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Water Vapor Variability in the
Tropics Observed by Airborne
Lidar and Modelling
Christoph Kiemle1 and Ann Kristin Naumann2
1: DLR Oberpfaffenhofen, Institut für Physik der Atmosphäre 2: MPI for Meteorology, Hamburg, Germany
with contributions by
Silke Gross and Martin Wirth (DLR), Daniel Klocke (MPI)
g/kg, (g/kg)²
ISTP 11, Toulouse > Airborne Lidar Observations of Water Vapor in the Tropics • Kiemle > 20.05.2019
wv mr
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Tropics and trade wind regions are key to Earth’s climate.
Water vapor influences radiation, clouds, and circulation.
Models have difficulties to reproduce the shallow convection.
ISTP 11, Toulouse > Airborne Lidar Observations of Water Vapor in the Tropics • Kiemle > 20.05.2019
Cloud layer humidity determines dilution of
clouds by entrainment
Vertical profile of water vapor determines
radiative cooling (e.g. Muller and Bony, 2015)
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DLR-WALES H2O Lidar on HALO
Water Vapour Lidar Experiment in Space: Airborne Demonstrator on board HALO
Differential Absorption Lidar, DIAL
solid-state laser, OPO
8 W power at 935 nm
High-Spectral-Resolution Lidar, HSRL
3 onlines for full troposphere coverage
max. height 15 km
max. range 9000 km
Tropical abs. line selection
WALES
weak
strong medium
reference
Tropical H2O
absorption
line selection:
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Water Vapour Lidar onboard HALO NARVAL Flight Experiment: Next Generation Aircraft Remote Sensing for Validation Studies
Lidar – Radar combination
in view of ESA EarthCare
See contribution on
Tuesday by S. Gross
presented by M. Hagen
km Kiemle et al., JTech 2007
Before 2010:
combination with wind
lidar for moisture
transport process studies
on DLR Falcon aircraft
Latent heat flux profile
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DLR.de • Chart 5
Tropical HALO Flights: North Atlantic, East of Barbados
Dec. 2013 Winter Trades Aug. 2016 Summer Trades Jan. 2020 EUREC4A
10.12.
11.12.
12.12.
14.12.
15.12.
16.12.
19.12.
Transfer flights from / to Germany
Local flights to the East
of Barbados with A-train
underflights
NARVAL1 flights, Dec. 2013:
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> Lecture > Author • Document > Date DLR.de • Chart 6
NARVAL-1 HALO
flights, Dec. 2013 11.12.
12.12.
15.12.
NARVAL-1 12. Dec. 13 MODIS 16:30 HALO track
ISTP 11, Toulouse > Airborne Lidar Observations of Water Vapor in the Tropics • Kiemle > 20.05.2019
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> Lecture > Author • Document > Date DLR.de • Chart 7
NARVAL-1 12. Dec. 13 MODIS 16:30 HALO track 2h∙15m/s=108 km
Aerosol, cloud tops and water vapor are observed simultaneously.
Profiles in narrow cloud gaps are possible.
WV resolution: 2.5 km hor., 200 m vert.
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> MPI, Uni Hamburg > Airborne Lidar Observations of Water Vapor in the Tropics • Kiemle > 12.9.2018 DLR.de • Chart 8
g/kg, (g/kg)² g/kg, (g/kg)²
skewness variance mean
Kiemle, Groß, Wirth, Bugliaro, Surv. Geophys. 2017
Winter trades: 15. Dec. 19. Dec.
vertical wv column =
water vapor path (wvp) =
∫ mmr(z) ∙ ρair(z) dz
air density ρair from dropsondes
total
BL
CL
FA
Winter trades: 15. Dec. 19. Dec.
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NARVAL-1 NARVAL-2
Dec. 2013 Aug. 2016
more north of ITCZ more close to ITCZ
42 sondes 81 sondes
Stevens, Brogniez, Kiemle, et al., Surv. Geophys. 2017
DLR.de • Chart 9
Tropical Winter – Summer Differences
in specific humidity profiles from dropsondes
ISTP 11, Toulouse > Airborne Lidar Observations of Water Vapor in the Tropics • Kiemle > 20.05.2019
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Summer Trades
12. Aug. 2016
MODIS 16:40
HALO track
12:30
17:20
Photos Bjorn Stevens
Flight report: “tenuous low clouds
in a dusty atmosphere”
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Rel.
Hum.
Summer Trades
12. Aug. 2016
Using ECMWF
temperature
profiles
ISTP 11, Toulouse > Airborne Lidar Observations of Water Vapor in the Tropics • Kiemle > 20.05.2019
mr
[g/kg]
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NARVAL 12 Aug. 16: WALES – Dropsonde – ECMWF Comparison
ppmv ppmv
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NARVAL 12 Aug. 16:
WALES Spectral Analyses
Integral length scale:
Integral of autocorrelation function
(Lenschow & Stankov, 1986)
Fourier spectra:
- across horizontal 600-km time series,
- vertically averaged to reduce noise
and sampling uncertainties (4 layers),
- normalized by n/ngood to restore
variance lost by gaps due to clouds.
(Kiemle et al., QJRMS, 2011)
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How can we compare Lidar and model results?
ICON model domains
1. Use average profiles across the domain: mean, variance, … 2. Use correlation functions, spectra, … 3. Sort all wv profiles from driest to wettest into „moisture space“
• all simulations without convective parameterization
• initial + boundary conditions: ECMWF reanalyses
• one-way nesting of higher resolution in low resolution simulations
Ann Kristin Naumann, Matthias Brück, Daniel Klocke, MPI for Meteorology, Hamburg
LEM
SRM
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NARVAL1 HALO
flight, 11. 12. 2013
How can we compare Lidar and model results?
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DLR.de • Chart 16
11.12.13.: From the Trades into the ITCZ
ISTP 11, Toulouse > Airborne Lidar Observations of Water Vapor in the Tropics • Kiemle > 20.05.2019
MODIS 17:25 UT
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DLR.de • Chart 17
How can we compare Lidar and model results?
Sort all wv profiles from driest to wettest into „moisture space“
Issue 1: Lidar misses 50 % of profiles,
and even more at the moist end of the
cumulative wvp distribution.
Issue 2: does ICON perform well?
Solution: use the collocated HALO
HAMP radiometer wvp data to span
up the full moisture space.
Then: tailor the ICON wvp distribution
to match the Lidar wvp range.
Radiometer data from Marek Jacob,
IGM, Univ. Köln
ISTP 11, Toulouse > Airborne Lidar Observations of Water Vapor in the Tropics • Kiemle > 20.05.2019
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DLR.de • Chart 18
Sort all wv profiles from driest to wettest into „moisture space“
How can we compare Lidar and model results?
ISTP 11, Toulouse > Airborne Lidar Observations of Water Vapor in the Tropics • Kiemle > 20.05.2019
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3 flights together: 11., 12., 15. Dec 2013
average profiles across the domain: cloud fraction mean wv stddev (wv)
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3 flights together: 12., 19., 24. Aug. 2016
average profiles across the domain: cloud fraction mean wv stddev (wv)
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DLR.de • Chart 21 ISTP 11, Toulouse > Airborne Lidar Observations of Water Vapor in the Tropics • Kiemle > 20.05.2019
3 flights in Dec 2013 3 flights in Aug 2016 Are the cases representative?
Only ICON
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Conclusions and Outlook
Airborne lidar profiles in the Trades can quantify the humidity variability.
Lidar sees wv gradients, dry layers, and profiles in between clouds.
ICON shows a good skill in reproducing the lidar wv path. Comparisons
with lidar profiles show a moist model bias near the cloud layer top.
An additional wind lidar would be nice to quantify wv fluxes & transport.
Our last proposal for an ESA Earth Explorer Water Vapor Lidar Mission
was not yet successful, despite a very high scientific ranking.
EUREC4A experiment 2020: cloud – wv – radiation – circulation coupling
DLR.de • Chart 22 ISTP 11, Toulouse > Airborne Lidar Observations of Water Vapor in the Tropics • Kiemle > 20.05.2019