1. Irene Garcia-Marti
Gerard van der Schrier
Jan-Willem Noteboom
7th November 2019
Assessing the quality
of volunteered
weather observations
to provide
high-resolution
weather maps

2. › Weather observations are
crucial!
› Spatial sparsity is a
challenge for high-res
weather forecasts
› Increasing number of
weather-related citizen
science projects
– WOW, Wunderground,
Netatmo, Meteoclimatic
Motivation
6 november 2019
Koninklijk Nederlands Meteorologisch Instituut 2

3. 1st
September 2019: 1,400 million observations and 17K stations worldwide

4. › 2015: KNMI partner of WOW
› Contributors: 400+ CWS
› Data NL+BE: 3.7M obs/month
› Devices: semi-professional
– Manufacturers: Davis, Oregon
scientific, Ventus, Alecto…
– Expected “reasonable” quality of
the observations
WOW-NL
4

5. › Quality not only related to
device:
– Good with respect to what? What
variables are (not) properly
monitored?
– Local processes: radiation,
shadowing, siting
› Classical challenges of citizen
science data:
– Gaps in data
– Noisy observations
WOW-NL
5

6. WOW-NL
6
› Quality not only related to
device:
– Good with respect to what? What
variables are (not) properly
monitored?
– Local processes: radiation,
shadowing, siting
› Classical challenges of citizen
science data:
– Discretization
– Scale of the phenomena

7. 7

8. What is the quality
of WOW-NL data?
1. Quality control
2. Interpolated maps
8

9. Preprocessing
WOW json csv
11.6M
observations
65 features
SVF
Feature engineering

10. 6 november 2019
Koninklijk Nederlands Meteorologisch Instituut 10

11. Quality control
11
› Narrowing down variables
– Not reinventing the wheel:
▪ Temperature:
• (Napoly, 2018) 🡪
• (Meier, 2017)
• (Lussana, Titan project)
– QC based on (Napoly, 2018)
▪ Feasible to implement on WOW-NL
▪ Levels have been compacted

12. Each of the 11.6M observations go through this workflow and is labeled with a quality level

13. Overview of the quality of WOW-NL
(Each square represents 10K observations)
M0: incorrect metadata
(not shown)
M1: insufficient Z-score
(presence outliers)
M2: insufficient day/mon
coverage
M3: insufficient (Pearson)
correlation
M4: OK

14. › Setting up a baseline:
– Kriging (or GP)
▪ Ordinary: no {external drift, trend}
▪ Temporal resolution: hourly
Interpolation
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Radiation
bias?

16. 16

17. › If good enough:
– Open the door for new research:
▪ Fine-grained interpolated layers
▪ Nowcasting / hi-res weather
▪ Gridded weather variables
▪ Study local phenomena
– Governance level:
▪ Lower cost for administration
▪ Better weather forecast for
underrepresented areas
▪ Bottom-up initiatives might work in
developing regions
Why the quality of citizen
science weather data is
important?
17

18. › Met offices have enough
knowledge to develop QC’s
– Does it make sense?
– Collaboration: “the more, the merrier”
› Re-use pieces of software:
– Corrections: radiation, air pressure
– QC’s for other variables:
precipitation, wind
› Ideally: work together to
build a pipeline with mechanistic,
corrective, and statistical filters
Not reinventing
the wheel
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19. Challenges and
opportunities ahead
19
Big data problem!
We are here
Imperfect data, but volume is difficult to ignore
› Big Data problem
– Paradigm shift: code 🡪 data
– Adoption of cloud technologies
› Data analysis:
– Machine learning for QC: Outlier
detection, add external drifts
– Coupling volunteered data with
weather models
– Study local patterns: urban wind
dynamics, urban rainfall, effect of
SVF on outliers
– Assess other quality control
schemas

20. Questions? ☺
Thanks!
6 november 2019
Koninklijk Nederlands Meteorologisch Instituut 20