CONSENS Priority Project Status report COSMO year 2008/2009
description
Transcript of CONSENS Priority Project Status report COSMO year 2008/2009
CONSENS Priority Project
Status report COSMO year 2008/2009
Involved scientists:Chiara Marsigli, Andrea Montani, Tiziana Paccagnella, Tommaso Diomede (ARPA-SIMC)
Flora Gofa, Petroula Louka (HNMS)Felix Fundel (MeteoSwiss)
Overview
Task 1: Running of the COSMO-SREPS suite suite maintenance implementation of the back-up suite
Task 2: Model perturbations perturbation of physics parameters perturbation of soil fields
Task 3: Ensemble mergingMulti-clustering
Task 4: Calibration
The COSMO-SREPS ensemble
COSMO-SREPS has been developed within the SREPS PP, aiming at the development of a Short-Range Ensemble Prediction System
3 days forecast range, 10 km of horizontal resolution
COSMO-SREPS provides boundary conditions for COSMO-DE-EPS, the 2.8 km ensemble system under development at DWD
Application: test of the use of COSMO-SREPS to estimate a flow-dependent B matrix in a 1D-Var DA of satellite data
COSMO-SREPS
COSMO at 25 km on IFSIFS – ECMWF global
by A
EMET
Sp
ain
COSMO at 25 km on GMEGME – DWD global
COSMO at 25 km on UMUM – UKMO global
COSMO at 25 km on GFSGFS – NCEP global
P1: control P2: physics pert p2P3: physics pert p3P4: physics pert p4…
•COSMO (v 4.7)
•00 UTC and12 UTC
•10 km
•40 levels
•16 members
•72 h
1. Running of the COSMO-SREPS suite
ARPA-SIMC Maintenance of the COSMO-SREPS suite at ECMWF
Adaptation of the data output for COSMO-DE-EPS
Implementation of a 12 UTC run (beside the 00 UTC one)
Implementation of the back-up suite: delayed (9 months)
The work involves also DWD, even if implicitly!
AEMET has provided the int2lm code adapted for the NCEP and UKMO models
An agreement with UKMO has been signed, in order to receive regularly the boundary conditions from the UM
Suite availability
COSMO-SREPS availability
0
10
20
30
40
50
60
70
80
90
100
giu-07
lug-07
ago-0
7se
t-07
ott-07
nov-0
7dic
-07
gen-0
8
feb-08
mar-08
apr-0
8
mag-08
giu-08
lug-08
ago-0
8se
t-08
ott-08
nov-0
8dic
-08
gen-0
9
feb-09
mar-09
apr-0
9
mag-09
giu-09
lug-09
ago-0
9
months
% o
f day
s
complete + incomplete
complete only
2.1 Model perturbations: parameters
CSPERT test suiteARPA-SIMC - HNMS In order to study new parameter perturbations, a test
suite (CSPERT) was already implemented at ECMWF, by ARPA-SIMC, during the SREPS PP. Results for SON 2007 can be found in the SREPS final report
According to the outcome of the SREPS PP, it was decided to analyse the impact of these perturbations on a dry season as well
New runs of the CSPERT suite were performed in autumn 2008, for the JJA 2008 period
Analysis of the results completed in May 2009
The CSPERT suite
16 LM runs at 10 km
P1: control (ope)P2: conv. scheme (KF)P3: parameter 1P4: parameter 2P5: …
IFS – ECMWF global
SON 07 + JJA 08
JJA 2008 – ITBIAS MAET2m
Td2m
|T BIAS| T RMSE |Td BIAS| Td RMSE |U BIAS| U RMSEKF = = ~ = = ~ = ~ =
tur_len=150 = = = > = = > (day)
= > (day)
tur_len=1000 ~ = = < = = =
pat_len=10000 < =(day)
< = (day)
> < (day)
> = (day) ~ = ~ =
rat_sea=1 > > > = > ~ = ~ =
rat_sea=60 < < > = > ~ = ~ =
crsmin=50 > > > > = > (day)
= > (day)
crsmin=200 < < = < = = =
c_lnd=1 = < (day)
> (day) > = = =
c_lnd=10 >< < > > (night) = < (day)
=
rlam_heat=0.1 > < (day)
= > (day)
> > = =
rlam_heat=10 < > (day)
= < (day)
> > > < ~ =
- -- +++=
Summary of the perturbation impact
Remarks from the CSPERT suite The effect of perturbing each physics parameter on
improving or worsening the statistical values of the results in comparison to the corresponding control was investigated
Based on these results, the next step was to explore the importance and the effect of selected physical perturbations further
It seems that the particular parameter perturbations do not influence greatly the mean horizontal wind apart from a few exceptions. Possibly looking at the vertical wind component would make the effects more apparent for some parameters
Remarks (cont)
Looking separately at each parameter perturbation compared to the control run:
scaling factors related to the laminar layer (rlam_heat, rat_sea), turbulent length scale (tur_len) and evapotranspiration (crsmin), all associated with the development of the turbulent surface layer, are the physical parameters on which the main focus is given
2.1 Model perturbations: parameters
the new COSMO-SREPS configurationARPA-SIMC - HNMS
On the basis of the analysis of these results, a new configuration of the COSMO-SREPS suite has been implemented in May 2009
An analysis of its performance over summer 2009 (JJA) has been carried out:in terms of 2m temperature only over the Alpine areaIn term of the continuous parameters (T, U and Td)
over GreecePrecipitation has not been considered up to now
mainly due to the summer season
member father itype_conv tur_len pat_len rlam_heat rat_sea crsmin1 ecmwf 0 150 500 1 20 1502 ecmwf 1 1000 500 1 20 1503 ecmwf 0 500 500 0.1 20 2004 ecmwf 1 500 10000 1 20 1505 gme 0 500 10000 1 20 1506 gme 1 500 500 0.1 20 1507 gme 0 500 500 1 1 2008 gme 1 500 500 1 1 1509 avn 0 1000 500 1 20 150
10 avn 1 150 500 1 20 15011 avn 0 500 500 10 20 15012 avn 1 500 500 10 20 15013 ukmo 0 500 500 1 60 15014 ukmo 1 500 500 1 60 15015 ukmo 0 500 500 1 20 5016 ukmo 1 500 500 1 20 50
COSMO-SREPS new configuration (from the 5th of May 2009)
convection scheme:0 Tiedtke 1 Kain-Fritsch
maximal turbulent length scale
length scale of thermal surface patterns
scaling factor of the laminar layer depth
ratio of laminar scaling factors for heat over sea
minimal stomata resistance
IFS
GME
NCEP
UM
Tiedtke
Tiedtke
Kain-Fritsch
Kain-Fritsch
tur_len <
tur_len >
rlam_heat <
crsmin >
pat_len >
pat_len >
rlam_heat < crsmin
>
rat_sea < rat_sea
<
tur_len >
tur_len <
rlam_heat >
rlam_heat >
rat_sea >
rat_sea >
crsmin <
crsmin <
Nearest grid point
Relationship between error and spread
JJA09Small sample, 30 days
only
SYNOP over the MAP D-PHASE domain
SYNOP over the whole domain
t2m
Relationship between error and spreadJJA09
SYNOP over the whole domain - Nearest grid point
00 UTC 12 UTCt2m
Nearest grid point
2m T – deterministic scoresglobal model
JJA09
SYNOP over the MAP D-PHASE domain
ecmwfgmencepukmo
MAE
BIAS
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
1.8
2.0
2.2
2.4
2.6
2.8
3.0
Nearest grid point
2m T – deterministic scoresconvection scheme
JJA09
SYNOP over the MAP D-PHASE domain
TiedtkeKain-Fritsch
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
1.8
2.0
2.2
2.4
2.6
2.8
3.0
MAE
BIAS
Nearest grid point
2m T – deterministic scorestur_len
JJA09
SYNOP over the MAP D-PHASE domain
tur_len=150 – ecmwf Ttur_len=1000 – ecmwf KFtur_len=1000 – ncep Ttur_len=150 – ncep KF
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
1.8
2.0
2.2
2.4
2.6
2.8
3.0
MAE
BIAS
tur_len: maximal turbulent length scale U10m: BIAS
-1.5
-0.5
0.5
1.5
6 12 18 24 30 36 42 48 54 60 66 72
m1 m2 m9 m10
U10m: RMSE
1.5
2
2.5
3
6 12 18 24 30 36 42 48 54 60 66 72
m1 m2 m9 m10
Nearest grid point
2m T – deterministic scorespat_len
JJA09
SYNOP over the MAP D-PHASE domain
pat_len=10000 – ecmwf KF
pat_len=10000 – gme T
MAE
BIAS
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
1.8
2.0
2.2
2.4
2.6
2.8
3.0
Nearest grid point
2m T – deterministic scoresrlam_heat
JJA09
SYNOP over the MAP D-PHASE domain
rlam_heat=0.1 crsmin=200 – ecmwf Trlam_heat=0.1 – gme KFrlam_heat=10 – ncep Trlam_heat=10 – ncep KF
MAE
BIAS
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
1.8
2.0
2.2
2.4
2.6
2.8
3.0
T2m: RMSE
1
2
3
4
5
6
7
8
9
6 12 18 24 30 36 42 48 54 60 66 72
m3 m6 m11 m12
T2m: BIAS
-2
-1
0
1
2
6 12 18 24 30 36 42 48 54 60 66 72
m3 m6 m11 m12
rlam_heat: scaling factor of the laminar layer depth
Td2m: BIAS
-7
-6
-5
-4
-3
-2
-1
0
1
6 12 18 24 30 36 42 48 54 60 66 72
m7 m8 m13 m14
Td2m: RMSE
1
2
3
4
5
6
7
8
9
0 6 12 18 24 30 36 42 48 54 60 66 72
m7 m8 m13 m14
rat_sea: ratio of laminar scaling factor for heat over sea
Nearest grid point
2m T – deterministic scorescrsmin
JJA09
SYNOP over the MAP D-PHASE domain
rlam_heat=1 crsmin=200 – ecmwf Trat_sea=1 crsmin=200 – gme Tcrsmin=50 – ukmo Tcrsmin=50 – ukmo KF
MAE
BIAS
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
1.8
2.0
2.2
2.4
2.6
2.8
3.0
crsmin: minimal stomata resistance Td2m: BIAS
-3
-2
-1
0
1
6 12 18 24 30 36 42 48 54 60 66 72
m3 m7 m15 m16
Td2m: RMSE
1
2
3
4
5
6
6 12 18 24 30 36 42 48 54 60 66 72
m3 m7 m15 m16
Remarks Some of the perturbations produce common effects on
both regions (e.g. rlam_heat, crsmin, tur_len) However, the impact of some of the physical
perturbations (e.g. rat_sea) depends on the geographical characteristics of the region
Large values of rlam_heat produce an increase in the error, implying that, theoretically, a deeper laminar layer suppresses the vertical fluxes
The value of pat_len will be decreased in the new implementation to be more consistent
A paper about the SREPS outcomes is in preparation!
Test of new parameter perturbations
(new CSPERT suite)member conv pat_len rlam_heat rat_sea crsmin cloud mu_rain gscp1 T 500 0.1 20 200 5.00e+08 0.5 42 KF 500 0.1 20 200 5.00e+08 0.5 43 T 500 1 1 200 5.00e+08 0.5 44 KF 500 1 1 200 5.00e+08 0.5 45 T 500 1 20 150 5.00e+07 0.5 46 KF 500 1 20 150 5.00e+07 0.5 47 T 500 1 20 150 5.00e+08 0 48 KF 500 1 20 150 5.00e+08 0 49 T 500 1 20 150 5.00e+08 0.5 3 (no gra)10 KF 500 1 20 150 5.00e+08 0.5 3 (no gra)11 T 10000 1 20 150 5.00e+07 0.5 412 KF 10000 1 20 150 5.00e+07 0.5 413 T 500 1 20 150 5.00e+07 0 414 KF 500 1 20 150 5.00e+07 0 415 T 500 1 20 150 5.00e+08 0.5 416 KF 500 1 20 150 5.00e+08 0.5 4
15: ctrl T16: ctrl KF
Nov 08 - MAMJ 09
2.2 Model perturbations: Developing perturbations for the lower
boundaryHNMS
AimImplement a technique for perturbing soil moisture
conditions and explore its impacts
ReasoningThe lack of spread is typically worse near the surface
rather than higher in the troposphere. Also, soil moisture is of primary importance in determining the partition of energy between surface heat fluxes, thus
affecting surface temperature forecasts
Soil Perturbation methodBased on the method proposed by Sutton and Hamill (2004)• Select a period that provides variability in soil moisture
e.g. spring • Use of data from a land–surface model analysis for the
defined period for a few years in order to create some “climatology” (DWD SMA)
• Implement the EOF (Empirical Orthogonal Function – Principal Component Analysis) to the data in order to generate random perturbations while retaining the spatial structure of the field
• Define the number of perturbations that will be initially used
• Test the impact of the perturbation within the COSMO-SREPS suite
3. Ensemble merging: development of the COSMO-LEPS
clustering(A. Montani, A. Corigliano)
A dynamical downscaling where driving members for COSMO are taken from different global ensembles is under testing
The cluster analysis is applied on a large set of members coming from different global ensembles
Up to now, ECMWF EPS and UKMO MOGREPS have been considered
initial conditions by EPS
initial conditions by MOGREPS
Issues
Consider both ECMWF EPS and UKMO MOGREPS and study the properties of the cluster analysis on multi-ensemble:
How many times do the 2 ensembles mix?Where do the RMs come from? How to they score depending on their “origin”?Is there added value with respect to single-model ensemble:
BEFORE dowscalingAFTER downscaling
Forecast and analysis datasets data from TIGGE-PORTAL (everything in GRIB2) 90 days (MAM09) of ECMWF-EPS and UKMO-MOGREPS run at 00 and
12 UTC use Z500 at fc+96h as clustering variable; for verifying analysis (at 00 and 12 UTC), consider Z500:
“consensus analysis” (average of UKMO and ECMWF high-res analyses), independent analysis (e.g. from NCEP);
generate the following global ensembles: EPS (50+1): 51 members MOGREPS (23+1): 24 members MINI-MIX (EPS24 + MOGREPS24): 48 members MEGA-MIX (EPS51 +MOGREPS24): 75 members
Strategy perform cluster analysis with 16 clusters and select RMs
(like in operations); generate 16-member global ensembles (EPS_REDU,
MOGREPS_REDU, MINI_REDU, MEGA_REDU). How do “REDUs” ensembles rank with respect to EPS,
MOGREPS, MINI-MIX, MEGA-MIX? Where do the best (and the worst) elements of REDU
ensembles come from? How do they score depending on their “origin”? BEFORE DOWNSCALING: is there added value with
respect to single-model ensemble?
Future plans
finish by March 2010!
Future future plans Implement dynamical downscaling: nest COSMO model in the selected RMs and generate “hybrid” COSMO-LEPS using boundaries from members of different global ensembles. For a number of case, compare operational COSMO-LEPS and “hybrid” COSMO-LEPS.
Summary results The availability of the COSMO-SREPS suite has been
around 90% during this year, but the system is complete only about 50-60% of the times -> back-up suite!
The analysis of the parameter perturbations introduced in the SREPS PP has been completed in Spring, and new selected perturbations have been introduced in the COSMO-SREPS suite in May
There is a good impact of the new perturbations on the spread of the system
A new set of perturbations, also for the microphysics scheme, is currently under testing
A methodology for soil moisture perturbation has been selected and is being implemented at HNMS
The work on multi-clustering has started, using the GRIB2 fields of the TIGGE-PORTAL
4. CalibrationARPA-SIMC - MeteoSwiss
At MeteoSwiss (F. Fundel, Sep 08-Feb 09):Sensitivity testsDocumentation/paper
At ARPA-SIMC (T. Diomede):Data collection:
• observations• MeteoSwiss reforecast• COSMO-LEPS forecasts
Choice of the methodsCode implementationEvaluation
Preparatory step: visit of Tom Hamill and Felix Fundel at ARPA-SIMC, June 2008
Calibration Method
x
Ret
urn
Perio
d
x
Reforecasts
Observations Reforecasts
Ret
urn
Perio
d
30 years COSMO-LEPS reforecasts (1971-2000) Observations (stations, gridded fields)
CDF (for one grid point)
Verification Results I
raw forecasts are overconfidentcalibrated forecasts nearly perfect reliable
strong improvements during winter
summer forecast already are reliable, onlylittle improvement possible
Sensitivity Study (precip)
current setup (18% improvement)best, cheap setup (15% improvement)
Cost for 1 member is ~equal to 2 reforecasts
15% improvement (over 16 member CLEPS DMO) using 11-12 members and calibrate with 8-10 years reforecasts
Depending on season:- more improvement during winter- less improvement during summer
rel. improvement in RPS over 16 Member CLEPS DMO
Observations Emilia-Romagna Region 24-h precipitation (08-08 UTC), 1970-2007
COSMO-LEPS reforecasts (done by MeteoSwiss) 30 years: 1971-2000 1 member, nested on ERA40, COSMO v4.0 1 run every third day (+90h)
COSMO-LEPS QPFs operational 5 years: 2003-2007
[m]
Emilia-RomagnaRegion
(22000 km2)
281 COSMO-LEPS grid points158 raingauges
Calibration – data collection
Calibration – choice of the methods choice of methodologies which enable a calibration of the
quantitative precipitation forecasts, not only of the probabilities of exceeding a threshold
aim:
improve COSMO-LEPS output (QPF)
hydrological applications
chosen methods up to now:
Cumulative Distribution Function (CDF) based
Linear regression
Analogues, based on the similarity of forecast fields:• precipitation
• geopotential height
CDF-based corrections
Ref: Zhu and Toth, 2005 AMS Annual Conf., and many others
For each model grid point:
• blue line CDF of COSMO-LEPS reforecasts
• red line CDF of historical observations
• “raw forecast” each member of the operational COSMO-LEPS
Calibration methodologies
Linear Regression
Ref: any applied statistics textbook
For each model grid point:
x-axis: COSMO-LEPS reforecasts
y-axis: historical observations
Calibration methodologies
yi 0 1xi i
1 analog date for the whole Emilia-Romagna Region
and for each 24-h forecast period
For each ensemble member’s forecast and 24-h forecast period (+ 20-44h , 44-68h , 68-92h , 92-116h):
- the analog search is performed in terms of 24-h rainfall pattern over the Emilia-Romagna Region
- the root-mean-square (rms) difference between the current forecast and each reforecast is computed, over all the grid points of the Emilia-Romagna Region
- the historical date with the smallest rms difference is chosen as the date of the analog, then the past raingauge recordings are used as the calibrated forecast
Calibration methodologiesAnalogues
Calibration – analogues
# #
# #
# #
# #
domain used for the analogue search
example on the methodology used for the analogue search in terms of geopotential at 700 hPa
Method comparisonautumn
threshold: 5 mm/24 h threshold: 20 mm/24 hAutumn 2003-2007 threshold: 5 mm/day fc: +20-44 h
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1forecast probability
obse
rved
rela
tive
frequ
ency
rawCDFANLLRanl Z
no resolution
no skill
Autumn 2003-2007 threshold: 20 mm/day fc: +20-44 h
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1forecast probability
obse
rved
rela
tive
frequ
ency
rawCDFANLLRanl Z
no resolution
no skill
+20-44h
Method comparison
threshold: 5 mm/24 h threshold: 20 mm/24 h
+68-92h
Autumn 2003-2007 threshold: 20 mm/day fc: +68-92 h
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1forecast probability
obse
rved
rela
tive
frequ
ency
rawCDFANLLRanl Z
no resolution
no skill
Autumn 2003-2007 threshold: 5 mm/day fc: +68-92 h
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1forecast probability
obse
rved
rela
tive
frequ
ency
rawCDFANLLRanl Z
no resolution
no skill
autumn
Method comparison
threshold: 5 mm/24 h threshold: 20 mm/24 h
+20-44h
Spring 2003-2007 threshold: 5 mm/day fc: +20-44 h
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1forecast probability
obse
rved
rela
tive
frequ
ency
rawCDFANLLRanl Z
no resolution
no skill
Spring 2003-2007 threshold: 20 mm/day fc: +20-44 h
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1forecast probability
obse
rved
rela
tive
frequ
ency
rawCDFANLLRanl Z
no resolution
no skill
spring
Autumn 2003-2007 Brier Skill Score threshold: 20 mm/24h fc +20-44 h
-1
-0.75
-0.5
-0.25
0
0.25
0.5
a c e g b d f hsub-areas
BSS
rawCDFANL LRanl Z
Different sub-areas
mountain plain
+20-44h20 mm/24 h
Spring 2003-2007 Brier Skill Score threshold: 20 mm/24h fc +20-44 h
-1
-0.75
-0.5
-0.25
0
0.25
0.5
a c e g b d f hsub-areas
BSS
rawCDFANL LRanl Z
Winter 2003-2007 Brier Skill Score threshold: 20 mm/24h fc +20-44 h
-1
-0.75
-0.5
-0.25
0
0.25
0.5
a c e g b d f hsub-areas
BSS
rawCDFANL LRanl Z
Summer 2003-2007 Brier Skill Score threshold: 20 mm/24h fc +20-44 h
-1
-0.75
-0.5
-0.25
0
0.25
0.5
a c e g b d f hsub-areas
BSS
rawCDFANL LRanl Z
raw
calibrated (CDF)
Spatial variability of the Mean ErrorMean Error of the ensemble mean
autumn
mm/24h
underestimation
overestimation
+20-44h
Spatial variability of the Mean Error
raw
calibrated CDF
mm/24h
underestimation
overestimation
Mean Error of the ensemble mean
spring
+20-44h
Spatial variability of the Mean Error
raw
calibrated CDF
Mean Error of the ensemble mean
summer
+20-44h
mm/24h
underestimation
overestimation
raw
calibrated CDF
Spatial variability of the Mean ErrorMean Error of the ensemble mean
winter
+20-44h
mm/24h
underestimation
overestimation
Autumn 2003-2007 Brier Skill Score threshold: 20 mm/24h fc +20-44 h
-1
-0.75
-0.5
-0.25
0
0.25
0.5
a c e g b d f hsub-areas
BSS
rawCDF no windCDF wind LR no windLR wind
Flow direction
mountain plain
+20-44h20 mm/24 h
Spring 2003-2007 Brier Skill Score threshold: 20 mm/24h fc +20-44 h
-1
-0.75
-0.5
-0.25
0
0.25
0.5
a c e g b d f hsub-areas
BSS
rawCDF no windCDF wind LR no windLR wind
Summer 2003-2007 Brier Skill Score threshold: 20 mm/24h fc +20-44 h
-1
-0.75
-0.5
-0.25
0
0.25
0.5
a c e g b d f hsub-areas
BSS
rawCDF no windCDF wind LR no windLR wind
Winter 2003-2007 Brier Skill Score threshold: 20 mm/24h fc +20-44 h
-1
-0.75
-0.5
-0.25
0
0.25
0.5
a c e g b d f hsub-areas
BSS
rawCDF no windCDF wind LR no windLR wind
0
5000
10000
15000
20000
25000
0 5000 10000 15000 20000 25000
forecast (mm*100 / 24h)
obse
rved
(mm
*100
/ 24
h)
upper mountainous macro-areas
overestimation underestimation
Wind S-SW-W
Linear Regression
autumn
[m2/s2][m2/s2]
Remarks
The lack of improvement can be ascribed to the lack of a strong relationship between forecast and observed data.
It is necessary to generate correction functions which are weather-type specific. The training sample should be divided into sub-samples which have similar characteristic with respect to the meteorological situation. Hence, a model error which is systematic with respect to the meteorological situation could be identified and reduced by a specific correction function.
Next developments improve the analogue search, by using upper air fields
(geopotential and specific humidity) at different levels and daytimes and testing the size of the domain used for the analogue search
apply LR and CDF on a limited sample of analogues verify results by the coupling with hydrologic models extend the calibration over other areas, if observed data will
be made available (data over Switzerland are available) reduce the size of the reforecast dataset (in order to use
more recent hourly data to calibrate precipitation forecasts accumulated over 12 or 6 h and enable more detailed hydrological applications)
Final remarks
Problems encountered
The implementation of the back-up suite has just started (with delay, not critical for the moment)
Difficulty in objectively evaluating COSMO-SREPS since the ensemble is often incomplete; problems in verifying precipitation in the summer season
Calibration: The performance of the calibration methodology is
dependent on the precipitation threshold and on the considered area
Difficulty in “catching the bias” of precipitation over Emilia-Romagna, dependent on weather type
Are good data over other areas available?
Decisions needed
In order to calibrate the ensemble over the whole domain, very good (dense and covering a log period, i.e. years) observations should be made available by other regions/countries within the Consortium. And in principle also outside!
Lessons learned
The development of a methodology always introduces new questions, not foreseen, which need to be answered within the project => increase of the amount of work needed (e.g. assess the effect of parameter perturbations in a robust manner, calibration)
Some re-shuffling of the timing of the tasks has been applied, but without influencing the project development, since the tasks are independent