Near real-time validation of EarthCARE
observations through monitoring within a data
assimilation system

Mark Fielding
Marta Janisková
ECMWF
ESA-JAXA EarthCARE validation workshop 15th Nov 2023
© ECMWF December 8, 2023

Towards a suite of observation operators for EarthCARE within the IFS
Mie backscatter
Rayleigh backscatter
Mie extinction

ECMWF IFS (Integrated ForecastSystem) model cloud fields

iwc

EarthCARE
Consistent set ofMicrophysical andradiative
assumptions
Radar reflectivity
Doppler velocity

swc
cwc
rwc

cswc

crwc

ZmVar (Di Michele et al., 2012;
Fielding and Janisková, 2020)

Visible and NIR radiances
MFASIS (Sheck et al., 2016 JSQRT)FLOTSAM (Hogan, in prep.)

Broadband fluxesecRad (Hogan andBozzo, 2017)

Observation data monitoring is a key component of data assimilation
All observations that are assimilated at ECMWF to produce the model initial conditions
also enter quality data monitoring system:– Assesses the availability and quality of observations by comparing them against
NWP model in near real-time.
– Uses in-house instrument simulators to monitor biases and variability within 12-hourdata assimilation.
– Rapidly detects instrument issues that could affect model analysis.
Example: Long-term Aeolus L2B HLOS
wind quality monitoring Monitoring and correction of Aeolus
horizontal line of sight (HLOS) winds
against ECMWF model was pivotal tomission success!
Degradations in HLOS winds improved
due to telescope-mirror temperaturedependent bias correction.

Thanks: Michael Rennie

Cloud and precipitation sensitive satellite observations: now and near future
Radar / Lidar

Passive Microwave
Solar

T-sounding

low-high-freq.
Cloudparticles,
lighter precip.

Verticallyresolved
Cloudparticle
size andnumberLarger
frozenparticles,
particle sizeand shape

Melting particles
Sub-FOV
heterogeneity& structure

InfraredCloud icewaterIce particle
size, shape,orientation

RO phase(+ attenuation)
Non-sphericalrain and ice
properties

Cloud fraction,
cloud top height,
multiple layers
Size of water
cloud particles

Cloud
liquid water

Cloud
cover

Passive
sub-mmRain, including
particle size

Rain gauges

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS

Mast to Mast(+ maybe to mobile)

Lightning
Ground radar
October 29, 2014

Slide adapted from Alan Geer

Representation of clouds in model is getting better and better!
comparison of day 1 forecasts with CERES SYN 1 degree daily mean TOA fluxes
shortwave
RMSE in1x1 degdaily
mean

longwave

Statistical basis of monitoring• Monitoring observations against NWP analyses allows faster detection ofinstrument issues by removing day-to-day variability from observations.
Observation only
Profiling sensor
observation‘True’ global distribution ofclouds

Systematic error

Observation minus modelsimulated profilingobservation

Variability insimulated clouds

Variability inclouds

Standard error in mean

Correlation between observations andsimulations are key to successful monitoringExample of monitoring observations from CloudSat against ECMWF model• Small changes in calibration can be detected because of low variability in global
mean, e.g., 1 dB drift in radar reflectivity would trigger ‘alarm’ within days.
Global12 h meanbias±3 s.d.

Global12 h std. dev.

Statistics can be generated for many other indicators based on model or satellite
EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS

7

Monitoring detects change in CALIPSO lidar tilting angle immediately

Global12 h meanbias

Global12 h mean
obs

Change in bias
quickly detected Reduction in specular reflection from ice crystals
when lidar pointing off-nadir

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS

Too much day-to-day
variability to detect bias 8

Investigation into tolerance for delays• EarthCARE data will be downlinked to two stations,and, with fast ground segment processing, the data willbe available in NRT.
• Using simulated EarthCARE orbit data, we can estimate
the expected data latency for a given period.

October 29, 2014
Example of data latency (minutes) for profiles in a 12-hour
windowEUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS

9ECMWF data assimilation cut-off times
CUT-OFF TIMES
Monitoring available

0145
06

09

observations
1218
21
1500

03

06

09

1215

Delayed cut-off 12-hour 4D-Var
Forecast from delayed cut-off analysis

Cut-off times for 00Z forecast:
0145 UTC for delayed cut-off
0400 UTC for early delivery-> Forecast dissemination ~ 0800UTCEUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS

Early delivery 6-hour 4D-Var
00Z forecast

October 29, 201410

Change of data missing cut-off times• Including 120-minute data processing time.

Both Stations• Including 30-minute data processing time.

Esrange only

80 % available
84 % of thetime

Two downlink stations enables good tolerance to delays in processing chain.

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS

October 29, 201411

Highlights from Chapter 6: Near-real time validation through data assimilation

Mark Fielding, Marta Janisková, Angela Benedetti, Will McLean, Michael Rennie – ECMWF, Mary Borderies, Rohit Mangla - Météo-France
Jerónimo Escribano - Barcelona Supercomputing Centre, Yasutaka Ikuta – JMA, Isaac Moradi - NASA, Derek Posselt - JPL-NASA, Jianglong Zhang - UND

GPM in arpege
Aeolus aerosol backscatter in CAMS

GPM at JMA

Community Radiative Transfer Model (CRTM)

Ikuta et al. (2021)

Summary• Monitoring observations against NWP data is a
powerful tool for detecting instrument issues,• A suite of observation operators for simulating
EarthCARE within IFS is now available.
• ECMWF is finalising preparations for near realtime validation of CPR radar reflectivity andATLID channels during commissioning phase.
• Recent developments in RTTOV and CRTM
should allow more operational centres tosimulate EarthCARE data.

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS

October 29, 201413