Post on 12-Apr-2020
WMO-SPICE Measurement and data methods, results and recommendations
Michael EarleObserving Systems and EngineeringMeteorological Service of CanadaEnvironment and Climate Change Canada
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Snow workshopForêt Montmorency, Québec March 28, 2019
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Modelling
Prediction
Monitoring
Forecast model development and application
Forecasts, verification, product development
Observing networks, data stewardship
Meteorological Service of Canada
Meteorological Research
Climate Research
Forecast improvement, remote sensing, quantitative precipitation estimation
Climate monitoring, modelling, analysis
Science and Technology Branch
Context of ECCC interest and involvement in measurement of snow
Provide guidance for snow measurements in context of global transition to automation
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WMO Solid Precipitation Intercomparison Experiment (WMO-SPICE)
Initiative of WMO Commission for Instruments and Methods of Observations (CIMO)
Demonstration project for Global Cryosphere Watch (GCW)
Recommend and characterize automatic field reference systems for the unattended measurement of snowfall and snow depth
Characterize the performance of existing and new/emerging technologies in different configurations, conditions
Provide comprehensive dataset for further data mining
Provide guidance to community, manufacturers
Key deliverables
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1. Caribou Creek, Saskatchewan, Canada
2. Bratt’s Lake, Saskatchewan, Canada
3. Marshall Site, Colorado, USA
4. CARE, Ontario, Canada
5. Tapado AWS, Region de Coquimbo, Chile
6. Formigal, Spain
7. Col de Porte, France
8. Weissfluhjoch, Davos, Switzerland
9. Forni Glacier, Italy
10. Hala Gasienicowa Station, Poland
11. Haukeliseter, Norway
12. Sodankylä, Finland
13. Valdai, Russia
14. Voljskaya Observatory, Gorodec, Russia
15. Pyramid Observatory, Nepal
16. Gochang, Korea
17. Joetsu, Japan
18. Rikubetu, Hokkaido, Japan
19. Guthega Dam, New South Wales, Australia
20. Mueller Hut Station, New Zealand
Characterization of instruments at test sites in different climate regimes
Tested instruments of varying type and technology
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Snowfall Snow on ground
Weighing gauges Heated tipping bucket gauges
Non-catchment instruments
Snow depth sensors
Snow water equivalent sensors
Measurements over 2013/2014 and 2014/2015 winter seasons
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Assessed influence of heating, configuration on instrument performance
Unshielded Single-Alter Double-ring shield (Alter slats) Double-ring shield (Belfort slats)
Wind shields with different size, slat design, porosity
Configuration of mounting infrastructure
Canadian double-Alter
All instruments measuring snowfall assessed relative to automated reference
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Double-Fence Automated Reference: DFAR
Heated weighing gauge in single-Alter shield within inner fence
Sensitive precipitation detector within inner fence; independent verification of precipitation occurrence
Common database and processing approach for all instruments, sites
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File formatting
Filtering
Correct number of records, fields
Remove outliers, jumps
Mitigate influence of noise
Aggregation
Common temporal resolution
Manual QC
Manual intervention, as required
Geonor T-200B3, CARE
Common approach to select precipitation events with confidence
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Weighing gauge in DFAR reports ≥ 0.25 mm precipitation
Precipitation detector in DFAR reports ≥ 60% precipitation occurrence
30 minute assessment intervals
Site event datasets (SEDS)
All 30 minute events at a given site over experiment
Reference accumulation
Accumulation for all test instruments
Ancillary data (temperature, wind speed)
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Advantages and limitations of approachAdvantages Comparability of results across
different instruments, sites
Traceability of field reference configuration to previous WMO standards
Provides framework for data quality control, processing
Limitations Accumulation threshold does not
capture light precipitation events
Event-based approach of limited operational utility
DFAR is an expensive requirement
Probability density functions of reported accumulation for 30-minute periods with no precipitation provide indication
of noise inherent in weighing gauge measurements in different shield configurations
References as composite datasets from automated instruments
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Snow depth Feasible to use composite datasets from
multiple automatic instruments as alternative to manual measurements
Applicability over shorter time intervals than manual measurements
Manual measurement approaches for SWE (top left), snow depth (top right), and platform with multiple automated
snow depth sensors (bottom)
Snowfall Composite dataset of precipitation amount,
precipitation type, ancillary measurements of wind speed, temperature
Snow water equivalent
No automatic measurement could be validated as reference
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Kochendorfer et al. (2017a,b) combined site event data from multiple WMO-SPICE sites
Derived “universal” transfer functions for weighing gauges in different shield configurations
Derivation and application of transfer functions
Transfer functions
Describe catch efficiency relative to DFAR
Function of wind speed and temperature
Reduce bias in snow measurements
Limited reduction in measurement uncertainty
Configuration of instruments for the measurement of snowfall
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Recommend double shields over single shields, single shields over unshielded
Mean wind speed [m/s]
Cat
ch e
ffici
ency
Guidance from transfer functions
Consider climate, exposure
Consider data impacts, durability, maintenance
Pluvio2 gauges at CARE
Catch efficiency for 30 minute snow events during WMO-SPICE, binned by mean wind speed (1 m/s bins)
Recommend heating
Capping prevention
Impact on response times
Configuration and environmental conditions have greater impact on results than specific gauge type
Selection and configuration of instruments for the measurement of snow depth
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Considerations for sensor selection (optical vs. acoustic)
Accuracy, precision, power requirements
Heating of sensors, infrastructure
Prevent accumulation, mitigate potential impacts on sample area
Use of artificial surface targets
Weigh benefits vs. potential drawbacks
Acoustic sensors may benefit more from artificial targets
Spatial variability a critical consideration
Heated SR50ATH on unheated boom at Sodankylä following snow event
Heated SR50ATH on heated, angled boom at Sodankylä following the same snow event
Feasibility of non-catchment instruments for the measurement of snowfall
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Significant scatter in catch efficiency results observed
Uncertainty in reported precipitation amount
Algorithm assumes particle shape, density
Operational considerations
Less sensitive to wind speed
More sensitive to orientation
Intervention requirements vs. data continuity risks
Sensors tested: disdrometers, present weather sensors, evaporative plates
Subject of future WMO studyCatch efficiency as a function of mean wind
speed for 30 minute snow events