Deutscher Wetterdienst
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1 Deutscher Wetterdienst Limited-area ensembles: finer grids & shorter lead times Susanne Theis COSMO-DE-EPS project leader Deutscher Wetterdienst
2 Thank You Neill Bowler et al. (UK Met Office) Andras Horányi et al. (Hungarian Meteorological Service) Trond Iversen and Jørn Kristiansen (Norwegian Meteorological Institute) Chiara Marsigli and Tiziana Paccagnella (ARPA SIMC) Olivier Nuissier et al. (Meteo France) Axel Seifert (Deutscher Wetterdienst)
3 Presentation Overview introduction to limited-area ensembles finer grids what do they promise? predictability issues constructing limited-area ensembles probability maps aim at finest grid?
4 Deutscher Wetterdienst Introduction to Limited-Area Ensembles
5 Limited-area ensembles computing resources are limited compromise between grid size / number of members / model complexity etc
6 Limited-area ensembles computing resources are limited compromise between grid size / number of members / model complexity etc Limited area area allows for for finer grid grid
7 Limited-area ensembles driven by members of global ensemble dynamical downscaling ensemble plus other perturbations today in Europe: more than 10 different ensemble systems (Marsigli et al. 2005, Frogner et al. 2006, Bowler et al. 2008, etc)
8 Limited-area ensembles in Europe ( large domains) system grid size lead time father EPS (global) MOGREPS-R COSMO-LEPS * 18 km 7 km 2.25 days 5.5 days MOGREPS-G ECMWF EPS selection * central / southern # northern GLAMEPS 13 km 1.75 days ECMWF EPS (v0: EuroTEPS) LAMEPS # 12 km 2.5 days ECMWF / EuroTEPS ALADIN-HUNEPS * 12 km 2.5 days PEARP ALADIN-LAEF 18 km 2.5 days ECMWF EPS selection COSMO-SREPS * 7 km 2 days multi-model AEMET-SREPS days multi-model + SRNWP-PEPS
9 Limited-area ensembles in Europe ( small domains) system grid size convectionpermitting lead time status MOGREPS-UK 2.2 km yes 1.5 days development AROME-EPS 2.5 km yes 1.5 days development COSMO-DE-EPS 2.8 km yes 21 hours running DMI-EPS 0.05 no 1.5 days running UMEPS 4km no research
10 Limited-area ensembles in Europe ( small domains) system grid size convectionpermitting lead time status MOGREPS-UK 2.2 km yes 1.5 days development AROME-EPS 2.5 km yes 1.5 days development COSMO-DE-EPS 2.8 km yes 21 hours running DMI-EPS 0.05 no 1.5 days running UMEPS 4km no research
11 Limited-area ensembles in Europe ( small domains) MOGREPS-UK rough projection COSMO-DE-EPS AROME-EPS - HyMeX campaign - future operational
12 Deutscher Wetterdienst Finer Grids - what do they promise?
13 Deutscher Wetterdienst Finer Grids - what do they promise? Examples from ALADIN-HUNEPS, COSMO-LEPS, UMEPS
14 Benefit shown by verification (ALADIN-HUNEPS) Horányi et al. (2011), Tellus 63A: DOI: /j x Figure 5 (right) Latest developments around the ALADIN operational short-range ensemble prediction system im Hungary grid size: 12 km
15 Benefit shown by verification (ALADIN-HUNEPS) Temperature ( C) m-temperature, RMSE of ens.mean Forecast-range (hours) Horányi et al. (2011), Tellus 63A: DOI: /j x Figure 5 (right) Latest developments around the ALADIN operational short-range ensemble prediction system im Hungary grid size: 12 km schematic reproduction of some features in original figure
16 Benefit shown by verification (ALADIN-HUNEPS) Temperature ( C) m-temperature, RMSE of ens.mean Forecast-range (hours) global ensemble spread Horányi et al. (2011), Tellus 63A: DOI: /j x Figure 5 (right) Latest developments around the ALADIN operational short-range ensemble prediction system im Hungary grid size: 12 km schematic reproduction of some features in original figure
17 Benefit shown by verification (ALADIN-HUNEPS) Temperature ( C) m-temperature, RMSE of ens.mean Forecast-range (hours) global limited-area Horányi et al. (2011), Tellus 63A: DOI: /j x Figure 5 (right) Latest developments around the ALADIN operational short-range ensemble prediction system im Hungary grid size: 12 km schematic reproduction of some features in original figure
18 Benefit shown by verification (ALADIN-HUNEPS) Temperature ( C) m-temperature, RMSE of ens.mean Forecast-range (hours) global limited-area Horányi et al. (2011), Tellus 63A: DOI: /j x Figure 5 (right) Latest developments around the ALADIN operational short-range ensemble prediction system im Hungary grid size: 12 km schematic reproduction of some features in original figure
19 Benefit shown by verification (ALADIN-HUNEPS) Temperature ( C) m-temperature, RMSE of ens.mean Forecast-range (hours) global limited-area Horányi et al. (2011), Tellus 63A: DOI: /j x Figure 5 (right) Latest developments around the ALADIN operational short-range ensemble prediction system im Hungary grid size: 12 km At 2m ( ) ALADIN HUNEPS limited area ensemble significantly improves in quality with decreasing values of RMSE (this being due to the higher resolution limited area model and with a better description of the corresponding surface).
20 Benefit shown by verification (ALADIN-HUNEPS) Temperature ( C) m-temperature, RMSE of ens.mean Forecast-range (hours) global limited-area Benefit: near-surface variables Horányi et al. (2011), Tellus 63A: DOI: /j x Figure 5 (right) Latest developments around the ALADIN operational short-range ensemble prediction system im Hungary grid size: 12 km
21 Benefit shown by verification (COSMO-LEPS) Marsigli et al. (2008), Meteorol. Appl. 15: DOI: /met.65 Figure 7(e) A spatial verification method applied to theevaluationof high-resolution ensemble forecasts grid size: 10 km
22 Benefit shown by verification (COSMO-LEPS) verification of precipitation ROC area limited-area global (51 membs) thresholds (mm/24h) Marsigli et al. (2008), Meteorol. Appl. 15: DOI: /met.65 Figure 7(e) A spatial verification method applied to theevaluationof high-resolution ensemble forecasts grid size: 10 km schematic reproduction of some features in original figure
23 Benefit shown by verification (COSMO-LEPS) verification of precipitation ROC area limited-area global (51 membs) thresholds (mm/24h) Marsigli et al. (2008), Meteorol. Appl. 15: DOI: /met.65 Figure 7(e) A spatial verification method applied to theevaluationof high-resolution ensemble forecasts grid size: 10 km schematic reproduction of some features in original figure
24 Benefit shown by verification (COSMO-LEPS) verification of precipitation ROC area limited-area global (51 membs) thresholds (mm/24h) Marsigli et al. (2008), Meteorol. Appl. 15: DOI: /met.65 Figure 7(e) A spatial verification method applied to theevaluationof high-resolution ensemble forecasts grid size: 10 km schematic reproduction of some features in original figure
25 Benefit shown by verification (COSMO-LEPS) verification of precipitation ROC area limited-area global (51 membs) thresholds (mm/24h) Marsigli et al. (2008), Meteorol. Appl. 15: DOI: /met.65 Figure 7(e) A spatial verification method applied to theevaluationof high-resolution ensemble forecasts grid size: 10 km COSMO-LEPS has the skill in forecasting the occurrence of precipitation peaks over an area, irrespective of the exact location.
26 Benefit shown by verification (COSMO-LEPS) verification of precipitation ROC area limited-area global (51 membs) Marsigli et al. (2008), Meteorol. Appl. 15: DOI: /met.65 Figure 7(e) A spatial verification method applied to theevaluationof high-resolution ensemble forecasts Benefit: precipitation peaks
27 The Polar Low Example (UMEPS) Kristiansen et al. (2011), Tellus 63A: DOI: /j x Figure 7 (c) (d) High-resolution ensemble prediction of a polar low development grid size: 4 km
28 The Polar Low Example (UMEPS) About polar lows: frequently accompanied by severe weather moist convective processes are important prediction of polar lows often fails example indicates added value of a high-resolution ensemble Kristiansen et al. (2011), Tellus 63A: DOI: /j x Figure 7 (c) (d) High-resolution ensemble prediction of a polar low development grid size: 4 km
29 The Polar Low Example (UMEPS) Probability [%] of wind speed > 25 m/s LAMEPS UMEPS grid size: 12 km grid size: 4 km Kristiansen et al. (2011), Tellus 63A: DOI: /j x Figure 7 (c) (d) 5 W 5 E 70 N 65 N 5 W 5 E 70 N 65 N >60% >80% schematic reproduction of some features in original figure High-resolution ensemble prediction of a polar low development
30 The Polar Low Example (UMEPS) Probability [%] of wind speed > 25 m/s LAMEPS UMEPS grid size: 12 km grid size: 4 km Kristiansen et al. (2011), Tellus 63A: DOI: /j x Figure 7 (c) (d) 70 N 65 N 70 N 65 N High-resolution ensemble prediction of a polar low development 5 W 5 E 5 W 5 E Benefit: schematic improved reproduction representation of some of of features processes in original leading figure to to severe weather
31 Benefit of ensembles with finer grids improved representation of atmospheric processes: subsynoptic, mesoscale, convective improved forecasts of near-surface variables: precipitation, 2m-temperature, wind gusts improved forecasts of severe weather (Horanyi et al. 2011) (Iversen et al. 2011) (Marsigli et al. 2008) (Bowler et al. 2008) etc
32 Entering Key Applications probabilistic forecasts of severe weather, near-surface variables, for short lead times: weather warnings flood warnings aviation wind energy etc
33 Deutscher Wetterdienst Predictability Issues
34 Deutscher Wetterdienst Predictability Issues General Remarks Supercell Example by COSMO-DE-EPS
35 Finer Grids gain in predictability?
36 Finer Grids gain in predictability? not necessarily!
37 scale diagram characteristic time scale 1 week 1 hour synoptic convective 100 m 10 km 1000 km characteristic length scale
38 scale diagram predictability characteristic time scale 1 week 1 hour synoptic convective 100 m 10 km 1000 km characteristic length scale lead time
39 scale diagram predictability characteristic time scale 1 week 1 hour synoptic convective 100 m 10 km 1000 km characteristic length scale lead time Uncertainties in in small scales grow faster (Lorenz 1969)
40 Finer Grids gain in predictability? smaller scales usually possess shorter life cycles, faster error growth, shorter predictability limits high-resolution model simulations are expected to contain a larger degree of randomness this can offset the benefits due to a smaller model grid box size if forecast uncertainties are not addressed explicitly (e.g. Mass et al. 2002)
41 Finer Grids gain in predictability? smaller scales usually possess shorter life cycles, faster error growth, shorter predictability limits high-resolution model simulations are expected to contain a larger degree of randomness this can offset the benefits due to a smaller model grid box size if forecast uncertainties are not addressed explicitly (e.g. Mass et al. 2002) Forecasts must be be addressed in in a probabilistic framework
42 The Supercell Example (COSMO-DE-EPS) by Axel Seifert with Thomas Hanisch, Christoph Gebhardt, Zied Ben Bouallègue, Michael Buchhold Deutscher Wetterdienst
43 The Supercell Example (COSMO-DE-EPS) COSMO-DE: convection-permitting model (2.8 km) can explicitly simulate severe storms, but deterministic forecasts of individual cells are not possible with 12 h lead time i.e. the model provides a possible scenario for the development of individual convective cells in this example: visualized by - simulated radar reflectivity -thesupercell detection index (SDI) Wicker et al. (2005) by Axel Seifert with Thomas Hanisch, Christoph Gebhardt, Zied Ben Bouallègue, Michael Buchhold Deutscher Wetterdienst
44 The Supercell Example (COSMO-DE-EPS) observed by radar forecast by COSMO-DE F2 tornado near Plate close to the Baltic coast 16:20 UTC by Axel Seifert Deutscher Wetterdienst
45 The Supercell Example (COSMO-DE-EPS) observed by radar forecast by COSMO-DE F2 tornado near Plate close to the Baltic coast 16:20 UTC the forecast shows many SDI events in that region by Axel Seifert Deutscher Wetterdienst
46 The Supercell Example (COSMO-DE-EPS) observed by radar forecast by COSMO-DE without ensemble F2 tornado near Plate close to the Baltic coast 16:20 UTC the forecast shows many SDI events in that region by Axel Seifert Deutscher Wetterdienst
47 The Supercell Example (COSMO-DE-EPS) forecast by COSMO-DE-EPS (Gebhardt et al., 2011) 20 scenarios of SDI events ensemble provides 20 scenarios with ensemble (2.8 km) by Axel Seifert Deutscher Wetterdienst
![, 2011) 20 scenarios of SDI events SDI probability [%] with ensemble](/docs-images/78/77724339/images/48-3.jpg "(2.8 km) ensemble provides 20 scenarios combined in a probability")
48 The Supercell Example (COSMO-DE-EPS) forecast by COSMO-DE-EPS (Gebhardt et al., 2011) 20 scenarios of SDI events SDI probability [%] with ensemble (2.8 km) ensemble provides 20 scenarios combined in a probability product useful guidance by Axel Seifert Deutscher Wetterdienst
49 The Supercell Example (COSMO-DE-EPS) forecast by COSMO-DE-EPS (Gebhardt et al., 2011) 20 scenarios of SDI events SDI probability [%] with ensemble (2.8 km) ensemble provides 20 scenarios combined in a probability product useful guidance Convection-permitting model can can simulate by process. Axel Seifert Ensemble accounts for for limited predictability and Deutscher Wetterdienst and derives useful guidance.
50 Deutscher Wetterdienst Constructing limited-area ensembles - same as global?
51 Finer grids: revision of ensemble techniques focus on short lead times account for uncertainties coming from the driving model introduce uncertainties in the relevant scales and processes some techniques are not applicable anymore high demand of computing resources
52 Finer grids: revision of ensemble techniques focus on short lead times account for uncertainties coming from the driving model introduce uncertainties in the relevant scales and processes some techniques are not applicable anymore high demand of computing resources new new scientific challenges
53 Deutscher Wetterdienst Probability maps: aim at finest grid?
54 Probability maps: aim at finest grid? forecast provider user
55 Probability maps: aim at finest grid? convection-permitting ensembles have a grid size of 1-3 km we can produce probability maps for this grid size
56 Probability maps: aim at finest grid? Example: 15 May UTC RADAR observation
57 Probability maps: aim at finest grid? Forecast: Probability of precipitation UTC derived from COSMO-DE-EPS 2.8 km
58 Probability maps: aim at finest grid? Forecast: Probability of precipitation UTC on the same grid as the model: 2.8 km derived from COSMO-DE-EPS 2.8 km
59 Probability maps: aim at finest grid? Forecast: Probability of precipitation UTC on the same grid as the model: 2.8 km derived from COSMO-DE-EPS 2.8 km
60 Probability maps: aim at finest grid? Forecast: Probability of precipitation UTC on the same grid as the model: 2.8 km derived from COSMO-DE-EPS 2.8 km
61 Probability maps: aim at finest grid? Forecast: Probability of precipitation UTC on the same grid as the model: 2.8 km derived from COSMO-DE-EPS 2.8 km
62 Probability maps: aim at finest grid? Forecast: Probability of precipitation UTC on a larger grid than the model: 28 km derived from COSMO-DE-EPS 2.8 km
63 Probability maps: aim at finest grid? Forecast: Probability of precipitation UTC on a larger grid than the model: 28 km derived from COSMO-DE-EPS 2.8 km
64 Probability maps: aim at finest grid? Forecast: Probability of precipitation UTC on a larger grid than the model: 28 km derived from COSMO-DE-EPS 2.8 km
65 Probability maps: aim at finest grid? think about scale of interest alert areas COSMO-DE-EPS example by DWD S.Theis, C.Gebhardt, Z. Ben Bouallègue, M.Buchhold
66 Probability maps: aim at finest grid? think about scale of interest alert areas e.g. UK Met Office: MOGREPS-W COSMO-DE-EPS example by DWD S.Theis, C.Gebhardt, Z. Ben Bouallègue, M.Buchhold derives area warnings from MOGREPS-R (18 km)
67 Probability maps: aim at finest grid? think about scale of interest alert areas e.g. UK Met Office: MOGREPS-W COSMO-DE-EPS example by DWD S.Theis, C.Gebhardt, Z. Ben Bouallègue, M.Buchhold derives area warnings from MOGREPS-R (18 km) End End product not not necessarily on on finest grid grid Beneficial if if underlying ensemble is is on on finest grid grid
68 Probability maps: aim at finest grid? users must know and understand the reference area of probabilities COSMO-DE-EPS example by DWD S.Theis, C.Gebhardt, Z. Ben Bouallègue, M.Buchhold
69 Probability maps: aim at finest grid? users must know and understand the reference area of probabilities COSMO-DE-EPS example by DWD S.Theis, C.Gebhardt, Z. Ben Bouallègue, M.Buchhold (Epstein, 1966) (Murphy, 1980) and (Gigerenzer et al., 2005) reference class
70 Probability maps: aim at finest grid? users must know and understand the reference area of probabilities COSMO-DE-EPS example by DWD S.Theis, C.Gebhardt, Z. Ben Bouallègue, M.Buchhold (Epstein, 1966) (Murphy, 1980) and (Gigerenzer et al., 2005) reference class Users (and providers) must achieve risk literacy
71 Summary ensembles are going to finer grids improved representation of atmospheric processes, improved forecasts of near-surface weather, severe weather even less deterministic predictability increased need for ensembles new challenges for ensemble techniques implication for ensemble applications entering key applications end products on scale of interest, not necessarily on finest grid risk literacy is essential
72
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