SREPS Priority Project: final report C. Marsigli, A. Montani, T. Paccagnella ARPA-SIMC - HydroMeteorological Service of Emilia- Romagna, Bologna, Italy F. Gofa, P. Louka HNMS – Hellenic National Meteorological Service, Athens, Greece

Last FTEs of Chiara were used for this TASK

ROMEO

Outline  COSMO-SREPS methodology  system set-up  analysis of the results on MAP D-PHASE DOP (JJA 2007+SON 2007)  role of different kind of perturbations  error vs spread  boundaries from mm vs different physics  ranking of different driving models and of different physics  conclusions

 analysis of the results on MAP D-PHASE DOP (JJA 2007+SON 2007)  role of different kind of perturbations  error vs spread  boundaries from mm vs different physics  ranking of different driving models and of different physics  conclusions

System set-up 16 COSMO runs 10 km hor. res. 40 vertical levels COSMO at 25 km on IFS IFS – ECMWF global by INM Spain COSMO at 25 km on GME GME – DWD global COSMO at 25 km on UM UM – UKMO global COSMO at 25 km on GFS GFS – NCEP global P1: control (ope) P2: conv. scheme (KF) P3: tur_len=1000 P4: pat_len= UTC JJA=53 SON=54

DOP SON=54 runs JJA=53 runs COSMO observations

intra-group distance Z500 COSMO analysis JJA daysSON days Same driving model Different model parameters Same model parameters Different driving model  role of different kind of perturbations

intra-group distance JJA days z500 COSMO analysis COSMO-SREPS Different driving models play the major role

intra-group distance SON days z500 COSMO analysis COSMO-SREPS

intra-group distance JJA days t850 COSMO analysis COSMO-SREPS

intra-group distance SON days t850 COSMO I7 analysis COSMO-SREPS

intra-group distance 2mT Northern Italy JJA daysSON days

SYNOP over D-PHASE area - Nearest grid point JJA07SON07 2m T - relationship between error and spread COSMO I7 analysis  error vs spread Underdispersive

Synop stations on the Alpine area 218 stations

spread/skill relationship 2mT Alpine area (synop stations) +12h+24h+36h +48h+60h+72h

spread/skill relationship 2mT Alpine area (synop stations) +12h+24h+36h +48h+60h+72h Underdispersive

t850 relationship between EM error and spread COSMO-I7 interpolated on SYNOP stations over the Alpine area JJA07

t850 relationship between error and spread COSMO-I7 interpolated on SYNOP stations over the Alpine area SON07

spread/skill relationship t850 Alpine area (COSMO analyses) +12h+24h+36h +48h+60h+72h

spread/skill relationship t850 Alpine area (COSMO analyses) +12h+24h+36h +48h+60h+72h

Z500 relationship between error and spread COSMO-I7 interpolated on Northern Italy stations and SYNOP stations over the Alpine area JJA07

Z500 relationship between error and spread COSMO-I7 interpolated on SYNOP stations over the Alpine area SON07

TP 24h – ave 0.5x0.5 JJA07 +30h noss IT + CH +54h Same driving models Same perturbation Big impact of multi model BCs!!!!!!! Looking at the right column it is evident that even with few members the skill does not decrease too much when the driving models are different. Same perturbation

TP 24h – ave 0.5x0.5 noss SON07 +30h IT + CH +54h Same driving model Same perturbation

JJA07SON07 pert father +30hIT + CH

JJA07 +30h ITIT + CH Same driving model Same perturbation

2m temperature - BIAS Synop observations over Alpine area Nearest grid point – lsm + altitude correction JJA07 ecmwf gme ncep ukmo p4

2m temperature - MAE Synop observations over Alpine area Nearest grid point – lsm + altitude correction JJA07 ecmwf gme ncep ukmo p4

2m temperature - BIAS Synop observations over Alpine area Nearest grid point – lsm + altitude correction SON07 ecmwf gme ncep ukmo p4

2m temperature - MAE Synop observations over Alpine area Nearest grid point – lsm + altitude correction SON07 ecmwf gme ncep ukmo p4

Deterministic scores – ave 0.5 x 0.5 IT 1mm/24h5mm/24h10mm/24h father

Deterministic scores – ave 0.5 x 0.5 IT 1mm/24h5mm/24h10mm/24h father

Deterministic scores – ave 0.5 x 0.5 IT pert 1mm/24h5mm/24h10mm/24h

Deterministic scores – ave 0.5 x 0.5 IT 1mm/24h5mm/24h10mm/24h pert

Test of more parameter perturbations (same father) 16 LM runs at 10 km P1: control (ope) P2: conv. scheme (KF) P3: parameter 1 P4: parameter 2 P5: … IFS – ECMWF global SON 07

T2m deterministic scores – npo IT ctrl KF tur_len=150 tur_len=1000 pat_len=10000 rat_sea=1 rat_sea=60 qc0=0.001 crsmin=50 crsmin=200 c_soil=0 c_soil=2 c_lnd=1 c_lnd=10 rlam_heat=0.1 rlam_heat=10

T2m deterministic scores – npo IT ctrl KF tur_len=150 tur_len=1000 pat_len=10000 rat_sea=1 rat_sea=60 qc0=0.001 crsmin=50 crsmin=200 c_soil=0 c_soil=2 c_lnd=1 c_lnd=10 rlam_heat=0.1 rlam_heat=10

Preliminary Conclusions  Perturbations -Multi Model ICs/BCs & Perturbations on Ph. Params:  the use of different driving models seems to dominate with respect to physics parameter perturbations as regards the contribution to the spread; these contributions are different in the two seasons (2mT)  the selected parameters produce a detectable spread among members with the same father (driving model)  spread-skill relationship : a correlation between error and spread exists, but the system is under-dispersive -> a better representation of model error is needed  the different driving models contribute differently to the ensemble skill, but there is a strong dependence on forecast range, season, verification area  the different perturbations can contribute differently to the ensemble skill as well

On-going activities and future plans  continue the analysis over the DOP MAP D-PHASE :  statistical analysis of the system  comparison with the other available mesoscale ensemble systems  verification carried out by HNMS  introduce the new parameter perturbations  analyse the impact of adding soil perturbations

run nr.parameter nameparameter descriptionrangedefaultused 1 ctrl ope 2 lconv convection scheme T or KFTKF 3 tur_len maximal turbulent length scale [100,1000] m tur_len maximal turbulent length scale [100,1000] m pat_len length scale of thermal surface patterns [0,10000] m rat_sea ratio of laminar scaling factors for heat over [1,100]201 7 rat_sea ratio of laminar scaling factors for heat over [1,100] qc0 cloud water threshold for autoconversion [0.,0.001] c_lnd surface area density of the roughness elements over land [1,10]21 14 c_lnd surface area density of the roughness elements over land [1,10] rlam_heat scaling factor of the laminar layer depth [0.1,10] rlam_heat scaling factor of the laminar layer depth [0.1,10]110 crsmin c_soil Minimal stomata resistance Minimal stomata resistance Surface area index of the evaporating soil [50,200] s/m ]0,c_lnd[

COSMO-SREPS methodology  i.c. and b.c. perturbations -> INM multi-model multi- boundary ensemble (SREPS)  LAM perturbations -> physics parameter perturbations LAM perturbations - smaller scale errors driving model perturbations (ics and bcs) - larger scale errors

System set-up 16 COSMO runs 10 km hor. res. 40 vertical levels COSMO at 25 km on IFS IFS – ECMWF global by INM Spain COSMO at 25 km on GME GME – DWD global COSMO at 25 km on UM UM – UKMO global COSMO at 25 km on GFS GFS – NCEP global P1: control (ope) P2: conv. scheme (KF) P3: tur_len=1000 P4: pat_len=10000

MAP D-PHASE DOP testing period  COSMO-SREPS was running during the DOP, at 00 UTC  107 runs out of 183 days, 53 in JJA and 54 in SON  ensemble verification

JJA07

intra-group distance JJA days 2mT Northern Italy COSMO-SREPS Same driving model Different model parameters Same model parameters Different driving model

intra-group distance JJA 2007 – 50 days tp6h Northern Italy Same driving model Different model parameters Same model parameters Different driving model

Mid Term comments Mid-upper troposphere: MULTI MODEL IC/BCs give the bigger contribution to the spread Surface/lower troposphere: model physics perturbations “gain ground”.

score evaluation +30h: ROC UKMO and GME the best, then ECMWF and NCEP P2 (KF) the best, then P4, P3, P1 (similar) BSS UKMO the best, ECMWF and GME the worst; NCEP improves with threshold P are similar, P2 (KF) slightly better JJA07 tp24 IT

score evaluation +54 : ROC similar for low thresholds, NCEP the best for high thresholds, then ECMWF P similar, P3 slightly better BSS ECMWF the best, GME the worst; NCEP improves with threshold P2 (KF) the worst, P3 the best but similar to P1 and P4 JJA07 tp24 IT

EC+30ROC BSS +54ROC BSS MO+30ROC BSS +54ROC BSS GME+30ROC BSS +54ROC BSS AVN+30ROC BSS +54ROC BSS

EC+30ROC****** * BSS +54ROC BSS MO+30ROC****** BSS +54ROC BSS GME+30ROC*************** BSS +54ROC BSS AVN+30ROC******** BSS +54ROC BSS

TP 24h – ave 0.5x0.5 JJA07 +30h noss IT +54h father pert

TP 24h – ave 0.5x0.5 JJA07 +30h IT +54h father pert noss Grosso impatto del MULTI MODEL ai boundaries!!!!!!! Looking at the right column it is evident that even with few members the skill does not decrease too much when the driving models are different.

score evaluation +30h: ROC UKMO > GME > ECMWF > NCEP P2 (KF) the best, the others are similar BSS NCEP better for high threshold; UKMO > ECMWF > GME P2 (KF) the worst, the others are similar JJA07 tp24 IT+CH

score evaluation +54 : ROC similar for low thresholds, NCEP and GME best for high thresholds P similar; P2 (KF) slightly better for high thresholds BSS ECMWF the best, NCEP the worst P similar; P2 (KF) slightly worse JJA07 tp24 IT+CH

TP 24h – ave 0.5x0.5 JJA07 +30h noss IT + CH +54h father pert

TP 24h – ave 0.5x0.5 JJA07 +30h noss IT + CH +54h father pert The performances reverse eith the leading time. Bad skill adding switzerlad.

Deterministic scores – ave 0.5 x 0.5 IT+CH 1mm/24h5mm/24h10mm/24h father

Deterministic scores – ave 0.5 x 0.5 IT+CH 1mm/24h5mm/24h10mm/24h father

Deterministic scores – ave 0.5 x 0.5 IT+CH 1mm/24h5mm/24h10mm/24h pert

Deterministic scores – ave 0.5 x 0.5 IT+CH 1mm/24h5mm/24h10mm/24h pert

Deterministic scores – ave 0.5 x 0.5 IT 1mm/24h5mm/24h10mm/24h father noss

Deterministic scores – ave 0.5 x 0.5 IT 1mm/24h5mm/24h10mm/24h father noss

Deterministic scores – ave 0.5 x 0.5 IT pert 1mm/24h5mm/24h10mm/24h noss

Deterministic scores – ave 0.5 x 0.5 IT 1mm/24h5mm/24h10mm/24h pert noss

6h accumulated precipitation relationship between error and spread obs Northern Italy L1

Relationship between error and spread Northern Italy observations Nearest grid point applicata correzione per la quota LAPSE=0.7 eliminati dati minori di –10 e maggiori di +42 t2m

Relationship between error and spread SYNOP over MAP area Nearest grid point t2m applicata correzione per la quota LAPSE=0.7 all (16 members)

Relationship between error and spread SYNOP over MAP area Nearest grid point t2m applicata correzione per la quota LAPSE=0.7 GME only (4 members)

Relationship between error and spread SYNOP over MAP area Nearest grid point t2m applicata correzione per la quota LAPSE=0.7 ECMWF only (4 members)

Relationship between error and spread SYNOP over MAP area Nearest grid point t2m applicata correzione per la quota LAPSE=0.7 NCEP only (4 members)

Relationship between error and spread SYNOP over MAP area Nearest grid point t2m applicata correzione per la quota LAPSE=0.7 UKMO only (4 members)

Relationship between error and spread SYNOP over MAP area Nearest grid point t2m applicata correzione per la quota LAPSE=0.7

2m temperature relationship between error and spread COSMO-I7 interpolated on Northern Italy stations and SYNOP stations over the Alpine area

SON07

6h precipitation - BSS Northern Italy observations Average over 0.5 x 0.5 deg boxes

6h precipitation – ROC area Northern Italy observations Average over 0.5 x 0.5 deg boxes

Daily precipitation - BSS Northern Italy + Switzerland observations Average over 0.5 x 0.5 deg boxes

Daily precipitation reliability and resolution Northern Italy + Switzerland observations Average over 0.5 x 0.5 deg boxes

Daily precipitation - ROC area Northern Italy + Switzerland observations Average over 0.5 x 0.5 deg boxes

Relationship between error and spread Northern Italy observations Nearest grid point eliminati dati minori di –10 e maggiori di +42 tp 6h

Relationship between error and spread Northern Italy observations Nearest grid point t2m applicata correzione per la quota LAPSE=0.7 eliminati dati minori di –10 e maggiori di +42

Relationship between error and spread SYNOP over MAP area Nearest grid point t2m applicata correzione per la quota LAPSE=0.7

2m temperature relationship between error and spread COSMO-I7 interpolated on SYNOP stations over the Alpine area

intra-group distance SON days 2mT Northern Italy COSMO-SREPS Same driving model Different model parameters Same model parameters Different driving model

intra-group distance SON days tp6h Northern Italy COSMO-SREPS

score evaluation +30h: ROC crossing; UKMO and ECMWF slightly better, GME worse P2 (KF) the best, the others are similar BSS ECMWF the best, GME the worst P are similar, P2 (KF) slightly better SON07 tp24 IT

score evaluation +54 : ROC NCEP the best, GME the worst P2 (KF) the best, the others are similar BSS similar SON07 tp24 IT

TP 24h – ave 0.5x0.5 SON07 +30h IT +54h father pert noss

TP 24h – ave 0.5x0.5 SON07 +30h IT +54h father pert noss

score evaluation +30h: ROC similar; for high thresholds ECMWF is the best and GME the worst similar; P2 (KF) slightly better BSS ECMWF the best and GME the worst,, especially with increasing threshold P are similar, P4 (tur_len=1000) better for the last thresholds SON07 tp24 IT+CH

score evaluation +54 : ROC NCEP the best, GME the worst similar, P2 (KF) slightly better BSS ECMWF the best, UKMO the worst similar SON07 tp24 IT+CH

TP 24h – ave 0.5x0.5 SON07 +30h IT + CH +54h father pert noss

TP 24h – ave 0.5x0.5 SON07 +30h IT + CH +54h father pert noss

Deterministic scores – ave 0.5 x 0.5 IT 1mm/24h5mm/24h 10mm/24h father

Deterministic scores – ave 0.5 x 0.5 IT 1mm/24h5mm/24h10mm/24h father

Deterministic scores – ave 0.5 x 0.5 IT pert 1mm/24h5mm/24h10mm/24h

Deterministic scores – ave 0.5 x 0.5 IT 1mm/24h5mm/24h10mm/24h pert

Deterministic scores – ave 0.5 x 0.5 IT+CH father 1mm/24h5mm/24h 10mm/24h

Deterministic scores – ave 0.5 x 0.5 IT+CH 1mm/24h5mm/24h10mm/24h father

Deterministic scores – ave 0.5 x 0.5 IT+CH pert 1mm/24h5mm/24h 10mm/24h

Deterministic scores – ave 0.5 x 0.5 IT+CH 1mm/24h5mm/24h10mm/24h pert

Deterministic scores – ave 0.5 x 0.5 IT 1mm/24h5mm/24h10mm/24h father noss

Deterministic scores – ave 0.5 x 0.5 IT 1mm/24h5mm/24h10mm/24h father noss

Deterministic scores – ave 0.5 x 0.5 IT pert 1mm/24h5mm/24h10mm/24h noss

Deterministic scores – ave 0.5 x 0.5 IT 1mm/24h5mm/24h10mm/24h pert noss

Test of more parameter perturbations (same father) 16 LM runs at 10 km P1: control (ope) P2: conv. scheme (KF) P3: parameter 1 P4: parameter 2 P5: … IFS – ECMWF global SON 07

run nr.parameter nameparameter descriptionrangedefaultused 1 ctrl ope 2 lconv convection scheme T or KFTKF 3 tur_len maximal turbulent length scale [100,1000] m tur_len maximal turbulent length scale [100,1000] m pat_len length scale of thermal surface patterns [0,10000] m rat_sea ratio of laminar scaling factors for heat over sea [1,100]201 7 rat_sea ratio of laminar scaling factors for heat over sea [1,100] qc0 cloud water threshold for autoconversion [0.,0.001] c_lnd surface area density of the roughness elements over land [1,10]21 14 c_lnd surface area density of the roughness elements over land [1,10] rlam_heat scaling factor of the laminar layer depth [0.1,10] rlam_heat scaling factor of the laminar layer depth [0.1,10]110 crsmin c_soil Minimal stomata resistance Minimal stomata resistance Surface area index of the evaporating soil [50,200] s/m ]0,c_lnd[

T2m deterministic scores – npo IT ctrl KF tur_len=150 tur_len=1000 pat_len=10000 rat_sea=1 rat_sea=60 qc0=0.001 crsmin=50 crsmin=200 c_soil=0 c_soil=2 c_lnd=1 c_lnd=10 rlam_heat=0.1 rlam_heat=10

T2m deterministic scores – npo IT ctrl KF tur_len=150 tur_len=1000 pat_len=10000 rat_sea=1 rat_sea=60 qc0=0.001 crsmin=50 crsmin=200 c_soil=0 c_soil=2 c_lnd=1 c_lnd=10 rlam_heat=0.1 rlam_heat=10

T2m deterministic scores – npo IT piamon

Td2m deterministic scores – npo IT ctrl KF tur_len=150 tur_len=1000 pat_len=10000 rat_sea=1 rat_sea=60 qc0=0.001 crsmin=50 crsmin=200 c_soil=0 c_soil=2 c_lnd=1 c_lnd=10 rlam_heat=0.1 rlam_heat=10 ctrl KF tur_len=150 tur_len=1000 pat_len=10000 rat_sea=1 rat_sea=60 qc0=0.001 crsmin=50 crsmin=200 c_soil=0 c_soil=2 c_lnd=1 c_lnd=10 rlam_heat=0.1 rlam_heat=10

Td2m deterministic scores – npo IT ctrl KF tur_len=150 tur_len=1000 pat_len=10000 rat_sea=1 rat_sea=60 qc0=0.001 crsmin=50 crsmin=200 c_soil=0 c_soil=2 c_lnd=1 c_lnd=10 rlam_heat=0.1 rlam_heat=10

Td2m deterministic scores – npo IT piamon

TP deterministic scores – ave 0.5 x 0.5 IT 1mm/6h ctrl KF tur_len=150 tur_len=1000 pat_len=10000 rat_sea=1 rat_sea=60 qc0=0.001 crsmin=50 crsmin=200 c_soil=0 c_soil=2 c_lnd=1 c_lnd=10 rlam_heat=0.1 rlam_heat=10 noss

TP deterministic scores – ave 0.5 x 0.5 IT 1mm/6h ctrl KF tur_len=150 tur_len=1000 pat_len=10000 rat_sea=1 rat_sea=60 qc0=0.001 crsmin=50 crsmin=200 c_soil=0 c_soil=2 c_lnd=1 c_lnd=10 rlam_heat=0.1 rlam_heat=10 noss

TP deterministic scores – ave 0.5 x 0.5 IT 1mm/6h ctrl KF tur_len=150 tur_len=1000 pat_len=10000 rat_sea=1 rat_sea=60 qc0=0.001 crsmin=50 crsmin=200 c_soil=0 c_soil=2 c_lnd=1 c_lnd=10 rlam_heat=0.1 rlam_heat=10 noss

TP deterministic scores – ave 0.5 x 0.5 IT 10mm/6h ctrl KF tur_len=150 tur_len=1000 pat_len=10000 rat_sea=1 rat_sea=60 qc0=0.001 crsmin=50 crsmin=200 c_soil=0 c_soil=2 c_lnd=1 c_lnd=10 rlam_heat=0.1 rlam_heat=10 noss

TP deterministic scores – ave 0.5 x 0.5 IT 10mm/6h ctrl KF tur_len=150 tur_len=1000 pat_len=10000 rat_sea=1 rat_sea=60 qc0=0.001 crsmin=50 crsmin=200 c_soil=0 c_soil=2 c_lnd=1 c_lnd=10 rlam_heat=0.1 rlam_heat=10 noss

TP deterministic scores – ave 0.5 x 0.5 IT 10mm/6h ctrl KF tur_len=150 tur_len=1000 pat_len=10000 rat_sea=1 rat_sea=60 qc0=0.001 crsmin=50 crsmin=200 c_soil=0 c_soil=2 c_lnd=1 c_lnd=10 rlam_heat=0.1 rlam_heat=10 noss

t BIAt MAEtd BIAtd MAEtp1 BStp1 TStp1 FAtp10 BStp10 TStp10 FA KF == >== <><>>><> tur_len=150 = <== >==<> <= <= tur_len=1000 = >= <===<> => <24= pat_len=10000 ><>= <= > >= <>> <24 24 rat_sea=1 = >= <>= >><> > = >24 rat_sea=60 = <= >< <<>= << = > qc=0.001 ====== <>=> <24 24 crsmin=50 = <== >= <= = >=== crsmin=200 ======= <=== c_soil=0 ><<><><= <=<> c_soil=2 <>>>>= <>==<> c_lnd=1 = <>>>====== c_lnd=10 ><<>= <= == rlam_heat=0.1 <>= >> ><>>= >== rlam_heat=10 <>= <<<>< <= <==

lconv=KFtur_len=150tur_len=1000 pat_len=10000rat_sea=1rat_sea=60qc0=0.001 crsmin=50crsmin=200c_soil=0c_soil=2 c_lnd=1c_lnd=10rlam_heat=0.1rlam_heat=10 ctrl CSPERT fc-ctrl differences +12h 04/05/ UTC mm/6h

CSPERT fc-obs differences +12h lconv=KFtur_len=150tur_len=1000 pat_len=10000rat_sea=1rat_sea=60qc0=0.001 crsmin=50crsmin=200c_soil=0c_soil=2 c_lnd=1c_lnd=10rlam_heat=0.1rlam_heat=10 ctrl mm/6h

CSPERT forecasts +12h UTC 04/05/07

Comparisons

SYNOP over MAP area - Nearest grid point JJA07SON07 2m T - relationship between error and spread COSMO-I7 analyses

intra-group distance 2mT Northern Italy JJA daysSON days

intra-group distance tp6h northern Italy JJA daysSON days

intra-group distance t850 COSMO analysis JJA daysSON days

intra-group distance Z500 COSMO analysis JJA daysSON days

JJA07SON07 pert father +30hIT + CH

JJA07SON07 pert father +30hIT

JJA07 pert father +30h ITIT + CH

SON07 pert father +30h ITIT + CH

score evaluation 2m t: BIA > 0 GME the largest (+), then UKMO, ECMWF, NCEP mixed P4 (tur_len=1000) the largest (+), for any father MAE GME the smallest, then UKMO, ECMWF, NCEP mixed P4 (tur_len=1000) the largest, especially for GME at night JJA07

score evaluation 2m t: BIA < 0 ECMWF the largest (-), then GME, NCEP and UKMO P4 (tur_len=1000) the more positive, for any father; P1, P2, P3 similar MAE ECMWF the largest, then GME, NCEP and UKMO P4 (tur_len=1000) the smallest; P1, P2, P3 similar SON07