# Presentation: 905123

## North European Snowfall Validation with Radars

**North European Snowfall Validation with Radars**

**Jarmo Koistinen**
**FMI, Finland**
**GPM GV Workshop, Taiwan, Sept 2005**

## Boreal forest climate:  

**Boreal forest climate:  **
** ****100-220 snow cover days/year**
** ****Snow depth in March 20-90 cm**

## 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

## VVP/VAD wind profiles

## Cold Boreal Forest Climate

- Temperature and snow depth in Sodankylä

- Oct Nov Dec Jan Feb Mar Apr May

- Months

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

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

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

## Helsinki Testbed 2005-2007- ?

- Mesoscale weather research
- Forecast and dispersion models development and verificationInformation systems and technology integrationEnd-user product development and demonstrationData distribution for public and research community

-  Jani Poutiainen / Finnish Meteorological Institute

- 15.6.2005

## 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).

-       IC lightning system + CG lightning system

## HTB Precipitation 

**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)

## Vaisala polarimetric radar at HTB, prototype resultsRHI scans across a bright banddBZ                  ρHV                         LDR

**Vaisala polarimetric radar at HTB, prototype results**RHI scans across a bright band**dBZ                  ****ρ****HV****                         LDR**

## NORDRAD-composite (25 radars)

## GEWEX: Global Energy and Water Cycle Experiment

- WCRP: World Climate Research Program

## 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

## 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)

## Growth of uncertainties in the GV process of snowfall at ground

##  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.

- Antennas tuned mechanically

## Clutter

- Jamming
- Ships       Stroboscope effect

- 12h accumulation of problems

## Slide 19

## But R(Z)-S(Ze) 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 **

**Range**

## Reality: Underestimation far from radarWHY?

- Reality: Underestimation far from radarWHY?

- Kuopio radar, 3 months winter
- accumulation (3 dB colour steps),
- range 250 km

- Average 12 hourly gauge/radar ratio (dB),
- summer

## Examples of measured reflectivity profiles

**Snow**

**Snow,melting close to ground**

## Overhanging snow 

- 1(2)

**Overhanging snow **
**(virga, Altostratus) **

**Snow, evaporation and**
**residual clutter**

## Automatic real time classification of VPRs based on radar and NWP data (556 471 profiles)

**Automatic real time classification of VPRs based on radar and NWP data (556 471 profiles)**

- VPR. type (at ground):

## Snowfall measurements require 20 dBs more sensitivity than those of rainfall

**Snowfall measurements require 20 dBs more sensitivity than those of rainfall**

- MDS of GPM

**Cumulative probability **
**distribution of snowfall **
**in 106 825 profiles**

## Reasonable boundary layer winds obtainable 90 % of time in winter with sensitive radars (ice crystals from the ground ?)

## Climatological profiles based on the measured 2.4 million precipitation profiles

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

- 1(2)

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

## Example: Bright band 

- 1(2)

**Example: Bright band **
**at ground**

**Radar bias i.e. VPR correction for 500 m PsCAPPI and dry snow S(Z****e****)**

## Example: A Snow Case

- 1(2)

**Profile correction for 500 m PsCAPPI**

## Yearly average radar bias for 500 m PsCAPPI as a function of range

- 1(2)

**Yearly average radar bias for 500 m PsCAPPI as a function of range**

## Improving validation data applying a spatially continuous VPR correction

- 1(2)

**24 h accumulated precipitation Nov 7, 2002, 14 UTC **

**Measured **

**Corrected to ground level**

**Improving ****validation data applying a spatially continuous VPR correction**

## Log(G/R), data as previous, note excellent VPR-effect

**Log(G/R), data as previous, note excellent VPR-effect**

## Overhanging precipitation (OP)

## Remaining problem: complete beam overshooting in a very shallow snowfall. 

## Precipitation top height March 2001 (snow)

- 1(2)

**Precipitation top height** **March 2001 (snow)**

## 24 h accumulated precipitation Dec 21, 2002,  21 UTC . VPR correction does not help at the edges where complete beam overshooting occurs (shallow snow).

- 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 correction**

**With VPR correction**

## Better accuracy with integrated data – but in proper order!

- Remove non-meteorological echoes and OP.
- Attenuation correction.
- Blocking- & VPR-correction & intelligent compositing => Precipitation at ground
- Time-space variable R(Z) / S(Ze) relations.
- Diagnose areas of total beam overshooting.
- Gauge-radar adjustment.