1 / 55

Forecasting Boot Camp

Learn the key steps in the forecast process, from data collection to model integration and post-processing. Understand the importance of data quality control and human interpretation in generating accurate forecasts.

peterkin
Download Presentation

Forecasting Boot Camp

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. Forecasting Boot Camp

  2. Major Steps in the Forecast Process • Data Collection • Quality Control • Data Assimilation • Model Integration • Post Processing of Model Forecasts • Human Interpretation (sometimes) • Product and graphics generation

  3. Data Collection • Weather is observed throughout the world and the data is distributed in real time. • Many types of data and networks, including: • Surface observations from many sources • Radiosondes and radar profilers • Fixed and drifting buoys • Ship observations • Aircraft observations • Satellite soundings • Cloud and water vapor track winds • Radar and satellite imagery

  4. Data Collection • Satellite data is now the dominant data source (perhaps 90%) • Huge increases in the numbers of surface stations and aircraft reports.

  5. Observation and Data Collection

  6. Quality Control • Automated algorithms and manual intervention to detect, correct, and remove errors in observed data. • Examples: • Range check • Buddy check (comparison to nearby stations) • Comparison to first guess fields from previous model run • Hydrostatic and vertical consistency checks for soundings. • A very important issue for the forecaster--sometimes good data is rejected and vice versa.

  7. Pacific Analysis At 4 PM 18 November 2003 Bad Observation

  8. Eta 48 hr SLP Forecast valid 00 UTC 3 March 1999 3 March 1999: Forecast a snowstorm … got a windstorm instead

  9. Objective Analysis/Data Assimilation • Numerical weather models are generally solved on a three-dimensional grid • Observations are scattered in three dimensions • Need to interpolate observations to grid points and to ensure that the various fields are consistent and physically plausible (e.g., most of the atmosphere in hydrostatic and gradient wind balance).

  10. Objective Analysis/Data Assimilation • Often starts with a “first guess”, usually the gridded forecast from an earlier run (frequently a run starting 6 hr earlier) • This first guess is then modified by the observations. • Adjustments are made to insure proper balance. • Objective Analysis/Data Assimilation produces what is known as the model initialization, the starting point of the numerical simulation.

  11. Model Integration: Numerical Weather Prediction • The initialization is used as the starting point for the atmospheric simulation. • Numerical models consist of the basic dynamical equations (“primitive equations”) and physical parameterizations.

  12. “Primitive” Equations • 3 Equations of Motion: Newton’s Second Law • First Law of Thermodynamics • Conservation of mass • Perfect Gas Law • Conservation of water With sufficient data for initialization and a mean to integrate these equations, numerical weather prediction is possible. Example: Newton’s Second Law: F = ma

  13. Simplified form of the primitive equations

  14. Physics Parameterizations • We need physics parameterizations to include key physical processes. • Examples include radiation, cumulus convection, cloud microphysics, boundary layer physics, etc. • Why? • Primitive equations lack the necessary physics • Lack sufficient resolution to resolve key processes. (called explicitly simulating a process) • Small scale physics has to be put in terms of larger scale variables

  15. Parameterization • Example: Cumulus parameterization • Most numerical models (grid spacing of 12-km is the best available operationally) cannot resolve convection (scales of a few km or less). • In parameterization, represent the effects of sub-grid scale cumulus on the larger scales.

  16. Numerical Weather Prediction • A numerical model includes the primitive equations, physics parameterization, and a way to solve the equations (usually using finite differences on a grid) • Makes use of powerful computers • Keep in mind that a model with a specific horizontal grid spacing is barely simulating phenomenon with a scale four times the grid spacing. So a 12-km model barely is getting 50 km scale features correct. • General rule of thumb: model resolves features 6X or larger than the grid spacing.

  17. Numerical Weather Prediction • Most operational modeling systems are run four times a day (00, 06, 12, 18 UTC), although some run twice a day (00 and 12 UTC) • The main numerical modeling centers in the U.S. are: • Environmental Modeling Center (EMC) at the National Centers for Environmental Prediction (NCEP)--part of the NWS. Located near Washington, DC. • Fleet Numerical Meteorology and Oceanography Center (FNMOC)-Monterey, CA • Air Force Weather Agency (AFWA)-Offutt AFB, Nebraska

  18. Major U.S. Models • Global Forecast System Model (GFS). Uses spectral representation rather than grids in the horizontal. Global, resolution equivalent to 13 km grid model. Run out to 384 hr, four times per day. • Weather Research and Forecasting Model (WRF). WRF is a mesoscale modeling system system that is used by the NWS and the university/research community. Two versions (different ways of representing the dynamics): WRF-NMM and WRF-ARW. Universities use WRF-ARW. The NWS runs WRF-NMM at 12-km grid spacing, four times a day to 84h. AFWA is also using WRF (ARW). Run here (36, 12, 4, 1.3 km)

  19. Major U.S. Models • MM5 (Penn. State/NCAR Mesoscale Model Version 5). Had been the dominant model in the research community. Run here at the UW (36, 12 resolution). • COAMPS (Navy). The Navy mesoscale model..similar to MM5. • There are many others--you will hear more about this in 452. • Forecasters often have 6-10 different models to look at. Such diversity can provide valuable information.

  20. Major International NWP Centers • ECMWF: European Center for Medium-Range Weather Forecasting. The gold standard. Their global model is considered the best. • UK Met Office: An excellent global model similar to GFS • Canadian Meteorological Center: GEM Model • Other lesser centers

  21. Accessing NWP Models • The department web site (go to weather loops or weather discussion) provides easy access to many model forecasts. • The NCEP web site is good place to start for NWS models. http://www.nco.ncep.noaa.gov/pmb/nwprod/analysis/ • The Department Regional Prediction Page gets to the department regional modeling output. http://www.atmos.washington.edu/mm5rt/

  22. A Palette of Models • Forecasters thus have a palette of model forecasts. • They vary by: • Region simulated • Resolution • Model physics • Data used in the assimilation/initialization process • The diversity of models can be a very useful tool to a forecaster. Also helps deal with uncertainty.

  23. Post-Processing • Numerical model output sometimes has systematic biases (e.g., too warm or too cold in certain situations). Why not correct the biases? • Numerical models may not have the resolution or physics to deal with certain problems (e.g., low level fog in a valley). Some information on local effects be derived from historical model performance. • The solution: post-processing of model forecasts.

  24. MOS • In the 1960s and 1970s, the NWS developed and began using statistical post-processing of model output…known as Model Output Statistics…MOS • Based on linear regression: Y=a0 + a1X1 + a2X2+ a3X3 + … • MOS is available for many parameters and greatly improves the quality of many model predictions.

  25. Post-Processing • There are other types of post-processing. • Here at the UW we have developed other ways of removing systematic bias. • Others have used “neural nets” as an approach. • Another approach is to combine several models, weighing them by previous performance (called Bayesian Model Averaging).

  26. Ensemble Forecasting • All of the model forecasts I have talked about reflect a deterministic approach. • This means that we do the best job we can for a single forecast and do not consider uncertainties in the model, initial conditions, or the very nature of the atmosphere. These uncertainties are often very significant. • Traditionally, deterministic prediction has been the way forecasting was done, but this is changing.

  27. A More Fundamental Issue • The work of Lorenz (1963, 1965, 1968) demonstrated that the atmosphere is a chaotic system, in which small differences in the initialization…well within observational error… can have large impacts on the forecasts, particularly for longer forecasts. • Similarly, uncertainty in model physics can result in large forecast differences and errors. • Not unlike a pinball game…. • Often referred to as the “butterfly effect”

  28. Probabilistic-Ensemble NWP • There are several ways to produce probabilistic information but the most viable and popular is ensemble prediction. • Instead of running one forecast, run a collection (ensemble) of forecasts, each starting from a different initial state or with different physics. • The variations in the resulting forecasts can be used to estimate the uncertainty of the prediction. • The ensemble mean is on average more skillful than any individual member.

  29. 12h forecast 24h forecast T T 36h forecast e a c u j n g Analysis Region t M 48h forecast Region

  30. Verification The Thanksgiving Forecast 2001 42h forecast (valid Thu 10AM) SLP and winds • Reveals high uncertainty in storm track and intensity • Indicates low probability of Puget Sound wind event 1: cent 5: ngps 11: ngps* 8: eta* 2: eta 3: ukmo 6: cmcg 9: ukmo* 12: cmcg* 4: tcwb 7: avn 13: avn* 10: tcwb*

  31. Ensemble Prediction • Can use ensembles to provide a new generation of products that give the probabilities that some weather feature will occur. • Can also predict forecast skill! • It appears that when forecasts are similar, forecast skill is higher. • When forecasts differ greatly, forecast skill is less.

  32. Ensemble Prediction • During the past decade the size and sophistication of the NCEP and ECMWF ensemble systems have grown considerably, with the medium-range, global ensemble system becoming an integral tool for many forecasters. • Also during this period, NCEP has constructed a higher resolution, short-range ensemble system (SREF) that uses breeding to create initial condition variations.

  33. Storm Prediction Center SREF Ensemble Plumes

  34. New Ensemble-Based Tools (Storm Prediction Center, SREF Visualization)

  35. Major Forecast Failure: Unnecessary if ensembles were used

  36. NAEFS: • North American Ensemble Forecast System

  37. Another Major Advance: Rapid Refresh and High Resolution Rapid Refresh

  38. HRRR • Every hour make a high-resolution analysis (3 km grid spacing) using all available observations. • They make a short-term forecast (now 15 hr)

  39. Human Interpretation • Once all the numerical simulations and post-processing are done, humans still play an important role: • Evaluating the model output • Making adjustments if needed • Attempting to consider features the model can’t handle • Communicating to the public and other users.

  40. Product Generation • Some completely objective and automated. • Others depend on human intervention • Example: the National Weather Service IFPS system

  41. Interactive Forecast Preparation System (IFPS) and National Digital Forecast Database (NDFD)

More Related