def nam_12km_hourly_to_hourly_instantaneous(latitude, longitude, elevation,
init_time, start, end,
interval_label,
load_forecast=load_forecast,
*, __model='nam_12km'):
"""
Hourly instantantaneous forecast.
GHI directly from NWP model. DNI, DHI computed.
Max forecast horizon 36 hours.
Parameters
----------
latitude : float
longitude : float
elevation : float
init_time : pd.Timestamp
Full datetime of a model initialization
start : pd.Timestamp
Forecast start. Forecast is inclusive of this point.
end : pd.Timestamp
Forecast end. Forecast is exclusive of this point.
interval_label : str
Must be instant
"""
start_adj, end_adj = adjust_start_end_for_interval_label(
interval_label, start, end, limit_instant=True)
ghi, air_temperature, wind_speed = load_forecast(
latitude, longitude, init_time, start_adj, end_adj, __model,
variables=('ghi', 'air_temperature', 'wind_speed'))
dni, dhi, solar_pos_calculator = _ghi_to_dni_dhi(
latitude, longitude, elevation, ghi)
# hourly instant in, hourly instant out
resampler = partial(forecast.resample, freq='1h')
return (ghi, dni, dhi, air_temperature, wind_speed,
resampler, solar_pos_calculator)
def nam_12km_cloud_cover_to_hourly_mean(latitude, longitude, elevation,
init_time, start, end, interval_label,
load_forecast=load_forecast,
*, __model='nam_12km'):
"""
Hourly average forecast.
GHI from NWP model cloud cover. DNI, DHI computed.
Max forecast horizon 72 hours.
Parameters
----------
latitude : float
longitude : float
elevation : float
init_time : pd.Timestamp
Full datetime of a model initialization
start : pd.Timestamp
Forecast start. Forecast is inclusive of this instant if
interval_label is *beginning* and exclusive of this instant if
interval_label is *ending*.
end : pd.Timestamp
Forecast end. Forecast is exclusive of this instant if
interval_label is *beginning* and inclusive of this instant if
interval_label is *ending*.
interval_label : str
Must be *beginning* or *ending*
"""
cloud_cover, air_temperature, wind_speed = load_forecast(
latitude, longitude, init_time, start, end, __model,
variables=('cloud_cover', 'air_temperature', 'wind_speed'))
return _resample_using_cloud_cover(latitude, longitude, elevation,
cloud_cover, air_temperature,
wind_speed, start, end, interval_label,
'interpolate')
def rap_ghi_to_instantaneous(latitude, longitude, elevation,
init_time, start, end, interval_label,
load_forecast=load_forecast,
*, __model='rap'):
"""
Hourly instantantaneous RAP forecast.
GHI directly from NWP model. DNI, DHI computed.
Max forecast horizon 21 or 39 (3Z, 9Z, 15Z, 21Z) hours.
Parameters
----------
latitude : float
longitude : float
elevation : float
init_time : pd.Timestamp
Full datetime of a model initialization
start : pd.Timestamp
Forecast start. Forecast is inclusive of this point.
end : pd.Timestamp
Forecast end. Forecast is exclusive of this point.
interval_label : str
Must be instant
"""
# ghi dni and dhi not in RAP output available from g2sub service
ghi, air_temperature, wind_speed = load_forecast(
latitude, longitude, init_time, start, end, __model,
variables=('ghi', 'air_temperature', 'wind_speed'))
dni, dhi, solar_pos_calculator = _ghi_to_dni_dhi(
latitude, longitude, elevation, ghi)
# hourly instant in, hourly instant out
resampler = partial(forecast.resample, freq='1h')
return (ghi, dni, dhi, air_temperature, wind_speed,
resampler, solar_pos_calculator)
def hrrr_subhourly_to_hourly_mean(latitude,
longitude,
elevation,
init_time,
start,
end,
interval_label,
load_forecast=load_forecast,
*,
__model='hrrr_subhourly'):
"""
Hourly mean HRRR forecast.
GHI, DNI, DHI directly from model, resampled.
Max forecast horizon 18 or 36 hours (0Z, 6Z, 12Z, 18Z).
Parameters
----------
latitude : float
longitude : float
elevation : float
init_time : pd.Timestamp
Full datetime of a model initialization
start : pd.Timestamp
Forecast start. Forecast is inclusive of this instant if
interval_label is *beginning* and exclusive of this instant if
interval_label is *ending*.
end : pd.Timestamp
Forecast end. Forecast is exclusive of this instant if
interval_label is *beginning* and inclusive of this instant if
interval_label is *ending*.
interval_label : str
Must be *beginning* or *ending*
"""
ghi, dni, dhi, air_temperature, wind_speed = load_forecast(
latitude, longitude, init_time, start, end, __model)
# Interpolate irrad, temp, wind data to 5 min to
# minimize weather to power errors. Either start or end is outside of
# forecast, but is needed for subhourly interpolation. After
# interpolation, we slice the extra point out of the interpolated
# output.
start_adj, end_adj = adjust_start_end_for_interval_label(
interval_label, start, end)
resample_interpolate_slicer = partial(forecast.reindex_fill_slice,
freq='5min',
start_slice=start_adj,
end_slice=end_adj)
ghi, dni, dhi, air_temperature, wind_speed = [
resample_interpolate_slicer(v)
for v in (ghi, dni, dhi, air_temperature, wind_speed)
]
# weather (and optionally power) will eventually be resampled
# to hourly average using resampler defined below
label = datamodel.CLOSED_MAPPING[interval_label]
resampler = partial(forecast.resample, freq='1h', label=label)
solar_pos_calculator = partial(pvmodel.calculate_solar_position, latitude,
longitude, elevation, ghi.index)
return (ghi, dni, dhi, air_temperature, wind_speed, resampler,
solar_pos_calculator)
def _load_gefs_member(member, solar_position):
cloud_cover_mixed, air_temperature, wind_speed = load_forecast(
latitude, longitude, init_time, start_floored, end_ceil, member,
variables=('cloud_cover', 'air_temperature', 'wind_speed'))
cloud_cover = _unmix_various_gefs_intervals(
init_time, start_floored, end_ceil, cloud_cover_mixed)
return _resample_using_cloud_cover(
latitude, longitude, elevation, cloud_cover, air_temperature,
wind_speed, start, end, interval_label, 'bfill',
solar_position=solar_position)
def gfs_quarter_deg_to_hourly_mean(latitude,
longitude,
elevation,
init_time,
start,
end,
interval_label,
load_forecast=load_forecast,
*,
__model='gfs_0p25'):
"""
Hourly average forecasts derived from GFS 1, 3, and 12 hr frequency
output. GHI from NWP model cloud cover. DNI, DHI computed.
Max forecast horizon 384 hours.
Parameters
----------
latitude : float
longitude : float
elevation : float
init_time : pd.Timestamp
Full datetime of a model initialization
start : pd.Timestamp
Forecast start. Forecast is inclusive of this instant if
interval_label is *beginning* and exclusive of this instant if
interval_label is *ending*.
end : pd.Timestamp
Forecast end. Forecast is exclusive of this instant if
interval_label is *beginning* and inclusive of this instant if
interval_label is *ending*.
interval_label : str
Must be *beginning* or *ending*
"""
start_floored, end_ceil = _adjust_gfs_start_end(start, end)
cloud_cover_mixed, air_temperature, wind_speed = load_forecast(
latitude,
longitude,
init_time,
start_floored,
end_ceil,
__model,
variables=('cloud_cover', 'air_temperature', 'wind_speed'))
cloud_cover = _unmix_various_gfs_intervals(init_time, start_floored,
end_ceil, cloud_cover_mixed)
return _resample_using_cloud_cover(latitude, longitude, elevation,
cloud_cover, air_temperature,
wind_speed, start, end, interval_label,
'bfill')
def hrrr_subhourly_to_subhourly_instantaneous(latitude,
longitude,
elevation,
init_time,
start,
end,
interval_label,
load_forecast=load_forecast,
*,
__model='hrrr_subhourly'):
"""
Subhourly (15 min) instantantaneous HRRR forecast.
GHI, DNI, DHI directly from model.
Max forecast horizon 18 or 36 hours (0Z, 6Z, 12Z, 18Z).
Parameters
----------
latitude : float
longitude : float
elevation : float
init_time : pd.Timestamp
Full datetime of a model initialization
start : pd.Timestamp
Forecast start. Forecast is inclusive of this point.
end : pd.Timestamp
Forecast end. Forecast is exclusive of this point.
interval_label : str
Must be instant
"""
start_adj, end_adj = adjust_start_end_for_interval_label(
interval_label, start, end, limit_instant=True)
ghi, dni, dhi, air_temperature, wind_speed = load_forecast(
latitude, longitude, init_time, start_adj, end_adj, __model)
# resampler takes 15 min instantaneous in, retuns 15 min instantaneous out
# still want to call resample, rather than pass through lambda x: x
# so that DatetimeIndex has well-defined freq attribute
resampler = partial(forecast.resample, freq='15min')
solar_pos_calculator = partial(pvmodel.calculate_solar_position, latitude,
longitude, elevation, ghi.index)
return (ghi, dni, dhi, air_temperature, wind_speed, resampler,
solar_pos_calculator)