MetEd: Meteorology Education and Training. Operated by the COMET Program

Description:
Forecasters must have an understanding of the general methodology of numerical weather prediction in order to develop realistic expectations for guidance produced by operational models. This series of Web-based modules explains how numerical models are designed and implemented, and will help forecasters interpret model guidance through an understanding of model constraints and features. The course will also be valuable for upper-division and graduate meteorology students wanting a deeper understanding of how atmospheric processes are modeled. The course contains six modules and will take approximately 16-20 hours to complete. Individual modules will take from 1 to 4 hours. A final exam helps you gauge your learning.

Estimated time to complete: 16-20 h

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Description:
This Webcast covers the ocean surface wind retrieval process, the basics of microwave polarization as it relates to wind retrievals, and several operational examples. Information on the development of microwave sensors used to retrieve ocean surface wind speed and the ocean surface wind vector (speed and direction) is also included.

Objectives:
State some key meteorological applications for ocean surface winds

• Describe the benefits of using microwave remote sensing to observe ocean winds
• Describe the differences between active and passive microwave remote sensing
• Describe in general terms, the emission, transmission, and scattering of microwave energy within the Earth-atmosphere system
• State the key assumptions for derivation of wind speed and direction from passive observation of microwave radiation
• Describe the limitations of passive microwave remote sensing and impacts on deriving wind speed and direction (this applies to both product limits and accuracy)
• Use cloud liquid water imagery to help assess the validity of the wind speed and direction vector

Estimated time to complete: 45 min

Includes audio: yes

Required plug-ins:   Flash RealPlayer Java Adobe® Reader®
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Last published on: 2005-11-28

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Description:
This module, the latest in our series on Numerical Weather Prediction, covers the theory and use of ensemble prediction systems (EPSs). The module will help forecasters develop an understanding of the basis for EPSs, the skills to interpret ensemble products, and strategies for their use in the forecast process. It contains six sections: an Introduction that briefly presents background theory; Generation, which describes how ensemble systems are constructed; Statistical Concepts, which provides a brief refresher on knowledge required for ensemble product interpretation; Summarizing Data, which describes common ensemble forecast products; Verification, which discusses how EPSs performance is assessed and documented; and Case Applications, which provides links to a number of forecast cases illustrating the use of EPSs in the forecast process. Questions and Exercises are offered throughout to help you test your learning and provide practical examples. The module also includes a pre-assessment and module final quiz.

Objectives:
Explain the basis for NWP ensemble prediction, and what we mean when we say that the atmosphere is chaotic (i.e. sensitive to initial conditions).

Describe the variety of methods used to generate the ensemble members of an ensemble prediction system, including perturbation of initial conditions, boundary conditions, and model configurations.

Understand the basic statistical concepts and methods used in the development of ensemble products, including probability distributions and their middleness, variability, and shape characteristics.

Recognize and interpret the variety of ensemble forecast products, including spatial and point forecast graphics, and including those that account for flow regimes (RMOP) and reveal NWP model bias and errors.

Interpret ensemble verification products, and apply them in using ensemble forecasts.

Estimated time to complete: 4-5 h

Includes audio: no

Required plug-ins:   Flash RealPlayer Java Adobe® Reader®
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Last published on: 2004-09-27

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Description:
This one-stop Ensemble Model Matrix provides information on the configurations of the NCEP Short-Range Ensemble Forecast (SREF) and Medium-Range Ensemble Forecast (MREF) systems. Information on ensemble perturbation methods; NWP model resolution, dynamics, physics (precipitation, radiation, land surface and turbulence); and ensemble post-processing and verification links are provided. As the ensemble prediction systems (EPSs) are improved, the information in the Ensemble Model Matrix will be updated. Additionally, as new EPSs are added to AWIPS, we will add new columns to the Ensemble Model Matrix.

Estimated time to complete:

Includes audio: no

Required plug-ins:   Flash RealPlayer Java Adobe® Reader®
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Last published on: 2006-04-05

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Description:
Forecasting Dust Storms is the latest module in the Mesoscale Meteorology Primer. The module starts by discussing the conditions required for a dust storm, including an appropriate source of dust, sufficient wind and turbulence, and an unstable atmosphere. The module then explores the fate of dust in the atmosphere including dispersion, advection, and settling. The concluding section on forecasting examines a case in the Middle East and demonstrates the use of a mesoscale NWP model, as well as next-generation dust forecasting models.

Objectives:
After completing this module, the learner should be able to do the following things:

With regard to dust storm characteristics:

• Describe how visibility varies near severe dust storms
• Recall the average height of dust storms
With regard to sources of dust:
• Describe the soil types in appropriate source regions for dust storms
• Recall that blowing dust usually does not occur for at least 24 hours after a rainfall
• Identify potential source regions with satellite imagery

With regard to atmospheric conditions required for dust storms:

• Recall the threshold wind speed for lifting fine dust particles.
• Describe the atmospheric conditions that promote lofting of dust in terms of stability and turbulence
• List the 3 ways that turbulence typically arises in the atmosphere
• Describe the effect of nightfall on dust storms

With regard to the dissipation and dispersion of dust storms:

• Describe the atmospheric factors that influence the dispersion of dust
• Describe the effect of precipitation on suspended dust and why this occurs
• Recall how quickly dust settles once winds die down

With regard to the climatology of dust storms:

• List the most common synoptic patterns for raising dust in the Middle East
• Define Shamal
• List at least 3 mesoscale weather phenomena that result in dust storms
• Describe how haboobs and dust devils originate
• Describe how winter dust storms differ from summer dust storms

With regard to the satellite detection of blowing dust:

• Describe how dust appears on IR images, during both day and night and over both land and water
• Describe how dust appears on visible images, during both day and night and over both land and water
• Describe the advantages of imagery from polar orbiting and geostationary satellites
• With regard to forecasting dust storms:
• List the tools available for observing dust storms.
• Describe how mesoscale NWP models can help with a dust storm prediciton
• List the dust storm forecasting models and describe their respective advantages

Estimated time to complete: 2 h

Includes audio: yes

Required plug-ins:   Flash RealPlayer Java Adobe® Reader®
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Last published on: 2003-10-23

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Description:
This Webcast is based on a COMET classroom presentation by Dr. Gary Lackmann at the 2nd MSC Winter Weather Course held in Boulder, Colorado on 22 February 2002. Dr. Lackmann reviews the basic thermodynamics of freezing and melting and how operational models represent these processes. He also touches upon the biases that occur in the models by looking at examples of melting snow aloft, melting snow at the surface, freezing aloft (ice pellets), and freezing rain. Dr. Lackmann is a faculty member in the Department of Marine, Earth, and Atmospheric Sciences at North Carolina State University.

Objectives:
1. Examine four thermodynamic scenarios closely, each of which produces a different precipitation situation.

2. Compare sounding, radar, and model signatures associated with these scenarios.

3. Compare the representation of these thermodynamic processes in operational models at and near the surface.

4. Become aware of potential problems with the model forecasts.

5. Examine the limiting processes and requirements for freezing rain.

Estimated time to complete: 35 min

Includes audio: yes

Required plug-ins:   Flash RealPlayer Java Adobe® Reader®
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Last published on: 2002-07-03

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Description:
The goal of this training module is to help you increase your understanding of how mesoscale models work. Such understanding, in turn, can help you more efficiently and accurately evaluate model-generated forecast products.

Objectives:
Terminal Objectives
By the end of this module you will be able to do the following:
1. Describe how mesoscale models work
2. Evaluate the strengths and weaknesses of different NWP models

Enabling Objectives
By the end of this module you will be able to do the following:
1. Describe the benefits and limitations of mesoscale NWP models.
2. Describe the relationship between grid spacing and model resolution for NWP models.
3. Describe the pros and cons of increasing model resolution
4. Describe hydrostatic balance and how hydrostatic NWP models differ from non-hydrostatic NWP models
5. Define Eta, sigma, and pressure vertical coordinates schemes and describe their respective advantages.
6. Define parameterization and describe the benefits of its use in NWP models
7. List at least 3 processes that are typically parameterized.
8. Describe limited area model (LAM), spin-up, and warm start, and how they are all related.
9. Describe the benefits and limitations of a warm start.

Estimated time to complete: 30 min

Includes audio: yes

Required plug-ins:   Flash RealPlayer Java Adobe® Reader®
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Last published on: 2002-04-22

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Description:
Part of the Numerical Weather Prediction Professional Development Series, this module explores how NWP models handle precipitation and cloud processes through parameterizations and/or explicit methods, with an emphasis on how a model's treatment of these processes affects its ability to depict and forecast precipitation and other related forecast variables.

The subject matter expert for this module is Dr. Ralph Petersen of the National Centers for Environmental Prediction, Environmental Modeling Center (NCEP/EMC).

Estimated time to complete: 3-6 h

Includes audio: no

Required plug-ins:   Flash RealPlayer Java Adobe® Reader®
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Last published on: 2000-07-27

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Description:
Impact of Model Structure & Dynamics, part of the Numerical Weather Prediction Professional Development Series and the NWP Distance Learning Course, provides operationally significant information about model type, horizontal resolution, vertical coordinate systems, vertical resolution, and domain and boundary conditions, with an emphasis on how each aspect can affect a model's ability to depict and forecast weather.

The subject matter expert for this module is Dr. Ralph Petersen of the National Centers for Environmental Prediction, Environmental Modeling Center (NCEP/EMC).

Estimated time to complete: 3-5 h

Includes audio: no

Required plug-ins:   Flash RealPlayer Java Adobe® Reader®
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Last published on: 2000-09-21

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Description:
This module describes model parameterizations of sub-surface, boundary-layer,and free atmospheric processes, such as surface snow processes, soil characteristics, vegetation, evapotranspiration, PBL processes and parameterizations, and trace gases, and their interaction with the radiative transfer process. It specifically addresses how models treat these physical processes and how they can influence forecasts of sensible weather elements.

Objectives:
Working through the material will help you to
• Develop a basic understanding of how radiation and associated processes are emulated in NWP models
• Understand when model physics are most important to the model forecast (versus model dynamics)
• Understand that model physics are specifically tuned to work best in certain situations and specific models
• Understand that model physics parameterizations affect other parameterizations, model dynamics, and data assimilation, which may result in feedbacks
• Identify impacts of model physics and their errors on model forecasts both at and around the forecast location
• Identify effects that are smaller than the model can emulate (for example, the resolution of surface characteristics is coarse but real effects occur at fine resolution)

Estimated time to complete: 1.5 h

Includes audio: no

Required plug-ins:   Flash RealPlayer Java Adobe® Reader®
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Last published on: 2000-11-17

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Description:
This module was developed and released in three sections: Postprocessing/Products, Statistical Guidance, and Assessment Tools. Specific topics covered include the impact of postprocessing and how to account for it, the statistical methods used to enhance raw model output including how statistical guidance products like MOS are generated, as well as NWP verification methodologies and use of daily model diagnostics.

The subject matter expert for this module is Dr. Ralph Petersen of the National Centers for Environmental Prediction, Environmental Modeling Center (NCEP/EMC), and J. Paul Dallavalle of the National Weather Service (NWS), Meteorological Development Laboratory, Statistical Modeling Branch (MDL/SMB). The primary content author was Kirby Cook, NWS, Western Region Headquarters (WRH)/Scientific Services Division (SSD).

Estimated time to complete: 1-2 h

Includes audio: no

Required plug-ins:   Flash RealPlayer Java Adobe® Reader®
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Last published on: 2000-10-02

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Description:
This webcast is a shorter companion to the Ensemble Prediction Explained module, focusing more directly on immediate operational needs. Introductory content includes the role of ensemble forecasts, presentation of basic ensemble forecasting terms, and discussion of how ensemble prediction systems (EPSs) are created. The largest section is focused on common ensemble forecast products, including how they differ from traditional NWP products, how we interpret ensemble forecast products, the advantages and limitations of each product, how EPS products are verified, and how to use ensemble products in conjunction with one another to increase your understanding of forecast uncertainty. Finally, three brief cases from cold and warm seasons illustrate the use of ensemble products in the forecast process.

Objectives:
1. State the benefits of including ensemble model forecasts in the NWP product suite.


2. Define the following terms used in ensemble forecasting:


* Ensemble perturbation

* Ensemble member

* Control forecast

* Perturbation forecast

* Ensemble Prediction System (EPS)


3. Describe three methods commonly used to produce the members of an EPS.


4. Describe how ensemble forecast products differ from traditional NWP products.


5. Interpret ensemble forecast products to determine the probabilistic EPS forecast.


* Interpret spaghetti plots, mean and spread plots, probability of exceedance plots, most likely or dominant event plots, plume diagrams, box and whisker diagrams, and ensemble soundings.

* State advantages and limitations to each of the above products.


6. Use ensemble products in conjunction with one another to increase your understanding of forecast uncertainty.


7. Use ensemble verification products to evaluate the performance of an EPS, including reliability and Talagrand diagrams.

Estimated time to complete: 59 min

Includes audio: yes

Required plug-ins:   Flash RealPlayer Java Adobe® Reader®
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Last published on: 2005-06-27

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Description:
The Marine Wave Model Matrix provides information on the formulation of wave models developed by the National Centers for Environmental Prediction (NCEP) and other modeling centers, including how these models forecast the generation, propagation, and dissipation of ocean waves using NWP model forecasts for winds and near-surface temperature and stability. Additionally, information is provided on data assimilation, post-processing of data, and verfication of wave models currently in operation. Within the post-processing pages are links to forecast output both in graphical and raw form, including links for data downloads. Links to COMET training on wave processes are also provided.

Estimated time to complete: 30 min

Includes audio: no

Required plug-ins:   Flash RealPlayer Java Adobe® Reader®
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Last published on: 2006-05-16

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Description:
Model Fundamentals, part of the Numerical Weather Prediction Professional Development Series and the NWP Distance Learning Course, describes the components of an NWP model and how they fit into the forecast development process. It also explores why parameterization of many physical processes is necessary in NWP models.

The module covers background concepts and terminology necessary for learning from the other modules in this series on NWP.

The subject matter expert for this module is Dr. Ralph Petersen of the National Centers for Environmental Prediction, Environmental Modeling Center (NCEP/EMC).

Estimated time to complete: 1 h

Includes audio: no

Required plug-ins:   Flash RealPlayer Java Adobe® Reader®
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Last published on: 1999-06-01

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Description:
Operational Models Matrix: Characteristics of Operational NWP Models, part of the Numerical Weather Prediction Professional Development Series, contains information about the characteristics and architecture of commonly used operational models, their operationally significant strengths and weaknesses, and model assessment tools. The information is updated whenever significant model changes are made.

The module is linked to the Impact of Model Numerics on Weather Depiction module (also in the NWP PDS), which provides background information about model components.

The subject matter expert for this module is Dr. Ralph Petersen of the National Centers for Environmental Prediction, Environmental Modeling Center (NCEP/EMC).

Estimated time to complete: 3-5 h

Includes audio: no

Required plug-ins:   Flash RealPlayer Java Adobe® Reader®
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Last published on: 2007-10-19

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Description:
The NCEP Real-Time Mesoscale Analysis (RTMA), provides current conditions in digital form on the NWS National Digital Forecast Database (NDFD) 5-km grid. This product was upgraded in early July 2007 to the point where its use by forecast offices is now encouraged for situational awareness, creating short-term forecast grids, and evaluating recent forecast grids and forecast bias. Unique to the RTMA is an uncertainty or error estimate for some of its analysis parameters. These uncertainty estimates perhaps could be used to determine when a forecast is “good enough”. This Webcast discusses why the RTMA and its parent project, the Analysis of Record, were created, how the RTMA is generated, and its capabilities, limitations, and possible applications. The Webcast includes extensive discussion about how representative individual observations are and how they are handled by the analysis. The topics covered include:

* The context for developing the RTMA and related future developments
* Use of the RTMA in the human forecast process
* The steps in generating RTMA products: forecast, downscaling, observation data sets, quality control, two-dimensional variational analysis (2d-var), “uncertainty” estimates, multisensor precipitation analysis, and GOES Effective Cloud Amount
* Limitations related to how RTMA products are generated
* How an observation affects the 2d-var analysis
* Issues raised by the analysis using accurate observations which are not representative of their surrounding area
* Preliminary performance assessment over complex terrain
* Key changes under development for future RTMA implementations

Objectives:

1. Big picture - Understand why RTMA was created and how it fits into the Analysis of Record project
   
2. Be able to apply RTMA in your forecast operations
   1. Be aware of which data types are used/not-used
      
   2. Recognize that a perfect analysis should not exactly fit observations
      
   3. Be familiar with what products exist
      
   4. Be familiar with how the products are made and therefore understand their capabilities and limitations
3. Be aware of how future changes already under development will affect the RTMA product suite
   

Estimated time to complete: 1 h

Includes audio: yes

Required plug-ins:   Flash RealPlayer Java Adobe® Reader®
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Last published on: 2007-08-14

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Description:
This module introduces forecasters to ten of the most commonly encountered or significant misconceptions about NWP models. This list of ten misconceptions includes issues surrounding data assimilation, model resolution, physical parameterizations, and post-processing of model forecast output.

Estimated time to complete: 100 min

Includes audio: yes

Required plug-ins:   Flash RealPlayer Java Adobe® Reader®
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Last published on: 2002-05-02

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Description: