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

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

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

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

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

Description:
This module explains the process by which data are used in NWP models and the ever-increasing importance that data assimilation has on the quality of numerical forecasts. It provides learners an appreciation for how models use data as a function of model resolution and data type, how data influence the analysis, the limitations of data assimilation systems, the importance of initial conditions on the quality of NWP guidance, as well as the challenges of assessing the quality of NWP guidance based on the initial conditions.

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: 3-4 h

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

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

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

Description:
This 7-page module provides a primer on geostrophic adjustment concepts. It discusses their application for understanding and forecasting real weather features, interpreting model forecasts, and recognizing the type and duration of impact that observations exert on the model forecast. The module also includes an interactive Exercises section.

Estimated time to complete: 1 h