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WIGOS Space 2040 PB 11/2015 Ⓒ ECMWF
Numerical Weather
Prediction in 2040
Peter Bauer, ECMWF
Acks.: N. Bormann, C. Cardinali, A. Geer, C. Kuehnlein, C. Lupu, T. McNally, S. English, N. Wedi
… will not discuss space weather, hydrology, biogeochemistry
10.8 µm GEO imagery (simulated!)
WIGOS Space 2040 PB 11/2015 Ⓒ ECMWF
(Increase expected from COSMIC-2 and more Chinese data)
assimilated monitored
Number of instruments from which data is assimilated
[Courtesy S. English]
WIGOS Space 2040 PB 11/2015 Ⓒ ECMWF
Quick look at observational impact on short-range forecast today
Forecast Sensitivity to Observation Impact (FSOI) as monitored at NWP centres (here ECMWF)
[Courtesy C. Cardinali]
(Error = Forecast – Analysis) (Error = Forecast – Observations)
WIGOS Space 2040 PB 11/2015 Ⓒ ECMWF
Microwave sounder & imager data can be assimilated in all-sky conditions over all surfaces
Infrared sounder data can be assimilated using the full spectrum via principal components
~200 channels ~5500 channels
Observation error formulations can include state dependence and error correlations
Cutting edge
[Courtesy A. Geer, N. Bormann, T. McNally]
WIGOS Space 2040 PB 11/2015 Ⓒ ECMWF
Limiting factors for observational data in NWP today
Global NWP Regional NWP
Models: • Resolution • Moist physics • Coupling with oceans/sea-ice/land • Composition
• Resolution • Moist physics • Coupling with land • Composition
Data assimilation: • Increment resolution (also vertical) • Linear algorithms • Above model shortcomings
• Increment resolution (also vertical) • Linear/nudging algorithms • Above model shortcomings
Observations: • Wind, low-level moisture, clouds, soil moisture, snow/sea-ice, ocean, aerosols, trace gases
• Sampling/coverage
• Wind, low-level moisture, clouds, precipitation, snow, aerosols
• Resolution • Sampling
Basic rule: • Use in data assimilation: Coverage over stability/accuracy (as long as errors can be characterized) • Use in model evaluation: Completeness (regarding processes) and accuracy
WIGOS Space 2040 PB 11/2015 Ⓒ ECMWF
Satellite data usage in NWP today
Popular question: Why does NWP only use ~5-10% of the globally available data? • Reduced sampling to avoid spatial, temporal and spectral error correlation
→ spectral can be done, spatial & temporal little benefit
• Reduced sampling to avoid unknown cloud and surface effects → increasingly improved with better models and data assimilation methods
Correct question: How much of the information content is used? • A lot more than 5-10%, but actual number is not known
→ spectral sampling will be optimized in the next few years (incl. use of residuals) → optimal temporal/spatial sampling should be addressed with more emphasis
WIGOS Space 2040 PB 11/2015 Ⓒ ECMWF
Fully coupled atmosphere – land – sea-ice – ocean
Fully coupled physics – chemistry
25 km 10 km 5 km 2 km
Greenhouse/reactive gases Atmosphere Aerosols Land surface Waves Sea-ice Ocean
Non-hydrostatic
2010 2015 2020 2025 2030
Models towards 2025-2030
• Single models at O (1-2km), 100 member ensembles at O (5 km), 200 vertical layers, O (100) prognostic variables • Non-hydrostatic, fully coupled models • Regional NWP models at O (100m)
WIGOS Space 2040 PB 11/2015 Ⓒ ECMWF
Models in 2040 As refining resolution globally may become uneconomic, hierarchical refinement in time/space seems favourable, but how to do this: • for coupled models (incl. composition), • consistently between data assimilation and
forecasts?
→The separation line between global and regional NWP will shift
[Courtesy C. Kuehnlein]
WIGOS Space 2040 PB 11/2015 Ⓒ ECMWF
Data assimilation in 2040
With increasing model complexity and a much wider range of observational information to be assimilated, the main challenges are:
• increasing number of degrees of freedom, • increasing non-linearity of processes, • increasing diversity of processes and resolutions.
→Can single method or data assimilation framework serve all purposes?
But: independent of algorithmic choices, further development of forward operators (radiative transfer models, LBL databases), observation and model error specifications will be required = safe investment!
WIGOS Space 2040 PB 11/2015 Ⓒ ECMWF
Computing and data constraints: What is the challenge?
Observations Models
Volume 20 million = 2 x 107 5 million grid points 100 levels 10 prognostic variables = 5 x 109
Type 98% from 80 different satellite instruments
physical parameters of atmosphere, waves, ocean
Observations Models
Volume 200 million = 2 x 108 500 million grid points 200 levels 100 prognostic variables = 1 x 1013
Type 98% from 100+ different satellite instruments
physical and chemical parameters of atmosphere, waves, ocean, ice, vegetation
Today:
Tomorrow:
Factor 10 Factor 2000 per day per time step
WIGOS Space 2040 PB 11/2015 Ⓒ ECMWF
Ensemble Single
HPC requirements and scalability
2015/6 2025
≈ M€ electricity/year
[Bauer et al. 2015]
affordable power limit
WIGOS Space 2040 PB 11/2015 Ⓒ ECMWF
Where computing constraints may make the decisions …
• High-resolution Eulerian, explicit time stepping models may be scalable but not efficient • Highly variable meshes in (unified) coupled models may be limited by load balancing • Sequential data assimilation methods may be too inefficient • Accuracy, stability and resilience may be impossible to achieve together • Data volumes (resolution x time steps x variables x ensemble members) may impose
upper limits
… and where not • Observational data volume handling may be manageable with compression methods • Forward operators (radiative transfer modelling) may be efficient on future architectures and because these can be parallelized more easily.
WIGOS Space 2040 PB 11/2015 Ⓒ ECMWF
• Today’s observational backbone is likely to remain backbone in the future: • (high-resolution) temperature & moisture (type advanced IR, MW, RO, conventional) • waves, currents, clouds, precipitation, ozone → optimal spatial/temporal staggering to ensure sustainability → better spatial/spectral resolution and spectral coverage needed
• Current break-through observations will be added to backbone:
• active: wind, moisture, clouds, precipitation, sea-ice, snow, vegetation • passive: composition, limb sounders, soil moisture → efficient transfer from experimental mission to ingestion in operational constellation
• Entirely new observations will appear: • high-spec instruments in geostationary orbit constellation, very low noise instruments, commodity → efficient transfer from technology demonstration to experimental mission
• Constellations require coordination: • gaps, inter-calibration, RT-modelling (LBL), pre-processing, dissemination, frequency protection etc. → global responsibility (WMO, space agencies)
Summary
WIGOS Space 2040 PB 11/2015 Ⓒ ECMWF
Concluding remarks
Observational impact is often limited by model and data assimilation shortcomings: → Space agencies need to start investing in both to achieve best value for money Space based observing system requires complementary ground based observing system (for assimilation and evaluation): → NWP has excellent metrics and tools to support observing system design NWP missions are climate missions are composition missions: → The toughest requirements from each community apply, respectively (eg NWP drives coverage, climate drives calibration, composition drives information content requirements for each instrument)