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The impact of moist singular vectors and ensemble size on predicted storm tracks for the winter storms Lothar and Martin
A. Walser1)
M. Arpagaus1)
M. Leutbecher2)
1)MeteoSwiss, Zurich
2)ECMWF, Reading, GB
Moist vs. operational singular vectorsCoutinho et al. (2004)
‚opr‘ SVs (T42L31, OT 48 h): linearized physics package with surface drag simple vertical diffusion
‚moist‘ SVs (T63L31, OT 24 h): linearized physics package includes additionally: gravity wave drag long-wave radiation deep cumulus convection large-scale condensation
Martin: Predicted storm tracks t+(42-66) < 980 hPa (1)
ensemble members: 2 tracks ▬ analysis
< 970 hPa< 960 hPa
Configuration:
• dry SVs/51 RMs
• moist SVs/51 RMs
Martin: Predicted storm tracks t+(42-66) < 980 hPa (2)
ensemble members: 12 tracks ▬ analysis
< 970 hPa< 960 hPa
Configuration:
• dry SVs/51 RMs
• moist SVs/51 RMs
moist SVs, x~10 km
Forecast storm Martin: max. wind gusts t+(30-54) (4)
Configuration:
• moist SVs/51 RMs
• moist SVs/10 RMs
Evaluation of the COSMO-LEPS forecasts for the floods in Switzerland in August 2005 and further recent results…
André Walser
MeteoSwiss, Zurich
Total precipitation over 3 days (20. – 22.8.)
About 400 stations, precipitation sum locally over 300 mm!
(06 - 06 UTC)
C. Frei, MeteoSwiss
Summary
COSMO-LEPS has proved to provide useful forecast uncertainty estimates for extreme events.
In the case of the flooding of August, warning has been issued on 21st August. No early warning has been given on 19th, 20st August, although aLMo, LEPS gave correct signal.
The effect of previous false alarms should not be underestimated.
Massimo Milelli, Daniele Cane
VII COSMO General MeetingZuerich, September 20-23 2005
Use of Multi-Model Super-Ensemble
Technique for complex
orography weather forecast
N
iiii FFaOS
1
N
ii OF
NOS
1
1
N
iii FF
NOS
1
1
As suggested by the name, the Multimodel SuperEnsemble method requires several model outputs, which are weighted with an adequate set of weights calculated during the so-called training period. The simple Ensemble method with bias-corrected or biased data respectively, is given by
(1) or (2)
The conventional SuperEnsemble forecast (Krishnamurti et. al., 2000) constructed with bias-corrected data is given by
(3)
Multimodel Theory
We use the following operational runs of the 0.0625° resolution version of LM (00 and 12 UTC runs)
Local Area Model Italy (UGM, ARPA-SIM, ARPA Piemonte) (nud00, nud12)Lokal Modell (Deutscher Wetterdienst) (lkd00, lkd12)aLpine Model (MeteoSwiss) (alm00, alm12)
Training: 180 days (dynamic)Forecast: from July 2004 to March 2005Stations: 102Method: mean and maximum values over warning areas
Precipitation
Me
an
Ma
xim
um
0,0
0,3
0,7
1,0
1,3
1,7
2,0
5 10 20 35
precipitation (mm)
BIA
S
-0,3
0,0
0,3
0,7
1,0
1,3
1,7
2,0
5 10 20 35 50 75
precipitation (mm)
BIA
S
0,0
0,1
0,2
0,3
0,4
0,5
0,6
5 10 20 35precipitation (mm)
ET
S
0,0
0,1
0,2
0,3
0,4
0,5
0,6
5 10 20 35 50 75precipitation (mm)
ET
S
36-60 h
We use the following operational runs of the 0.0625° resolution version of LM (00 and 12 UTC runs)
Local Area Model Italy (UGM, ARPA-SIM, ARPA Piemonte) (nud00, nud12)Lokal Modell (Deutscher Wetterdienst) (lkd00, lkd12)aLpine Model (MeteoSwiss) (alm00, alm12)
Training: 90 days (dynamic)Forecast: March 2005Stations: 53 (h<700m), 34 (700m<h<1500m), 15 (h>1500m)Method: bilinear interpolation horizontally, linear vertically (using Z)
Temperature
h < 700 m
700 m < h < 1500 m
h > 1500 m
0
2
4
6
8
10
12
14
18 24 30 36 42 48 54 60 66 72
Mea
n T
emp
erat
ure
(°C
)
-1
0
1
2
3
4
5
6
7
8
18 24 30 36 42 48 54 60 66 72
Mea
n T
emp
erat
ure
(°C
)
-4
-3
-2
-1
0
1
2
3
18 24 30 36 42 48 54 60 66 72
Forecast Time (h)
Mea
n T
emp
erat
ure
(°C
)
-4,0
-2,0
0,0
2,0
4,0
18 24 30 36 42 48 54 60 66 72
ME
AN
ER
RO
R (
°C)
0,0
1,0
2,0
3,0
4,0
5,0
6,0
7,0
18 24 30 36 42 48 54 60 66 72
RM
SE
(°C
)
-4,0
-2,0
0,0
2,0
4,0
18 24 30 36 42 48 54 60 66 72
ME
AN
ER
RO
R (
°C)
0,0
1,0
2,0
3,0
4,0
5,0
6,0
7,0
18 24 30 36 42 48 54 60 66 72
RM
SE
(°C
)
-4,0
-2,0
0,0
2,0
4,0
18 24 30 36 42 48 54 60 66 72
forecast time (h)
ME
AN
ER
RO
R (
°C)
0,0
1,0
2,0
3,0
4,0
5,0
6,0
7,0
18 24 30 36 42 48 54 60 66 72
forecast time (h)
RM
SE
(°C
)
h <
700
m70
0 m
< h
< 1
500
mh
> 1
500
m
We use the following operational runs of the 0.0625° resolution version of LM (00 and 12 UTC runs)
Local Area Model Italy (UGM, ARPA-SIM, ARPA Piemonte) (nud00, nud12)Lokal Modell (Deutscher Wetterdienst) (lkd00, lkd12)aLpine Model (MeteoSwiss) (alm00, alm12)
Training: 90 days (dynamic)Forecast: March 2005Stations: 53 (h<700m), 34 (700m<h<1500m), 15 (h>1500m)Method: bilinear interpolation horizontally, linear vertically (using Z)
Relative Humidity
h <
700
m70
0 m
< h
< 1
500
mh
> 1
500
m
0,0
5,0
10,0
15,0
20,0
25,0
30,0
18 24 30 36 42 48 54 60 66 72
RM
SE
(%
)
0,0
5,0
10,0
15,0
20,0
25,0
30,0
18 24 30 36 42 48 54 60 66 72
RM
SE
(%
)
0,0
5,0
10,0
15,0
20,0
25,0
30,0
18 24 30 36 42 48 54 60 66 72
forecast time (h)
RM
SE
(%
)
-8,0
-4,0
0,0
4,0
8,0
12,0
18 24 30 36 42 48 54 60 66 72
ME
AN
ER
RO
R (
%)
-8,0
-4,0
0,0
4,0
8,0
12,0
18 24 30 36 42 48 54 60 66 72
ME
AN
ER
RO
R (
%)
-8,0
-4,0
0,0
4,0
8,0
12,0
18 24 30 36 42 48 54 60 66 72
forecast time (h)
ME
AN
ER
RO
R (
%)
Conclusions
• Multimodel Ensemble and SuperEnsemble permit a strong improvement of all the considered variables with respect to direct model output.
• In particular, SuperEnsemble is always superior to Ensemble, except for mean precipitation over warning areas and for ETS in general.
Basis: Operational COSMO-LEPS starting at 12 UTC of the 9th April 2005 (intense precipitation over Emilia Romagna region between 0 UTC of the 10 and 0 UTC of the 12 April 2005)
Set-up of the operational system:
model: LM version 3.9, no nudging, QI and prognostic precipitation
perturbations: b.c. and i.c. from 10 Representative Members selected out of 2 EPS; use of Tiedtke or Kain-Fritsch random
Experiment set-up:
Adding perturbations of the physical parameters of the model -> runtime perturbations
Forecast range: +72h
Member PerturbationM 1 ctrl - T (ope)
M 2 KF only (ope)
M 3 KF + crsmin=200. and c_soil=c_lnd=2
M 4 KF (no ope) + pat_len=0., c_diff=0. and tur_len=1000
M 5 T + pat_len=10000. and c_diff=2.
M 6 KF + crsmin=200., c_soil=0. and rlam_heat=50.
M 7 T (no ope) + lcape=.true.
M 8 KF + l2tls=.true.
M 9 KF + epsass=0.05
M 10 epsass=0.15 (default) + hd_corr_q=0.75
COSMO 2005
Interpretation of the new high-resolution model LMK
Heike Hoffmann [email protected]
Volker Renner [email protected]
Susanne Theis [email protected]
COSMO 2005
Method
We plan a two step approach
1. Using information of a single model forecast by applying the Neighbourhood Method (NM)
2. Using information resulting from LMK forecasts that are started every 3 h (LAF-Ensemble)
COSMO 2005
Assumption: LMK-forecasts within a spatiotemporal neighbourhood are assumed to constitute a sample of the forecast at the central grid point
Neighbourhood Method
COSMO 2005
Products
Smoothed fields for deterministic forecasts
• Expectation Values from spatiotemporal neighbourhood• simple averaging over quadratic grid boxes
Probabilistic Products
• Exceedance Probabilities for certain threshold values for different parameters, especially for hazardous weather warnings
COSMO 2005
Verification results for precipitation
DataLMK forecasts; 3.-17.01.2004; 13.-27.07.2004; 1 h values, 00 UTC and 12 UTC starting time; 7-18 h forecast timeall SYNOPs available from German stationscomparison with nearest land grid point
Neighbourhood-Method-Parametersvers_01: 3 time levels (3 h); 10 s ( 28 km)vers_02: 3 time levels; 5 s vers_03: 3 time levels; 15 s
Averagingsquare areas of different sizes (5x5,15x15)
COSMO 2005
LMK, 3.-17. Jan., vv=7-18h, 00 UTC, FBI
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
0.1 0.2 0.5 1.0 2.0 5.0
threshold [mm/h]
LMK DMO
expectation value v2
5*5
15*15
COSMO 2005
LMK, 3.-17. Jan., vv=7-18h, 00 UTC, HSS
0
10
20
30
40
50
60
0.1 0.2 0.5 1.0 2.0 5.0threshold [mm/h]
LMK DMO
expectation value v2
5*5
15*15
COSMO 2005
LMK, 13.-27. July 2004, vv=7-18h, 00 UTC, FBI
0.0
0.5
1.0
1.5
2.0
2.5
3.0
0.1 0.2 0.5 1 2 5
threshold [mm/h]
LMK DMO
5*5
15*15
expectation value v2
COSMO 2005
LMK, 13.-27. July 2004, vv=7-18h, 00 UTC, HSS
0
5
10
15
20
25
30
35
0.1 0.2 0.5 1 2 5
threshold [mm/h]
LMK DMO
5*5
15*15
expectation value v2
COSMO 2005
– scores in winter are much better than in summer, but in summer there is more effect in postprocessing
– expectation values of NM do not integrate into simple averaging, they show some advantages for intermediate thresholds
– there is, however, no obvious overall improvement by using the NM instead of simple spatial averaging
– therefore, simple averaging over 5x5 domain will be applied followed by a re-calibration of the distributions of the smoothed field towards the distribution of the original field
Conclusions for deterministic precipitation forecasts
COSMO 2005
Exceedance Probability of 1mm/h, 13. Jan. 2004, 00 UTC, vv=17-18h
[%]
calculated with the NM with radius 10 grid steps, 3 time intervals (t-1, t, t+1)
COSMO 2005
LMK, BSS, 3.-17. Jan. 2004, 00 UTC
-30
-20
-10
0
10
20
30
40
50
0.1 0.2 0.5 1 2 5
threshold [mm/h]
BSS Jan v1 climate
BSS Jan v1 LMK
BSS Jan v2 LMK
BSS Jan v2 climate
COSMO General MeetingZurich, 2005
Simple Kalman filter – a “smoking gun” of shortages of models?
Andrzej MazurInstitute of Meteorology and Water Management
COSMO General MeetingZurich, 2005
Introduction
Model forecasts vs. observations
Warsaw, Jan-Mar 2005`
COSMO General MeetingZurich, 2005
“Raw” results vs. Kalman filtering - meteograms
Application of simple Kalman filter for air temperature and wind speed (station Wroclaw)
COSMO General MeetingZurich, 2005
Application of simple Kalman filter for road temperature assessment during winter period
“Raw” results vs. Kalman filtering - (post-processing) applications
COSMO General MeetingZurich, 2005
Conclusions
Method seems to work quite good as far as “continuous” meteorological parameters, like temperature, wind speed or air pressure, are concerned.
Other parameters, like precipitation, should be studied in a similar way. They might require different approach due to their different “nature”.
In both cases, careful selection of predictors is strongly advised.
Results also seem to depend on differences between observations and “raw” results (i.e., BEFORE filter is applied). The greater difference, the better result.
Method - even in this simple approach - can “detect” not only any factor “aside” of the model, but also systematic errors in results.