Forecast Tutorial

This tutorial will walk through forecast data from Unidata forecast model data using the forecast.py module within pvlib.

Table of contents:

Setup
Intialize and Test Each Forecast Model

This tutorial has been tested against the following package versions:

Python 3.4.3
IPython 4.0.1
pandas 0.17.1
matplotlib 1.5.0
netcdf4 1.2.1
siphon 0.3.2

It should work with other Python and Pandas versions. It requires pvlib >= 0.2.0 and IPython >= 3.0.

Authors:

Derek Groenendyk (@moonraker), University of Arizona, November 2015
Will Holmgren (@wholmgren), University of Arizona, November 2015

Setup



In [1]:

    
%matplotlib inline
import matplotlib.pyplot as plt
try:
    import seaborn as sns
    sns.set(rc={"figure.figsize": (12, 6)})
except ImportError:
    print('We suggest you install seaborn using conda or pip and rerun this cell')

# built in python modules
from datetime import datetime, timedelta
import os

# python add-ons
import numpy as np
import pandas as pd
try:
    import netCDF4
    from netCDF4 import num2date
except ImportError:
    print('We suggest you install netCDF4 using conda rerun this cell')

# for accessing UNIDATA THREDD servers
from siphon.catalog import TDSCatalog
from siphon.ncss import NCSS

from pvlib.forecast import GFS,HRRR_ESRL,NAM,NDFD,HRRR,RAP









    



C:\Users\DareRock\Anaconda3\envs\pvlibdev\lib\site-packages\matplotlib\__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.
  warnings.warn(self.msg_depr % (key, alt_key))



In [2]:

    
# Choose a location and time.
# Tucson, AZ
latitude = 32.2
longitude = -110.9 
tz = 'US/Arizona'

start = datetime.now() # today's date
end = start + timedelta(days=7) # 7 days from today
timerange = pd.date_range(start, end, tz=tz)

Instantiate GFS forecast model



In [3]:

    
# GFS model, defaults to 0.5 degree resolution
fm = GFS()



In [4]:

    
# retrieve data
data = fm.get_query_data(latitude, longitude , timerange)



In [5]:

    
data['temperature'].plot()
plt.ylabel(fm.var_stdnames['temperature'] + ' (%s)' % fm.var_units['temperature'])









    Out[5]:





<matplotlib.text.Text at 0x80a32e8>



In [6]:

    
cloud_vars = ['total_clouds','low_clouds','mid_clouds','high_clouds']



In [7]:

    
for varname in cloud_vars:
    data[varname].plot()
plt.ylabel('Cloud cover' + ' %')
plt.xlabel('Forecast Time ('+str(data.index.tz)+')')
plt.title('GFS 0.5 deg')
plt.legend(bbox_to_anchor=(1.18,1.0))









    Out[7]:





<matplotlib.legend.Legend at 0x8090748>



In [8]:

    
total_cloud_cover = data['total_clouds']



In [9]:

    
total_cloud_cover.plot(color='r', linewidth=2)
plt.ylabel('Total cloud cover' + ' (%s)' % fm.var_units['total_clouds'])
plt.xlabel('Forecast Time ('+str(data.index.tz)+')')
plt.title('GFS 0.5 deg')









    Out[9]:





<matplotlib.text.Text at 0x7fcee48>

Instantiate GFS forecast model



In [10]:

    
# GFS model at 0.25 degree resolution
fm = GFS(res='quarter')



In [11]:

    
# retrieve data
data = fm.get_query_data(latitude, longitude , timerange)



In [12]:

    
for varname in cloud_vars:
    data[varname].plot(ls='-', linewidth=2)
plt.ylabel('Cloud cover' + ' %')
plt.xlabel('Forecast Time ('+str(data.index.tz)+')')
plt.title('GFS 0.25 deg')
plt.legend(bbox_to_anchor=(1.18,1.0))









    Out[12]:





<matplotlib.legend.Legend at 0x7fc7550>

Instaniate NAM forecast model



In [13]:

    
fm = NAM()



In [14]:

    
# retrieve data
data = fm.get_query_data(latitude, longitude, timerange)



In [15]:

    
for varname in cloud_vars:
    data[varname].plot(ls='-', linewidth=2)
plt.ylabel('Cloud cover' + ' %')
plt.xlabel('Forecast Time ('+str(data.index.tz)+')')
plt.title('NAM')
plt.legend(bbox_to_anchor=(1.18,1.0))









    Out[15]:





<matplotlib.legend.Legend at 0x853898>



In [16]:

    
data['ghi'].plot(linewidth=2, ls='-')
plt.ylabel('GHI W/m**2')
plt.xlabel('Forecast Time ('+str(data.index.tz)+')')









    Out[16]:





<matplotlib.text.Text at 0x800be0>

Instantiate NDFD forecast model



In [17]:

    
fm = NDFD()



In [18]:

    
# retrieve data
data = fm.get_query_data(latitude, longitude, timerange)



In [19]:

    
total_cloud_cover = data['total_clouds']
temp = data['temperature']
wind = data['wind_speed']



In [20]:

    
total_cloud_cover.plot(color='r', linewidth=2)
plt.ylabel('Total cloud cover' + ' (%s)' % fm.var_units['total_clouds'])
plt.xlabel('Forecast Time ('+str(data.index.tz)+')')
plt.title('NDFD')
plt.ylim(0,100)









    Out[20]:





(0, 100)



In [21]:

    
temp.plot(color='r', linewidth=2)
plt.ylabel('Temperature' + ' (%s)' % fm.var_units['temperature'])
plt.xlabel('Forecast Time ('+str(data.index.tz)+')')









    Out[21]:





<matplotlib.text.Text at 0x9d1d588>



In [22]:

    
wind.plot(color='r', linewidth=2)
plt.ylabel('Wind Speed' + ' (%s)' % fm.var_units['wind_speed'])
plt.xlabel('Forecast Time ('+str(data.index.tz)+')')









    Out[22]:





<matplotlib.text.Text at 0x843f98>

Instantiate RAP forecast model



In [23]:

    
fm = RAP()



In [24]:

    
# retrieve data
data = fm.get_query_data(latitude, longitude, timerange)



In [25]:

    
cloud_vars = ['total_clouds','high_clouds','mid_clouds','low_clouds']



In [26]:

    
for varname in cloud_vars:
    data[varname].plot(ls='-', linewidth=2)
plt.ylabel('Cloud cover' + ' %')
plt.xlabel('Forecast Time ('+str(data.index.tz)+')')
plt.title('RAP')
plt.legend(bbox_to_anchor=(1.18,1.0))









    Out[26]:





<matplotlib.legend.Legend at 0x9cdc898>

Instantiate HRRR forecast model



In [27]:

    
fm = HRRR()



In [28]:

    
# retrieve data
data = fm.get_query_data(latitude, longitude, timerange)



In [29]:

    
cloud_vars = ['total_clouds','high_clouds','mid_clouds','low_clouds']



In [30]:

    
for varname in cloud_vars:
    data[varname].plot(ls='-', linewidth=2)
plt.ylabel('Cloud cover' + ' %')
plt.xlabel('Forecast Time ('+str(data.index.tz)+')')
plt.title('HRRR')
plt.legend(bbox_to_anchor=(1.18,1.0))









    Out[30]:





<matplotlib.legend.Legend at 0x9df56d8>

Instanciate HRRR ESRL forecast model



In [31]:

    
fm = HRRR_ESRL()









    



c:\programmingfolder\repositories\pvlib\pvlib\forecast.py:539: UserWarning: HRRR_ESRL is an experimental model and is not always available.
  + 'available.')






    



---------------------------------------------------------------------------
HTTPError                                 Traceback (most recent call last)
<ipython-input-31-2164a2bcd853> in <module>()
----> 1 fm = HRRR_ESRL()

c:\programmingfolder\repositories\pvlib\pvlib\forecast.py in __init__(self, set_type)
    558         'high_clouds',
    559         'ghi', ]
--> 560         super(HRRR_ESRL, self).__init__(model_type, model, set_type)
    561 
    562 

c:\programmingfolder\repositories\pvlib\pvlib\forecast.py in __init__(self, model_type, model_name, set_type)
    130             raise ParseError(self.model_name + ' model may be unavailable.')
    131 
--> 132         self.model = TDSCatalog(model_url)
    133         self.datasets_list = list(self.model.datasets.keys())
    134         self.set_dataset()

c:\programmingfolder\repositories\siphon\siphon\catalog.py in __init__(self, catalog_url)
     57         # get catalog.xml file
     58         resp = session.get(self.catalog_url)
---> 59         resp.raise_for_status()
     60 
     61         # If we were given an HTML link, warn about it and try to fix to xml

C:\Users\DareRock\Anaconda3\envs\pvlibdev\lib\site-packages\requests\models.py in raise_for_status(self)
    835 
    836         if http_error_msg:
--> 837             raise HTTPError(http_error_msg, response=self)
    838 
    839     def close(self):

HTTPError: 404 Client Error: Not Found for url: http://thredds-jumbo.unidata.ucar.edu/thredds/catalog/grib/HRRR/CONUS_3km/surface/catalog.xml



In [ ]:

    
# retrieve data
data = fm.get_query_data(latitude, longitude, timerange)



In [ ]:

    
cloud_vars = ['total_clouds','high_clouds','mid_clouds','low_clouds']



In [ ]:

    
for varname in cloud_vars:
    data[varname].plot(ls='-', linewidth=2)
plt.ylabel('Cloud cover' + ' %')
plt.xlabel('Forecast Time ('+str(data.index.tz)+')')
plt.title('HRRR_ESRL')
plt.legend(bbox_to_anchor=(1.18,1.0))



In [ ]:

    
data['ghi'].plot(linewidth=2, ls='-')
plt.ylabel('GHI W/m**2')
plt.xlabel('Forecast Time ('+str(data.index.tz)+')')



In [ ]: