new data quality of aeolus wind measurements · 2020. 5. 4. · data quality of aeolus wind...

Data quality of Aeolus wind measurements

Isabell Krisch1

Christian Lemmerz1, Oliver Lux1, Uwe Marksteiner1, Nafiseh Masoumzadeh1, Oliver Reitebuch1, Fabian Weiler1, Benjamin Witschas1, Fabio Bracci2, Markus Meringer2, Karsten Schmidt2, Alexander Geiß3, Dorit Huber4, Ines Nikolaus5, Michael Vaughan6, Alain Dabas7, Thomas Flament7, Dimitri Trapon7, Saleh Abdalla8, Lars Isaksen8, Michael Rennie8, Dave Donovan9, Jos de Kloe9, Gert-Jan Marseille9, Ad Stoffelen9, Thomas Kanitz10, Anne-Grete Straume10, Denny Wernham10, Jonas von Bismarck11, Sebastian Bley11, Peggy Fischer11, Tommaso Parrinello11

1DLR, Institute of Atmospheric Physics 2DLR, Remote Sensing Technology Institute 3Ludwig-Maximilians-Universität München 4DoRIT 5Munich University of Applied Sciences 6OLA

7Météo-France 8ECMWF 9KNMI 10ESA-ESTEC 11ESA-ESRIN

The Aeolus Data Innovation and Science Cluster (DISC)

DLR.de Chart 2 EGU General Assembly 4-8 May 2020 © Aeolus DISC. All rights reserved.

Evolution of Aeolus random and systematic errors ECMWF operational monitoring of Aeolus Rayleigh and Mie winds

DLR.de Chart 3 EGU General Assembly 4-8 May 2020

Random error of ALADIN Rayleigh winds is in the order of 5-7 m/s and 3-4 m/s for Mie winds (mostly clouds): random errors in both channels were increased since launch and did not improve.

Systematic errors (bias) for both Mie and Rayleigh winds were enhanced since launch (several m/s), and show strong temporal variations (slow drifts), orbital variations, differences for ascending and descending orbits, and occurrence in some range-gates combination of 4 unexpected sources for the bias identified up to now

Rayleigh clear global, daily O-B statistics Mie cloudy global, daily O-B statistics

O-B: Difference between Aeolus observation and ECMWF forecasted HLOS wind

FM-B FM-B FM-A FM-A

FM-B with FM-A calibration

FM-B with FM-A calibration

© M. Rennie (ECMWF). All rights reserved.

Evolution of Aeolus random and systematic errors Comparison of Aeolus Rayleigh and Mie winds with 4 German radar wind-profilers


Radar wind profilers support error estimates obtained by ECMWF O-B.

© A. Geiß (University LMU Munich). All rights reserved.

1. Laser emit energy

Lower than expected (60mJ instead of 80mJ)

Negative trend

2. Optical signal throughput in receive path for atmospheric signal

Lower than expected (factor 2-3)

Negative trend

What drives the random errors?


Rayleigh atmospheric path signal and laser energy for FM-B

Discrepancy between these lines indicates that laser energy is not representative for instrument performance. This hints to a signal

loss in optical emit and/or receive path.

© K. Schmidt (DLR). All rights reserved.

1. Laser emit energy


Negative trend

2. Optical signal throughput in receive path for atmospheric signal


Negative trend

3. Solar background noise

Impact higher than expected due to lower atmospheric signal

Seasonal variation of solar background by factor 18: Rayleigh random errors of 7-8 m/s were obtained in summer months for polar regions

What drives the random errors?


Seasonal variation of Rayleigh solar background noise

Orbital variation of Rayleigh solar background noise

March 1, 2019

© K. Schmidt (DLR). All rights reserved.

What causes systematic errors? Combination of 4 unexpected error sources with different temporal characteristics

Systematic dark signal offsets with 10-3 to 10-4 of signal or 1% -10% of noise

1. Higher dark current rates for some “hot pixels”

affects specific range gates; currently 13 pixels on Mie ACCD and 14 pixels on Rayleigh ACCD


April 2020, Hot pixel map Hot pixel evolution

Oct Jan Apr Jul Oct Jan Apr Jul Oct 2018 | 2019 | 2020

30

25

20

15

10

5

0

Nu

mb

er o

f h

ot

pix

els

Hot pixels (mean per DCMZ/DUDE measurement)

Mie ACCD

Rayleigh ACCD

© F. Weiler (DLR). All rights reserved.

Correction of hot pixels implemented in June 2019

Enhanced dark signals (hot pixels) cause

systematic errors of ±(1-3 m/s) for Rayleigh

winds for some range gates

=> horizontal stripes

New instrument modes introduced, algorithms developed and implemented in operational processors on June, 14, 2019 for correction of hot pixels by measuring dark signals 4 times per day for real-time datasets

Hot pixels appearing in between the dark signal measurements still cause biases in NRT dataset (are flagged in L2B winds) !

DLR.de Chart 8 EGU General Assembly 4-8 May 2020 R

ayleigh H

LOS w

ind

in m

/s

June 14, 2019

Alt

itu

de

in m

© Fig: VirES for Aeolus; correction developed by

F. Weiler (DLR) and D. Huber (DoRIT) All rights reserved.



2. Error in the on-board software in calculation of residual projection of the satellite ground speed on the line-of-sight LOS:

harmonic variation of bias along the orbit

correction with on-board calculated vSAT in L1B and L2B processors de-activated in summer 2019

correction for on-ground L1A processing identified and implementation envisaged for autumn 2020


vSAT during June 29 – Jul 05, 2019

Amplitude of vLOS is zero at equator and maximum at poles with around 0.6 m/s, but opposite phase as in December

vSAT during Dec 18 – 24, 2018

© I. Nikolaus (Psol). All rights reserved.

© B. Witschas (DLR). All rights reserved.



2. Error in the on-board software in calculation of residual projection of the satellite ground speed on the line-of-sight LOS

3. Slow drifts in the illumination of the Rayleigh/Mie spectrometers causing a slowly, linear drifting constant bias


09.2018 11.2018 01.2019 03.2019 05.2019 07.2019 09.2019 11.2019 01.2020 03.2020

5320

5340

5360

5380

5400

5420

5440

5460

5480

5500

5520

-(0.07 ± 0.02) MHz/d

Switch

to FMB

-(0.32 ± 0.02) MHz/d

-(0.87 ± 0.06) MHz/d

Spacin

g/(

MH

z)

Date

-(1.52 ± 0.03) MHz/d

-(0.12 ± 0.04) MHz/d

Rayleigh spectrometer filter spacing

Rayleigh filter A and B spectral difference nominal 2.3 pm = 5460 MHz

=> change of 0.4 fm/d (2∙10-4 /d) => wind speed dependent error

filter A

Response

𝑹 =𝑰𝑨 − 𝑰𝑩𝑰𝑨 + 𝑰𝑩

filter

B






Rayleigh spectrometer center frequencies

07.2019 08.2019 09.2019 10.2019 11.2019 12.2019 01.2020 02.2020 03.2020 04.2020

844.95526

844.95528

844.95530

844.95532

844.95534

844.95536

844.95538

844.95540

(0.29 ± 0.02) MHz/day

(0.11 ± 0.1) MHz/day

(0.26 ± 0.03) MHz/day

f0/(

TH

z)

- d

irect

cha

nn

el

Date

150 MHz

Modell Line

Gleichung y = A + B*x

Chi-Quadr Reduziert

4.8999998E-12

Kor. R-Quadrat -0.5

Wert Standardfehler

f0/(THz)A 844.6743259 0.3477182

Modell Line


Chi-Quadr Reduziert

2.6666664E-13

Kor. R-Quadrat -0.3333333

Wert Standardfehler

f0/(THz)A 844.2528445 0.0573589

Laser energy increase by 0.5 mJ

positive T-change CP

filter A

07.2019 08.2019 09.2019 10.2019 11.2019 12.2019 01.2020 02.2020 03.2020 04.2020

844.96060

844.96062

844.96064

844.96066

844.96068

844.96070

844.96072

844.96074

(0.14 ± 0.05) MHz/day.

(0.11 ± 0.06) MHz/day.

(0.27 ± 0.03) MHz/day.

f0/(

TH

z)

- re

fle

cte

d c

han

ne

l

Date

150 MHz

Modell Line


Chi-Quadr Reduziert

8.7689859E-13

Kor. R-Quadrat 1.0065909

Wert Standardfehler

f0/(THz) A 844.2868598 0.0677506

filter B

filter A

Response


filter

B

© B. Witschas (DLR). All rights reserved.





Was in the past corrected manually by tuning of input parameters for L2B processing

Now corrected in real-time products as part of M1-correction (see next slide)


filter A

Response


filter

B

Temporal evolution of internal Response 𝑹𝑰𝒏𝒕

Aug 2019 Jan 2020

0.01 = 20 MHz = 5.8 m/s HLOS

slowly, linear drifting constant bias

© F. Weiler (DLR). All rights reserved.





4. Thermal variations of the M1 telescope mirror

Rayleigh bias with orbital phase (argument of latitude) and longitude

Use correlation between M1 temperatures and mean model bias for correction

DLR.de Chart 13 EGU General Assembly 4-8 May 2020 Mean telescope M1 mirror temperatures

M1

Te

mp

. /

°C

11.95

11.90

11.85

11.80

11.75

11.70

180° 150°W 120°W 90°W 60°W 30°W 0° 30°E 60°E 90°E 120°E 150°E 180°

90°N

75°N

60°N

45°N

30°N

15°N

0°

15°S

30°S

45°S

60°S

75°S

90°S July 1 – Oct 25, 2019

Rayleigh bias against ECMWF model (O-B)

Aug 13 – Sep 14, 2019 400hPa

ascending orbits

© F. Weiler (DLR) and M. Rennie (ECMWF). All rights reserved.

Correction of bias caused by thermal variations of the M1 mirror


Multiple linear regression (1d of data)

Effects of M1 correction: 1. „flattens out“ orbital variation (M1 telescope mirror) – reduces std. deviation of O-B (here: 2.62 m/s to 0.76 m/s) 2. Corrects for bias drifts (e.g. illumination effects)

stdev(<O-B>): • 2.62 m/s • 1.05 m/s • 0.76 m/s

Rayleigh bias versus time on 09/08/2019

Hour of day

HLO

S w

ind

me

an(O

-B)

/ m

/s

6

4

2

0

-2

-4

-6

© F. Weiler (DLR) and M. Rennie (ECMWF). All rights reserved.

Correction of bias caused by thermal variations of the M1 mirror Activated on April 20, 2020


New instrument modes introduced, algorithms developed and implemented in operational processors on April, 20, 2020 for correction of M1 temperature biases and slowly, linear drifting constant biases by using daily mean correlation between ECMWF model bias (O-B) and M1 temperature measurements.

Additional investigations are on-going to use ground measurements instead of ECMWF O-B to regain model independence.

Mie cloudy global, daily O-B statistics Rayleigh clear global, daily O-B statistics

© M. Rennie (ECMWF). All rights reserved.

Re-processing of Aeolus data

• Re-processing activities started beginning of this year

• It involves multiple manual and only semi-automated steps, e.g. Manual production of calibration files and

processing up to L1B Correction of hot pixels also in time periods

between dark signal measurements Semi-automated processing up to L2B Manual estimation of M1 temperature correction Verification of bias correction and delivery of all

calibration files to ESA Re-processing at ESA Validation and quality control of re-processed

dataset

• First re-processed dataset is FM-B data from 2019. It will be available in Sept. 2020.


Sept 2020

early 2021

2022

Re-processed FM-B data of 2019 including M1 correction will become

available

Re-processed FM-A data with hot-pixel and M1 correction or re-processed FM-B

data of 2020 will become available

Re-processing of complete Aeolus dataset since launch with optimized processor settings and all available corrections.

© Aeolus DISC. All rights reserved.

Summary and Conclusion

• The Aeolus DISC consortium is responsible for calibration, processor evolution, product quality and impact studies.

• Both systematic and random errors after launch were higher than expected

• Precise instrument characterization and use of O-B statistics enabled a drastic reduction of systematic errors

• Hot pixel correction was implemented in June 2019 for NRT data stream

• M1 temp. correction was implemented in April 2020 for NRT data stream and is currently examined by experts.

Public data release on May 12, 2020

• Re-processing started recently and first re-processed data (June – December 2019) will be available in Sept. 2020.


Sept. 2020 early 2021 2022

FM-B 2019 data FM-A data Complete

Aeolus dataset

Reprocessing

Hot pixel correction implemented

M1 bias correction implemented

Public NRT data release 12 May

2020

© Aeolus DISC. All rights reserved.

new data quality of aeolus wind measurements · 2020. 5. 4. · data quality of aeolus wind...

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