april 2005: 19 nws/ 21 forecast products (1) austria aladin-lace (9.6 km) arpege
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1Deutscher WetterdienstMärz 2005
April 2005: 19 NWS/ 21 forecast products
(1) Austria ALADIN-LACE (9.6 km) ARPEGE
(2) Czech Repub ALADIN-LACE (9 km) ARPEGE
(3) Croatia ALADIN-LACE (9 km) ARPEGE
(4) Hungary ALADIN-LACE (11 km) ARPEGE
(5) Slovakia ALADIN-LACE (11 km) ARPEGE
(6) France ALADIN (11 km) ARPEGE
(7) Belgium ALADIN (15 km) ARPEGE
(8) Slovenia ALADIN (9.5 km) ARPEGE
(9) UK UM-EU/LAM (20/12 km) UM-global
(10) Denmark HIRLAM (16 km) ECMWF
(11) Finland HIRLAM (22km) ECMWF
(12) Netherlands HIRLAM (22 km) ECMWF
(13) Spain HIRLAM (22 km) ECMWF
(14) Ireland HIRLAM (16 km) ECMWF
(15) Norway HIRLAM (22/11 km) ECMWF
(16) Switzerland aLMo (7 km) ECMWF
(17) Italy EuroLM (7km) EuroHRM
(18) Germany LM (7 km) GME
(19) Poland Institute of Meteorology and Water Management
SRNWP-PEPSa regional multi-model ensemble in Europe
Internet: www.dwd.de/PEPS
Jean Quiby
Sebastian Trepte
Michael Denhard
jean.quiby@meteoswiss.ch
sebastian.trepte@dwd.de
michael.denhard@dwd.de
2Deutscher WetterdienstMärz 2005
Generation
PEPS grid with a grid spacing of 0.0625° (~7 km) covering Europe
The ensemble size depends on location and every PEPS grid point has its own probability distribution
thresholdaisandpointgridatforecastsofnumbertotaltheiswhere
)(
TiN
N
iatTexceedingxforecastsofNumberTxP
i
ii
Methodology
Ensemble
3Deutscher WetterdienstMärz 2005
Ensemble Products
4. Ensemble size per grid point (at least two members)
1. Ensemble mean. Forecast periods +06...+30h (24 hours), +06...+18h and +18...+30h (12 hours)
• Total precipitation (accumulation), sum of convective and large scale precipitation• Total snow (accumulation) ), sum of convective and large scale snow• Maximum 10 m wind speed• Maximum 10 m wind gust speed• 2 m minimum/maximum temperature
2. Probabilistic products. Forecast period +06...+30h (24 hours)
• Probabilities of total precipitation Thresholds: > 25, > 40, > 70 mm• Probabilities of total snow Thresholds: > 1, > 5, > 10, > 20 cm• Probabilities of maximum wind speed Thresholds: > 10, > 15, > 20, > 25 m/s• Probabilities of maximum wind gust speed Thresholds: > 10, > 15, > 20, > 25, > 33 m/s
3. Probabilistic products. Forecast periods +06...+18h and +18...+30h (12 hours)
• Probabilities of total precipitation Thresholds: > 20, > 50, > 100 mm • Probabilities of total snow Thresholds: > 1, > 5, > 10, > 20 cm • Probabilities of maximum wind speed Thresholds: > 10, > 15, > 20, > 25 m/s• Probabilities of maximum wind gust speed Thresholds: > 10, > 15, > 20, > 25, > 33 m/s
4Deutscher WetterdienstMärz 2005
Maximum Ensemble Size
MaximumEnsemble Size
Totalprecip.
Totalsnow
Windspeed
Wind gustspeed
Temperature
00 UTC 20 19 20 8 20
06 UTC 7 6 7 - 7
12 UTC 20 19 20 8 20
18 UTC 8 7 8 1 8
depends on main run and on meteorological parameter
5Deutscher WetterdienstMärz 2005
Ensemble Mean
21/01/2005 00 UTC +06...30
6Deutscher WetterdienstMärz 2005
21/01/2005 00 UTC +06...30
probability forecasts
7Deutscher WetterdienstMärz 2005
Cut-off times
Model run cut-off time
00 UTC 05.30 UTC
06 UTC 11.30 UTC
12 UTC 17.30 UTC
18 UTC 23.30 UTC
SRNWP-PEPS runs operationally since December 2004 (Distribution of forecasts to the contributing NWS)
8Deutscher WetterdienstMärz 2005
The SRNWP-PEPS project
SRNWP-PEPS workshop 6th April 2005, ARPA-SIM, Italy
products validation further developement rights of use
9Deutscher WetterdienstMärz 2005
Workshopproducts
Mask of areas without sufficient models
Wind gusts
provided by COSMO and some ALADIN countries using different parametrisationsstatistical estimation of wind gusts within PEPS?
Statistics of availability of models
Additional productsmore sysoptic oriented parametersindices of convectivity
Precipitationmedian instead of meanlower thresholds
PEPS-Meteograms (provided by Meteoswiss)
10Deutscher WetterdienstMärz 2005
validation
Workshop
• Comparison with COSMO-LEPS
• Scoring probabilistic forecasts- error measures- FBI, POD, FAR, ETS, HSS, Odds Ratio- BS, BSS, RPS, ROC
• Scale-/Object oriented techniques
- contiguous rain area method (Ebert &McBride)
• Severe weather Problem
- linear error in probability space (LEPS)
• Online verification
WG on Verifcation to coordinate verification with high resolution observations in the contributing countries and to provide scientific expertise.
11Deutscher WetterdienstMärz 2005
further developement
Workshop
Ensemble Calibration
Calibrated: Intervals or events that we declare to have probability P happen a proportion P of the time
Sharp: Prediction intervals are narrower on average than those obtained from climatology; the narrower the
better
Dressing the probability distriubtion of the ensemble with observational errors and give different weights to the ensemble members
12Deutscher WetterdienstMärz 2005
further developement
Workshop
Using Bayesian Model Averaging (BMA) to calibrateforecast ensembles
„The model is estimated from a training set of recent data by maximum likelihood using the EM algorithm. Good results with a 25-day training period.“
Adrian E. Raftery, Fadoua Balabdaoui, Tilmann Gneiting and Michael PolakowskiDepartment of Statistics, University of Washington, Seattle, Washington
),y~()~,...,y~|p(y 2k1 kk
kkn bawy
is the observed value
is the k th forecast
y
1y~
13Deutscher WetterdienstMärz 2005
further developement
Workshop
BMA
work on precipitation is in progress
Software R package EnsembleBMA is available
Sourcewww. stat. washington. edu/ rafterywww. stat. washington. edu/ MURI
14Deutscher WetterdienstMärz 2005
further developement
Workshop
The SRNWP-PEPS consits of different model grids with different horizontal and vertical resolutions.
Question:How can we account for these differences in an appropriate way ?
Statistical downscaling ?
Neighbourhood Ensemble ?
15Deutscher WetterdienstMärz 2005
further developement
Workshop
Neighbourhood Ensemble ?consider all gridpoints within a given distance of a point
Size of Area
Form of Area
spatial temporal
x
t
Neighbourhood members from different grids should not have equal weightsSystematic errors (e.g. due to orography) should be corrected
16Deutscher WetterdienstMärz 2005
further developement
Workshop
Hybrid LAM-Ensemble ?concatenate SRNWP-PEPS with other ensemble systems
• COSMO-LEPS
• INM Ensemble• Meteo France PEACE Ensemble• UK-Met Office LAM• met-norway LAMEPS
Concerning GLOBAL PEPS:„According to most skill measures, these hybrid configurations outperform the ECMWF-EPS at short range for most variables, regions and thresholds“from: Test of a Poor Mans Ensemble Prediction System for short range probability forecasting Arribas, A., Robertson, K.B., Mylne, K.R.
17Deutscher WetterdienstMärz 2005
research projects using PEPS forecasts & products
Workshop
Hydrological Ensemble Forecasts for the „MULDE“ catchment
Hybrid Ensemble
COSMO-LEPS (+120h)
SRNWP-PEPS (+48h)
LMK (2.8km)"lagged average forecast"
Ensemble(+18h)
consistent forecast scenarios of precipitation for the Mulde catchment
up to +120h
International projects which use or may use SRNWP-PEPS forecasts
- EURORISK Prev.I.EW windstorms workpackage- MAP D-Phase (Mesoscale Alpine Program)
18Deutscher WetterdienstMärz 2005
rights of use
Workshop
Scientific use products as well as individual forecastshistoric as well as live data
Commercial useproducts only
Request to DWDDWD distributes the request to the contributing NWS
NWS give their permission
products have to be added to the ECOMET list withpermission of the NWS
19Deutscher WetterdienstMärz 2005
any questions or remarks ?
Thank you to all contributing Weather Services !
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