A Beginners Guide to Weather & Climate Data, Margriet Groenendijk

@ M A R G R I E T G R
B E G I N N E R S G U I D E T O
W E AT H E R A N D C L I M AT E D ATA
D R M A R G R I E T G R O E N E N D I J K
D E V E L O P E R A D V O C AT E - I B M WAT S O N D ATA P L AT F O R M
1 2 J U N E 2 0 1 7 - P Y PA R I S

B E G I N N E R S G U I D E T O
W E AT H E R A N D C L I M AT E D ATA
S L I D E S
H T T P S : / / W W W. S L I D E S H A R E . N E T /
M A R G R I E T G R O E N E N D I J K / P R E S E N TAT I O N S

W E AT H E R
F O R E C A S T

W E AT H E R
F O R E C A S T I N A
N O T E B O O K

J U P Y T E R
N O T E B O O K
https://jupyter.org

T H E W E AT H E R
C O M PA N Y A P I
D ATA
Get access here:
https://console.ng.bluemix.net/

W E AT H E R
F O R E C A S T I N A
N O T E B O O K
Try it out here:
https://datascience.ibm.com
Notebook:
https://github.com/ibm-cds-labs/python-notebooks

S C R E E N S H O T O F N O T E B O O K

%%javascript
navigator.geolocation.getCurrentPosition(function(position) {
console.log(position.coords.latitude, position.coords.longitude);
});

%%javascript
setTimeout(function() {
IPython.notebook.kernel.execute('lat="' + position.coords.latitude + '";')
IPython.notebook.kernel.execute('lon="' + position.coords.longitude + '";')
},5000)
});

%%javascript
setTimeout(function() {
IPython.notebook.kernel.execute('lat="' + position.coords.latitude + '";')
IPython.notebook.kernel.execute('lon="' + position.coords.longitude + '";')
},5000)
});
import requests
import json
line='https://'+username+':'+password+
'@twcservice.mybluemix.net/api/weather/v1/geocode/'+
lat+'/'+lon+'/forecast/intraday/10day.json?&units=m'
r=requests.get(line)
weather = json.loads(r.text)
Get access to the API here: https://console.ng.bluemix.net/

print json.dumps(weather, indent=4, sort_keys=True)

print json.dumps(weather, indent=4, sort_keys=True)
{
"forecasts": [
{
"class": "fod_long_range_intraday",
"clds": 42,
"dow": "Tuesday",
...
"temp": 13,
"wdir": 261,
},
],
"metadata": {
"expire_time_gmt": 1491904587,
"latitude": 51.45,
"longitude": -2.58,
...
}
}

import pandas as pd
from datetime import datetime
df =
pd.DataFrame.from_dict(weather['forecasts'][0],orient='index').transpose()
for forecast in weather['forecasts'][1:]:
df =
pd.concat([df,pd.DataFrame.from_dict(forecast,orient='index').transpose()])

import pandas as pd
from datetime import datetime
df =
pd.DataFrame.from_dict(weather['forecasts'][0],orient='index').transpose()
for forecast in weather['forecasts'][1:]:
df =
pd.concat([df,pd.DataFrame.from_dict(forecast,orient='index').transpose()])
df['date'] = df['fcst_valid_local'].apply(lambda x: datetime.strptime(x,
'%Y-%m-%dT%H:%M:%S+0200'))

L A M B D A A N D PA N D A S D ATA F R A M E S
A N O N Y M O U S F U N C T I O N
df['date'] = df['fcst_valid_local'].apply(lambda x: datetime.strptime(x,
'%Y-%m-%dT%H:%M:%S+0200'))

S O M E M O R E C L E A N I N G U P
df = df.drop([‘expire_time_gmt’],1)
df['temp']=df['temp'].apply(pd.to_numeric)
df.head()

P L O T W I T H M AT P L O T L I B
import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline

fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(14, 8))
import matplotlib
%matplotlib inline

df['rain'].plot(ax=axes[0], kind='bar', color='#C93D79',sharex=True)
axes[0].set_title('Chance of rain',loc='left',fontsize=20)
import matplotlib
%matplotlib inline

import matplotlib
%matplotlib inline
df['rain'].plot(ax=axes[0], kind='bar', color='#C93D79',sharex=True)
axes[0].set_title('Chance of rain',loc='left',fontsize=20)
df['temp'].plot(ax=axes[1], color='#6EEDD8',lw=4.0,sharex=True)
axes[1].set_title('Temperature',loc='left',fontsize=20)

cities = [
('Bristol',51.44999778,-2.583315472),
...
('Portsmouth',50.80034751,-1.080022218)]
icons=[]
temps=[]
for city in cities:
lat = city[1]
lon = city[2]
line='https://'+username+':'+password+'@twcservice.mybluemix.net/api/
weather/v1/geocode/'+str(lat)+'/'+str(lon)+'/observations.json?&units=m'
r=requests.get(line)
weather = json.loads(r.text)
icons=np.append(icons,weather['observation']['wx_icon'])
temps=np.append(temps,weather['observation']['temp'])

from mpl_toolkits.basemap import Basemap
from matplotlib.offsetbox import AnnotationBbox, OffsetImage
from matplotlib._png import read_png
from itertools import izip
import urllib
matplotlib.style.use('bmh')
# background maps
m1 = Basemap(projection='mill',resolution=None,llcrnrlon=-7.5,llcrnrlat=49.84,urcrnrlon=2.5,urcrnrlat=59,ax=axes[0])
m1.drawlsmask(land_color='dimgrey',ocean_color='dodgerBlue',lakes=True)
m2 = Basemap(projection='mill',resolution=None,llcrnrlon=-7.5,llcrnrlat=49.84,urcrnrlon=2.5,urcrnrlat=59,ax=axes[1])
m2.drawlsmask(land_color='dimgrey',ocean_color='dodgerBlue',lakes=True)
# weather icons map
for [icon,city] in izip(icons,cities):
lat = city[1]
lon = city[2]
try:
pngfile=urllib.urlopen('https://github.com/ibm-cds-labs/python-notebooks/blob/master/weathericons/icon'+str(int(icon))+'.png?raw=true')
icon_hand = read_png(pngfile)
imagebox = OffsetImage(icon_hand, zoom=.15)
ab = AnnotationBbox(imagebox,m1(lon,lat),frameon=False)
axes[0].add_artist(ab)
except:
pass
# temperature map
for [temp,city] in izip(temps,cities):
lat = city[1]
lon = city[2]
if temp>16:
col='indigo'
elif temp>14:
col='darkmagenta'
elif temp>12:
col='red'
elif temp>10:
col='tomato'
elif temp>0:
col='turquoise'
x1, y1 = m2(lon,lat)
bbox_props = dict(boxstyle="round,pad=0.3", fc=col, ec=col, lw=2)
axes[1].text(x1, y1, temp, ha="center", va="center",
size=11,bbox=bbox_props)

P I X I E D U S T
O P E N S O U R C E
https://ibm-cds-labs.github.io/pixiedust/
!pip install --upgrade pixiedust

P I X I E D U S T
A N D M A P B O X
dfmap = pd.DataFrame(
cities,
columns=['city','lat','lon'])
dfmap['temp']=temps
dfmap[‘icon']=icons
display(dfmap)

P I X I E A P P S

W H E R E D O E S W E AT H E R
D ATA C O M E F R O M ?
B U T

O B S E R VAT I O N S + M O D E L S

O B S E R VAT I O N S
• Temperature
• Humidity
• Windspeed and direction
• Air pressure
• Rainfall
• Radiation
http://www.metoffice.gov.uk/public/weather/climate-network/#?tab=climateNetwork

H I S T O R I C W E AT H E R
• http://
www.metofﬁce.gov.uk/
datapoint/
• https://
business.weather.com/
products/the-weather-
company-data-packages
• https://climexp.knmi.nl
• http://www.ecmwf.int/en/
forecasts/datasets

I WA N T A M A P…
B U T

P O I N T S T O
G R I D
T H E P R O B L E M

from scipy.interpolate import griddata
# grid of latitude and longitude values
x = np.linspace(49.0,59.0,100)
y = np.linspace(-6,2,100)
X, Y = np.meshgrid(x,y)
px = points['lat'].as_matrix()
py = points['lon'].as_matrix()
pz = points['temp'].as_matrix()

from scipy.interpolate import griddata
# grid of latitude and longitude values
x = np.linspace(49.0,59.0,100)
y = np.linspace(-6,2,100)
X, Y = np.meshgrid(x,y)
px = points['lat'].as_matrix()
py = points['lon'].as_matrix()
pz = points['temp'].as_matrix()
fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(18, 8))
for i, method in enumerate(('nearest', 'linear', 'cubic')):
Ti = griddata((px, py), pz, (X, Y), method=method)
ax[i].contourf(X, Y, Ti)
ax[i].set_title('method = {}'.format(method))
ax[i].scatter(px, py, c='k', marker='o')

H O W C A N I W O R K W I T H
T H I S D ATA ?
C O O L , B U T …

N E T C D F B I N A RY F I L E S

from netCDF4 import Dataset, num2date
import numpy as np

import numpy as np
cfile = 'assets/HadCRUT.4.5.0.0.median.nc'
dataset = Dataset(cfile)
print dataset.data_model
print dataset.variables
Data is here:
https://crudata.uea.ac.uk/cru/data/temperature/

import numpy as np
cfile = 'assets/HadCRUT.4.5.0.0.median.nc'
dataset = Dataset(cfile)
print dataset.data_model
print dataset.variables
NETCDF4
OrderedDict([(u'latitude', <type 'netCDF4._netCDF4.Variable'>
float32 latitude(latitude)
standard_name: latitude
long_name: latitude
point_spacing: even
units: degrees_north
axis: Y
unlimited dimensions:
ts: days since 1850-1-1 00:00:00
calendar: gregorian
start_year: 1850
...
Data is here:
https://crudata.uea.ac.uk/cru/data/temperature/

import scipy
import matplotlib
from pylab import *
from mpl_toolkits.basemap import Basemap, addcyclic, shiftgrid, maskoceans
# define the area to plot and projection to use
m =
Basemap(llcrnrlon=-180,llcrnrlat=-60,urcrnrlon=180,urcrnrlat=80,projection=
'mill')

import scipy
import matplotlib
from pylab import *
from mpl_toolkits.basemap import Basemap, addcyclic, shiftgrid, maskoceans
# define the area to plot and projection to use
m =
Basemap(llcrnrlon=-180,llcrnrlat=-60,urcrnrlon=180,urcrnrlat=80,projection=
'mill')
# covert the latitude, longitude and temperatures to raster coordinates to
be plotted
t1=temperature[0,:,:]
t1,lon=addcyclic(t1,lons)
january,longitude=shiftgrid(180.,t1,lon,start=False)
x,y=np.meshgrid(longitude,lats)
px,py=m(x,y)

rcParams['font.size']=12
rcParams['figure.figsize']=[8.0, 6.0]
figure()
palette=cm.RdYlBu_r
rmin=-30.; rmax=30.
ncont=20
dc=(rmax-rmin)/ncont
vc=arange(rmin,rmax+dc,dc)
pal_norm=matplotlib.colors.Normalize(vmin = rmin, vmax = rmax, clip =
False)
m.drawcoastlines(linewidth=0.5)
m.drawmapboundary(fill_color=(1.0,1.0,1.0))
cf=m.pcolormesh(px, py, january, cmap = palette)
cbar=colorbar(cf,orientation='horizontal', shrink=0.95)
cbar.set_label('Mean Temperature in January')
tight_layout()

W H AT A B O U T F O R E C A S T S
A N D P R E D I C T I O N S ?
T H I S D ATA I S A L L B A S E D O N M E A S U R E M E N T S …

C L I M AT E
M O D E L S

C L I M AT E W I T H D I F F E R E N T S C E N A R I O S
M O D E L E X P E R I M E N T S

G L O B A L
T E M P E R AT U R E
E X P L A I N E D
https://www.bloomberg.com/graphics/2015-whats-warming-the-world/

W H AT C A N I U S E
W E AT H E R D ATA F O R ?

in vehicle hail damage
claims every year
increase in temperature
means $24M more in
electricity spending per day
drop in sales for areas
with more than a 10% drop
in temperature
I N S U R A N C E
E N E R G Y
R E TA I L
A P P L I C AT I O N S

W E AT H E R A N D
T R A F F I C
C O L L I S I O N S
E X A M P L E
https://www.pexels.com/photo/blur-cars-dew-drops-125510/

N Y P D T R A F F I C
C O L L I S I O N S
E X A M P L E
https://data.cityofnewyork.us/
Public-Safety/NYPD-Motor-
Vehicle-Collisions/h9gi-nx95

8 1 2 , 5 2 6
T R A F F I C
C O L L I S I O N S
S I N C E A P R I L 2 0 1 4

N Y P D T R A F F I C C O L L I S I O N S
https://apsportal.ibm.com/exchange/public/entry/view/5a7051906b8fe9cc1ba126b53edd948e

T E M P E R AT U R E F O R T H E 5 B O R O U G H S

H O W T O C O M B I N E T H E D ATA
manhattan_merged = pd.merge_asof(manhattan.sort_values(by='Date'),
weather.sort_values(by=‘date’),
left_on='Date',right_on='date',
tolerance=pd.Timedelta('6h'))

def perdelta(start, end, delta):
curr = start
while curr < end:
yield curr
curr += delta
for result in perdelta(datetime(2017,4,1,0), datetime(2017,4,15,23),
timedelta(hours=1)):
colhour = manhattan.loc[manhattan['Date'] == result]
hour = pd.DataFrame([[result,borough,len(colhour.index),
colhour['Persons Injured'].sum(),
colhour['Persons Killed'].sum(),
if result == datetime(2017,4,1,0):
newhour = hour.copy()
else:
newhour = newhour.append(hour)

Find out how weather impacts traffic collisions in New York:
https://medium.com/ibm-watson-data-lab

R E F E R E N C E S
• IBM Bluemix - https://console.ng.bluemix.net/
• IBM Data Science Experience - https://datascience.ibm.com
• PixieDust - https://ibm-cds-labs.github.io/pixiedust/
• Slides - https://www.slideshare.net/MargrietGroenendijk/
presentations
• Notebooks - https://github.com/ibm-cds-labs/python-notebooks
• Me - mgroenen@uk.ibm.com - @MargrietGr

A Beginners Guide to Weather & Climate Data, Margriet Groenendijk

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A Beginners Guide to Weather & Climate Data, Margriet Groenendijk