The power of monitoring EarthCARE
observations at ECMWF andpreparations towards their assimilation.
2nd ESA EarthCARE Validation Workshop
25th May 2021

Mark Fielding, Marta Janisková
Thanks: Michael Rennie, Mohamed Dahoui© ECMWF June 9, 2021

Preparations for EarthCARE assimilation – Radar and Lidar (PEARL)
Joint ECMWF-ESA project to maintain and improve
developments for monitoring and potential assimilationof EarthCARE cloud radar and lidar observations.
1. Port assimilation developments to latest modelcycle and maintain.
2. Optimise observation impact throughimprovements of observation operator, biascorrection, observation error.
3. Explore synergies with other on-board sensors
4. Prepare observation processing so monitoring
can begin as soon as possible after satellite
launch.

Observation data monitoring at ECMWF• Observational data monitoring is a key component of the data
assimilation system:– Assesses the availability and quality of observations bycomparing them against NWP model.
– Typically, biases and variability in ‘observation minus background’(O-B), also known as ‘First Guess’ (FG) departures, aremonitored.
– Detects instrument and model issues that could affect quality ofanalysis.

Long-term L2B HLOS wind quality monitoring using backgrounddepartures (O-B statistics)
L2B Rayleigh-clear winds; daily, global data, whole profile
FM-A laser

FM-B laser

Relaxed QC:
𝑂 − 𝐵 > 15 𝑚/𝑠
rejected• Random error
fairly stable
recently; ~5.5 m/s• Improvements inground processing
algorithms andsettings helped
mitigate signal
lossesDue to radiometricperformance issues
the Rayleigh winds as~twice as noisy as
expected pre-launch

4Courtesy: Michael RennieIn late 2019, an explanation for the largest source of Rayleigh wind bias wasfound• Using ECMWF departures and satellite house-keeping data it was identified
Rayleigh wind bias depends linearly on the telescope primary mirror’s (M1)temperature readings (particularly outer minus inner gradients)• M1 temperature varies with Earthshine (short and long-wave radiation) andonboard thermal control• Physical mechanism for bias: temperature changes affect mirror shape andfocus, causing angular changes of atmospheric path (backscattered) light upon
the spectrometers– Spectrometer response is sensitive to frequency (Doppler shift) and toangle of incidencePrimary (M1) mirror• Bias correction trained on ECMWF departures, using M1 temperatures aspredictors, was implemented in ground processing chain on 20 April 2020
– Required major restructuring of the ground processing software– Regression updated every 12 hours with 24 hours of past data (atECMWF) using DLR’s software (multiple linear regression)– Regular updates required primarily to correct for a global bias drift (internalpath laser pointing drift)– The method works very well
Courtesy: Michael Rennie

5Long-term L2B HLOS wind quality monitoring using backgrounddepartures (O-B statistics)
L2B Rayleigh-clear winds; daily, global data, whole profile
FM-A laser

FM-B laser

Relaxed QC:
𝑂 − 𝐵 > 15 𝑚/𝑠
rejected• Random error
fairly stable
recently; ~5.5 m/s• Improvements inground processing
algorithms andsettings helped
mitigate signal
lossesDue to radiometricperformance issues
the Rayleigh winds as~twice as noisy as
expected pre-launch

Bias improved due totelescope-mirror
temperature dependentbias
6
correction

Monitoring EarthCARE observations requires sophisticated operator
see Fielding and Janisková, 2020
doi:10.1002/qj.3878

Cloud overlap

Particle habit

Radar reflectivity

iwc
swc
cswc
size
Particle

cwc
rwc

Radiative
transferScattering
properties

Lidar backscatter

crwc

Subgrid scalecondensate variability+cc, pfra

MicrophysicalRepresentativity

MacrophysicalScale dependent

Model space

Scale invariant

Particle size distribution

Observation space

Observation operators (and pre-processing) tested using EarthCARE test data
EarthCARE simulator applied to GEM

IFS forward model applied to ECMWFØ Screening helps to improve quality of monitoring and assimilation••

Screening example based on C-NOM test data from
‘Halifax scene’ (courtesy of Aleksandra Tatarevic,Zhipeng Qu)
C-NOM data was also used to test obs. Preprocessing (e.g., conversion to BUFR and ODB)Monitoring cloud radar reflectivity from CloudSat against ECMWF model
Globalmean±3 s.d.

GlobalStd. dev.

12-hour global statistics for all observations passing screening
EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS

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Monitoring cloud lidar backscatter from CALIPSO against ECMWF model
Globalmean

GlobalStd. dev.

12-hour global statistics for all observations passing screening
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Benefits of monitoring EarthCARE observations at ECMWF• Rapid feedback on changes of calibration or drifts• Indications of regional variations of calibration that would be
difficult to detect otherwise (e.g., ECMWF pivotal in detectingand correcting Aeolus wind biases)• Offer a sanity check on absolute calibration

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Assimilating radar and lidar observations improves NWP analysis…
WindSonde wind - Global

Water vapour

Temperature

Geostationary IR - Global

AMSUA - Global

OSE: 6-month (08.-10. 2007 and 02.-04. 2008) CloudSat and CALIPSO
October 29, 2014
observations in addition to regularly assimilated observations vs control

Verification of forecast against own analysis
Day 4Day 5Day 60.04
REDworseWind0.02

significant 0.5 - 1% improvements in global
upper tropospheric winds at day 5-6!
Temperature

0.00

Difference inRMS error
normalised by
RMS error ofcontrol– 0.02

GeopotentialBLUE
better– 0.04

…leading to improvements in medium-range weather forecasts
October 29, 2014

Preparations for the monitoring of additional EarthCARE observations are underway

L2 HSRL products

Radar Doppler velocity

October 29, 2014

Summary• Monitoring observations against ECMWF forecast data is a tried andtested method for quality control of satellite data.
• ECMWF is preparing for the monitoring of EarthCARE observations aspart of the joint ESA-ECMWF project ‘PEARL Cloud’.
• Radar reflectivity and lidar backscatter operators are mature: individual
HSRL channels, extinction, Doppler velocity, MSI radiances are underdevelopment.
• Assimilation of cloud radar reflectivity and lidar backscatter shows hugepromise for improving model cloud analysis AND subsequent forecast.

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October 29, 201415