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A Comprehensive History of Atmospheric Model Development and Evolution of NCEP Operational Models

This overview chronicles the development of atmospheric models from the early 1900s to present-day advancements. It highlights pivotal figures such as Richardson, Charney, and Phillips, who simplified complex prediction equations, leading to improved models in the 1950s and 1970s. The evolution includes significant milestones such as the introduction of the General Circulation Model and the transition to ensemble forecasting. Key developments at institutions like NCEP and ECPC show the integration of numerical weather prediction methods and the continuous refinement of model resolution and physics for accurate weather forecasting.

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A Comprehensive History of Atmospheric Model Development and Evolution of NCEP Operational Models

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  1. Brief history of atmospheric model developmentNCEP operational modelsECPC (Scripps) model Masao Kanamitsu

  2. Short history (1) • Early 1900. Richardson • Used most complicated form of hydro-dynamical and thermo-dynamical equations (primitive equation). • Spent several months to make 24 hour forecast • Failure!! • 1940’s. Charney, Phillips • Simplification of prediction equations • 2-D regional. • Limited success • General Circulation model • NWS started Numerical Weather Prediction • 1950’s – 1960’s • Gradual increase in the complexity of the equations. • Barotropic Vorticity equation • Quasi-geostrophic models • Balance equation models • General Circulation Model development • 1970’s • Successful use of primitive equations • Hemispheric to global • Development of sophisticated physical processes • Parameterization (convection) • Radiation, boundary layer, etc.

  3. Short History (2) • 1980’s • Further development of numerics and physics • Global model • Long integrations became possible • Shallow convection, gravity wave drag…. • Development of data assimilation • 1990’s-2000’s • Development of variational data assimilation • Ensemble forecast • Further increase in resolution (Parallel computer) • Move towards non-hydrostatic equations

  4. Three streams of modeling • For weather forecasting Global model Regional model • For general circulation study • GFDL, NCAR, UCLA, NASA, ….. Global model still not merged with weather forecasting model • For regional simulation study • NCAR, Navy, CSU, Penn State, …. Becoming merged with weather forecasting models

  5. GCM developments in 1970’s • GFDL (Princeton) under Smagorinsky Manabe • UCLA under Yale Mintz Arakawa • NCAR Kasahara and Washington • NASA(Somerville), Wisconsin(Johnson)…

  6. NCEP (NMC) model development • Research Modeling moved to Princeton (GFDL) in 1960’s. The model development has been done independently between NMC and GFDL, with very little collaboration (radiation). NMC – Shuman, Cressman, … GFDL – Miyakoda, Kurihara NMC was world leader until 1978.

  7. Establishment of ECMWF • 1976 • UK, Germany, France, Italy, Spain, Sweden, Finland, Belgium, Austria, Yugoslavia, Turkey, Greece, Denmark, Holland, Ireland, Portugal • Headed by Winn Nielsen, Lennart Bengtsson.

  8. NCEP operational models

  9. NCEP Models • Global model • Aviation (also provide B.C. for regional) • Medium range • Seasonal range • Regional model • Short range • Hurricane model (on call) • Rapid update cycle • Very short range (order of hourly)

  10. Global model • Global Forecast System (MRF/GFS). • Mother of all models. • Covers the entire globe. • Troposphere to upper stratosphere • Reasonably high horizontal and vertical resolution (T362 L64, about 35km) • Hydrostatic • Spectral

  11. Resolution comparison • U.S. T362L64 • ECMWF T799L91 • UK ~40km L50 (~T360L50)

  12. Regional model • NGM (Phillips)  Eta  WRF • Used to be eta model, but recently replaced with WRF model developed at NCAR. • 12 km resolution • Non-hydrostatic • Grid point

  13. Ensemble forecasting • Uncertainties in the forecast due to uncertainties in the initial condition. • Generate better forecast with ensemble average • Start forecast from slightly different initial conditions. • How to perturb the initial condition • Use most unstable mode • Use perturbation breeding

  14. Merit and demerit of ensemble forecasting • Merit • Better forecast (ensemble average) • Probabilistic information • Demerit • Ensemble spread is too small. The ensemble member does not distribute around the truth. • Future • Initial perturbation • Perturbation of the model physics • Super ensemble (use of multiple models)

  15. NWP and weather forecast • Model Output Statistics (MOS) • Obtain weather element statistically from model output. • Include model error correction • Contrast to Perfect Prog. Method (PPM) • Considered as evil from modelers

  16. Performance comparison

  17. What determines model performance • Atmospheric model • Higher resolution • Better numerics and physics • Analysis • Complexity in variational analysis • Data quality control • Telecommunication lines, decoders • Stability of computer system

  18. NCEP operational data • http://www.emc.ncep.noaa.gov/mmb/nomads/index.shtml

  19. Atmospheric modeling at Scripps

  20. Experimental Climate Prediction Center (ECPC) G-RSM (1) • Global to Regional Spectral Model • Originated from NCEP global and Regional Spectral Model • Improved significantly at Scripps • Global and regional models merged together. • User friendly interface, but with modeling research in mind.

  21. Experimental Climate Prediction Center (ECPC) G-RSM (2) • Runs on a variety of computers, including laptop, workstation, linux cluster, Mac cluster to Earth Simulator. • Need Linux operating system with fortran compiler. • User can change physics packages, and other components of the model. • Good tool to understand the capability of the model.

  22. Difference in simulation due to difference in convection

  23. Experimental Climate Prediction Center (ECPC) G-RSM (3) • Model, detailed instructions available from: g-rsm.wikispaces.com

  24. ECPC forecasts • Seasonal (7-month) ensemble forecast once a month. • Downscaling of the ensemble seasonal forecast over U.S. • Coupled single member forecast once a month. • Daily 3 month forecast and downscaling.

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