I gave this talk at the Bristol Data Science Meetup. This talk is an overview of where to find and how to use weather data.

1. A Beginners guide to
weather and climate data
Bristol Data Scientists Meetup | 24 January 2017
Margriet Groenendijk | Developer Advocate | IBM Watson Data Platform
@MargrietGr
https://medium.com/ibm-watson-data-lab

3. https://github.com/MargrietGroenendijk/Bristol

4. https://github.com/MargrietGroenendijk/Bristol

6. https://github.com/MargrietGroenendijk/Bristol

7. Observations + Models
Forecast =

8. Temperature
Humidity
Windspeed and direction
Air pressure
Rainfall
Radiation
http://www.metoffice.gov.uk/public/weather/
climate-network/#?tab=climateNetwork

9. Historic weather
Mean daily maximum temperature
Mean daily minimum temperature
Days of air frost
Total rainfall
Total sunshine duration
http://www.metoffice.gov.uk/datapoint/

10. https://business.weather.com/products/the-
weather-company-data-packages
Note: data not cleaned
Historic weather
Example: London City airport 1997-2017

11. https://climexp.knmi.nl

12. http://www.cru.uea.ac.uk/data/

13. https://developer.ibm.com/clouddataservices/2016/04/18/predict-
temperatures-using-dashdb-python-and-r/

14. pseudo-code… test before using!

16. Models

17. 3D grid
Atmosphere
Energy
Water
Differential equations
Boundary conditions
Land
Ocean
Lots of parameters
http://www.climateprediction.net/

20. 3 and 4 and that they should be equal:
WUEe =
GPP
ET
(5)
2GPP ET

21. http://www.earth-syst-dynam.net/7/525/2016/esd-7-525-2016.pdf

22. 0 50 100 150
0
100
200
300
400
500
600
vcm,25
(µmol m−2
s−1
)
jm,25
(µmolm−2
s−1
)
1 2 3 4 5 6 7
0 200 400 600
0
0.2
0.4
0.6
0.8
1
jm,25
(µmol m−2
s−1
)α(molmol−1
)
k-means clustering
0 50 100 150
0
vcm,25
(µmol m−2
s−1
)
0 50 100 150
0
200
400
600
800
1000
vcm,25
(µmol m−2
s−1
)
λ(molmol−1
)
0 200 400 600
0
jm,25
(µmol m−2
s−1
)
0 0.5 1
0
200
400
600
800
1000
α (mol mol−1
)λ(molmol−1
)

23. 1 2 3 4 5 6 7
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Group
Relativevegetationdistribution(−)
CRO
SAV
DBF
EBF
ENF
GRA
MFO
1 2 3 4 5 6 7
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Group
Relativeclimatedistribution(−)
AR
BO
TM
TE
TC
TR
k-means clusters vs. vegetation and climate classifications

24. 0.25
0.15
0.20
A CB
http://www.earth-syst-dynam.net/7/525/2016/esd-7-525-2016.pdf

26. Climate models
=
Algorithms + Observations + Parameters
=
Machine Learning

27. 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
Insurance
Energy & Utility
Retail
Some
applications

28. Next… find the data!
Open Data

29. Open data in Bristol
https://opendata.bristol.gov.uk
Air quality
River water quality
Water levels
Flood alerts
M32 traffic flow
Road works
Crime stats
Census data
Energy consumption by ward
Hospital admissions
NHS

30. Weather + Driving Difficulty Index API
+
M32 traffic flow + Road works
+
air quality observations
=
Predict air quality

31. Weather and Climate Data
Historical time series
Historical maps
Climate model data
Real-time APIs
Forecast APIs

32. Thanks!
https://github.com/MargrietGroenendijk/Bristol
http://www.slideshare.net/MargrietGroenendijk/presentations
@MargrietGr
https://medium.com/ibm-watson-data-lab