1. 10/30/031 Northern European Possibilities for Ground Validation of Snowfall Jarmo Koistinen FMI, Finland IPWG/GPM/GRP Workshop on Snowfall, Madison, October 2005

2. 10/30/031 Most of Finland belongs to boreal forest climate: 100-220 snow cover days/year Average snow depth in March 20-90 cm

3. 10/30/031 FMI weather radar network 8 C-band Dopplers Polar V, dBZ (dBT, W) archived since 2000 Data availability 99.3 % incl. maintenance and telecommunications in 2004

4. 10/30/031 VVP wind profiles: horizontal drifting of snow

5. 10/30/031 Cold Boreal Forest Climate Temperature and snow depth in Sodankylä Oct Nov Dec Jan Feb Mar Apr May Months

6. 10/30/031 Global CEOP* validation site (Nr 29) *Coordinated Enhanced Observing Period (CEOP) Potential GV site for snow Sodankylä (the northernmost radar, 67°N)

7. 10/30/031 Helsinki Testbed (HTB), 60°N, 2005-2007-? A coastal, mesoscale high latitude research and development facility (WMO/WWRP endorsement tbd). All other stations shown except Road Weather. Average WS distance 9 km (FMI regular 50 km). 1 IC lightning system + CG lightning system

8. 10/30/031 HTB Precipitation Measurements -circles: radar 20-60km (0-250 km) -dot: manual obs -big diamond: FD12P -small diamond: potential FD12P -triangle: autom snow depth -square: weighing gauge - plan: 2 POSS to be implemented http://testbed.fmi.fi public realtime data during the campaigns (6 months during Aug 2005 – Aug 2006, snow: Nov, Jan-Feb)

9. 10/30/031 Vaisala polarimetric radar at HTB, prototype results RHI scans across a bright band dBZ ρ HV LDR

10. 10/30/031 NORDRAD-composite (25 radars, operational)

11. 10/30/031 GEWEX: Global Energy and Water Cycle Experiment WCRP: World Climate Research Program

12. 10/30/031 WCRP/GEWEX/BALTEX: DBZC - Composites of radar reflectivity ● More than 30 radars in 11 countries: BALTRAD ● Radar Data Centre at SMHI, Sweden (Daniel Michelson) ● Continuous operation since October 1, 1999 ● Resolutions: 2  2 km, 15 minutes, 0.4 dBZ BALTRAD composite 2005-05-28 14:15 UTC BALTEX Radar Data Center

13. 10/30/031 RR - 3 and 12-hour Gauge-adjusted Accumulated Precipitation + Gauges-only Accumulation ● 2  2 km horizontal resolution ● Every 3 and 12 hours ● 32-bit depth ● Wind corrected gauge observations ● 3-hour BALTRAD area ● 12-hour BALTEX Region (see example)

14. 10/30/031 Eumetnet OPERA Programme : “The aim of OPERA is to harmonize European radar data and products, raise their qualities, facilitate their exchange, and support their application” Opera runs projects on –Quality information –Radar data use –New technologies –Products to exchange –New data formats –BUFR software –Radar data hub

15. 10/30/031 European Co-operation in the Field of Scientific and Technical Research (COST)

16. 10/30/031 EUMETSAT: Hydrology SAF SAF = Satellite Application Facility under EUMETSAT contract HSAF lead by Servizio Meteorologico dell’Aeronautica, Italy Hydrology SAF –Precipitation (Italy) –Soil Moisture (Austria) –Snow parameters (Finland) Mainly EUMETSAT operational satellites, but also other (research) satellites are used, when applicable Developme nt work for improved precipitation data quality Routine production of precipitation data for systematic value assessment Developme nt work for improved snow data quality Routine production of snow data for systematic value assessment Developme nt work for improved soil moisture data quality Routine production of soil moisture data for systematic value assessment Developme nt work for improved data assimilation schemes Space-time continuisati on for gridded data at specified times by assimilation Measure d precipitat ion Compute d precipitat ion Compute d soil moisture Computed snow parameter s Measure d soil moisture Measured snow parameter s Assessment programme to evaluate the benefit of satellite- derived precipitation, soil moisture and snow information in European hydrology and water resource management Fig. 01 – Logic of the H-SAF Development phase.

17. 10/30/031 Hydrology SAF Products Snow recognition (SR) Snow effective coverage (SCA) Snow status (wet or dry) Snow Water Equivalent(SWE) Satellites/instruments during development: NOAA (AVHRR) + MetOp (AVHRR, ASCAT) + Meteosat (SEVIRI) + EOS-Terra/Aqua (MODIS) + DMSP (SSM/I, SSMIS) + EOS-Aqua (AMSR-E) + QuickSCAT (SeaWinds) Satellites/instruments during operations: MetOp (AVHRR, ASCAT) + Meteosat (SEVIRI) + NPOESS (VIIRS, CMIS) + MW radiometers of the GPM constellation

18. 10/30/031 Hydrology SAF Fig. 18 -Three snow melt maps produced by applying the HUT snow melt model (Eq. 2) for SAR images and the land-use map. The maps describe the snow cover percentage at (a) May 25, (b) May 28 and (c) June 4, 1997.

19. 10/30/031 Helsinki University of Technology: Modeling polarimetric microwave scattering, e.g. snow, sleet, boreal forest, insects, birds

20. 10/30/031 Growth of uncertainties in the Ground Reference process of snowfall at ground GPM estimates Radar estimates SFWE(Ze) In situ estimates (SFWE) gauges, POSS etc Real snowfall at ground including density GV main tasks Major sources of error are hiding here Rarely available Calibrations Wind correction WMO inter- comparison

21. 10/30/031 Work to improve quality (implemented) Absolute calibration is still an issue (at best 1-3 dB): A relative calibration method based on comparing precipitation accumulation in the overlapping area of radar pairs. Elevation angle calibration to better than 0.05 degrees (high latitude sun hits the operational scans densely during rise and set). Cold climate phenomena diagnosed applying pattern recognition and fuzzy logics (in future applying polarimetry): Anomalous propagation common introducing strong sea and ship clutter. Migration of birds and insects.

22. 10/30/031 Antennas tuned mechanically

23. 10/30/031 Clutter Jamming Ships Stroboscope effect 12h accumulation of problems

24. 10/30/031 Better accuracy with integrated data: Hydrometeor phase analysis (rain, sleet, snow) based on Kriging- analysis of SYNOP data (T,RH). Resolution 5 min & 1 km (extrapolation). Time-space variable Z – R / Z e – S relations. Operational since 1999: Grey background: snow Blue background: rain Pink background: mixed

25. 10/30/031 But R(Z)-S(Z e ) relations play only a minor role Gauge/Radar 3000 gauge/radar winter comparisons  Variable-phase (R or S) method in solid line  Z-R only in dashed line  Snow cases in orange, all cases in grey  Correct Z-R or Z-S is negligible compared to the increasing bias as a function of range due to the vertical profile of reflectivity! Range

26. 10/30/031 Ideal Reality: Underestimation far from radar WHY? Kuopio radar, 3 months winter accumulation (3 dB colour steps), range 250 km Average 12 hourly gauge/radar ratio (dB), summer

27. 10/30/031 Examples of measured reflectivity profiles Snow Snow,melting close to ground

28. 10/30/031 1(2) Overhanging snow (virga, Altostratus) Snow, evaporation and residual clutter

29. 10/30/031 1(2) Automatic real time classification of VPRs based on radar and NWP data (556 471 profiles) VPR. type (at ground):

30. 10/30/031 Snowfall measurements require 20 dBs more sensitivity than those of rainfall MDS of GPM Cumulative probability distribution of snowfall in 106 825 profiles (range 2-40 km)

31. 10/30/031 Reasonable boundary layer winds obtainable 90 % of time in winter with sensitive radars (ice crystals from the ground ?)

32. 10/30/031 Climatological profiles based on the measured 220 000 precipitation profiles

33. 10/30/031 1(2) Vertical profiles of reflectivity (VPR) in winter introduce large biases (S) in the radar estimates of surface precipitation

34. 10/30/031 1(2) Example: Bright band at ground Radar bias i.e. VPR correction for 500 m PsCAPPI and dry snow S(Z e )

35. 10/30/031 Example: A Snow Case 1(2) Profile correction for 500 m PsCAPPI

36. 10/30/031 1(2) Yearly average ground reference bias for 500 m PsCAPPI as a function of range In snowfall sample size 106 000 VPRs

37. 10/30/031 1(2) 24 h accumulated precipitation Nov 7, 2002, 14 UTC Measured Corrected to ground level Improving reference data applying a spatially continuous VPR correction

38. 10/30/031 Log(Gauge/Radar), note excellent VPR-effect

39. 10/30/031 Overhanging precipitation (OP)

40. 10/30/031 Remaining problem: complete beam overshooting in very shallow snowfall.

41. 10/30/031 1(2) GPM Blind Zone may mask shallow precipitation Case: Precipitation top height March 2001 (snow). Note: 0.3-1 km high snowfall regular in Finland (difficult to diagnose from VPRs). Not known

42. 10/30/031 1(2) 24 h accumulated precipitation Dec 21, 2002, 21 UTC. VPR correction does not help at the edges where complete beam overshooting occurs (shallow snow). No VPR correctionWith VPR correction

43. 10/30/031 Better accuracy with integrated data – but in proper order! 1.Remove non-meteorological echoes and OP. 2.Attenuation correction (sleet!). 3.Blocking- & VPR-correction & intelligent compositing => Precipitation at ground 4.Time-space variable R(Z) / S(Z e ) relations. 5.Diagnose areas of total beam overshooting and POD of snowfall detection. 6.Gauge-radar adjustment.