Northern European Possibilities for Ground Validation of Snowfall

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Northern European Possibilities for Ground
Validation of Snowfall

• Jarmo Koistinen
• FMI, Finland
• IPWG/GPM/GRP Workshop on Snowfall,
• Madison, October 2005

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• Most of Finland belongs to boreal forest climate
  
• 100-220 snow cover days/year
• Average snow depth in March 20-90 cm

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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
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VVP wind profiles horizontal drifting of snow
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Cold Boreal Forest Climate

• Temperature and snow depth in Sodankylä

Months
Oct Nov Dec Jan Feb Mar Apr May
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Potential GV site for snow Sodankylä (the
northernmost radar, 67N)
Global CEOP validation site (Nr 29) Coordinated
Enhanced Observing Period (CEOP)
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Helsinki Testbed (HTB), 60N, 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

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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)
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Vaisala polarimetric radar at HTB, prototype
resultsRHI scans across a bright banddBZ
?HV LDR
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NORDRAD-composite (25 radars, operational)
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GEWEX Global Energy and Water Cycle
Experiment WCRP World Climate Research Program
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WCRP/GEWEX/BALTEX DBZC - Composites of radar
reflectivity
BALTRAD composite 2005-05-28 1415 UTC

• 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

BALTEX Radar Data Center
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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)

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Eumetnet OPERA ProgrammeThe 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

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European Co-operation in the Field of Scientific
and Technical Research (COST)
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EUMETSAT Hydrology SAF

• SAF Satellite Application Facility under
  EUMETSAT contract
• HSAF lead by Servizio Meteorologico
  dellAeronautica, Italy
• Hydrology SAF
• Precipitation (Italy)
• Soil Moisture (Austria)
• Snow parameters (Finland)
• Mainly EUMETSAT operational satellites, but also
  other (research) satellites are used, when
  applicable

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Hydrology SAF
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)

• Products
• Snow recognition (SR)
• Snow effective coverage (SCA)
• Snow status (wet or dry)
• Snow Water Equivalent(SWE)

Satellites/instruments during operations MetOp (A
VHRR, ASCAT) Meteosat (SEVIRI) NPOESS (VIIRS,
CMIS) MW radiometers of the GPM constellation
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Hydrology SAF
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Helsinki University of Technology Modeling
polarimetric microwave scattering, e.g. snow,
sleet, boreal forest, insects, birds
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Growth of uncertainties in the Ground Reference
process of snowfall at ground
WMO inter- comparison
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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.

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Antennas tuned mechanically
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12h accumulation of problems
Clutter Jamming Ships Stroboscope effect
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(No Transcript)
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But R(Z)-S(Ze) relations play only a minor role
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!

Gauge/Radar
Range
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Reality Underestimation far from radarWHY?
Average 12 hourly gauge/radar ratio (dB), summer
Kuopio radar, 3 months winter accumulation (3 dB
colour steps), range 250 km
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Examples of measured reflectivity profiles
Snow,melting close to ground
Snow
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Overhanging snow (virga, Altostratus)
Snow, evaporation and residual clutter
1(2)
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Automatic real time classification of VPRs based
on radar and NWP data (556 471 profiles)
VPR. type (at ground)
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Snowfall measurements require 20 dBs more
sensitivity than those of rainfall
Cumulative probability distribution of snowfall
in 106 825 profiles (range 2-40 km)
MDS of GPM
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Reasonable boundary layer winds obtainable 90
of time in winter with sensitive radars (ice
crystals from the ground ?)
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Climatological profiles based on the measured 220
000 precipitation profiles
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Vertical profiles of reflectivity (VPR) in winter
introduce large biases (S) in the radar estimates
of surface precipitation
1(2)
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Example Bright band at ground
Radar bias i.e. VPR correction for 500 m PsCAPPI
and dry snow S(Ze)
1(2)
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Profile correction for 500 m PsCAPPI
Example A Snow Case
1(2)
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Yearly average ground reference bias for 500 m
PsCAPPI as a function of range
In snowfall sample size 106 000 VPRs
1(2)
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Improving reference data applying a spatially
continuous VPR correction
Corrected to ground level
Measured
24 h accumulated precipitation Nov 7, 2002, 14
UTC
1(2)
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Log(Gauge/Radar), note excellent VPR-effect
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Overhanging precipitation (OP)
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Remaining problem complete beam overshooting
in very shallow snowfall.
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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
1(2)
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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 correction
With VPR correction
1(2)
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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 gt Precipitation at ground
4. Time-space variable R(Z) / S(Ze) relations.
5. Diagnose areas of total beam overshooting and POD
   of snowfall detection.
6. Gauge-radar adjustment.