers using Siphon NCSS and converts the netcdf data to a pandas DataFrame.
get_processed_data(*args, **kwargs) Get and process forecast data.
gust_to_speed(data[, scaling]) Computes standard wind speed from gust.
isobaric_to_ambient_temperature(data) Calculates temperature from isobaric temperature.
kelvin_to_celsius(temperature) Converts Kelvin to celsius.
process_data(data[, cloud_cover]) Defines the steps needed to convert raw forecast data into processed forecast data.
rename(data[, variables]) Renames the columns according the variable mapping.
set_dataset() Retrieves the designated dataset, creates NCSS object, and creates a NCSS query object.
set_location(time, latitude, longitude) Sets the location for the query.
set_query_latlon() Sets the NCSS query location latitude and longitude.
set_time(time) Converts time data into a pandas date object.
uv_to_speed(data) Computes wind speed from wind components.

Attributes

access_url_key
base_tds_url
catalog_url
data_format
units
vert_level