The journey towards assimilation ofEarthCARE cloud radar and lidar
observations
Marta Janisková and Mark Fielding

ECMWF, Shinfield Park, Reading, UK
marta.janiskova@ecmwf.int

Thanks to: T. Wehr, S. Di Michele, O. Stiller, R. Forbes, A. Beljaars, S. English, P. Bauer,
P. Lopez, P. Bauer, A. Benedetti, M. Crepulja, P. Lean, M. Chrust, R. Hogan© ECMWF December 6, 2023

Frascati, November 2023

Introduction• Cloud processes are characterized by a wide range of spatial and temporal scales:– observing cloud 4D-variability is challenging– parametrization of cloud processes in global NWP models is difficult
 Global NWP models describe clouds with a reasonable degree of realism (at least in horizontal
structure), however, uncertainties in representation of cloud vertical structure may be substantial.
 Introduction of cloud radar and lidar observations from space provide a large volume of informationon the vertical structure of clouds and aerosols (CloudSat, CALIPSO, EarthCARE, …):– very important for model evaluation & further model development– opening new possibilities to explore model improvement through assimilation of data related toclouds However, assimilation of vertically resolved cloud information in a global numerical weather systembring a lot of challenges to succeed

2
EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS

Requirements for data assimilation• To succeed to assimilate new type of observations, especially cloud profiling, requires:‒ reasonable representation of the physical processes related to the observations‒ observation operator being able to provide realistic model equivalents to the observations‒ linearity and regularity of the observation operator used in the variational assimilation
framework‒ appropriate screening of observations‒ removal of systematic biases via a bias correction scheme‒ characterizing the components of observation error including representativity issues‒ inclusion of observations into automatic monitoring system

3
EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS

ERM

ESA projects at ECMWF

ECAREnwp

QuARL

ECAREassim1

ECAREassim2

PEARL Cloud1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

• ERM project (ERM):
ERM 1999 – 2001
• Impact of EarthCARE products on NWP (ECAREnwp):
ECAREnwp 2002 – 2004

Model
validation• Quantitative assessment of the Operational Value of Space-Borne Radara and Lidar
Measurements of Cloud and Aerosol Profiles (QuARL):QuARL Sept 2008 – Aug 2010
• STSE Study – EarthCARE Assimilation (ECAREassim1):
ECAREassim1 Sept 2011 – March 2014
• GSP Study – Operational Assimilation of Space-borne Radar and Lidar Cloud Profile
Observations for NWP (ECAREassim2):
ECAREassim2 May 2016 – Sept 2018
• ESA-ECMWF Study – Preparations for EarthCARE Assimilation – Radar and Lidar Cloud
Observations (PEARL
Cloud): October 2019 – …
PEARL CloudData
assimilation

ERA of Tobias Wehr (✞) as science officer for the projects:
QuARL

2008

ECAREassim1

2009 2010 201120122013 2014 2015

ECAREassim2

20162017 2018 2019

PEARL Cloud

2020

2021 2022 2023

2024

Tobias’ invaluable contributions helped drive all thoseprojects and mission towards data assimilation
forward. He is missed greatly.
4

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTSERMECAREnwp

ECAREnwp project
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

Impact of EarthCARE products on NWP

The objectives of the study :• Assess the importance of EarthCARE observations for validation & improvement of physical parametrizations• Perform sensitivity studies to help in defining EarthCARE requirements (e.g. required accuracy of measurements for NWP)
• Improve assimilation tools in view of EarthCARE use (introducing the cloud-radiation processes in the assimilating model)
Model evaluation: Comparison of the model cloudinessNew cloud scheme for assimilation:

Feasibility studies for assimilation:

profiles against radar estimates at Chilbolton, June 2003

12-hour FC of total column cloud water

1D-Var using OBS from ARM Program

Observed cloud fraction

Prognostic scheme of the forecast model

OBS: cloud radar reflectivity from MMCR

FG: model first guess

ECMWF model cloud fraction

Old diagnostic scheme for assimilating model

AN: 1D-Var analysis• Model is able to correctly capture the major cloud
events although errors in the details are clearly visible.
• Potential of using space-borne radar to derive vertical
cloud distributions for model comparison.

January 2001

New diagnostic scheme for assimilating modeldBZ / 10• 1D-Var AN closer to assimilated obs• 1D-Var able to modify temperature (T) &humidity (q) ⟶ important for consistency
between clouds & dynamics

ERM

ECAREnwp

QuARL

QuARL project
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

Quantitative Assessment of the Operational Value of Space-Borne Radar and Lidar Measurements of Cloud and Aerosol Profiles• The purpose of this project was to make maximum use of the CloudSat/CALIPSO data in the context of the ECMWF
atmospheric data assimilation and forecasting system in order to evaluate model errors, to improve the initial conditions
and to prepare the utilization of similar observations as will be produced by the EarthCARE mission.

Global validation with CloudSat/CALIPSO
Comparison of IWC vs. temperature – July 2006, NH

Frequency of occurrence of hydrometeors [%]Model (previous)

Model (updated)

Model

Obs

CloudSat/CALIPSO

Significant improvement of the icewater content (IWC) with the clouddevelopment changing representationof snow & mixed-phase ice cloud.
Frequency ofoccurrence ofhydrometeors (%)
EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS

Validation studies highlight the value of theobservations for the model validation and triggering
6
new model developments.

ERM

ECAREnwp

QuARL

QuARL project
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

Quantitative Assessment of the Operational Value of Space-Borne Radar and Lidar Measurements of Cloud and Aerosol Profiles

1D-Var retrievals of cloud information from CloudSat-18 - -15

-12 - -9-15 - -12

-9 - -6-6 - -3

0 - 3-3 - 0

3 - 6

6 - 9

9 - 12Cloud optical depth - MODIS vs. model

15 - 18

12 - 15

Cloud reflectivity
from 94GHz

OBS

9.3
7.6
5.9
4.3
3.0
1.9
-24 - -21

-21 - -18

60 N

-18 - -15

58 N

-12 - -9-15 - -12

56 N

54 N

52 N

-9 - -6

50 N-6 - -3

48 N

46 N

0 - 3

-3 - 0

44 N

42 N

3 - 6
40 N

6 - 9
38 N

9 - 12

36 N

12.5

100

-21 - -18

-18 - -15

-15 - -12

Period:20080424,
America-12 - -9
-9 - -6-6 - Area:-3-3North
-0
0 - 3
3 - 6

6 - 9

9 - 12

opt_MODIS
opt_FG
opt_AN80
9.3

BIAS STDV
FG -23.81 28.25
4.3
40
AN -8.52 26.29
7.6

60

5.9

3.0

32 N

1.9

30 N

Lat

100 W

FG

99 W

98 W

97 W

96 W

95 W

94W

93 W

92 W

91 W

90 W

89W

88W

1.1
0

87 W

Lon

62 N
0

2058 N

56 N

99 W

98 W

60 N

100 W

52 N
40 54 N 60
97 W

48 N
80

46 N

95 W

94W

50 N

96 W

42 N 40 N140
38 N
10044 N 120
93 W

92 W

91 W

34 N 32
N 30 N
160
180
Lat

36 N

90 W

89W

88W

Precipitation - NEXRAD vs. model
Hourly precipitation
rates from NEXRAD

7.6-24 - -21

12.5

5.9

10

3.0
1.9

1.1
62 N
12.5

-21 - -18

60 N

-18 - -15

58 N

56 N

99 W

98 W

-12 - -9-15 - -12

54 N

52 N

-9 - -6

50 N-6 - -3

48 N

46 N

95 W

94W

0 - 3

-3 - 0

44 N

42 N

3 - 6
40 N

6 - 9

38 N

36 N

9 - 1234 N

15 - 18

12 - 15

32 N

30 N

Lat

REF_AT

100 W

AN

97 W

96 W

93 W

92 W

91 W

90 W

89W

88W

87 W

Lon

-21 - -18

-18 - -15

-15 - -12

Period:20080424,
Area:-3 North
America-12 - -9
-9 - -6-6 - -3
-0
0
- 3
3 - 6

6 - 9

REF_AT8
9.3
7.6

9 - 1215 - 18

12 - 15precip_NEXRAD
precip_FG
precip_AN

BIAS STDV
FG -0.175 1.452
AN -0.006 1.471

10.9

precipitation
Height (km)[mm/h]

Period: 24 April 2008

4.3

-24 - -21

87 W

Lon

9.3

10.9

15 - 18

12 - 15

REF_AT

10.9

15 - 18

12 - 15

34 N

REF_AT(km)
HeightHeight (km)-24 - -21

20

1.1
12.5 62 N

10.9

Cloud optical depth from MODIS
cloud optical depth

HeightHeight (km) (km)10.9

-21 - -18

REF_AT

Height (km)-24 - -21

12.5

Comparison with independent observations

6
5.9
4.3
4
3.0

2

9.3

1.9

Height (km)Height (km)

7.6

1.1
0

5.9

2058 N

56 N

99 W

98 W

60 N

100 W

52 N
40 54 N 60
97 W

48 N
80

46 N

95 W

94W

50 N

96 W

42 N 40 N140
38 N
10044 N 120
93 W

92 W

91 W

34 N 32
N 30 N
160
180

36 N

Lat

90 W

89W

88W

87 W

Lon

4.3

• 1D-Var analysis gets closer not only to the assimilated, butalso to the independent observations

3.0
1.9
1.1

0

62 N

62 N

60 N

58 N

56 N

99 W

98 W

54 N

52 N

50 N

48 N

46 N

95 W

94W

44 N

42 N

40 N

38 N

36 N

34 N

32 N

30 N

Lat
100 W

97 W

96 W

93 W

92 W

91 W

90 W

89W

88W

87 W

Lon

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS• Analysis could benefit from assimilation of spaceborne cloudradar observations ⟶ efforts to be made to further7 explore
their possible use in the assimilation system of NWPERM

ECAREnwp

QuARL

QuARL project
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

Quantitative Assessment of the Operational Value of Space-Borne Radar and Lidar Measurements of Cloud and Aerosol Profiles

1D+4D-Var technique to study the impact of CloudSat obs on 4D-Var analyses & subsequent
forecasts

Impact on analyses: Standard deviation for FG & AN departures from 4D-Var
assimilating T & q pseudo-observations from 1D-Var of cloud radar reflectivity

Temperature [K]Specific humidity [g/kg]

Impact on forecasts: Difference of 200-hPa wind rms errors for the differences• Information on temperature (T) & humidity (q) retrieved
from 1D-Var of cloud radar data & used as pseudo-obs
in 4D-Var can improve initial conditions & partly forecast• Getting more impact from the new data would require tocarefully tune their usage in the assimilation system• Promising results indicate that it would be highly
desirable for NWP to have space-borne radar and lidar
observations in near-real time.

a)between the forecasts (FC) starting from 4D-Var of T & q pseudo-obs (exp) andfrom the reference (ref) analysis (AN) andcorresponding to reference ANb) Difference of rms erros for wind at 200 hPa - 20080424 12 UTC T+24

Differenceof rms90°W
errors forwind at 70°W
200 hPa 60°W
- 20080424
12UTC40°W
t+12
100°W
80°W
50°W

T+12

70°N

70°N
4

90°W

80°W

70°W

60°W

50°W

40°W

T+24

70°N

60°N
1

50°N

50°N
0.2

60°N
1

50°N

50°N
0.2

0.5

-0.2

-0.2

-0.5

-0.5

40°N

30°N

40°N

40°N

-1

-1

-2

-2

30°N

30°N

30°N
-4

-4
-6

90°W

80°W

70°W

60°W

50°W

40°W

6

70°N
4

60°N

0.5

40°N

worse

2

2

60°N

100°W

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS

100°W

6

100°W

90°W

80°W

70°W

60°W

8

50°W

-6

40°W

betterERM

ECAREnwp

QuARL

ECAREassim1

ECAREassim1 project
STSE Study – EarthCARE assimilation

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

• The project concentrated on preparing off-line data assimilation and monitoring systems to exploit combined
space-borne lidar and radar cloud observations for their assimilation in NWP models.

• The necessary developments for monitoring and assimilation studies have been done:− observation operator for cloud radar reflectivity and lidar backscatter− quality control and bias corrections schemes & definition of observation errors
1D-Var of cloud radar reflectivity [dBZ]

1D-Var of cloud lidar backscatter [km-1 sr -1]

Observations

ObservationsFirst guess

First guess

Analysis_R

Analysis_L92007012400 over PacificERM

ECAREnwp

QuARL

ECAREassim1

ECAREassim1 project
STSE Study – EarthCARE assimilation

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

1D-Var fit to assimilated observations

1D-Var increments of RH (derived from T & q)

Single 12-hour assimilation window: 20070123 21UTC – 20070124 09UTC

2007012400 over Pacific
1D-Var radar

RMS (OBS – FG) – RMS (OBS – AN)

Relative humidity [%]1D-Var lidar
Cloud radarreflectivity

1D-Var radar+lidar

1D-Var fit to independent
observations
Comparison for:
FG, AN and OBS ≤ 50
Cloud optical depth(independent OBS)

• T & q increments from lidar, smaller than from radar.
• Lidar increments at higher altitudes, i.e. complimentary

FG – First Guess
AN – Analysis
OBS - Observation

Cloud lidar
backscatter

1D-Var analysis getscloser to assimilated &independent obs:
impact of cloud radarreflectivity larger than oflidar backscatter
10

ERM

ECAREnwp

QuARL

ECAREassim1

ECAREassim1 project
STSE Study – EarthCARE assimilation

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

1D+4D-Var of T,q pseudo-observations from 1D-Var of radar + lidar: Impact on subsequent forecast
Specific humidity [g/kg]

T+24
Single 12-hour assimilation window:
20070123 21UTC – 20070124 09UTC

Generally, a positive impact of the newobservations on the subsequent
forecast:
Even though it decreases in time, it isstill noticeable up to 48-hour forecasts
Temperature [K]T+24• Information on T & q retrieved from
1D-Var of cloud radar/lidar data andused as pseudo-observations in 4D-Var
can lead to improve initial conditions &better forecast• But direct 4D-Var of cloud radar/lidar
observations to be developed:
1D+4D-Var too expensive to be used for
operational application
11

ERM

ECAREnwp

QuARL

ECAREassim1

ECAREassim2

ECAREassim2 project
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

GSP Study – Operational assimilation of Space-borne Radar and Lidar Cloud Profile Observations for NWP

The objectives of the study :• Development of an assimilation system that will be able to monitor the data quality of the EarthCARE radar and lidar
profiles (limited to cloud observations) and assimilate them into an operational global weather prediction model.
• Direct (in-line) data assimilation and monitoring system allowing extended research studies beneficial for futureapplications of EarthCARE.
Direct 4D-Var assimilation of all observations including cloud radar reflectivity & lidar backscatter
OBS radar reflectivity (dBZ)

OBS lidar backscatter (dBβ)

FG radar reflectivity (dBZ)

FG lidar backscatter (dBβ)

AN radar reflectivity (dBZ)

AN lidar backscatter (dBβ)

Situation: 20070801 00 UTC
12
EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTSERMECAREnwp

QuARL

ECAREassim1

ECAREassim2

ECAREassim2 project
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

GSP Study – Operational assimilation of Space-borne Radar and Lidar Cloud Profile Observations for NWP

Verification of 4D-Var assimilation runs
against assimilated radar & lidar observations

Verification of 4D-Var assimilation runs
against other assimilated observations
Radiosonde – U-wind

N.HemisN.HemisModel level

Pressure (hPa)Radar reflectivity

Radiosonde – T

Model level

Pressure (hPa)

Lidar backscatter

Analysis departure
Background departure

Analysis departure
EXP
Backgrounddeparture EXP
Analysis departure
REF
Backgrounddeparture REF

CloudSat & CALIPSO
assimilation indicated:• 4D-Var analysis
getting closer toassimilated cloudradar reflectivity &lidar backscatter• a positive impact
on analysis fit toother assimilated
observations
13

10-day period – 20 assimilation cycles)

Analysis: 20070801 00:00 UTC – 20070810 12:00 UTC

ERM

ECAREnwp

QuARL

ECAREassim1

ECAREassim2

ECAREassim2 project
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

GSP Study – Operational assimilation of Space-borne Radar and Lidar Cloud Profile Observations for NWP

4D-Var using new observations – impact on subsequent forecast (FC)
CHANGE IN ERROR: EXP – CONTROL
31-day period: 20070801 00 UTC – 20070831 12 UTC
VW – 850 hPa

T + 12better

T – 1000 hPa

T + 12

VW – 850 hPa

Verification of FC against TRMM datafor 10 days of 4D-Var cycling (20070801 – 20070810)
T + 24worse

T – 1000 hPa

T + 24EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS

Assimilation of CloudSat & CALIPSO observations:• a positive impact on the subsequent short-term FC• improving forecasts of rain rates in Tropics• The direct assimilation of space-borne cloud radar & lidar
into a global NWP model demonstrated for the first time.
• Further gains in forecast skill could be achieved byrefining obs.error, bias correction & tuning of superobbing.
• Encouraging results pave the way for potential operational
14
assimilation of EarthCARE observations in the future.

ERM

ECAREnwp

QuARL

ECAREassim1

ECAREassim2

PEARL Cloud

PEARL Cloud project
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

Preparations for EarthCARE assimilation - Radar and Lidar Cloud (Observations)• Joint ECMWF-ESA project to maintain & improve developments for monitoring & potential assimilation ofEarthCARE cloud radar & lidar observations in preparation for its launch:⁃⁃⁃⁃

Port assimilation developments to the latest model cycle and maintain
Optimise observation impact through improvements of: observation operator, bias correction, observation error, screening, …
Explore synergies with other on-board sensors
Prepare observation processing so monitoring can begin as soon as possible after satellite launch

Developments for additional EarthCARE products: observation operators & monitoring system
Lidar cloud extinction

Radar Doppler velocity

Rayleigh backscatter

CALIPSO extinction [10*log10(m-1)]

Pseudo-obs Doppler velocity [m.s-1]

Pseudo-obs Rayleigh backscatter [10*log10(m-1sr-1)]

FG extinction [10*log10(m-1)]

FG Doppler velocity [m.s-1]

FG Rayleigh backscatter [10*log10(m-1.sr-1)]

AN extinction [10*log10(m-1)]

AN Doppler velocity [m.s-1]

AN Rayleigh backscatter [10*log (m-1.sr-1)]

AN – FGAN – FGAN – FGEUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS

Transect on 20070801 00 UTC

15

ERM

ECAREnwp

QuARL

ECAREassim1

ECAREassim2

PEARL Cloud

PEARL Cloud project
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

Preparations for EarthCARE assimilation - Radar and Lidar Cloud (Observations)

4D-Var experiments using CloudSat radar reflectivity & CALIPSO lidar backscatter
Improvements to medium-range FC skills

Improved fit to other assimilated obs
In-situ wind - Global

Wind

Temperature

Day 1

Day 4

0.04

Vector Wind

0.02

0.0

Temperature AMSUA - Global
Geopotential

-0.02

-0.04

Difference in RMS error normalised by RMS error of control

RED
worse

BLUE
better

Forecast error reduction grows with forecast lead time
significant 0.5 - 1% improvements in global upper
tropospheric temperature & winds at day 4-7!

worse

betterImprovements to forecast of TOA
radiation based on verification against
independent CERES observations
16
11-month combined period:
1 August 2007 – 31 August 2008

ERM

ECAREnwp

QuARL

ECAREassim1

ECAREassim2

PEARL Cloud

PEARL Cloud project
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

Number of obs

Preparations for EarthCARE assimilation - Radar and Lidar Cloud (Observations)2006

How does the impact of assimilating CloudSat and CALIPSO change over time?
Day-time only2018
Temperature (T)

2010

2016
Temperature (T)Temperature (T)0.02

Vector Wind (VW)

Vector Wind (VW)

Vector Wind (VW)

0.0

Geopotential (Z)Geopotential (Z)Geopotential (Z)-0.02

11-month period

3-month period

3-month period

Difference in RMS error normalised by RMS error of control

2008

RED
worse
0.04

-0.04
BLUE
betterSummary• ECMWF is preparing for the near real-time monitoring andassimilation of EarthCARE cloud radar and lidar
observations as part of a joint ESA-ECMWF project.
• If assimilated, EarthCARE cloud radar and lidar data is
expected to have a significant positive impact on mediumrange weather forecasts (Janisková and Fielding, 2020).
• Instrument simulators (Fielding and Janisková, 2020) built
within ECMWF model provide support for the scientificexploitation of EarthCARE data, for example to improve therealism of digital twins within the DestinE framework.
• Observation quality monitoring against NWP data is aninvaluable tool for validation of meteorological satellite data,
including from EarthCARE.
Fielding, M. D. and M. Janisková, 2020: Direct 4D-Var assimilation of space-borne cloud radar reflectivity
and lidar backscatter. Part I: Observation operator and implementation. Quarterly Journal of the Royal
Meteorological Society, 146(733), 3877–3899.

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS

Janisková, M. and M.D. Fielding, 2020: Direct 4D-Var assimilation of space-borne cloud radar reflectivity
and lidar backscatter. Part II: Impact on analysis and subsequent
Quarterly Journal of the Royal
October 29,forecast.
2014
Meteorological Society, 146(733), 3900–3916.