WMO-SPICE Measurement and data methods, results and recommendations

Michael EarleObserving Systems and EngineeringMeteorological Service of CanadaEnvironment and Climate Change Canada

1

Snow workshopForêt Montmorency, Québec March 28, 2019

2

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

3

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

4

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

5

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

6

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

7

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

8

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

9

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)

10

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

11

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

12

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

13

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

14

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

15

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