ECMWF Report 2010-2011...Slide 1 ECMWF report - WGNE 2011 Slide 1, ©ECMWF ECMWF Report 2010-2011 Jean-Noël Thépaut ECMWF October 2011 and many colleagues from the Research Department

Slide 1

ECMWF report - WGNE 2011 Slide 1, ©ECMWF

ECMWF Report 2010-2011

Jean-Noël Thépaut ECMWF

October 2011

and many colleagues from the Research Department

Slide 2

Forecasting system upgrades 2010-11  Cycle 36R4 – 11 November 2010

  5-species prognostic cloud scheme

  SEKF soil moisture and OI snow analysis   Convection entrainment, all-sky assimilation refinement, 2 outer loops in

early-delivery 4D-Var, etc.

 Cycle 37R2 – 18 May 2011   Reduced AMSU-A radiance observation errors

  Use of EDA variances in 4D-Var background error formulation

  Cloud scheme modification, GPSRO obs operator improvement, etc.

 Cycle 37R3 – November 2011   Aircraft temperature bias correction

  Combined UV-IR ozone observation assimilation   Cloud scheme & surface roughness modification, assimilation of Stage-IV,

stratospheric model error cycling , convection detrainment, etc.

 Outlook ECMWF report - WGNE 2011 Slide 2, ©ECMWF

November 2010 IFS cycle 36r4

  Selected contents"

•  Prognostic rain and snow with more comprehensive cloud microphysics!

•  EKF for soil moisture analysis!

•  New snow analysis (O-I)!

•  Enhancement of all-sky radiance assimilation!

Slide 4

New prognostic cloud microphysics scheme

WATER VAPOUR

CLOUD Liquid/Ice

PRECIP Rain/Snow

Evaporation CLOUD FRACTION CLOUD

FRACTION

Old Cloud Scheme New Cloud Scheme

•  2 prognostic cloud variables + w.v. •  Ice/water diagnostic Fn(T)

•  Diagnostic precipitation

•  5 prognostic cloud variables + water vapour •  Ice and water now independent

•  More physically based, greater realism •  Significant change to degrees of freedom

•  Change to water cycle balances in the model •  More than double the lines of “cloud” code!

Slide 4, ©ECMWF ECMWF report - WGNE 2011

Slide 5

New prognostic cloud microphysics Representation of mixed phase

•  One of the most significant changes in the new scheme is the physical representation of the mixed phase.

•  Old scheme: Single prognostic condensate and diagnostic fn(T) split between ice and liquid cloud

•  New scheme: Prognostic liquid and prognostic ice and any fraction of supercooled liquid water allowed for a given T (determined by sources+sinks)

PDF of liquid water fraction of cloud for

the diagnostic mixed phase scheme

(dashed line) and the prognostic ice/

liquid scheme (shading)


Slide 6

Tem

pera

ture

Ice Water Content (g m-3)

-80

-60

-40

-20

0 106 105 104 103 102 101 100

-80

-60

-40

-20

0

-80

-60

-40

-20

0 106 105 104 103 102 101 100 106 105 104 103 102 101 100

Ice Water Content (g m-3) Ice Water Content (g m-3)

CloudSat/CALIPSO observations

ECMWF old scheme without snow

ECMWF new scheme with snow

New scheme with prognostic ice and snow allows much higher ice water contents (seen by the radiation scheme)

Relative frequency of occurrence of ice/snow for NH mid-latitudes in June 2006: ECMWF model vs. Cloudsat/Calipso retrievals

CY36R4 highlights: New cloud scheme


Slide 7

Ice water content zonal cross-sections (1 year average)

•  Previous scheme had unrealistic peaks of ice water content in 0 to -23°C temperature range.

•  New scheme reduces cloud ice water content in closer agreement with CloudSat derived estimate.

•  Careful comparison required at lower altitudes due to limitations of CloudSat observations.

Previous scheme

New scheme

mg m-3

CloudSat (non-convective, non-precipitating derived ice water)


Slide 8

Mod

el le

vel Cy37r3 super-cooled liquid (red) and ice (green)

52°S 63°S

Cy37r2 super-cooled liquid (red) and ice (green)

Mod

el le

vel

Southern Ocean SLW layer from CALIPSO

Ice

SLW

50S 60S

(4) Super-cooled liquid water Super-cooled liquid water

•  Super-cooled liquid water (SLW) cloud frequently occurs in atmosphere down to -30°C and below (as seen in aircraft obs, lidar etc.)

•  Fine balance between turbulent production of water droplets, nucleation of ice, deposition growth and fallout.

•  New cloud scheme represents microphysical processes in mixed-phase cloud rather than a diagnostic.

•  Cy36r4/Cy37r2 had less SLW, Cy37r3 increases SLW, particularly at cloud top (as often observed).

SLW layer (blue)


Slide 9

Land surface data assimilation evolution

1999 2004 2009 2010/2011

Optimum Interpolation (OI)

screen level analysis

Douville et al. (2000)‏ Mahfouf et al. (2000) ‏

Soil moisture analysis based on Temperature and relative

humidity analysis

Revised snow analysis

Drusch et al. (2004)‏ Cressman snow depth

analysis using SYNOP data improved by using NOAA / NSEDIS Snow cover extend

data

Recent developments/implementations: •  SEKF (Simplified Extended Kalman Filter) surface analysis •  Use of active microwave data: ASCAT soil moisture product •  Use of passive microwave SMOS Brightness Temperature product •  New snow analysis and use of NOAA/NESDIS 4km snow cover product

Structure Surface Analysis

OI snow analysis and high resolution NESDIS data (4km)

SEKF Soil Moisture analysis Simplified Extended Kalman Filter

METOP-ASCAT SMOS


Slide 10

ECMWF report - WGNE 2011

CY36R4 highlights: SEKF soil moisture analysis

  ECMWF land surface analysis:   Snow depth SYNOP

  Snow cover NESDIS (AVHRR, SSM/I, sta?ons)

  2 m T SYNOP

  2 m RH SYNOP

  Soil moisture SYNOP (T2m, RH2m) → ASCAT → SMOS → SMAP

  Soil temperature SYNOP (T2m)

Land and atmospheric analyses are performed separately indirect coupling

  OI: computa?onally cheap

  simple error covariance formula?on

  only based on screen-‐level observa?ons, fixed rela?onship

  EKF: computa?onally expensive (currently w/ finite differences for Jacobians)

  physically based, i.e. uses land surface model

  open for using more diverse observa?ons (at correct ?me)

  (SEKF: B is sta?c) Slide 10, ©ECMWF

Slide 11

EKF soil moisture analysis

-  Dynamical estimates of the Jacobian Matrix that quantify accurately the

physical relationship between observations and soil moisture

-  Flexible to account for the land surface model H-TESSEL evolution

-  Makes it possible to combine different sources of information

-  Possible to investigate the use of new generation of satellite data:

- Active microwave (C-Band MetOp/ASCAT) ‏

- Passive microwave (L-band SMOS, SMAP)‏

SYNOP ASCAT SMOS


Slide 12

Impact on 2-meter Temperature Forecasts

Global mean RMS (against SYNOP)

T2m 48h FC errors (OI-SEKF) EKF improves T2m

- EKF consistently improves SM & T2m - Makes it possible to assimilate satellite

data to analyse soil moisture Impr

oved

de

grad

ed


Slide 13

CY36R4 highlights: SEKF soil moisture analysis

36R4 – 11/2010

as 32R3 – 11/2007 as ERA-Interim

colder than ERA-I analysis warmer than ERA-I analysis

Mean annual 2-metre temperature errors from 13-month model integrations


Slide 14

A new snow analysis Snow analysis uses SYNOP snow depth data and

NOAA/NESDIS IMS snow cover

2010 implementation: -  New Snow analysis based on the Optimum

Interpolation with Brasnett 1999 structure functions

- A new IMS 4km snow cover product to replace the 24km product

- Improved QC (monitoring, Blacklisting)

2011: - Assimilate additional snow data From Sweden (New Report Type)


Slide 15

ECMWF report - WGNE 2011 Slide 15

Upgrades to all-sky assimilation at 36r4

 All-sky assimilation of microwave imager radiances over ocean (SSMIS, TMI, AMSRE):   Improves analysed water vapour, cloud and precipitation.

  Direct 4D-Var assimilation was implemented in March 2009 in cycle 35r2, replacing the previous 1D+4D-Var approach.

  Initially, large observation errors were applied, giving relatively weak observational control over oceanic water vapour.

 36r4 upgrade substantially increases weight of microwave-imager observations, due to:   Decreased observation errors, with errors now dependent on the

mean cloud amount in observations and model – a “symmetric error” approach.

  More observations getting through an improved quality control that can distinguish real information (e.g. missing cloud) from instrument problems.

Slide 16

ECMWF report - WGNE 2011 Slide 16

Forecast fits to assimilated AMSU-B: Standard deviation of FG departures

Upper-tropospheric humidity

Mid-tropospheric humidity

Lower-tropospheric humidity

36r4 36r2 No all-sky Experimental: even smaller obs errors

Slide 17

Normalized anomaly correlation difference - z500 CY36R4 vs CY36R2

SEEPS: impact of CY36R4


May 2011 IFS cycle 37R2


•  Increased weight to AMSU-A data!

•  Direct use of EDA in 4D-Var!

•  Retuning of new physics!

•  GRIB-2 for model level fields!

Slide 19

CY37R2 highlights: AMSU-A data

Comprehensive analysis of observation error covariances (spatial, spectral) for IR and MW sounders:

evaluate current data thinning, Different estimates of σo

•  produce covariances for 4D-Var.

Spatial error correlation AMSU-A ch. 9


Slide 20

CY37R2 highlights: EDA variances for 4D-‐Var

Analysis Xib(tk)

y+εio

Boundary pert. i & SPPT

Xia(tk) Forecast Xib(tk+1)

ε1m

InBalize EPS Produce σb for B in 4D-‐Var

10-‐member 4D-‐Var ensemble (T399 w/ T95/T159):

Std. dev. of vor?city ML64 (~ 500 hPa): old new


Similar strategy to that of Meteo-‐France (Raynaud, Berre and Desroziers, 2008)

Slide 21

Slide 21

CY37R2 highlights: EDA variances for 4D-‐Var

Tropical cyclone Aere

MSLP:

MSLP variances:

MSLP increments:

old New ECMWF report - WGNE 2011

Slide 22

EDA – flow dependent co-variances (based on 20 members EDA)

Background error correlaBon length scale and pmsl

km


Slide 23

Normalized anomaly correla?on difference -‐ z500 CY36R4 vs CY36R2 CY37R2 vs CY36R4


Slide 24

Overall performance: CY37R2-‐CY36R4

Europe

N. Hem.

Tropics

S. Hem.

Generally very posiBve – mostly range day 1-‐5 (AMSU-‐A, EDA)

IFS cycle 37R3 (November 2011)


  Bias correction of aircraft temperature observations

  Activation of stratospheric model error cycling (weak constraint 4DVAR)

  Improved ozone by activation of combined IR radiance and derived TCO analysis

  Entrainment/detrainment of convection

  Supersaturation and deposition rate for clouds   Active assimilation of accumulated rainfall from NEXRAD"

Slide 26

Aircraft temperature bias correction   Based on the variational bias correction scheme developed at ECMWF

  Each aircraft is bias corrected individually using a constant predictor

  First predictor: constant temperature correction. Second predictor: function of the vertical aircraft velocity (dp/dt) to account for ascend/descend bias conditions

aircraft R/S R/S


Slide 27


Slide 28


Slide 29

Mixed-Phase Cloud Motivation for recent work (input for CY37R3)

•  Cold temperature bias reported for IFS over Scandinavia in early January. Investigation showed correlation with regions of low-level mixed-phase cloud (cloud with super-cooled liquid water and ice)

•  Previous cloud scheme (36R3 and before) had liquid everywhere (by definition) between 0 and -23°C.

•  New cloud scheme (36R4 and 37R1/37R2) has similar liquid water, except in weakly forced situations (where sink due to deposition greater than source due to condensation).


Slide 30

CY36R4 CY37R3

T2m 60h f/

c error

TCLW 60h f/

c

CY37R3 highlights: Revised cloud scheme

Sodankyla

Slide 31

Ceilometer observations

Sodankyla/Finland:

<CY36R4 CY36R4 CY37R3

T2m 60h f/c error

clw/ciw 60h f/c

CY37R3 highlights: Revised cloud scheme

Slide 32

CY37R3 highlights: O3 analysis

•  Combined assimilaBon of ozone-‐sensiBve observaBons from UV (SBUV, SCIAMACHY, OMI) and IR (HIRS, AIRS, IASI):

→  more consistent ozone analysis, parBcularly in absence of daylight

AN – MLS (SBUV) AN – MLS (SBUV+IR)

(12/07-11/08/2011)

Slide 33

Normalized anomaly correlation difference - z500 CY36R4 vs CY36R2 CY37R2 vs CY36R4 CY37R3 vs CY37R2


Slide 34

Outlook - 2012  Cycle 38 – October 2011:

  Common cycle with Météo-France

  Seasonal Forecasting System 4 – November 2011   Higher resolution, updated model cycle, more members   New NEMO ocean model

  Larger hindcast data (15 members for 30 years)

 Cycle 38R1 – spring 2012:   Revised L91 background error covariance statistics

  Aliasing noise removal in spectral transforms

  Lagged 10+10 member EDA   EDA mean instead of control as reference for EPS initial perturbations

 Cycle 38R2 – autumn 2012:   Vertical resolution upgrade L91 → L137 for high-resolution forecast

model and DA-system. ECMWF report - WGNE 2011 Slide 34, ©ECMWF

Slide 35

S4 shows higher skill then S3 over Europe, a very difficult area, as indicated by 2mT reliability for m2-‐4 hindcasts for JJA (30 years, 1981-‐2010). Note that S3 has 11*T159L40 members, and S4 has 15*T255L91 members.

New seasonal system-4 (S4, Nov 2011): reliability over EU

S3 S4

Slide 36

Vertical resolution increase from 91 to 137 levels (1)

•  Still a lot of work to do, in particular background error covariances generation •  Implementation: Early 2012 Slide 36, ©ECMWF ECMWF report - WGNE 2011

Slide 37

Outlook – continued  More hybrid EDA/long-window weak constraint 4D-Var

  Require fundamental algorithmic changes

 Seamless EDA/EPS-monthly  Enhanced cloud analysis  Numerical experimentation into the “grey zone”

  Fast Legendre transforms at T3999

  Physics-dynamics coupling for NH

 Increased horizontal resolution   T2047 by 2015

 IFS maintenance and optimisation   Continuous Observation Preprocessing

 Object Oriented Prediction System   Modularity, flexibility for new algorithmic developments


Slide 38

Long-window, weak-constraint 4D-Var Results based on a two-layer quasi-geostrophic model indicates that increasing the length of the analysis window is beneficial, despite the lack of realism in the model error representation.

Slide 39

Cloud assimilaBon increment (500hPa) from a single cloud

observaBon (red circle) in a very dry area. NoBce the asymmetry of the increments caused by the flow-‐dependent background error

variances.

Cloud assimilaBon increment (500hPa) from a single cloud

observaBon (red circle) in a nearly saturated area (Colour=rh_bg,

black=qc_incrE6, white=q_incrE6). The q increments come from a balance relaBonship with qc.

Cloud analysis: Add new control variable: cloud condensate


Slide 40

Reanalysis Highlights   ERA-interim has been

extended back to 1979

  ERA-CLIM has been kicked-off in January 2011 (3 year project funded under EU FP-7)


Slide 41

Work on “old observations” in ERA-CLIM


Feedback from Marco Matricardi, explaining why the new coefficients would be so much befer

“there are three main differences to the 2001 SSU coefficient files:

1) The new coefficients have been computed using accurate line-‐by-‐line computaBons and the most recent spectroscopic data available at the Bme. 2)A more realisBc value of the cell pressure (which is now plahorm dependent). 3)The CO2 profile can be varied to reflect actual concentraBons (although I do not know how you have addressed this in your computaBons).

It is difficult to disentangle all contribuBons. What I can say is that the transmikances used to derive in the 2001 coefficient files were based on a 20 years old spectroscopy and were performed using a parameterized model rather that accurate line-‐by-‐line computaBons. I expect this to have a rather large impact on the computaBons (more than the contribuBons from 2) and 3). “

Impact of more recent RTTOV on obs-‐guess departures within ERA-‐interim : SSU channel 1

Slide 42

ERA-CLIM pilot reanalyses

What Period Resolution Ens When Vol

ERA-Int Interim reanalysis 1989-NRT T255L60 1 ongoing 33 Tb

ERA-P0 AMIP ensemble 1900-2011 T159L91 10 Jun 2011 (9M)

ERA-P1 EDA using sfc obs only 1900-2011 T159L91 10 Sep 2011 (15M) 655 Tb

ERA-S1 Land surface using ERA-P1 1900-2011 T799 1 Sep 2012 (9M) 77 Tb

ERA-P2 Reanalysis using all obs 2 early decades T511L91 1 Sep 2012 (9M) 180 Tb

ERA-E2 As ERA-P2 but with

SST/sea-ice perturbations 2 early decades T159L91 10 Jan 2013 (9M) 180 Tb

ERA-P3 To replace ERA-Interim 1979-NRT T511L91 1 Jan 2012 (24M+) 234 Tb

ERA-20C 20th-century reanalysis 1900-NRT T511L91 1 2014 (36M+) 1062 Tb

ER

A-C

LIM

ECMWF report - WGNE 2011

Slide 42, ©ECMWF

Slide 43

Thank You


Documents

ECMWF Report 2010-2011...Slide 1 ECMWF report - WGNE 2011 Slide 1, ©ECMWF ECMWF Report 2010-2011 Jean-Noël Thépaut ECMWF October 2011 and many colleagues from the Research Department