This document summarizes research using weather observation data from the Weather Observations Website (WOW) citizen science project in the Netherlands. It finds that while citizen science weather data has challenges like gaps, noise and scale issues, the WOW data for the Netherlands contains over 11.6 million observations from 400+ contributors. After preprocessing and quality control analysis of the temperature data, the WOW data is found to capture daily temperature cycles at a fine-grained scale, though spatial patterns differ from numerical models due to local effects. With improvements, citizen science weather data could open new research opportunities and improve forecasts, especially for underrepresented areas.

1. Using volunteered weather
observations to explore
urban and regional
weather patterns in the
Netherlands
Irene Garcia-Marti
Marijn de Haij
Jan-Willem Noteboom
Gerard van der Schrier
Cees de Valk
AGU Fall Meeting 2019
IN22A - Making Data Usable and Accessible:
Gaining Insight from Citizen Science Applications
10th December 2019

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
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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
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Province of Utrecht
94 CWS

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
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6. WOW-NL
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› 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
What is the quality of WOW-NL?
R2 correlation between citizen and official weather stations

8. 8
What is the quality of WOW-NL?

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

10. Quality control
› Based on (Napoly, 2018)
› Variable: temperature
– Feasible to implement on
WOW-NL
– Levels have been
compacted
– Each of the 11.6M
observations is labeled with
a quality level
M0: incorrect metadata
M1: insufficient Z-score
(presence outliers)
M2: insufficient day/mon
coverage
M3: insufficient (Pearson)
correlation
M4: OK
(Napoly et al., 2018)
Development and Application of a Statistically-Based
Quality Control for Crowdsourced Air Temperature Data
Frontiers in Earth Science

11. 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

12. › Heat wave 27-07-2018
› Methodology:
– Kriging interpolation:
▪ WOW-NL observations per hour
▪ Calibrated for this day
▪ No external drift
– Visual comparison with
HARMONIE
▪ Regional numerical model
▪ Provides forecast up to 48h in
advance
Exploring regional
temperature
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13. › Results:
– WOW-NL captures the daily
temperature cycle
– Spatial patterns are different:
▪ Radiation: proximity to buildings
▪ Cooling: shadowing of trees
▪ Meaning:
• Predicted weather in the
column different to what
happens at ground level
• More work to reduce this gap
Exploring regional
temperature
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14. › If good enough:
– Open the door for new research:
▪ Fine-grained interpolated layers
▪ Nowcasting / hi-res weather
▪ Crowdsourced NWP
– 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?
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Imperfect data, but volume is difficult to ignore
Big data problem!
We are here

15. Questions? ☺
garciamarti@knmi.nl
Thanks!
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