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Northern European Possibilities for Ground Validation of Snowfall

Jarmo Koistinen

FMI, FinlandIPWG/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 telecommunicationsin 2004

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Global CEOP* validation site (Nr 29)*Coordinated Enhanced Observing Period (CEOP)

Potential GV site for snow Sodankylä (the northernmost radar, 67°N)

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Helsinki Testbed (HTB), 60°N, 2005-2007-?A coastal, mesoscale high latitude research and development facility (WMO/WWRP endorsement tbd).

testbed

46 FMI weather stations34 FMI precipitation stations5 new weighing gauges

13 off-line temperature loggers10 Weather transmitters in urban area

191 Road weather stations299 Surface weather stations, total42 Instrumented towers (telecommuni-

cation masts) with weather trans-mitterson 2 or 3 levels each

3 Mobile ship weather stations5 Optical backscatter profilers

(ceilometers)4 Doppler radars1 Dual-polarization Doppler radar3 RAOB sounding stations1 Wind profiler with RASS

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 monthsduring Aug 2005 – Aug 2006, snow: Nov, Jan-Feb)

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Vaisala polarimetric radar at HTB, prototype resultsRHI scans across a bright band

dBZ ρHV LDR

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NORDRAD-composite (25 radars, operational)

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WCRP/GEWEX/BALTEX: WCRP/GEWEX/BALTEX: DBZC - Composites of DBZC - Composites of radar reflectivityradar reflectivity

●More than 30 radars in 11 countries: BALTRAD

● Radar Data Centre at SMHI, Sweden (Daniel Michelson)● Continuous operation since October 1, 1999●Resolutions: 22 km, 15 minutes, 0.4 dBZ

BALTRAD composite2005-05-28 14:15 UTC

BALTEX Radar Data Center

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RR - 3 and 12-hour RR - 3 and 12-hour Gauge-adjusted Gauge-adjusted

Accumulated Accumulated Precipitation +Precipitation +Gauges-only Gauges-only AccumulationAccumulation

●22 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 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

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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 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

Development work for improved precipitatio

n data quality

Routine production

of precipitation data for systematic

value assessment

Development work for improved snow data

quality

Routine production

of snow data for

systematic value

assessment

Development work for improved

soil moisture

data quality

Routine production

of soil moisture data for

systematic value

assessment

Development work for improved

data assimilation

schemes

Space-time continuisati

on for gridded data at

specified times by

assimilationMeasured

precipitation

Computed

precipitation

Computed soil

moisture

Computed snow

parameters

Measured soil

moisture

Measured snow

parameters

Assessment programme to evaluate the benefit of satellite-derived precipitation, soil moisture

and snow information in European hydrology and water resource managementFig. 01 – Logic of the H-SAF Development phase.

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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/instrumentsduring operations:

MetOp(AVHRR,ASCAT)

+Meteosat(SEVIRI)

+NPOESS

(VIIRS, CMIS)+

MW radiometersof the GPMconstellation

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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

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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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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 / Ze – S relations.

Operational since 1999:

Grey background: snowBlue background: rainPink background: mixed

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But R(Z)-S(ZBut R(Z)-S(Zee) relations play only a minor role) 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

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Examples of measured reflectivity profiles

Snow Snow,melting close to ground

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1(2)

Overhanging snow (virga, Altostratus)

Snow, evaporation andresidual clutter

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1(2)

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

MDS of GPM

Cumulative probability distribution of snowfall in 106 825 profiles (range 2-40 km)

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Climatological profiles based on the measured 220 000 precipitation profiles

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1(2)

Vertical profiles of reflectivity (VPR) in winter introduce large biases (S) in the radar estimates of surface precipitation

),(

),0(log10

rhZ

rZS

e

e

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Example: A Snow Case

1(2)

Profile correction for 500 m PsCAPPI

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1(2)

Yearly average ground reference bias for 500 m PsCAPPI as a function of range

In snowfall sample size 106 000 VPRs

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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

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Log(Gauge/Radar), note excellent VPR-effect

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Remaining problem: complete beam overshooting in very shallow snowfall.

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1(2)

0

5

10

15

20

25

0 1000 2000 3000 4000 5000 6000 7000 8000 9000

Height (m)

%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).

Notknown

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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 => 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.