Use of Satellite data at ECMWF© ECMWF JCSDA seminar 20/10/2014 Satellite Data Assimilation at ECMWF Stephen English with special thanks to: Tony McNally,

Use of Satellite data at ECMWF © ECMWF

JCSDA seminar 20/10/2014

Satellite Data Assimilation at ECMWFStephen English

with special thanks to:Tony McNally, Niels Bormann, Alan Geer, Marco Matricardi, Sean Healy, Cristina Lupu, Marta Janisková, Michael Rennie, Massimo Bonavita, Lars Isaksen, Mike Fisher, Richard Engelen, Peter Bauer, David Richardson, Thomas Laiden and Erland Källén.


• General introduction to ECMWF

• Satellite data assimilation overview at ECMWF

• Data assimilation strategy at ECMWF

• Recent highlights in satellite DA at ECMWF

• Current impact – recent ECMWF OSEs

What to expect in this talk


General introduction

Use of Satellite data at ECMWF © ECMWF~8-10km~210km ~63km ~39km ~16km

Evolution of ECMWF forecast skill E. Källén


Forecast error growth500hPa height, NH

Forecast lead time (days)

RM

S e

rror

(m

)199020002010

1.5 day gain

2 day gain

2.5 day gain

E. Källén


Skill gain relative to ERA-Interim (day 5)

Skill relative to ERA-I at day 5. Verification against analysis for 500 hPa geopotential (Z500), 850 hPa temperature (T850), mean sea level pressure (MSLP) and 2 m temperature (T2M_AN), using RMSE as a metric; against SYNOP for 2 m temperature (T2M), 10 m wind speed (V10), and total cloud cover (TCC), using error standard deviation.

T. Haiden


Recent implementations:• November 2010 (36r4): New cloud scheme, SEKF soil moisture analysis,

SKEB• June 2011 (37r2): AMSU-A obs. error, EDA variances in 4D-Var• November 2011 (37r3): Rev. cloud scheme, aircraft b/c, NEXRAD assimilation• May 2012 (38r1): New Jb, EDA-filtering, clouds/convection• June 2013 (38r2): HRES/4DVAR/EDA L91 → L137• November 2013 (40r1): ENS L62 → L91, ocean day-0, 25M EDA,

GWD/diffusion

Note: Satellite introductions often between cycles e.g. first use of China’s FY3B satellite in September 2014.

Future implementations• December 2014 (40r3): 4DVAR 3xT255, 2x weekly 11M reforecasts, new

surface fields and trace gas climatologies, clouds & convection improvement (e.g. supercooled water), lake model (FLAKE), “all-sky” MHS, 2D RO operator

• Autumn 2015 (41r2): Horizontal resolution → 8 or 10km

System updatesP. Bauer


ECWMF Satellite Data Assimilation


5.5 more instruments per year

Around 80 satellite instruments are processed in the operational IFS. Sounders (microwave and

infrared) are the most critical observations for accurate NWP and must be properly maintained.


M Janiskova S Di MicheleNew technologies in space: Cloud radar and lidar

Radar Lidar

O

B

A

Observed (O), Background (B) and Analysis (A) for spaceborne radar (Cloudsat) and lidar (Calipso) data. ECMWF short range forecast

captures 2D cloud structures seen in the observations.

New technology can fill gaps in the Global Observing System. Many years of sustained effort are needed to develop the capability to fully understand and make use of highly innovative new observations in a data assimilation system.


Clear-sky MHS impact All-sky MHS impact

T+72 RMS difference normalised by RMS of control

-4% -2% 0% 2% 4%

Improved assimilation: “all-sky” microwave soundingModelling cloud effects extract more benefit (blue colours) from MHS

than trying to screen out cloud-affected data.

All sky assimilation of microwave imagers improves wind and humidity fields. SSMIS and MHS humidity sounders are being moved to all-sky, and infrared humidity observations may follow.

A. Geer


Water vapour in the presence of cloud - 183±1

StartAssimilation window

UTHincrement (200-500 hPa mean RH)

Zonal windincrementat 400 hPa

Time of observation

Humidity reduction at observation time generated by changes in wind (and other dynamical variables) 1000km away, 9h earlier!

A. Geer


ECMWF Data Assimilation


Ensemble of data assimilation

• 25 members of 2 inner-loop 4D-Var’s at T95/159 L137, T399 outer lops• Perturbations from observations, SST, SPPT; noise filtering, scaling


DA future strategy at ECMWF

Hybrid EDA & 4D-VarLong window 4D-var with weak constraint.

- 24h weak constraint beats 24h strong constraint.

- More work needed on weak constraint to beat current operational 12h strong constraint.

Scalability of 4D-var is an issue – but we anticipate solvable, at least for the time being (time-parallel saddlepoint formulation).

Theoretically the longer the window the better, but devil is in the detail (formulating model error!).


• EDA is a very skilful system, but it is expensive. How well do cheaper alternatives to error cycling compare?

• An EnKF-based error cycling system without perturbed observations could be a cheaper alternative

Other options being investigated

• One among many options: If we have EnKF and 4D-var we can blend the Kalman gain – we call this “hybrid-gain”

xa=xb +(αKEnKF + (1- α)K4DVar)(y-Hx)

M. Bonavita


Recent satellite DA highlights


Recent highlights and progress in Satellite DA

ECMWF funded (6 people) All sky MHS assimilation

(2014, CY40R3) Metop + S-NPP instruments

(2014) IVER, ODB-IFS, cycle testing

(2014, CY40R3) High profile case studies and

OSEs e.g Haiyan, Sandy (2014) Improved understanding of

observation impact diagnostics (2014)

Observation error correlations for hyperspectral instruments (2015)

All sky IR assimilation (2016)

Externally funded (12 people)• Evaluation of Chinese FY3A/B/C (2014)

• Skin temperature analysis (2014, 40R2)

• RTTOV-11 (2014, 40R2+3)

• Improved MW emissivities (2014, CY40R3)

• Improved use of AMVs (2014, CY40R3)

• 2D RO operator (2014, CY40R3)

• MW observation error model (2015)

• Improved aerosol detection for IASI, CrIS and AIRS (2015)

• Improved MW imager assimilation e.g. cold air outbreaks (2015)

• PC assimilation demonstrated for cloud-free scenes (2016+)

• New ideas to exploit correlated error, rather than just allow for ir (2016+)


surface

ray path

Improvement of the 2D approach in 40R3

The outer loop uses 31 profiles to describe the 1200 km “occultation plane”. 7 profiles used for inner loop.

Interpolate 2D information to the ray path

Tangent height of the ray-path determined by the impact parameter provided with the observation, .

S. Healy


Impact of tangent point drift (37R2) and the 40R3 2D operator

S. Healy


Aeolus wind product statusLaunch mid 2016?

- 2014: L2B processing facility: prototype → operational

- Summer 2014: ESA ground Segment testing started

M. Rennie


Humidity

Better background fit to other observations over the tropics, especially for humidity-sensitive observations:

Temperature

Updated R for IASI and AIRS(new σO + correlations vs operational σO without correlations)

N. Bormann


Updated R for IASI and AIRS (2)

S.Hem. N.Hem.

Normalised RMSE differences over

6 months: Aug-Oct 2013; Jan-Mar 2014,

(vs own analysis)

New worse

New better

Forecast day

Tropics

New worse

New better

Forecast day Forecast day Forecast day

Forecast day

N. Bormann


Fastem – development history

Dielectric properties

Surface roughness

Directional anisotropy

Whitecapping

As full model

Fast fit to full model

As full model

As full model

Full model Fast model

RTTOV, CRTM etc.

Integration into RTM

Fastem-1: English and Hewison 1998: Created for 20-90 AMSU-A.Fastem-2: Deblonde and English 2002: Extend to MW imagers.Fastem-3: English 2007: Extend to polarimetric imagers.Fastem-4: Liu et al. 2011: Extend to 1-20 GHz and 90-200 GHz.Fastem-5: Liu et al. 2012: Fixed some weaknesses identified in Fastem-4 by users.Fastem-6: Kazumori and English 2014: Fixed anisotropy model in Fastem-5

V1 V2 V3 V4 V5 V6


Fastem-6: more accurate ocean emissivity for RTTOV-11/CY40R3 + 2 QJ papersUse of the Ocean Surface Wind Direction Signal in Microwave Radiance Assimilation

Masahiro Kazumori and Stephen J. English

Asymmetric features of oceanic microwave brightness temperature in high surface wind speed condition

Masahiro Kazumori, Akira Shibata, and Stephen J. English

June 20 to October 3

= Fastem-5 = Fastem-6

M. Kazumori (JMA)


Recent OSEs


Whilst the impact of a single instrument may appear small, when we test multiple instruments together, like

all the Metop-B instruments, the benefit is clear.

Metop-B IASI only

A. McNally

S Healy

Metop-B IASI+AMSU-A +MHS+GRAS+ASCAT

Long term gains in forecast skill can arise from many small changes. We should not always expect to get measurable

medium range positive impact from adding new observations.

-2%

to +

2%

-2%

to +

2%


OSE 500z (NH-24h)

NO SCAT

NO GEO

NO MWI

NO GPS

NO IRS

NO MWS

NO CONV

0 0.05 0.1 0.15 0.2 0.25 0.3

T. McNally


OSE 500z (SH-24h)

NO SCAT

NO GEO

NO MWI

NO IRS

NO GPS

NO MWS

NO CONV

-0.02 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16

T. McNally


72hr Tropics 200hPa VW

NO SCAT

NO IRS

NO GPS

NO MWS

NO CONV

NO GEO

0 0.005 0.01 0.015 0.02 0.025 0.03

Series1

T. McNally


OSE 500z (NH-24h) v FSO (NH-24h)

NO SCAT

NO GEO

NO MWI

NO GPS

NO IRS

NO MWS

NO CONV

0 0.05 0.1 0.15 0.2 0.25 0.3

T. McNally


Do results of OSE and FSO disagree ?

RMSE(IASI) minus RMSE(NO-IASI)RMSE(IASI*) minus RMSE(NO-IASI)

C. Lupu


Summary and thoughtsForecast skill continues to improve rapidly.By 2016 90+ satellite instruments processed operationally – a peak?Most satellite DA is achieved through collaboration (e.g. NWPSAF,

CMA-ECMWF partnership for FY3, Horizon-2020, Copernicus…).

New instruments: Radar, lidar, L-band, Limb sounders all demonstrating impact. Who will pay for an operational service?

Handling cloud and rain affected satellite data will become mainstream – potential being realised, this is the way forward.

System is robust to losing ANY observation type, even the most important. But note in situ data remains important.

ECMWF still thinks 4D-var has a bright future, but we are evaluating other approaches.



Atmospheric CompositionIFS code has been extended with full chemistry, aerosols, and

greenhouse gases -> “C-IFS”Strong reliance on ageing satellite systems: ENVISAT (failed), EOS

AURA (OMI & MLS), MOPITT, MODISMetop, Sentinel-5p, -4 and -5 therefore very important for

sustaining and improving current capabilitiesFrequently retrievals are assimilated

Use of Averaging Kernel in data assimilation removes sensitivity to prior information used in retrievals

Global mean CO analysis profiles

With AK

Without AK

R. Engelen


P de Rosnay, J Muñoz Sabater, C AlbergelLand Surface

Snow cover preprocessing and 24 to 4 km resolution improvement

NH 1000 hPa Z

SMOS brightness temperature assimilation: soil moisture increments

Root zone soil moisture analysis from EKF assimilation of ASCAT

Marine, land surface and atmospheric composition also need high quality satellite observations though lack of mature tools means

retrievals are often assimilated: however we now know better how to assimilate retrievals.


24h weak vs 24h strong24h weak-constraint (red) significantly better than 24h strong-constraint in NH.Small improvement also in SH.Verification is against own analyses.

Mike Fisher


24h weak vs 12h strong24h weak-constraint (red) remains worse than 12h strong-constraint in NH.Similar scores in SH (although some degradation at short range).Verification is against own analyses.

M. Fisher


T399 EnKF (100 member)T95/T159/T399 4DVar with static BT399 Hybrid Gain EnKF (100 member)

Z500 hPa AC - NHem Z500 hPa AC - SHem

M. Bonavita


2D impact at short-range (winter experiment)

NH SH

Clear improvement in GPS-RO statistics of ~ 5 % in troposphere.

More importantly, the 2D framework enables us to investigate further improvements include more physics.

S. Healy


Winds assimilated in an ECMWF cycle

• Very uneven distribution• AMV coverage good in tropics, but

obs errors large• Stratosphere poorly sampled

log10(number obs per area)

M. Rennie


Forecast rms errors for the 850 hPa relative humidity in the Tropics

Verification against radiosondes: Temperature in the Tropics

RADPC_CORR

RADPC_CORR

Assimilation of PC scores derived from 305 IASI channels

M. Matricardi


Hurricane Sandy


Hurricane SandyT. McNally


Hurricane SandyT. McNally


Analysis differences that led to failed (NO –LEO SAT) forecast

Control minus NO-LEO SAT MSLP 2012102500z (after 5 days of data denial)

T. McNally

Documents

Use of Satellite data at ECMWF© ECMWF JCSDA seminar 20/10/2014 Satellite Data Assimilation at ECMWF Stephen English with special thanks to: Tony McNally,