FRM4RADAR

FRM4RadarCloud Profiling Radar Network for Satellite Validation
Lukas Pfitzenmaier1, Pavlos Kollias1,2, Annika Daehne1,Salomon Eliasson3, Anke Thoss3, Anca Nemuc4, Bogdan Antonescu4, Ewan O’Connor5,Jonas von Bismarck6
1) University of Cologne, Germany, 2) Stony Brook University, NY, USA 3) SMHI, Norköping, Sweden 4) INOE, Bucharest,Romania, 5) Finish
Meteorological Institute, Helsinki, Finland 6) Earth Observation Ground Segment Department, ESA / ESRIN, Frascati, Italy
2nd ESA EarthCARE Validation Workshop
ESA UNCLASSIFIED – For ESA Official Use Only

125-28 May 2021 (online)Objectives of the FRM4Radar project§ Pave the way towards Fiducial Reference Measurements (FRM) for theEarthCARE Cloud Profiling Radar (CPR) L2 ESA products§ Increase confidence in the L2 ESA CPR products by means of ground-based radarobservations§ Quality check of data: C-FMR, C-CD
§ Quality check of retrieval products: C-CT, C-CLD§ Complementary 94-GHz radar network sites as part of CloudNet§ Use to the same wavelength as the EarthCARE CPR§ Fill geographical gaps in the European ground-based network§ Foster the development of new Cal/Val products§ Long term monitoring of the data – over years2

THE FRM4Radar NETWORK

https://cloudnet.fmi.fi/§ Compliment the existing ground based radar
network in the EU§ fill gaps in under sampled regions(Sweden and Romania)§ coverage of different cloud climate regimes(Svalbard and Babados)§ Operation of the radars§ 24-7 operation§ near real time data processing and visualization§ with "GEOMS data format"§ Quality checks§ received power calibration§ antenna pointing characterization

Existing CloudNet station (35 GHz)
Planed CloudNet station (94 GHz)
Existing CloudNet station running a 94-GHz3

EXAMPLE – Received power calibration

Radar
disdrometer

Disdrometer: opticalparticle counter tomeasure the droplet
size distribution N(D)

Forward modeling of
expected radar Ze basedon measured N(D)

Offset calculation by thestatistical comparison of
Ze_disdro to the Ze_radar
Kollias et al., 2019, AMT ; Myagkov et al., 2020, AMT 4

EXAMPLE – Received power calibration
radarZedisdrometer
N(D)disdrometervt(D)disdrometer
rain rate

Disdrometer: opticalparticle counter tomeasure the droplet
size distribution N(D)Radar
disdrometer

Forward modeling of
expected radar Ze basedon measured N(D)

Offset calculation by thestatistical comparison of
Ze_disdro to the Ze_radar
Kollias et al., 2019, AMT ; Myagkov et al., 2020, AMT 5

EXAMPLE – Received power calibration
radarZedisdrometer
N(D)disdrometervt(D)disdrometer
rain rate

Disdrometer: opticalparticle counter tomeasure the droplet
size distribution N(D)Radar
disdrometer

Forward modeling of
expected radar Ze basedon measured N(D)

Offset calculation by thestatistical comparison ofZe_disdro to the Ze_radar
Kollias et al., 2019, AMT ; Myagkov et al., 2020, AMT 6

EXAMPLE – Received power calibration
radarZeLong termstatistics toidentify trends

disdrometer
N(D)disdrometervt(D)disdrometer
rain rate

Disdrometer: opticalparticle counter tomeasure the droplet
size distribution N(D)

Radaration
Strong cooper
ommunitycIS
RTCA
hitw

disdrometer

Forward modeling of
expected radar Ze basedon measured N(D)

Offset calculation by thestatistical comparison of
Ze_disdro to the Ze_radar
Kollias et al., 2019, AMT ; Myagkov et al., 2020, AMT 7

Example – antenna pointing characterization
zenith pointingb)

vhvm

vpvv

zvhvm

altitudezaltitudea)

off-zenith pointing

00

vpvv

ϕ

Error in the measured Doppler
velocity (vm) is a function of 𝜙, vh,
and wind direction

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Example – antenna pointing characterization
zenith pointingb)

vhvm

vpvv

zvh
vm

altitudez

altitudea)

off-zenith pointing

00

vpvv

ϕ

For cirrus clouds:
Miss-pointing can causeup to 0.2 ms-1 error in vm

Error in the measured Dopplervelocity (vm) is a function of 𝜙, vh,and wind direction
Retrieval is based on: 3 monthstatistical analysis of wind andDoppler velocity
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Example – antenna pointing characterization
zenith pointingb)

vh
vm

vp
vv

z

vh
vm

altitudez

off-zenith pointingaltitudea)

Kaband
vm

00

vp
vv

ϕXbandvm

if antennas point zenith
X-band vm- Ka-band vm ~ 0 ms-1 in the cloud
this is not the case here

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Example – antenna pointing characterization
zenith pointingzb)

vhvm

vp
vv

z00corrected off-zenith pointing in the vm fields:
X-band vm- Ka-band vm ~ 0vhvm

altitudea)

Kabandvm

off-zenith pointing

altitudeRetrieval of miss-pointing angles:
Ka-band = 0.04°
X-band = 0.15°

vpvv

ϕ

Xband
vm

if antennas point zenith
X-band vm- Ka-band vm ~ 0 ms-1 in the cloud
this is not the case here11

https://cloudnet.fmi.fi/§ Compliment the existing ground based
radar network in the EU§ fill gaps in under sampled regions§ coverage of different cloud climate regimes(Svalbard and Babados)§ Operation of the radars§ Quality checks§ Collaboration with the ACTRSI network§ Instrumental synergy with ceilometer§ run 94-GHz version of Cloudnet algorithm (nomicrowave radiometer needed)*§ target classification algorithm*§ improve CloudNet coverage in Europa*Illingworth, 2007, BAMS, and Tukiainen et al., (2020)

THE CLOUDNET ALGORITHM

Existing CloudNet station (35 GHz)
Planed CloudNet station (94 GHz)
Existing CloudNet station running a 94-GHz
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THE CLOUDNET ALGORITHM

https://cloudnet.fmi.fi/

25th May NyAlesund

25th May JOYCE

25th May Bucharest
Existing CloudNet station (35 GHz)
Planed CloudNet station (94 GHz)
Existing CloudNet station running a 94-GHz
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Example:
Comparison of INOE and SMHI winter 2020 Observations
Comparison of liquid precipitation and target classification
Norunda, Sweden

Bucharest, Romania

Ze liquidvm allvm liquid

blind zone

Ze allZe liquid

detection limit

detection limit

Ze allvm allvm liquid

blind zone

almost all rain below detection limit

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Use of long term observations for model and CPR evaluation§ Long data set with good quality(GEOMS)§ Cloudnet target classification§ Covering different cloud climate
regimes (Arctic, mid and continentalEU, Caribbean)§ EarthCARE has an improved detection§ We need to account for differences insensitivity, attenuation effects, and thesurface blind zone

Lamer et al., 2020, AMT and IEEE 15

Use of long term observations for model and CPR evaluation

FRM4RADAR

In the next phase:§ develop a simple radar forward model basedon Lamer et al., 2020 that will account fordifferences in:§sampling§
sensitivity§
surface clutter
between the ground-based and space-based
observations.
§ Apply the simulator to the
FRM4Radar observations to convert them to a
top-down view (CPR)§ enable a more objective comparison between
the CPR and FRM4Radar measurements.
§ The same framework can be applied to model CPR comparisons.
Lamer et al., 2020, AMT and IEEE 16

FRM4Radar project summary
FRM4Radar home page:ØDocumentation of radar operation and procedure for quality control

Øhttps://geomet.uni-koeln.de/forschung/frm4radar

Online quicklook archiveØMeasurements, instrument status and soon quality control products

Øhttps://geomet.uni-koeln.de/forschung/frm4radar/quicklooks

Data available via CloudNet web portalØ

https://cloudnet.fmi.fi/

3 month test data set till end of the year (GEOMS)
ØFRM4RADAR

Spectral data -> l.pfitzenmaier@uni-koeln.de17

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