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)