1 / 38

Introduction to NWP

Introduction to NWP. What is NWP?. Numerical Weather Prediction: Predicting the future state of the atmosphere by use of the mathematical equations that describe it. Equations of motion. 1. Momentum. Conservation Laws. Mass:. Energy:.

norah
Download Presentation

Introduction to NWP

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Introduction to NWP

  2. What is NWP? • Numerical Weather Prediction: Predicting the future state of the atmosphere by use of the mathematical equations that describe it.

  3. Equations of motion 1. Momentum Conservation Laws Mass: Energy: Hydrostatic: eliminate acoustic, high-frequency buoyancy waves

  4. “Physics” equations • model processes in atmosphere and at earth’s surface.

  5. Radiation

  6. Convection

  7. Boundary Layer

  8. Precipitation

  9. Precipitation

  10. Equations • Equations can only be solved in modifed form, on super-computer. • First successful NWP was in 1950 with simplified (barotropic) model run on ENIAC.

  11. Discretization: of fields • bring irregularly spaced data to regular grid

  12. xi, at time t-∆t xi, at time t+∆t Discretization: of equations Simple example: 1D advection equation: approximated by differences in space and time:

  13. GEM grids Global Regional LAM (east)

  14. Discretization: Vertical Levels GEM: 80 levels LAM: 58 levels Terrain-following “eta” coordinate becomes level with increasing height

  15. CMC Model Hierarchy

  16. CMC Model Hierarchy

  17. Parametrization

  18. Parameterization e.g.: • convective cloud processes • formation of cloud droplets and precipitation • solar and infrared radiative transfer • surface-air exchanges of energy, momentum, and moisture

  19. Use J(xi-1) to find xi Data assimilation concept Data Acquisition Error Statistics (Observation and Forecast) Data Quality Control Analysis (Spatial QC) Cost function J(x,..) First Guess xa MIN J(x) NWP model

  20. Observations assimilated at CMC Type Variables Thinning radiosonde/dropsonde U, V, T, (T-Td), ps 28 levels Surface report T, (T-Td), ps, (U, V over water) 1 report / 6h Aircraft (BUFR, AIREP, AMDAR, ADS) U, V, T 1o x 1o x 50 hPa per time step Ocean Land AMSU-A 3-10 6-10 AMSU-B / MHS 2-5 3-4 ATOVS NOAA 15-16-17-18, AQUA 250 km x 250 km per time step Water vapor channel GOES 11-12 IM3 (6.7 m) 2o x 2o 3-hourly U,V (IR, WV, VI channels) 1.5o x 1.5o 11 layers, per time step AMV’s (METEOSAT 5-8, GOES 11-12, MTSAT-1R) ~180 km boxes 11 layers, per time step MODIS polar winds (Aqua, Terra) U,V Profiler (NOAA Network) U,V (750 m) Vertical hourly

  21. e.g. AMSU-A data received at CMC

  22. e.g. AMSUA data retained after thinning

  23. e.g. AMSUB data received at CMC

  24. e.g. AMSUB data retained after thinning

  25. e.g. satellite winds received at CMC(6 hr period, 1 single level wind vector per obs, ~380k obs, from GTS)

  26. e.g. satellite winds retained after thinning

  27. e.g. radiosonde data received at CMC(6 hr period, multi-level U,V,T,Es, ~600 stations, from GTS and MSC)

  28. e.g. aircraft data received at CMC

  29. e.g. SYNOP data received at CMC(

  30. e.g. Buoy data received at CMC(6 hr period, multiple elements, ~12k reports, from GTS and MSC, ARGO also avail.)

  31. Use J(xi-1) to find xi Data assimilation and forecast Data Acquisition Error Statistics (Observation and Forecast) Data Quality Control Analysis (Spatial QC) Cost function J(x,..) First Guess xa MIN J(x) NWP model Forecasts

  32. CMC Assimilation & Forecast cycle (Only 12-hour Regional cycle shown)

  33. Forecast Error: verification

  34. Forecast Error: verification

  35. Forecast Error • 10 day MLSP forecast is completely off: Approaching theoretical limit of 2 weeks for weather forecasts (Lorenz: “butterfly effect”)

  36. Ensemble Prediction System Final states (forecasts) Initial states (analyses) True initial state True final state

  37. Ensemble Prediction System observations random numbers perturbed observations trial field or data assimilation 6-h integration with model i perturbed trial field perturbed analysis random numbers and perturbed physics model physics medium-range forecast medium-range integration with model i

  38. Summer SW Point forecasts Winter SW Aviation Air Quality Meteorological Fields Operational Charts EER Plain language Graphics Fcst Product Generation Systems NWP Fields Weather Elements Numerical Weather Prediction Models Verification seasons Long range fcst months Public fcsts EPS Error Feedback Statistics Weather Elements Contingency Tables Perfect Prog Direct Model Output MOS Analog Neural Net Fuzzy Logic Kalman Filters Diagnostic

More Related