The MetEd website, COMET’s signature offering, is a free collection of hundreds of training resources intended for the geoscience community. We deliver over 240,000 hours of online education each year in disciplines such as aviation weather, climate, convective weather, emergency management, hydrology, numerical modeling, satellite meteorology and winter weather, among many others.
A variety of MetEd lessons are translated in multiple languages. COMET partners with many international stakeholders and is sponsored by global agencies such as, National Oceanic and Atmospheric Administration (NOAA), European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), Meteorological Service of Canada (MSC), National Science Foundation (NSF), World Meteorological Organization (WMO) and many more.
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Latest MetEd Publications
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Aviation Hazards: Thunderstorms
This lesson introduces aviation forecasters to the impact of thunderstorms on aviation functions within the US National Airspace System (NAS) and to how National Weather Service (NWS) programs coordinate the production of relevant aviation thunderstorm forecast products. In the lesson, learners will adopt the role of an aviation forecaster at one of four key aviation program offices, including a Weather Forecast Office, a Center Weather Service Unit, the Aviation Weather Center, and AWC National Aviation Meteorologists at the Air Traffic Control System Command Center. Learners will assess a real convective forecast situation and apply their knowledge of observations and forecast tools to create initial aviation thunderstorm forecast products. They will then coordinate the issuance of final products and collaborate with relevant external program offices as needed to clarify and update the products. By the end of the lesson, learners will understand not only the impact of thunderstorms on the NAS, but also the breadth and depth of collaborative work required to provide forecast product continuity and clear decision support services.
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NBM Precipitation Products
The National Blend of Models (NBM) is a critical component of forecasting in the National Weather Service (NWS) that uses different statistical techniques to produce numerical weather prediction data. There are many different probabilistic products that are available for use for summertime precipitation events. With many different uses, some of the applicability and correct usage may be lost to forecasters. This lesson focuses on frequently used NBM products for summertime precipitation and explains the construction and probabilistic background of these products through primarily an application based approach. Products covered are Quantile Mapping and Dressing (QMD), Percentiles, Quantitative Precipitation Forecast (QPF) Mean, and Probability of Exceedance.
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Integrating Scatterometer Wind Data in Weather Forecasting: Tropical Pacific Cases
In this lesson, learners will interpret scatterometer wind data and apply their knowledge of scatterometry to analyze and diagnose weather features. Learners will further integrate this information alongside other available satellite and NWP datasets from two real tropical Pacific case studies. Both case studies challenge learners to use their findings within everyday forecasting processes and decision support tasks. The intended audience for this lesson is operational forecasters and students familiar with how scatterometers estimate surface winds and who want to integrate those wind estimates within their forecast and service delivery processes. To get the most out of the lesson, learners should have at least a Basic Instruction Package for Meteorologists (BIP-MT) level background in meteorology and have completed the “Introduction to Scatterometer Winds” lesson, which is part one of this series.
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Understanding NBM v4.0 Snowfall Products
In this short vignette, you will learn about the different National Blend of Models (NBM) snowfall products available in v4.0/4.1 and some of the caveats associated with them. These products include Snow Amount (SnowAmt), Snow-to-liquid Ratio (SLR), and Snow Level (SnowLvl).
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Introduction to Scatterometry
This lesson describes the basics of scatterometers, including how they work, what they measure, and how scatterometer wind estimates are derived. It then discusses some of the challenges inherent in using the wind estimates, such as dealing with ambiguities and rain contamination. The lesson then steps through several tropical scatterometer wind estimate plots in which learners can practice assessing where these challenges are present as well as identifying current weather circulations in the region. To get the most out of the lesson, you should have a Basic Instruction Package for Meteorologists (BIP-MT) level background in meteorology and/or concurrent knowledge of the topics in COMET’s Wind and Wave Forecasting Distance Learning Course.
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Satellite-NWP Integration: Nighttime Fog Analysis Challenge
This short lesson uses GOES-R red-green-blue (RGB) composites in combination with numerical weather prediction (NWP) model output for analysis of nighttime fog. In this lesson, the learner is challenged to use the Nighttime Microphysics RGB, 2-m relative humidity, and 10-m wind fields to locate fog overnight. Learners will also use the datasets in combination to forecast the dissipation of fog which is not easily achieved by using satellite imagery or NWP fields in isolation. The lesson highlights the benefits of using animated satellite products and combining satellite RGB imagery with NWP model fields to be able to make more informed forecasting decisions. This lesson addresses the following primary performance components from the WMO Competency Framework Satellite Skills: 2.3 Identify fog and discriminate between fog and low cloud 7.2 Identify and assess various weather features by integrating satellite and NWP products 7.4 Use NWP information to enhance the understanding of the features shown in the satellite images 7.5 Use satellite data in conjunction with NWP at different stages of the analysis and forecast process
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Stream Height Forecasts via NASA/SPoRT Machine Learning
Developed and hosted by NASA's Short-term Prediction, Research, and Transition (SPoRT) program, this training serves as a reference for NASA SPoRT’s stream height forecast product. The product is derived from a machine learning (ML) model where inputs to the model include soil moisture values from the NASA SPoRT Land Information System (LIS) as well as QPE/QPF and stream gauge height. The Streamflow ML Height forecast product is intended for applications in areas without existing forecast information and provides an extended outlook (i.e., beyond two days) of stream flooding potential.
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Communicating Potential Flash Flood & Debris Flow Threats
This 45-minute lesson begins with an overview on the importance of building partnerships and understanding partner needs prior to the wildfire season. Then, the learner takes on the role of a forecaster conversing with two different partners to understand their information needs regarding post-wildfire flash flood and debris flow events. With this understanding, the learner works through two post-wildfire case exercises to practice providing decision-support to each partner based on their needs.
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The National Tidal Datum Epoch
Tide and water level datums are standard elevations used to reference water level heights and depths. Each type of datum represents a unique phase or behavior of the water along the coast or in the Great Lakes. CO-OPS and its partners are currently in the midst of two large-scale datum updates for the National Tidal Datum Epoch (NTDE) and the International Great Lakes Datum (IGLD). This video explains what the NTDE is, along with the importance and application of tidal datums for marine commerce, environmental restoration, and coastal resilience. You can learn more at https://tidesandcurrents.noaa.gov/datum-updates/.
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Satellite Applications for Winter Weather: Mesoscale Banded Snowfall
New-generation GOES-R and JPSS satellite products offer improved capabilities for detecting and monitoring some of the more subtle elements of winter weather events, including mesoscale banded snowfall. In this lesson learners will investigate an April 2020 banded snowfall event that occurred over the Great Plains of the United States. Throughout the lesson, learners will use several satellite tools in tandem with model forecasts and observations including radar, sounding and surface data. As learners analyze the data they will be asked to determine how particular satellite products can aid in different aspects of the forecast process, from situational awareness and precursor ingredient assessment, to monitoring the initiation and evolution of the snowband. This lesson is intended for forecasters familiar with the dynamics of winter storms and experienced in using a broad suite of observational tools and model outputs.