ESA Explorer mission
EarthCARE:
Earth Clouds, Aerosols
and Radiation
Explorer
• Joint ESA/JAXA mission
• Launch 2016
• Budget 700 MEuro
EarthCARE Mission Objectives
To quantify cloud-aerosol-radiation interactions so they
may be included correctly in weather & climate models
 To observe vertical profiles of natural and anthropogenic
aerosols, their radiative properties and interaction with
clouds
 To observe vertical distributions of liquid water and ice,
their transport by clouds and their radiative impact
 To observe cloud distribution and the characteristics of
vertical motions within clouds
 To retrieve profiles of radiative heating rate through the
combination of the retrieved cloud and aerosol properties
EarthCARE measurements will link cloud, aerosols and
radiation at a target accuracy of 10 Wm-2
Atmospheric Lidar (ATLID)
EarthCARE Payload
– 355 nm (UV) with depolarization channel
– High spectral resolution capability providing
direct cloud/aerosol extinction measurements
Broadband
Radiometer (BBR)
– Long-wave & totalwave
– 3 views to get
fluxes
Cloud Profiling Radar (CPR)
–
–
–
–
94 GHz
2.5-m dish
Doppler capability
Min detectable signal -35 dBZ
Multispectral Imager (MSI)
– 4 solar and 3 thermal infrared channels
– 150-km swath
24/01/2013
ECARE overview KNMI
EarthCARE
Viewing geometry
•
•
•
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Satellite mass: 2000 kg
Solar panel area: 21 m2
Altitude: 393 km to maximize sensitivity
Radar and lidar power consumption: 2.5 kW
EarthCARE: UK Involvement
• Lead European Scientist
– Professor Anthony Illingworth (University of Reading)
• Development of synergy algorithms
– Professor Robin Hogan (University of Reading)
– Sustained funding from NCEO and NERC in support of this activity
• Development of Doppler radar simulator
– Dr Alessandro Battaglia (University of Leicester)
• Prime contractor
– Astrium UK
• Multi-Spectral Imager
– Surrey Satellite Technology Ltd. (Sevenoaks)
• Broad-Band Radiometer
– SEA, Bristol
• Thermodynamic data for for EarthCARE retrievals;
Real-time assimilation of EarthCARE data
– European Centre for Medium Range Weather Forecasts (Reading)
Single
Raw
instrument
measurements
products
EarthCARE products
Lidar
Radar
Imager
BB Radiometer
ATLID Level 1
Attenuated backscatter in
• Rayleigh channel
• Co-polar Mie channel
• Cross-polar Mie channel
CPR Level 1
Radar reflectivity profile,
Doppler profile
MSI Level 1
TOA radiances for 4 solar
channels, TOA brightness
temperatures for 3 thermal
channels
BBR Level 1
TOA long-wave and total-wave
radiances
ATLID Level 2
Feature mask and target
classification, extinction,
backscatter and depolarization
profiles, aerosol properties, ice
cloud properties
CPR Level 2
Radar echo product, feature
mask, cloud type, liquid and ice
cloud properties, vertical
motion, rain and snow
estimates
MSI Level 2
Cloud mask, cloud microphysical parameters, cloud top
height, aerosol parameters
BBR Level 2
Unfiltered TOA short-wave and
long-wave radiances,
TOA short-wave and longwave fluxes
Synergy
products
Synergistic Level 2
Cloud and aerosol products
derived from synergistic
retrievals using combinations
of ATLID, CPR, MSI
Radiation and
closure
products
Radiative Transfer Products
Fluxes, heating rates, TOA
radiances calculated from
constructed 3D cloud-aerosol
scenes (1D & 3D rad. transfer)
Assessment
Comparison of Radiative
Transfer Products (fluxes,
radiances) to BBR radiances
and fluxes
The A-Train versus EarthCARE
The A-Train (fully launched 2006)
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NASA
Multiple platforms
700-km orbit
CloudSat 94-GHz radar
Calipso 532/1064-nm lidar
CERES broad-band radiometer
MODIS multi-wavelength radiometer
EarthCARE (launch 2016)
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–
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ESA and JAXA
Single platform
393-km: higher sensitivity
94-GHz Doppler radar
355-nm High spectral res. lidar
3-view broad-band radiometer
Multi-spectral imager
Example A-train observations
Radar sees rain but
lidar is attenuated
Lidar more sensitive to ice cloud
Aerosol
Air molecules
Supercooled liquid cloud
What would
EarthCARE see?
• Method: Use a
variational retrieval
algorithm to retrieve
microphysical
properties of liquid
cloud, ice cloud, rain
and aerosol
• Next: Forward model
the EarthCARE
instruments
CloudSat
radar
EarthCARE radar significantly more sensitive than CloudSat
Much more information on thin ice clouds
Simulated
EarthCARE
radar
Doppler in convective clouds
Ice fall speed:
important uncertainty
in global models
Convective updrafts:
first time they will be
sampled globally
− NASA ER2:
high flying
aircraft
High spectral resolution lidar (HSRL)
• Lidars detect particles (clouds and aerosol) and air molecules
• Molecules are fast moving and so have a large Doppler shift
• EarthCARE’s HSRL can separate the two components using the
frequency spectrum of the returned signal
• EarthCARE will derive extinction profile (crucial for radiative
transfer studies) from attenuated air backscatter
Calipso
lidar
EarthCARE can separate out particle (Mie) from air (Rayleigh) backscatter
Cloud and aerosol extinction profiles can be retrieved directly
Attenuated particle backscatter
Simulated
EarthCARE
lidar
Attenuated air backscatter
Tropical cyclone: model and observations
Visible image
Tropical
Cyclone
Fengsheng
Observations
Simulated
observations
from NICAM
model
Brightness temp. (10.8 m)
Longwave flux
CloudSat 94 GHz
CloudSat 94 GHz
radar echo [dBZ]
CALIPSO 532 nm lidar
Evaluation of model ice clouds using A-train retrievals
Gridbox-mean ice water content
In-cloud mean ice water content
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•
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Both models lack high cirrus; Met Office has too narrow a distribution of in-cloud IWC
Using this work, ECMWF have developed a new scheme that performs better
Ice water content and particle size will be considerably more accurate from EarthCARE
Delanoe et al. (QJRMS 2011)
Case 20070123
over Pacific
ECMWF data assimilation
activities
Marta Janiskova, Sabatino Di Michele et al.
• In preparation for the launch of
EarthCARE, ECMWF are testing the
assimilation of CloudSat and Calipso
data using a 1D+4D-Var approach
(Janikova et al. 2012)
• Bias correction applied
• Results show a significant impact on
analyses and forecasts
• EarthCARE data are anticipated to be
assimilated by ECMWF into their model
when the satellite is launched
Reading, UK
© ECMWF 2013
1D-Var of cloud radar reflectivity - assimilation
Case 20070123
over Pacific
Observations
First guess
Analysis
Reading, UK
Improved
match to
observations
after
assimilation
© ECMWF 2013
Increments due to 1D-Var assimilation
• Temperature increments:
• Specific humidity increments:
Reading, UK
© ECMWF 2013
Measures of success: PDF of first-guess minus analysis
• Cloud radar reflectivity comparison
– assimilated observations
– PDF has narrowed: indicates
assimilation is working
• Cloud lidar backscatter comparison
– independent observations
– PDF has narrowed: indicates
analysis has been improved
Reading, UK
© ECMWF 2013
Impact of 1D+4D-Var assimilation on subsequent forecast
a) 10.7µm100°W
TB GOES12 2008042421
Mean=278.1380°W
K
90°W
K
320
10m
simulated TB
from 9-hour
forecast:
|obs-exp||obs-ref|
312
304
296
288
40°N
40°N
280
GOES-12
observations
272
264
256
248
240
232
30°N
30°N
224
216
208
200
100°W
c)
90°W
80°W
Precipitation
20080424 12:00 for t9
- t6
100°W- NEXRAD OBS:90°W
80°W
33
333
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3
40°N
3-hour
accumulated
NEXRAD
precipitation
30°N
d)
Precip. EXP=refl_2err:
20080424
100°W
90°W12:00 t9 - t6 for |obs-exp|-|obs-ref|
80°W
33
333
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3
12
10
8
6
40°N
40°N
4
2
1
30°N
30°N
0.5
15
10
8
6
4
2
1
40°N
0.5
0.1
-0.1
3-hour
accumulated
model
precipitation:
|obs-exp||obs-ref|
-0.5
-1
-2
-4
30°N
-6
-8
Case
20080424
-10
0.25
100°W
90°W
Reading, UK
80°W
-15
100°W
90°W
80°W
© ECMWF 2013
EarthCARE and the Met Office
Improving the simulation of clouds in our models is important
for both short-term forecasting and for reducing uncertainty in
our projections of future climate change. EarthCARE will
provide information of relevance to both of these aims.
• We plan to use EarthCARE data to compare with our
weather and climate models to see how realistically we
represent clouds.
• This will help us to improving the representation of
clouds in our models, including their formation,
development and evolution over different time scales.
© Crown copyright Met Office