HNMS contribution to CONSENS Petroula Louka & Flora Gofa Hellenic National Meteorological Service [email protected]@hnms.gr, [email protected]@hnms.gr

HNMS contribution to CONSENS

Petroula Louka & Flora Gofa

Hellenic National Meteorological Service

[email protected], [email protected]

mailto:[email protected]

mailto:[email protected]

11th COSMO General Meeting

HNMS involvement

Task 2.1. Test new parameter perturbations, including combination of parameters. – 2.1.1 select new parameters to be perturbed, select

parameter combinations – 2.1.3 analyse the impact with respect to the control

Task 2.2. Develop perturbations for the lower boundary fields. – 2.2.1 develop techniques for lower boundary fields

perturbation.


Physics perturbations

2 suites– CSPERT: 16 members of the same initial conditions

with different values for selected physics parameters.– New: 16 members of different initial conditions with

different combinations of physics perturbations. 3 testing periods of investigation and statistical analysis

– Summer 2008 and Spring 2009 with CSPERT suite– May – August 2009 with the new suite

The focus of the study is mainly on the continuous parameters (T, U and Td) rather than precipitation.


CSPERT suite

ECMWF initial conditions


2m Temperature T2m: RMSE

1

2

3

4

3 6 9 12 15 18 21 24

T2m: RMSE

1

2

3

4

3 6 9 12 15 18 21 24

T2m: BIAS

-2.5

-1.5

-0.5

0.5

1.5

2.5

3 6 9 12 15 18 21 24

T2m: BIAS

-2.5

-1.5

-0.5

0.5

1.5

2.5

3 6 9 12 15 18 21 24

Spring 2009

Summer 2008


10m Wind U10m: BIAS

-1.5

-0.5

0.5

1.5

3 6 9 12 15 18 21

U10m: BIAS

-1.5

-0.5

0.5

1.5

3 6 9 12 15 18 21

U10m: RMSE

2

2.5

3

3 6 9 12 15 18 21 24

Summer 2008

U10m: RMSE

2

2.5

3

3 6 9 12 15 18 21 24

Spring 2009


2m Dew point temperature Td2m: BIAS

-2.5

-1.5

-0.5

0.5

1.5

2.5

3 6 9 12 15 18 21 24

Td2m: BIAS

-2.5

-1.5

-0.5

0.5

1.5

2.5

3 6 9 12 15 18 21 24

Td2m: RMSE

2

3

4

5

3 6 9 12 15 18 21 24

Summer 2008

Td2m: RMSE

2

3

4

5

3 6 9 12 15 18 21 24

Spring 2009

|T BIAS| T RMSE |Td BIAS| Td RMSE |U BIAS| U RMSE

KF = = ~ = = ~ = ~ =

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)

> > > < ~ =

- -- +++=

Based on Summer case study


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 default was investigated.

The spread of the results is larger for the summer case for temperature and dew point mainly during the hours of intense solar radiation (as expected).

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.


Remarks (cont) For the summer case, looking separately at each parameter

perturbation compared to the control run:

– rlam_heat (scaling factor of the laminar layer depth): Changing its value affects mainly temperature. This is an important parameter since it defines the layer with non-turbulent characteristics (molecular diffusion effects only). Therefore, the depth of the laminar layer will influence the vertical fluxes from the surface.

– rat_sea (ratio of laminar scaling factor for heat over sea): Changing its value affects only temperature, as it has to do with laminar heat flux.

– crsmin (minimal stomata resistance): This seems to be an important parameter for all results with its largest value producing better statistics.


Remarks (cont)

– tur_len (maximal turbulent length scale): Changing its value affects the wind only during daytime. This parameter is used mainly in the calculation of the characteristic length scale for vertical mixing and thus into the calculation of the vertical transport momentum coefficient.

– pat_len (length scale of thermal surface patterns): Changing its value affects only temperature. This was expected as this parameter is mainly used in the calculation of the heat flux.

– c_lnd (surface area density of the roughness elements over land): affects mainly temperature but also slightly the wind values.


First discussion

– tur_len (maximal turbulent length scale): This parameter is related to vertical rather than the horizontal transport. It is possible that if the vertical wind component were examined separately the effect would have been more evident.(?) Theoretically, an increased value of tur_len would lead to increasing the coefficient of vertical transport of momentum, which in turn may imply a more turbulent surface layer (mainly due to shear production of turbulent kinetic energy).

– pat_len (length scale of thermal surface patterns): As this parameter is directly related to the horizontal sub-grid scales perhaps the value of 10000m that has been chosen for the perturbation (default=500) is too large for the horizontal grid size of the ensemble (=10km, i.e. equal to pat_len). Maybe a value less than 10000m is more appropriate to extract useful information for the sub-grid heat flux. (?)


New CONSENS suite Random combination of parameter perturbation and initial

model conditions. The results are investigated in a different manner as no

control run is available. Reminder: The period is summer!


Major discrepancies for T2m

T2m: BIAS

-2

-1

0

1

2

6 12 18 24 30 36 42 48 54 60 66 72

m1 m3 m4 m5 m6

m7 m8 m11 m12

T2m: RMSE

1

2

3

4

5

6

7

8

9

6 12 18 24 30 36 42 48 54 60 66 72

m1 m3 m6 m8 m11 m12


Major discrepancies for U10m

U10m: BIAS

-1.5

-0.5

0.5

1.5

6 12 18 24 30 36 42 48 54 60 66 72

m1 m4 m6 m8 m10

m11 m12 m15

U10m: RMSE

1.5

2

2.5

3

6 12 18 24 30 36 42 48 54 60 66 72

m2 m6 m7 m8 m10

m11 m15


Major discrepancies for Td2m

Td2m: BIAS

-7

-6

-5

-4

-3

-2

-1

0

1

6 12 18 24 30 36 42 48 54 60 66 72

m3 m6 m7 m8 m11 m12

Td2m: RMSE

1

2

3

4

5

6

7

8

9

6 12 18 24 30 36 42 48 54 60 66 72

m1 m3 m6 m7 m8

m11 m12


rlam_heat: Scaling factor of the laminar layer depth

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

Effects of physics perturbations

While rlam_heat increases the depth of the laminar layer increases and produces the largest errors especially during the daytime underestimating T (suppresses the exchange of heat from the surface ?).


U10m: BIAS

-1.5

-0.5

0.5

1.5

6 12 18 24 30 36 42 48 54 60 66 72

m3 m6 m11 m12

Although there are differences among the members, they do not obtain a clear behaviour.


U10m: RMSE

1.5

2

2.5

3

6 12 18 24 30 36 42 48 54 60 66 72

m3 m6 m11 m12


Td2m: BIAS

-7

-6

-5

-4

-3

-2

-1

0

1

6 12 18 24 30 36 42 48 54 60 66 72

m3 m6 m11 m12

Td2m: RMSE

1

2

3

4

5

6

7

8

9

0 6 12 18 24 30 36 42 48 54 60 66 72

m3 m6 m11 m12

While rlam_heat increases the depth of the laminar layer increases and produces the largest errors especially during the daytime underestimating largely Td (even greater than T).



rat_sea: Ratio of laminar scaling factor for heat over sea

T2m: BIAS

-2

-1

0

1

2

6 12 18 24 30 36 42 48 54 60 66 72

m7 m8 m13 m14

T2m: RMSE

1

2

3

4

6 12 18 24 30 36 42 48 54 60 66 72

m7 m8 m13 m14

It seems that there is a diurnal pattern here with the largest rat_sea producing less error during daytime than the smaller value, while the opposite occurs during the night hours.


U10m: BIAS

-1.5

-0.5

0.5

1.5

6 12 18 24 30 36 42 48 54 60 66 72

m7 m8 m13 m14

It seems that the largest rat_sea produces less error for wind?


U10m: RMSE

1.5

2

2.5

3

6 12 18 24 30 36 42 48 54 60 66 72

m7 m8 m13 m14


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

It seems that the largest rat_sea largely underestimates Td especially during daytime (opposite than T and U).



crsmin: Minimal stomata resistance

T2m: BIAS

-2

-1

0

1

2

6 12 18 24 30 36 42 48 54 60 66 72

m3 m7 m15 m16

T2m: RMSE

1

2

3

4

6 12 18 24 30 36 42 48 54 60 66 72

m3 m7 m15 m16

It seems that there is a smaller diurnal pattern here with the largest crsmin producing more error during daylight (photosynthesis and evapotranspiration hours), while the opposite occurs during the night hours.



U10m: BIAS

-1.5

-0.5

0.5

1.5

6 12 18 24 30 36 42 48 54 60 66 72

m3 m7 m15 m16

U10m: RMSE

1.5

2

2.5

3

6 12 18 24 30 36 42 48 54 60 66 72

m3 m7 m15 m16

Although there are some differences among the members, they do not obtain a clear behaviour.



It seems that the largest crsmin produces more error during daylight (photosynthesis and evapotranspiration hours).

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


tur_len: Maximal turbulent length scale

T2m: RMSE

1

2

3

4

5

6

7

6 12 18 24 30 36 42 48 54 60 66 72

m1 m2 m9 m10

T2m: BIAS

-2

-1

0

1

2

6 12 18 24 30 36 42 48 54 60 66 72

m1 m2 m9 m10

The largest value of tur_len using AVN seems to produce larger errors during the daytime.



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

Not a clear effect although it seems that for the first forecast day the largest value of tur_len using AVN produces larger errors during the daytime.

U10m: RMSE

1.5

2

2.5

3

6 12 18 24 30 36 42 48 54 60 66 72

m1 m2 m9 m10



Not a clear effect.

Td2m: BIAS

-4

-3

-2

-1

0

1

6 12 18 24 30 36 42 48 54 60 66 72

m1 m2 m9 m10

Td2m: RMSE

1

2

3

4

5

6

6 12 18 24 30 36 42 48 54 60 66 72

m1 m2 m9 m10


Remarks It is difficult to identify the effect (small or not) of the combined

physics perturbations altogether. Some of the members of the new CONSENS suite produce large

errors compared to the CSPERT suite. The physical importance of the value of each perturbed parameter is

apparent. Based on that it could be suggested– large values of rlam_heat produce large errors (independently of

the crsmin and iconv).– rat_sea affects differently the parameters with its largest value

producing less error during daytime than its smaller value (for T and U), while for Td its largest value leads to large errors.

– the largest crsmin produces more error during daylight (photosynthesis and evapotranspiration hours) for T and Td.

– the largest value of tur_len using AVN produces larger errors during the daytime for T and U although the effect is not as clear as the other perturbations (but it is difficult to study w).


Suggestions for next steps on physics perturbations

Based on HNMS results: The perturbation of rlam_heat, tur_len, crsmin should be

further considered although the largest value of rlam_heat producing the worst results should be re-examined.

The perturbed values of pat_len should be carefully examined (consider smaller values than 10km?).

New physics perturbations?

– The dissipation rate for turbulent kinetic energy (TKE) is defined by using a “tuning constant” cε ( d_heat or d_mom). Obviously this is an important parameter that affects the dissipation rate and therefore the consumption of TKE. Could we “touch” this value?


Developing perturbations for the lower boundary

Task 2.2.1 techniques

AIMImplement a technique for perturbing soil moisture

conditions and explore its impacts

Reasoning

The lack of variability 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 method

Based 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”

Implement the EOF (Empirical Orthogonal Function – Principal Component Analysis) to the data in order to generate random perturbations

Define the number of perturbations that will be initially used Decide to which member of the CONSENS suite (control or

other?) the method will be applied.


EOF analysisThis analysis will generate random perturbations that will have the same spatial

structure as the daily deviations of soil moisture from a running mean

climatology.

Based on the selected period data, a 30-day period running mean will be

calculated. This mean will be subtracted from the daily soil moisture analysis to

get daily deviations. Using these deviations to EOF, the perturbations will be

calculated and ordered from largest to smallest variability.

n

iiij ur1

Where: εj is the jth perturbation

ri is the standard normally distributed

random number

ui is the ith singular vector

σi is the ith singular valuesThe perturbations will be added to the soil moisture data to create the final perturbed field to be adopted in the ensemble run


Task Contributing scientist(s) FTE- years Start Deliverables Date of delivery

Preceding tasks

1.1 Chiara Marsigli (ARPA-SIM) Andrea Montani (ARPA-SIM)

0.16 01.09.2008 COSMO-SREPS suite running for the whole period 31.08.2010 -

1.2 Davide Cesari (ARPA-SIM) Chiara Marsigli (ARPA-SIM) Andrea Montani (ARPA-SIM)

0.15 01.02.2009 Implementation of the back-up suite 31.12.2009 -

2.1 Flora Gofa (HNMS) Petroula Louka (HNMS) Chiara Marsigli (ARPA-SIM) Andrea Montani (ARPA-SIM)

0.85 01.09.2008 A definite set of physics perturbations is implemented in COSMO-SREPS (Feb 2010) and applied to the final ensemble system (Spring 2010)

28.02.2010 -

2.2 Flora Gofa (HNMS) Petroula Louka (HNMS) Chiara Marsigli (ARPA-SIM) Andrea Montani (ARPA-SIM)

1.0 01.01.2009 The algorithm for lower boundary perturbation is implemented in COSMO-SREPS (Feb 2010) and applied to the final ensemble system (Spring 2010)

28.02.2010 -

3.1 Andrea Corigliano (ARPA-SIM) Chiara Marsigli (ARPA-SIM) Andrea Montani (ARPA-SIM)

1.0 01.01.2009 Decision on the “multi-perturbation-strategy” to be adopted in the final ensemble

28.02.2010 -


0.2 01.03.2010 The “multi-perturbation-strategy” COSMO ensemble will be implemented at ECMWF as operational suite

31.08.2010 2 and 3.1

4.1 Felix Fundel (MeteoSwiss) 0.2 01.09.2008 Availability of one calibration algorithm tuned over Switzerland 31.04.2009 -

4.2 Tommaso Diomede (ARPA-SIM) 0.6 02.09.2008 Implementation of several calibration algorithms for COSMO-LEPS 28.02.2009 -

4.3 Tommaso Diomede (ARPA-SIM) Andrea Montani (ARPA-SIM)

0.7 01.03.2009 Decision about the best calibration technique to be used in the final ensemble

31.02.2010 4.2


0.2 01.03.2010 Implementation into the operational suite of calibration as post-processing

31.08.2010 4.1 4.2 4.3

All 5.06 01.09.2008 31.08.2010

Based on Spring case study

|T BIAS| T RMSE |Td BIAS| Td RMSE |U BIAS| U RMSE

KF

tur_len=150

tur_len=1000

pat_len=10000

rat_sea=1

rat_sea=60

crsmin=50

crsmin=200

c_lnd=1

c_lnd=10

rlam_heat=0.1

rlam_heat=10

- -- +++=


2m Temperature T2m: RMSE

1

2

3

4

5

6

7

8

9

6 12 18 24 30 36 42 48 54 60 66 72

m1 m2 m3 m4m5 m6 m7 m8m9 m10 m11 m12m13 m14 m15 m16

T2m: BIAS

-2

-1

0

1

2

6 12 18 24 30 36 42 48 54 60 66 72



10m Wind U10m: BIAS

-1.5

-0.5

0.5

1.5

6 12 18 24 30 36 42 48 54 60 66 72


U10m: RMSE

1.5

2

2.5

3

6 12 18 24 30 36 42 48 54 60 66 72



2m Dew point Td2m: BIAS

-7

-6

-5

-4

-3

-2

-1

0

1

6 12 18 24 30 36 42 48 54 60 66 72


Td2m: RMSE

1

2

3

4

5

6

7

8

9

6 12 18 24 30 36 42 48 54 60 66 72



6h FAR: >0.1mm

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

6 12 18 24

CSPERT precipitation (deterministic approach)

6h POD: >0.1mm

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

6 12 18 24 6h ETS: >0.1mm

0.2

0.3

0.4

0.5

6 12 18 24


CSPERT precipitation (probabilistic approach)

BSS (March-May 2009)

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0 1 2 3 4 5 6Threshold (mm/24hr)

ROC (March-May 2009)

0.6

0.7

0.8

0.9

0 1 2 3 4 5 6

Threshold (mm/24hr)

Small sample!

Analytical results of CSPERT for summer 2008


T2m – Summer 2008tur_len

T2m: BIAS

-2.5

-1.5

-0.5

0.5

1.5

3 6 9 12 15 18 21 24

Forecast time

Default = 500

Maximal turbulent length scale

T2m: RMSE

1

2

3

4

3 6 9 12 15 18 21 24

Forecast time


U10m – Summer 2008tur_len

Default = 500


Wind10m: BIAS

-1.5

-0.5

0.5

1.5

3 6 9 12 15 18 21 24

Wind10m: RMSE

1

2

3

3 6 9 12 15 18 21 24

Forecast time


Td2m – Summer 2008tur_len

Default = 500


Td2m: BIAS

-2.5

-1.5

-0.5

0.5

1.5

3 6 9 12 15 18 21 24

Td2m: RMSE

1

2

3

4

5

3 6 9 12 15 18 21 24

Forecast time


T2m – Summer 2008pat_len

T2m: BIAS

-2.5

-1.5

-0.5

0.5

1.5

3 6 9 12 15 18 21 24

Forecast time

Default = 500

Length scale of thermal surface patterns

T2m: RMSE

1

2

3

4

3 6 9 12 15 18 21 24

Forecast time


U10m – Summer 2008pat_len

Default = 500


Wind10m: BIAS

-1.5

-0.5

0.5

1.5

3 6 9 12 15 18 21 24

Wind10m: RMSE

1

2

3

3 6 9 12 15 18 21 24

Forecast time


Td2m – Summer 2008pat_len

Default = 500


Td2m: BIAS

-2.5

-1.5

-0.5

0.5

1.5

3 6 9 12 15 18 21 24

Td2m: RMSE

1

2

3

4

5

3 6 9 12 15 18 21 24

Forecast time


T2m – Summer 2008rat_sea

T2m: BIAS

-2.5

-1.5

-0.5

0.5

1.5

3 6 9 12 15 18 21 24

Forecast time

Default = 20

Ratio of laminar scaling factor for heat over sea

T2m: RMSE

1

2

3

4

3 6 9 12 15 18 21 24

Forecast time


U10m – Summer 2008rat_sea

Default = 20


Wind10m: BIAS

-1.5

-0.5

0.5

1.5

3 6 9 12 15 18 21 24

Wind10m: RMSE

1

2

3

3 6 9 12 15 18 21 24

Forecast time


Td2m – Summer 2008rat_sea

Default = 20


Td2m: BIAS

-2.5

-1.5

-0.5

0.5

1.5

3 6 9 12 15 18 21 24

Td2m: RMSE

1

2

3

4

5

3 6 9 12 15 18 21 24

Forecast time


T2m – Summer 2008crsmin

T2m: BIAS

-2.5

-1.5

-0.5

0.5

1.5

3 6 9 12 15 18 21 24

Forecast time

Default = 150

Minimal stomata resistance

T2m: RMSE

1

2

3

4

3 6 9 12 15 18 21 24

Forecast time


U10m – Summer 2008crsmin

Default = 150


Wind10m: BIAS

-1.5

-0.5

0.5

1.5

3 6 9 12 15 18 21 24

Wind10m: RMSE

1

2

3

3 6 9 12 15 18 21 24

Forecast time


Td2m – Summer 2008crsmin

Default = 150


Td2m: BIAS

-2.5

-1.5

-0.5

0.5

1.5

3 6 9 12 15 18 21 24

Td2m: RMSE

1

2

3

4

5

3 6 9 12 15 18 21 24

Forecast time


T2m – Summer 2008rlam_heat

T2m: BIAS

-2.5

-1.5

-0.5

0.5

1.5

3 6 9 12 15 18 21 24

Forecast time

Default = 1

Scaling factor of the laminar layer depth

T2m: RMSE

1

2

3

4

3 6 9 12 15 18 21 24

Forecast time


U10m – Summer 2008rlam_heat

Default = 1


Wind10m: BIAS

-1.5

-0.5

0.5

1.5

3 6 9 12 15 18 21 24

Wind10m: RMSE

1

2

3

3 6 9 12 15 18 21 24

Forecast time


Td2m – Summer 2008rlam_heat

Default = 1


Td2m: BIAS

-2.5

-1.5

-0.5

0.5

1.5

3 6 9 12 15 18 21 24

Td2m: RMSE

1

2

3

4

5

3 6 9 12 15 18 21 24

Forecast time


T2m – Summer 2008c_lnd

T2m: BIAS

-2.5

-1.5

-0.5

0.5

1.5

3 6 9 12 15 18 21 24

Forecast time

Default = 2

Surface area density of the roughness elements over land

T2m: RMSE

1

2

3

4

3 6 9 12 15 18 21 24

Forecast time


U10m – Summer 2008c_lnd

Default = 2


Wind10m: BIAS

-1.5

-0.5

0.5

1.5

3 6 9 12 15 18 21 24

Wind10m: RMSE

1

2

3

3 6 9 12 15 18 21 24

Forecast time


Td2m – Summer 2008c_lnd

Default = 2


Td2m: BIAS

-2.5

-1.5

-0.5

0.5

1.5

3 6 9 12 15 18 21 24

Td2m: RMSE

1

2

3

4

5

3 6 9 12 15 18 21 24

Forecast time


T2m – Summer 2008qc0

T2m: BIAS

-2.5

-1.5

-0.5

0.5

1.5

3 6 9 12 15 18 21 24

Forecast time

Default = 0

Cloud water threshold for autoconversion

T2m: RMSE

1

2

3

4

3 6 9 12 15 18 21 24

Forecast time


U10m – Summer 2008qc0

Default = 0


Wind10m: BIAS

-1.5

-0.5

0.5

1.5

3 6 9 12 15 18 21 24

Wind10m: RMSE

1

2

3

3 6 9 12 15 18 21 24

Forecast time


Td2m – Summer 2008qc0

Default = 0


Td2m: BIAS

-2.5

-1.5

-0.5

0.5

1.5

3 6 9 12 15 18 21 24

Td2m: RMSE

1

2

3

4

5

3 6 9 12 15 18 21 24

Forecast time


T2m – Summer 2008c_soil

T2m: BIAS

-2.5

-1.5

-0.5

0.5

1.5

3 6 9 12 15 18 21 24

Forecast time

Default = 1

Surface area index for evaporating soil

T2m: RMSE

1

2

3

4

3 6 9 12 15 18 21 24

Forecast time


U10m – Summer 2008c_soil

Default = 1


Wind10m: BIAS

-1.5

-0.5

0.5

1.5

3 6 9 12 15 18 21 24

Wind10m: RMSE

1

2

3

3 6 9 12 15 18 21 24

Forecast time


Td2m – Summer 2008c_soil

Default = 1


Td2m: BIAS

-2.5

-1.5

-0.5

0.5

1.5

3 6 9 12 15 18 21 24

Td2m: RMSE

1

2

3

4

5

3 6 9 12 15 18 21 24

Forecast time

Documents

HNMS contribution to CONSENS Petroula Louka & Flora Gofa Hellenic National Meteorological Service [email protected]@hnms.gr, [email protected]@hnms.gr