Francesca Marcucci and Lucio Torrisi

CNMCA, National Meteorological Center,Italy

KENDA MINI-WORKSHOP Feb 28, 2014

LETKF on an EU/Mediterranean scale

Outline

Overview of last changes in operational suite:

dynamical land emissivity retrieval for AMSUA

pseudo-RH variable (J. Liu,2007)

reduced horizontal length for humidity obs

Experimental suites of the CNMCA-LETKF system: MHS observation assimilation

self evolving additive noise

7 km40 v.l.

2.8 km65 v.l.

- compressible equations- parameterized convection

- compressible equations- explicit convection

CNMCA NWP SYSTEM since 1 June 11CNMCA NWP SYSTEM since 1 June 11LETKF analysis ensemble (40+1 members) every 6h using RAOB (also 4D), PILOT, SYNOP, SHIP, BUOY, Wind Profilers, AMDAR-ACAR-AIREP, MSG3-MET7 AMV, MetopA-B/Oceansat2 scatt. winds, NOAA/MetopA-B AMSUA radiances+ Land SAF snow mask, IFS SST analysis once a day

Local Area Modelling: COSMO

Ensemble Data Assimilation:

COSMO-ME (7km) ITALIAN MET SERVICE

10 km45 v.l.

Control StateAnalysis

COSMO-ME EPS(pre-operational)

LETKFAnalysis

Changes in CNMCA LETKF systemChanges in CNMCA LETKF system

Recent changes in the operational suite are: COSMO model (tuning and adaptation)Space and time displacement in radiosoundings (only BUFR messages)Humidity bias correction for Vaisala RS AMSU-A radiances over sea and land Additive noise from IFS forecasts instead from model climatology

Dynamical land emissivity retrieval for AMSUAPseudo-RH variableReduced horizontal length for humidity obsHumidity bound check

Dynamical land emissivity retrievalDynamical land emissivity retrieval

The method proposed in Karbou et al. (2005) is used to improve the specification of land surface emissivity The emissivity is dynamically retrieved from suitable window channels, using background information to estimate the required terms in the radiative transfer equation. The retrieved emissivity is then applied for the forward calculations for the sounding channels The method is applied to AMSU-A and MHS data: AMSU-A channel 3 and MHS channel 1 are used to estimate the emissivity for the other sounding channels

BIAS STDV SPREAD

AMSU-A OBSERVATION INCREMENT STATISTICS

•A reduction of AMSU-A temperature bias is observed if the dynamical land A reduction of AMSU-A temperature bias is observed if the dynamical land emissivity retrieval is appliedemissivity retrieval is applied• No significant impact on standard deviation No significant impact on standard deviation

Period:Period:16-09-2012 16-09-2012

to to 05-10-2012 ) 05-10-2012 )

Dynamical land emissivity retrievalDynamical land emissivity retrieval

•The impact is evaluated through the relative difference (%) in RMSE computed against IFS analysis with respect to the configuration without dynamical emissivity retr. (MHS+AMSU-A assimilation) for 00 UTC COSMO runs from 16-09-2012 to 05-10-2012 • A clear positive impact is observed at day 2

Dynamical land emissivity retrievalDynamical land emissivity retrieval

+36h +48h +36h +48h +36h +48h

TEMPERATURE WIND VECTORTEMPERATURE REL. HUMIDITY

PSEUDO-RH VARIABLE

Two options were evaluated:1- LIU pseudo-RH normalization qv

i / qv_satbg

specific humidity normalized by the mean bg saturation specific humidity 2- pseudo-RH variable qv

i / qv_satbg

i

change of variable (from specific humidity to pseudo-RH) using the bg saturation specific humidity for each i-member

“Among the other choices of humidity variable types, the best result is from pseudo-RH assimilation. The error distribution of pseudo-RH is more Gaussian than specific humidity observations. It has similar error distribution as the relative humidity observations, but unlike relative humidity observations, it has no error correlation with the other observation variables” (J. Liu, 2007, PhD thesis )

Changes in CNMCA LETKF systemChanges in CNMCA LETKF system

Relative difference (%) in RMSEcomputed against IFS analysisfor 00 UTC COSMO runs from16-09-2012 to 05-10-2012negative value = positive impact

PSEUDO-RH VARIABLE

+12h +24h +36h +48h+12h +24h +36h +48h

HORIZONTAL localization lengths of humidity observations

The use of different localization lengths for humidity obervations have been evaluated:

1- HUM loc. Length = 0.5 TUV loc. length

2-adapted from humidity correlation length

Changes in CNMCA LETKF systemChanges in CNMCA LETKF system

Relative difference (%) in RMSE computed against IFS analysis for 00 UTC COSMO runs from 16-09-2012 to 05-10-2012 negative value = positive impact

A positive impact is observed at day 2 changing the humidity-obs localization

+36h +48h+36h +48h

HUM obs horizontal localization lenghts

1- HUM loc. length = 0.5 TUV loc. length 2- NEW

MAXIMUM-BASED MAXIMUM-BASED METHODMETHOD

MHS are treated as MHS are treated as “single-level” obs“single-level” obs

Assign radiance to the Assign radiance to the pressure level obtained pressure level obtained by a weighted average by a weighted average using the normalized using the normalized weighting function weighting function (WF) larger than 0.8 (WF) larger than 0.8

Radiances Radiances from from RTTOVRTTOV

ny

Mean FG Mean FG time = ntime = n

Bias CorrectionBias CorrectionQuality ControlQuality Control

Observation Observation IncrementsIncrements

a a an n nx x X= +

Mean FG Mean FG Radiances from Radiances from RTTOV RTTOV

Observation Observation Increments withIncrements withMean FG Mean FG

LETKF LETKF Analysis Analysis time = ntime = n

Radiances Radiances

FG FG Ensemble Ensemble

bnx

ANALYSIS ANALYSIS Ensemble Ensemble

Obs from NOAA16-18-19 and Obs from NOAA16-18-19 and MetOpAMetOpA RTTOV v 10.2RTTOV v 10.2 Off Line Dynamic Bias Off Line Dynamic Bias CorrectionCorrection Obs Error 2 °K Obs Error 2 °K (no FOV 1-8 and 83-90)(no FOV 1-8 and 83-90) Horizontal thinning 120 kmHorizontal thinning 120 km Channels 3-4Channels 3-4 Rain check on CH 2:Rain check on CH 2: 5 °K 5 °K Discarded over high Discarded over high orographyorography

Use Ensemble Mean as Use Ensemble Mean as reference for BC and QCreference for BC and QC

)x(H bn)xH( b

n

)H(xbn

MHS rad. assimilation

Weighting function (transmittance vert. derivative)

Experimental suites

Relative difference (%) in RMSEcomputed against IFS analysisfor 00 UTC COSMO runs from16-09-2012 to 05-10-2012negative value = positive impact

MHS rad. assimilation

MHS+AMSU

AMSU

Self-Evolving Additive NoiseSelf-Evolving Additive Noise• A new additive inflaction formulation is needed, because IFS

additive noise is not consistent with COSMO model errors statistics. • The self-evolving additive inflaction (idea of Mats Hamrud –

ECMWF) was chosen. The idea is different from the evolved additive noise of Hamill and Whitaker (2010)

• Difference between ensemble forecasts valid at the analysis time is calculated. The mean difference is subtracted to yield a set of perturbations that are scaled and used as additive noise. The ensemble forecasts are obtained by the same ensemble DA system extending the end of the model integration.

• The self-evolving additive perturbations are both consistent with

model errors statistics and a flow-dependent noise • The error introduced during the first hours may have a component

that will project onto the growing forecast structures having probably a benificial impact on spread growth and ensemble-mean error

Experimental suites

IFS ADD SELF EV. ADD

Self-Evolving Additive NoiseSelf-Evolving Additive Noise

Relative difference (%) in RMSE,computed against IFS analysis, with respect to NO-ADDITIVE runfor 00 UTC COSMO runs from16-09-2012 to 05-10-2012negative value = positive impact

+12h +24h +36h +48h+12h +24h +36h +48h

• Self-evolving additive inflaction / Stochastics physics• COSMO-ME Short-Range EPS based on LETKF is experimentally running

• ATMS radiances and GPS zenith total delays assimilation tests• Improved drifting radiosounding thinning • Use of KENDA• Shorter assimilation window

Current and future developments

Thanks for your attention!