The document serves as a beginner's guide to accessing and utilizing weather and climate data, highlighting various data sources and tools for analysis, including the IBM Watson platform and PixieDust. It discusses the importance of weather data in sectors like insurance and energy, and provides examples of its applications, including traffic collision analysis. Multiple references and links to resources are provided for further exploration of the subject.

1. 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
I B M WAT S O N D ATA P L AT F O R M
@ M A R G R I E T G R
2 9 A P R I L 2 0 1 7 - D ATA S C I E N C E F E S T I VA L - L O N D O N

2. 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

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

4. 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
D E M O

5. 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/

6. 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:
datascience.ibm.com
Notebook:
https://github.com/MargrietGroenendijk/Data-Science-Festival

7. 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

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

9. 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

10. 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

11. I WA N T A M A P…
B U T

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

13. 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')

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

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

18. 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 …

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

20. 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

21. 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/

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

25. 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

26. W E AT H E R A N D
M A C H I N E
L E A R N I N G
https://www.pexels.com/photo/buildings-silhouette-rain-window-27472/

27. W E AT H E R A N D
M A C H I N E
L E A R N I N G
https://www.pexels.com/photo/green-palm-tree-near-body-of-
water-149671/

28. 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/

29. 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

30. 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

31. 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

32. 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

33. 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

34. S U M M A RY
• Weather data
• json
• timeseries
• maps
• netcdf
• models
• Example of weather and
traffic collisions

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

36. Hackathon
June 10-11, 2017
Galvanize, San Francisco
Code Challenge
June/July 2017
The SETI Institute
Register Now!
seti.org/ML4SETI