Trond Iversen Overview of GLAMEPS activities

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Norwegian Meteorological Institute

met.no

GLAMEPS
:

G
rand
L
imited
A
rea
M
odel
E
nsemble
P
rediction
S
ystem


Overview of activities


EWGLAM


Dubrovnik, October 2007


Trond Iversen

with contributions from

Inger
-
Lise Frogner, Edit Hágel,

Kai Sattler, Roeland Stappers + more


Norwegian Meteorological Institute

met.no

is in real time to provide to all HIRLAM and ALADIN partner countries:

an operational, quantitative basis for

forecasting probabilities of weather events

in Europe up to 60 hours in advance

to the benefit of highly specified as well as general
applications,

including risks of high
-
impact weather.

The GLAMEPS objective

Norwegian Meteorological Institute

met.no

Expectations from Short Range

ensemble prediction



How certain is today’s weather forecast?



What are the risks of high
-
impact events?


Forcasted risk = probability
x

potential damage (vulnerability)



Lower predictability of “free flows” as scales decrease;


i.e.: higher resolution increases the need for information about spread
and the timing of spread saturation



Predictability of “forced flows” is longer than “free flows”:


i.e.: beneficial to separate unpredictable “free flows” from those
strongly influenced by surface contrasts: e.g. topography, coast
-
lines,
land
-
use, etc.


Norwegian Meteorological Institute

met.no

Basic Ideas in GLAMEPS


An array of LAM
-
EPS models or model versions:


Each partner runs a unique sub
-
set of ensemble members


Partners who run the same model version,


use different lower boundary data,


or different initial and lateral boundary perturbations


Partners who run with DA, produce 5
-

21 ensemble members


based on initial and lateral boundary perturbations


(one control with DA + pairs of symmetric initial perturbations)


Partners who do not run DA produce 6
-
20 ensemble members (pairs)


Grid resolution



Now 20km, later: 10km or finer, 40 levels, identical in all model
versions (
should be increased to at least 60
)


Forecast range




60h (shorter?)
-

starting daily from 00UT and 12 UT


A common pan
-
European integration domain


Or alternatively: a minimum common overlap

Norwegian Meteorological Institute

met.no

Aspects to consider

1.
Operational aspects

-

In particular data storage and Real
-
Time distribution

2.
Constructing initial and lateral boundary perturbations


Imported global eps
-
members enhanced


w.r.t. resolution, European target, moist physics


LAM
-
specific perturbations (SVs, ETKF)

3.
Lower boundary data perturbations


Stochastic perturbations


Switch surface schemes


Targetted Forcing Singular Vectors or Forcing Sensitivities

4.
Model perturbations


Switching models (e.g. Aladin and Hirlam)


Switching physical packages (e.g. Straco, RKKF, ECMWF
-
physics)


Stochastic perturbations (EC: Cellular automata.)


Forcing Singular Vectors

5.
EPS
-
calibration and probabilistic validation

6.
Post
-
processing, graphical presentation, products

7.
Further downscaling to meso
-

and convective scales

Norwegian Meteorological Institute

met.no

Quality objective

To operationally produce ensemble forecasts with


a spread reflecting known uncertainties in data and
model;


a satisfactory spread
-
skill relationship (calibration); and


a better probabilistic skill than the operational ECMWF
EPS;


for


the chosen forecast range of

60 hours (could be shorter);


our common target domain; and


weather events of our particular interest


(European extremes
-

probabilistic skill parameters).

Norwegian Meteorological Institute

met.no

GLAMEPS Common Domain

ALADIN


Resolution: 22km


320 x 300 x 37

HIRLAM
(EPS71)



Resolution 0.2 deg.


306 x 260 x 40

Norwegian Meteorological Institute

met.no


GLAMEPS_v0: Laboratory at ECMWF


To select a small set of model versions which are equally valid but
significantly different,


3 different models:



ALADIN, HIRLAM
-
STRACO, HIRLAM RK
-
KF


To construct initial/lateral boundary perturbations


New ECMWF TEPS: define TSVs targeted to 3 domains


all TSVs are orthogonal to NH SVs (EPS) and mutually


TSVs: OT=24h, T159, (not yet diabatic)


Use: 30 TSVs and 50 NH SVs, Gaussian sampling to 20 members +
control


Probabilistic estimation (e.g. BMA),


Not started yet: awaits results


Products; Quality and Value


INM package based on Magics


Predictability of the day, event risks


Reliability, BSS, ROC, Value, …

Norwegian Meteorological Institute

met.no

TEPS FOR EUROPE

Inger
-
Lise Frogner

GLAMEPS integration domain

(HIRLAM version)

Target area north

(82N,15W,50N,50E
)

Target area central

(62N,20W,33N,44E)

Target area south

(47N,23W,24N,32E)

Norwegian Meteorological Institute

met.no

First experimental setup for

“TEPS for Europe”


Singular vectors are computed with:


T159L62

(as opposed to T42 for operational NH SVs at


ECMWF)


24h optimization time

(as opposed to 48h)


Targeted in the vertical to the troposphere


Targeted SVs (TSVs)

based on total energy norm


The TSVs are orthogonal NH SVs and mutually orthogonal



TEPS perturbations are made from 80 SVs:


10 SVs for each of the three targets


and 50 NH SVs and evolved SVs from EPS.


Includes standard stochastic physics



Different amplitudes is assigned to the different sets of SVs, to
give the desirable spread/skill relation


Presently under development



Norwegian Meteorological Institute

met.no

EXPERIMENTS


21 days in summer 2007:


20070618
-
20070624, 20070808
-
20070814
and 20070820
-
20070826


The amplitude of NH SVs is kept as in
EPS for the first experiment: 0.020


The amplitude of TSVs from the three
target areas for the first experiment:
0.008


Under adjustment!

Norwegian Meteorological Institute

met.no

COST


TSVs for all three target areas: ca 450 SBUs



TEPS for Europe: ca 3000 SBUs


A total cost of ~3500 SBUs per run /case

Norwegian Meteorological Institute

met.no

Problems and obstacles


Technical issues regarding running with
three orthogonal sets had to be solved



The high resolution of the TSVs caused
unforeseen problems.


The vertical diffusion scheme in the SV
calculations in IFS had to be changed.


The original scheme caused spurious growth.



Norwegian Meteorological Institute

met.no

Example of TSVs

Lowest level, SV no. 2

NHSV

TSV
-
north

TSV
-
central

TSV
-
south

Norwegian Meteorological Institute

met.no

Spread/Skill relationship

MSLP, 21 summer cases 2007

___
error of Ensemble


Mean (EM), EPS

___
error of EM, Norwegian


TEPS

___
error of EM, European


TEPS

----

spread around EM, EPS

----

spread around EM,


Norwegian TEPS

----

spread around EM,


European TEPS

Norwegian Meteorological Institute

met.no

RMS Difference in spread between European
TEPS and EPS over the 21 cases

+12h

+24h

+36h

+48h

Norwegian Meteorological Institute

met.no

Skill scores MSLP (example)

21 summer cases 2007


Black: EPS, 50 members


Blue: European TEPS,


20 members


Red: Norwegian TEPS,


20 members


Event: anom <
-
5 hPa

ROC area

BSS

Norwegian Meteorological Institute

met.no

Further work


Experimentation with the amplitudes of the SVs and
TSVs will be carried out.


At the moment we are testing:


Increasing the amplitude from the TSVs and at the same time
reduce the amplitude from the SVs, in order to get better spread
/ skill in the range 0 to 60 hours as well as better
scores
.


A winter period of 21 days will also be run.


Scores for more parameters will be calculated:


T850, ff10m, Z500, T2m



After the tuning of TEPS is satisfactory, LAMEPS will be
run with TEPS as initial and boundary conditions

Norwegian Meteorological Institute

met.no

HIRLAM EPS and ALADIN EPS

Norwegian Meteorological Institute

met.no

Main characteristics of HIRLAM EPS

Kai Sattler

1. Calculates a control from HIRLAM 3d
-
Var (later 4D Var)


+ ensemble of perturbations

2. Can be used with downscaled ECMWF EPS:



* 50+1 members



* 12h cycle frequency



* data availability:




* online data




* boundary data pool

(intermediate storage, hindcast)

3. Old Norwegian TEPS:



* 20+1 members



* 24h cycle frequency



* boundary data pool

4. New “TEPS for Europe” (not fully implemented yet):



* 3 target areas



* 12h cycle frequency



* boundary data pool

5. Ensemble member specific environment settings



choice of parameterization schemes

6. Selection of convection/condensation scheme for each perturbed member

Norwegian Meteorological Institute

met.no

ALADIN EPS


Scripts for the execution of ALADIN/EPS system for the
GLAMEPS domain(s) at ECMWF


(Stjepan Ivatek
-
Sahdan, ZAMG EPS team, Joao Ferreira)



Operational ALADIN EPS (“ALADIN LAEF”) built in Austria
at ECMWF machines


used as starting point for the GLAMEPS
-
v0 laboratory


So far tested: Downscaling ECMWF EPS for ALADIN.


quasi
-
operational manner.


Joao Ferreira (“Downscaling ECMWF EPS with ALADIN”)

Norwegian Meteorological Institute

met.no

Costs

EPS_71T_15: Coarse test domain

EPS_71_20: Full 0.2 deg. dom: 60
-
70 SBU/48hfcst


22km: around 80 SBU/54h fcst

HIRLAM

ALADIN

Norwegian Meteorological Institute

met.no

Further R&D in parallel

To gradually increase sources of spread, quality and reliability

Include lower boundary perturbations and other types of model
perturbations,
e.g.:


vary model coefficients


Targeted Forcing SVs or Forcing Sensitivities,


weak 4D
-
Var perturbed tendencies


Stochastic physics

Include alternative initial/lateral boundary perturbations


ETKF generalized breeding,


HIRLAM and ALADIN LAM SVs,

Pdf
-
estimation, presentation, validation etc.


BMA,


Products


Validation


Norwegian Meteorological Institute

met.no

ALADIN SVs

Efit Hágel, Richard Mladek

Norwegian Meteorological Institute

met.no

Case study



2
7

August

200
7
,
00

UTC


ALADIN singular vector
s were computed


GLAMEPS

domain was used for the computations,
but the
target (
optimization
)

area itself was
smaller:
5
6
N/3
4
S/
2
W/
40
E
(see on figure in green)


Opt.

area

Norwegian Meteorological Institute

met.no

Case study


2
7

August

200
7
,
00

UTC


ALADIN SVs
-

choices for
case study
:


Norms:

total energy norm (initial and final time)


Optimization area
: 5
6
N/3
4
S/
2
W/
40
E


Optimization time
: 12
and 24
hours


Vertical optimization
: level 1
-

46 (all levels)


Resolution
: 2
2 and 44

km


LBC
Coupling
: every 3 hours

(ARPEGE)


Opt. area

Norwegian Meteorological Institute

met.no

Case study


ALADIN SVs


Leading singular values
for the experiments with
different optimization
time and resolution



Lowest singular values
with 44 km resolution and
12 hours optimization
time



Highest singular values
with 22 km resolution and
24 hours optimization
time

Norwegian Meteorological Institute

met.no

Case study


ALADIN SVs


ALADIN leading singular vector

at T+0h (
top
) and evolved singular
vector at T+12h

and T+24h (bottom)

for
wind v

at
model level
2
2
.



44 km

22 km

44 km

22 km

0h

12h

/24h

Norwegian Meteorological Institute

met.no

HIRLAM SVs

Jan Barkmeijer, Roel Stappers

Norwegian Meteorological Institute

met.no

Hirlam Case I101 (Fine)


Analysis June 28
th

2006 at 3 UTC


Resolution 0.2 x 0.2 degrees


Optimization time 12 hours


Dry total energy norm


No projection operator


Vertical diffusion in TL
-
model


No diabatic processes in TL
-
model


Leading singular value 6.6

Norwegian Meteorological Institute

met.no

Temperature and wind field

of the
leading singular vector

at model
level 19 (500
hPa
) at initial (left) and final time (right) using the same temperature contour
interval and unit wind vector.

Initial

Evolved

Temp.

Wind

Norwegian Meteorological Institute

met.no

Vertical energy distribution

of the leading singular vector for
the wind (black) and temperature field

(red) at initial (left) and final time( right). Notice the difference in scaling of the horizontal axis.

Initial

Evolved

wind

Temp.

Norwegian Meteorological Institute

met.no

SV
-
spectrum

Fine (0.2x0.2),
coarse (0.4x0.4)

Norwegian Meteorological Institute

met.no

GLAMEPS_v1: distributed laboratory

To set up a first phase suite tested in distributed mode (2008)


based on experience from _v0


Run in hindcast mode use and use ECMWF for data exchange /storage



Run ECMWF TEPS



Store necessary LBC
-
data on accessible ECMWF disk for < 24 h



Each partner downloads data and run a set of predefined




LAM
-
EPS members



Each partner to store selected results on ECMWF disk



A set of probabilistic products made in batch
-
mode



Each partner to download the entire produc suite



Store on mars for future quality evaluation / validation /calibration


NB: The success of GLAMEPS relies critically on dedicated partners.


Most probably we need funding for supporting staff and storage at ECMWF
.

Norwegian Meteorological Institute

met.no

ECMWF and GLAMEPS

Operationally produce enhanced value intial/lateral boundary perturbations


“TEPS for Europe” as a Prediction Application Facility (PAF)?

Data exchange central in RT operation


A selected set of data from TIGGE
-
list copied to ECMWF in RT each LAM
-
EPS.


At an agreed time, all partners can download the set of GLAMEPS members.

Archiving


Archiving EPS and TEPS for use by GLAMEPS


Archiving GLAMEPS raw data and products

Use software developed at ECMWF for


Selected probabilistic products,


Probabilistic verification and validation

Calibrate and validate the entire GLAMEPS

Develop and maintain


Prototype codes and scripts for downloading by partners,


Testing and quasi
-
operationalization in research mode,

Further co
-
operate with ECMWF staff, scientifically and operationally.


Norwegian Meteorological Institute

met.no

Thank You!