WIVERN: a Mission to Observe Global in-cloudWinds, Clouds and Precipitationas part of the ESA Earth Explorer 11 Programme

A. Illingworth, A. Battaglia, andall the WIVERN MAG and ESA teamalessandro.battaglia@polito.it

ESA Earth Explorer 11 roadmap
Launch 2031
www.wivern.polito.it
https://www.esa.int/esatv/Videos/2023/10/Earth_Explorer_11_User_Consultation_Meeting_WIVERN

About 20
proposals

1. CAIRT
2. WIVERN

1. CAIRT
2. NITROSAT
3. SEASTAR
4. WIVERNCAIRT: unravel the intricate connections between circulation and composition through the middle atmosphere.
NITROSAT: detecting reactive nitrogen from farms, industries, transport, fires and urban areas.
SEASTAR: observing small-scale ocean dynamics in coastal areas,shelf1seas
and themarginal ice zone.
ICONkm globalmodel
2
WIVERN: deliver the first space-based observations of in-cloud winds.

Why WIVERN? The next decade perspective
Mediterranean cyclone Daniel(3-11 September 2023)

ICON 1 km global model3

Three Scientific Pillars of WIVERNWind➢ Can we measure 3-dimensional winds within clouds to better
define the state of the Earth’s atmosphere and its dynamics on aglobal scale?

Clouds &Precipitation➢ Can WIVERN improve clouds and precipitation representation in thenext generation of km scale global models, and enhance confidence inour prediction of regional climate change?

Convection➢ Can WIVERN help better understanding the dynamics of convection and its
impact on moisture transport?

The science impact question: can WIVERN also demonstrate a significant
positive impact on weather forecasting?
4How? The WIVERN Instrument➢ W-band (94 GHz, -15 dBZ single-pulse sensitivity, -20 dBZ @1 km integration)➢ 500 km altitude, conically scanning at 42o (830 km swath), ~12 rpm (8 pulses per km)➢ Doppler (polarization diversity, THV=20 s) for up to ±40 m/s line-of-sight (LoS) horizontal winds➢ Big >3 m antenna (3dB0.07o) (vertical resolution of 600 m, horizontal < 1km),➢ Radiometric mode with km-scale resolution and ~2 K relative accuracy➢ Polar orbit with 1.5 day average revisit time5Through what? WIVERN L2 Data Products
Doppler measurements

Windproducts

Convectiveclassificationand verticalHorizontal
motionwind speedcomponent
WIVERN
Flagship
Product

Reflectivity and brightness temperature measurements

Cloud & precipitation
products
Rain/Snow Rates
Liquid Water Path
Ice Water Content• Wind Horizontal Line-of-Sight product
𝑉𝐿𝑜𝑆 − 𝑉𝑁𝐺 𝑖 ,  − 𝑤 + 𝑉𝑇𝐷 cos 𝑖
𝑉𝐻𝐿𝑜𝑆 =sin 𝑖• Synergy with EC for mass-relevant cloud/precipitation&convection products
6

Horizontal Line-of-Sight Wind

https://dev.explorer.wivern.adamplatform.eu/

7

Horizontal Line-of-Sight Doppler velocity error budget

Total uncertainty HLoS
2.65 m/s (G) / 2.80 m/s (T)Level 2B HLoS
Total uncertainty

Level 2B HLoS
Random/systematic decomposition

Level 2A LoS
Horizontal to Line-of-sight projection

Random HLoS
2.6 m/sRandomLoS
1.6 m/s

HLoS
WMO requirements forSystematicNWP

Vertical motion
correction
1 m/s➢ Bias=0.6 m/s
Level 1B LoS
Geophysical/Non-geophysicaldecomposition

Geophysical contribution➢ Random=Non-geophysical
2.5m/s(incl. NUBF, wind shear, etc.)
contribution
1 m/s
1.3 m/s

0.5 m/s (G) / 1 m/s (T)Systematic LoS
0.33 m/s (G) / 0.66 m/s (T)uncertainty of ~2.7 m/s
Non-Geophysicalcontribution
2 m/s (G) / 4 m/s (T)threshold compliant

Geophysicalcontribution(negligible)

Pre-calibration

8

WIVERN + Aeolus-2: complementary Impact•

The assimilation of Aeolus (spaceborne Doppler lidar) clear-air and Mie winds significantly improved theforecasts of NWP models (e.g. Rennie et al., 2021) ➔ wind obs provide the largest impact per observation•Does the assimilation of the WIVERN in-cloud winds further improve NWP forecast accuracy?

EDA experiment performedat Meteo France9Reduction of the EDA spread

Level-1B

Level-2B

Z&TB-based products: snowfall rate
Microphysicalproducts

Rain/SnowRates

Liquid Water Path (LWP)
Ice Water Content (IWC)

Noise powermeasurements
TbV and TbH

Calibrated
reflectivities
ZH and ZV❑ Snowfall is paramount to understand cryosphere (ice sheet massbalance) and the energy budget in polar regions (latent heat).
❑ Radar retrievals based on well established power-laws: Snow
Rate=f(Z,T)❑ CloudSat was the best source for snowfall climatology and training
Kulie et al., 2020datasets but poor samplingThree advantages of WIVERN

1. 70 times better spatial sampling than CloudSat
and upcoming EarthCARE due to scanning
2. Radiometer mode & polarimetricmeasurements can reduce uncertainties

10x10
snowfallclimatology(2006–2016)

3. Reduced ground clutter blind zone especiallyover ocean
10Gauging the anvil mass in organized mesoscale systems
CloudSat 1 km wide track
GEO provides generalcontext via VIS/IR images

CloudSat provides a single
vertical slice of radarreflectivity
Overshooting convection
Extensive anvil

11Gauging the anvil mass in organized mesoscale systems

WIVERN will
providemany more verticalslices through thewhole storm.

Geostationary will fill
the gaps at cloud
top between theWIVERN slices andalso provide
temporal evolution
12

Gauging the anvil mass in organized mesoscale systems

WIVERN will
providemany more verticalslices through thewhole storm.

Estimate of ice watercontent in the anvil.
Helps estimate convective
mass flux in the storm.
13Conclusions and outlook• WIVERN was one of the 4 ESA EE11 candidate missions, now recommended for Phase A by ACEO
• Hinges upon a single cutting-edge conically scanning W-band radar with polarization diversity
Doppler ➔ large Nyquist for sampling winds in high-impact weather such as tropical cyclones• Flagship product: vertically resolved in-cloud winds over a large swath ➔ plenty of accurate(<3m/s) winds (>2 million line of sight wind measurements at 20 km resolution per day) with asignificant impact on data assimilation (complement Aeolus 2 in cloudy mid/low troposphere)

Synergy/complementarity with EC❖ EarthCARE: characterization of W-band vertical Doppler (NUBF corrections, sedimentation)❖ WIVERN: extension of mass-important cloud/precipitation products for climate record (no other94 GHz proposed for 2030-40 decade), lower sensitivity but 30-40 times better sampling( revisit
each 20x20 km2 box on average every 1.6 days at the equator and daily at mid-latitudes).
❖ Possible synergistic activities• Algorithm development (e.g. warm rain/convective snowfall, based on Z&TB)• Development of synergy with GEO (e.g. for convection/anvil characterization)• CAL/VAL and data assimilation of Z and TB (EDA experiment planned for WIVERN at ECMWF)
14

Thanks
for theattention

Paper and info @ www.wivern.polito.it«The future’s in the air
I can feel it everywhere
blowing with the wind of change»from Wind of change by Skorpions