Amateur Weather Observations
Opportunities and Challenges
Simon Bell1, Dan Cornford1, Lucy Bastin1, Mike Molyneux2
1Aston
University, Birmingham
Office, Exeter
2Met
Contents
1. The current state of amateur observations
2. Possible applications
3. Challenges facing amateur observers
4. Intercomparison field study
5. Estimating observation bias and uncertainty
The current state of amateur
observations
State of Amateur Observations
What is an Amateur Observer?
By ‘Amateur’ I do not mean Amateurish!!
Rather they regularly collect weather
observations due to a passion for
meteorology rather than because it is
their job.
Who you calling
‘Amateur’!?!
Automatic and/or Manual Observations
State of Amateur Observations
Automatic Weather Station Setup
Step 1. Buy your weather station.
Step 2. Install your weather
station
Step 3. Dedicate a
computer to your
weather station
Step 4. Connect the
display console
(datalogger) to the
computer.
Step 5. Install the appropriate
software.
Notable software packages
include:
• Weather Display
• Cumulus (Windows only)
• Weathersnoop (Mac only)
Step 6. Decide where
to send your data…
State of Amateur Observations
Where does the amateur data end up?
Met Office
Weather Observations
Website
(WOW)
- Launched early 2011.
- Near real-time observations.
Weather Underground
- Longer established than
WOW.
- Not weather ‘under’ the
ground.
Climatological Observers
Link
- Greater focus on daily
measurements.
- Monthly Bulletins.
- Over 40 years old!
State of Amateur Observations
Spatial Resolution
Met Office MMS
• ~250 stations
•
Professional network
Met Office WOW
• 592 stations (as of last week)
Weather Underground
• 1554 stations (as of last week)
•
•
•
•
Amateur network
~60% also upload to WU,
Amateur Network
~20% also upload to WOW
(based on 100m proximity)
•
•
Climatological Observers Link, COL, ~350 active members
Citizen Weather Observing Program, CWOP, 491 ‘Active Members’
State of Amateur Observations
Temporal Resolution
• Majority are sub-hourly
• Most commonly at 5 minute intervals
• Near real-time observations
State of Amateur Observations
Weather Stations models
Weather stations used to automatically upload data to Weather Underground in February 2012. A total of 1353
stations were investigated, of which 16.6% were of unknown type and have been excluded from the diagram.
Possible Applications
Possible Applications
Assimilation into NWPs or Post-Processing
Could resolve more grid cells
within high resolution NWP
model.
Could help correct site-specific
forecasts, by learning from a
sequence of past observations.
Station locations (various networks) for Reading area. Grid = 1.5 by 1.5km
Met Office Android App
Possible Applications
Most useful variables
Useful
Not so useful
Temperature – Urban Climate studies
Pressure
Humidity – Deep Convection
Wind speed & direction
Precipitation – Flood forecasting
Snow depth/cover
Boscastle, Cornwall. 16th Aug 2004.
Birmingham heat island derived
from MODIS satellite images.
Tomlinson et al, 2012. Int. J. Clim.
uksnowmap.com, snow as recorded by Twitter users on 28th Jan 2013
Challenges facing Amateur
Observers
Challenges facing Amateur Observers
Exposure and Siting
Fair Isle MMS station (Dave Wheeler)
Well exposed rural professional Met Office site
Sheltered urban Amateur site
The urban nature of many amateur stations presents the following issues:
• Nearby trees and buildings can alter the catch of the rain gauges.
• Warm or reflective surfaces nearby can bias temperature readings.
• Making representative wind measurements within the turbulent urban boundary
layer is very difficult.
Challenges facing Amateur Observers
Representativity
Scales
Scales
Representative
Microscale : < hundreds of metres
Yes
Local scale: one to several kilometres
Maybe
Mesoscale : tens of kilometres
No
Image via NASA
Challenges facing Amateur Observers
Ventilation and Radiation Shielding
Affects temperature
and humidity readings.
Consequence of:
1. Sheltered sites
2. Poor radiation
shield design.
Challenges facing Amateur Observers
Calibration
• Calibration may be expensive, e.g.
calibrating humidity sensors.
• Or time consuming, e.g. rainfall.
• Thermistor may not be waterproof,
ice/water bath impossible.
• COL do have a calibration initiative with a
tinytag sensor. Can collocate with amateur kit.
Challenges facing Amateur Observers
Other design flaws
Tiny buckets will be
sensitive to accumulating
debris.
Snow easily accumulates on
them, wind vanes can freeze.
However could be spotted
earlier at an amateur site.
Challenges facing Amateur Observers
Other design flaws
Some components are
painted black,
therefore prone to
overheating.
Wireless transmitters
and batteries can often
fail leading to missing
data.
Intercomparison field study
Intercomparison field study
The field site
Winterbourne No.2 MMS site,
Birmingham
Data from August 2012 - Present
Intercomparison field study
The Stations
Davis Vantage Pro 2
With wireless
transmission and
FARS. (x2)
Name
Davis Vantage Vue
(x2)
Price (approx.)
Oregon Scientific
WMR200 (x2-ish)
Accompanying
Software
Software used
for data retrieval
La Crosse
WS2350
Maplin USB Wireless
Weather Forecaster
(N96FY). (Fine Offset
WH1080, Ambient
Weather, Watson.)
Observation
Frequency
(minutes)
Memory at this
obs. freq.
Davis VP2
£1000
Weatherlink
Weatherlink
10
~ 2.5 weeks
Davis Vue
£450
Weatherlink
Weatherlink
10
~ 2.5 weeks
Oregon Scientific
£350
Weather OS
Virtual Weather
Station
10
291 days
La Crosse
£100
HeavyWeather
HeavyWeather
60
7.29 days
EasyWeather
EasyWeather
10
~ 30 days
Maplin
£60-125
Intercomparison field study
Temperature Bias
Average Bias : +0.19
Residual Variance: 0.04
Average Bias : +0.18
Residual Variance: 0.06
Davis Vantage Pro 2
• Slight warm bias at night, fairly neutral during the day.
• Hints that the aspirating fan should really be on continuously.
Intercomparison field study
Temperature Bias
Average Bias : +0.15
Residual Variance: 0.07
Davis Vantage Vue
• Potential longwave radiation bias
Average Bias : -0.11
Residual Variance: 0.08
Intercomparison field study
Temperature Bias
Average Bias: +0.44
Residual Variance: 1.27
Oregon Scientific WMR200
• Shortwave radiation bias
• Poorly ventilated shielding
Average Bias: +0.75
Residual Variance: 1.33
NB: change of colour scale!
Intercomparison field study
Temperature Bias
Average Bias: +0.75
Residual Variance: 4.34
La Crosse WS2350
• Shortwave radiation bias
Average Bias: +0.45
Residual Variance: 0.66
Maplin Weather Station
• Shortwave radiation bias
Intercomparison field study
Temperature Bias as a function of Radiation
NB: Be aware of small sample sizes.
Images taken by a Flir i5 thermal imaging camera, the colour-scale is consistent.
Intercomparison field study
Humidity
A typical time series plot
In wet situations: Amateur stations tend to read drier than Met Office sensors.
In dry situations: VP2’s read close to or wetter than mms. Vue’s and Maplin normally wetter. OS drier, often
dramatically so! La Crosse often drier, but has a mind of it’s own.
Intercomparison field study
Humidity
Intercomparison field study
Precipitation
Intercomparison field study
Precipitation
Corrected
Intercomparison field study
Precipitation
• Measuring long term totals = poor
• Picking up time of rainfall events and indicating intensity = good
Estimating observation bias and
uncertainty
Estimating observation bias and uncertainty
What do we need to find out?
So we have an observation from an amateur weather station, for which we need to
know the following:
• Is the data biased? If so we need to correct it.
• How certain are we the observation is correct?
- Need an estimate of the residual variance.
In order to provide a value for the bias and
residual variance we need an initial estimate
of what the weather is at the amateur location.
Estimating observation bias and uncertainty
First guessing the weather at amateur location
Two options
1. Interpolate observations
from nearby professional sites.
2. Use a short range forecast
from a high resolution NWP.
Estimating observation bias and uncertainty
Interpolation Model – the predictors
Elevation
Coastality
Land Cover (urban?)
Roughness
Temperature
Foehn Wind
Exposure/Shelter
Aspect
Soil Composition
Data Acknowledgements: UGS GMTED, CEH LCM2007, CEH Soil Parent Map.
Estimating observation bias and uncertainty
Interpolation Model – the predictors
Elevation
Coastality
Easting
Land Cover (urban?)
Roughness
Northing
Temperature
Foehn Wind
Exposure/Shelter
Aspect
Soil Composition
Data Acknowledgements: UGS GMTED, CEH LCM2007, CEH Soil Parent Map.
Estimating observation bias and uncertainty
UKV Model vs Interpolation Model
For Air
Temperature
12th – 25th July 2012
Verification with MMS stations only
Estimating observation bias and uncertainty
For Dew Point
12th – 25th July 2012
Verification with MMS stations only
Estimating observation bias and uncertainty
Interpolation Model – planned update
• Regression Model
• Primarily with linear relationships, but could handle quadratic, etc…
• Will smooth in time, not just space, should hopefully improve our prediction
power significantly.
• Dynamic clustering, the tricky bit!
Once you have an estimate of
the truth at an amateur locations
then what….
Estimating observation bias and uncertainty
Distinguishing natural differences from artificial errors
Differences due to geospatial
changes should already be
captured
Estimating observation bias and uncertainty
Distinguishing natural differences from artificial errors
Consistent bias
• E.g. ‘Out of the box’ sensor
bias.
• Can try to learn this during
periods when synoptic
dependent biases are
negligible.
Estimating observation bias and uncertainty
Distinguishing natural differences from artificial errors
Bias dependent on synoptic situation
• Siting issue (e.g. near wall or road)
• Design flaw (ventilation,
radiation shielding)
Crucial to learn these dependencies
(e.g. radiation bias) so we can
parameterise the bias in the model.
Estimating observation bias and uncertainty
Distinguishing natural differences from artificial errors
Natural differences (we want to capture!!)
• Station is in frost hollow?
But, only want to capture this
natural variation if our weather
model is competent to resolve it!
i.e. is it representative?
Estimating observation bias and uncertainty
How metadata can help
Good metadata would give us some prior
information regarding the artificial bias we’d
expect to see.
WOW star rating helps indicate how well
exposed a site is, the quality and siting of
the temperature, rainfall and wind devices.
At present the only quality control on WOW
comes from the amateurs themselves.
However, metadata is often missing or unstructured,
will have to rely heavily on the data itself to inform us
about the characteristics of a site.
Estimating observation bias and uncertainty
How does it all fit together?
Synoptic
Situation
Geospatial
information
(e.g. elevation)
Met Office
Observations
(e.g. radiation
levels)
Interpolation
Model
Short-range
model forecast
First guess of
weather at amateur
location
Clusters
Estimating observation bias and uncertainty
How does it all fit together?
Knowledge from
intercomparison field
study
Metadata
(e.g. station type)
Synoptic
Situation
Geospatial
information
(e.g. elevation)
Met Office
Observations
(e.g. radiation
levels)
Prior information about the
synoptic and instrument
dependent bias
Interpolation
Model
Short-range
model forecast
First guess of
weather at amateur
location
Clusters
Estimating observation bias and uncertainty
How does it all fit together?
Knowledge from
intercomparison field
study
Metadata
Synoptic
Situation
(e.g. station type)
(e.g. elevation)
Met Office
Observations
(e.g. radiation
levels)
Prior information about the
synoptic and instrument
dependent bias
Amateur Weather
Observation
Geospatial
information
Interpolation
Model
Short-range
model forecast
First guess of
weather at amateur
location
Clusters
Model
Bias
Correction
Amateur weather
observation
(corrected for artificial bias)
Residual
Variance
Estimate of
uncertainty
Estimating observation bias and uncertainty
How does it all fit together?
Knowledge from
intercomparison field
study
Metadata
Synoptic
Situation
(e.g. station type)
(e.g. elevation)
Met Office
Observations
(e.g. radiation
levels)
Prior information about the
synoptic and instrument
dependent bias
Amateur Weather
Observation
Geospatial
information
Interpolation
Model
Short-range
model forecast
First guess of
weather at amateur
location
Clusters
Model
Bias
Correction
Amateur weather
observation
(corrected for artificial bias)
Residual
Variance
Estimate of
uncertainty
Data Assimilation
Scheme
Initial conditions in
NWP model
In Summary:
Opportunities
•
Freely available, near-real time weather observations available from >1700 amateur station
locations in GB alone.
•
Sparser professional networks (+high-res forecast models) can help quantify the bias and
uncertainty associated with these amateur observations.
•
Amateur observations, once bias corrected and given a reliable uncertainty estimate, could
have a significant impact on data assimilation schemes, post-processing, flood forecasting,
urban climate studies, etc…
Challenges
•
Amateur equipment prone to significant biases due to poor siting, calibration and shielding
design, and/or because poor exposure --> unrepresentative.
•
Identifying whether an amateur site is well exposed, calibrated and maintained can be difficult
to ascertain from just the sporadic metadata.
•
Disentangling natural spatial variations in weather from the artificial biases present within
amateur observations could prove very tricky.
•
Working out over what scale an amateur, or even a professional, weather station is
representative is a difficult undertaking.
•
Any Questions?