Quantifying Errors and Biases in Snowfall Records: Perspectives from WMO Solid Precipitation Intercomparison

1. Quantifying Errors and Biases in Snowfall Records: Perspectives from the WMO Solid Precipitation Intercomparison Daqing Yang National Hydrology Research Centre (NHRC) Environment Canada (EC) Saskatoon, Canada Barry Goodison WMO and MSC/EC (with contributions from many colleagues)

2. Sources of Measurement Errors Systematic errors for manual catchment-type gauge: • WIND (temperature) • wetting loss • evaporation loss • non-zero trace • capping of gauge orifice • blowing snow

3. WMO Solid Precipitation Measurement Intercomparison sites and people Barry Goodison Chairman, International Organizing Committee Canada Barry Goodison Paul Louie John Metcalfe Ron Hopkinson China Daqing Yang Ersi Kang Yafen Shi Croatia Janja Milkovic Denmark Henning Madsen Flemming Vejen Peter Allerup Finland Esko Elomaa Reijo Hyvonen Bengt Tammelin Asko Tuominen S. Huovila India N. Mohan Rao B. Bandyopadhyay Virendra Kumar Col K.C. Agarwal Germany Thilo Günther Japan Masanori Shiraki Hiroyuki Ohno Kotaro Yokoyama Yasuhiro Kominami Satoshi Inoue Norway Eirik Førland Romania Violete Copaciu Russian Federation Valentin Golubev A. Simonenko Slovakia Miland Lapin Sweden Bengt Dahlstrom Switzerland Boris Sevruk Felix Blumer Vladislav Nešpor UK J. Fullwood R. Johnson USA Roy Bates Timothy Pangburn H. Greenan George Leavesley Larry Beaver Clayton Hanson Albert Rango Douglas Emerson David Legates P. Groisman WMO Klaus Schulze Stephan Klemm CRN modified DFIR • Intercomparison was the result of Recommendation 17 of the ninth session of the CIMO-IX. • Started in the northern hemisphere winter of 1986/87. • Field work carried out at 26 sites in 13 Member countries for 7 years • Final report WMO-TD no. 827 published in 1998

4. WMO Solid Precipitation Measurement IntercomparisonGoal and Objective • The goal of the intercomparison: • to assess national methods of measuring solid precipitation against methods whose accuracy and reliability were known, including past and current procedures, automated systems and new methods of observation • The intercomparison especially designed to: • Determine wind related errors in national methods of measuring solid precipitation, including consideration of wetting and evaporative losses • Derive standard methods for adjusting solid precipitation measurements • Introduce a reference method of solid precipitation measurement for general use to calibrate any type of precipitation gauge

5. WMO Double Fence International Reference(DFIR) for Solid Precipitation Secondary reference

6. WMO Solid Precipitation Measurement Intercomparison, manual and auto gauges

7. Data analyses: Mean catch ratio (NWS 8-in gauge / DFIR, %) and mean wind speed at 3 meter height, 3 WMO Intercomparison sites (Nov. 1987 - Apr. 1993)

8. Daily catch ratio vs. daily wind speed

9. WMO Intercomparison Study Results: Catch Efficiency vs. Wind for 4 most widely used gauges Daily time scale

10. Report and Publication • About 30 papers in international journals • National reports • WMO TD, No- 827, 1998 Barry Goodison, Paul Louie, and Daqing Yang, the 14th Professor Vilho Vaisala Award in 1998

11. Recommendations from the WMO Intercomparison Study • WMO correction methods (available for different types of gauges and for different types of precipitation and various time intervals) should be adopted and applied to current and archived data; • both measured and corrected precipitation data should be reported and archived; • trace precipitation should be treated as a non-zero event; effort to determine mean trace amount is needed in Arctic conditions; • additional wind speed measurements be taken at the level of the gauge orifice and hourly mean wind data be archived in order to correct for wind-induced undercatch; • use of heated tipping-bucket gauges for winter precipitation measurement should be carefully assessed; their usefulness is severely limited in regions where temperatures fall below 0C for prolonged periods of time; • timing and type of precipitation be recorded by automatic instruments in order to conduct the correction on the basis of precipitation event.

12. Precip (mm) Precip days Application of WMO result: Barrow daily precipitation correction 1982-83 (Yang and Goodison, 1998)

14. Mean Gauge-Measured (Pm) and Bias-Corrected (Pc) Precipitation, and Correction Factor (CF) for January a) Pm (mm) b) Pc (mm) c) CF • Total 4827 stations located north of 45N, with data records longer-than 15 years during 1973-2004. • Similar Pm and Pc patterns – corrections did not significantly change the spatial distribution. • CF pattern is different from the Pm and Pc patterns, very high CF along the coasts of the Arctic Ocean.

15. GaugeIntercomparisonin Canada and Alaska DFIR Bratt’s Lake Intercomparison Facility/Smith Geonor-DF - - - Barrow, UAF Wyoming snow fence, Mar/03 Barrow, UAF DFIR, Mar 03 Barrow, UAF DFIR, Mar 3/03 -

16. WMO Solid Precipitation Inter-Comparison Experiment (WMO-SPICE) EC Snow Workshop, Toronto Dec 01, 2010 Rodica Nitu

17. CIMO-XV (Sept 2010) • An instrument intercomparison for solid precipitation measurements at AWS: a priority!   • WMO-SPICE: WMO Solid Precipitation Instrument Intercomparison Experiment • Canada committed to a leadership role if other Members participate and share the work • Support and commitment expressed by China, Finland, Japan, New Zealand, Switzerland, Russian Federation, and USA. • In CIMO, SPICE positioned in the context of WIGOS, EC-PORS, GCW.

18. Steps of CIMO Coordination • Goal: snowfall in cold and alpine regions; • WMO-CIMO to establish an International Organizing Committee (IOC) and a project lead; • Define objectives (primary, secondary, ….); • Selection of sites based on objectives and climatology; • Logistics: use to the extent possible existing configurations, etc; • Ensure consistency of datasets and results; • Data management and analysis.

19. WMO-SPICE: Proposed objectives • Evaluate the performance and configuration (catching, non-catching type, instrument & shield) of measurements in field conditions; • Develop multi-parameter algorithms to improve AWS precipitation data; • Develop adjustment procedures of systematic errors; • Establish a field reference standard using automatic gauges; • Develop long-term capacity to support validation of satellite measurements (e.g. Global Precipitation Measurement); • Develop comprehensive datasets to support future research objectives; • Provide feedback to manufacturers; • Pilot project for WIGOS, EC-PORS, GCW.

20. Proposed Ancillary Measurements • Radar – for horizontal (PPI scans) and vertical profiling (RHI or Vertical scans) for variation of precipitation. Dual-pole for precipitation typing. • Radiometer – determine the presence of liquid water (determine if particles are rimed). • Wind measurements (3D anemometers) – for turbulence, gustiness, at sensor height. • Precipitation Type Sensors – present weather sensors, intensive human observations. • Temperature and humidity – point and profiling, to determine habit types • Particle size, particle density and shape information – for aerodynamic collection efficiency issues; • Snowpack properties – snow depth, snow morphology, snow (freshly fallen and snow pack) density; • Lidarfor cloud properties; • Upper Air soundings for air mass stability.

21. Summary • Large biases/errors in historical gauge snow data • Corrections for biases is necessary, using the WMO methods • Need good meta data and info for the corrections • Impacts of precip bias corrections • changes in max P, mean, variation and trend • SWE, snowmelt runoff, river flow, water budget • decision making – FEMA • Compatibility among gauge observations, manual vs. auto gauges, i.e. NWS 8” vs. CRN, snow depth vs. snowfall obs