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.

dataframe_variables¶

Common variables present in the final set of data.

Type:	list

model¶

Name of the UNIDATA forecast model.

Type:	string

model_type¶

UNIDATA category in which the model is located.

Type:	string

variables¶

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

Type:	dict

units¶

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

Type:	dict

__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(tz, latitude, longitude) Sets the location for the query.

set_query_latlon() Sets the NCSS query location latitude and longitude.

set_query_time_range(start, end)

param start:	Must be tz-localized.

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`