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WMO Competencies > NWP Skills and Knowledge for Operational Meteorologists
NWP Skills and Knowledge for Operational Meteorologists |


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Using NWP Lightning Products in Forecasting
This lesson introduces two numerical weather prediction (NWP) lightning hazard products that forecasters can use during a convective meteorological watch and to assess lightning risk at Day 2 and beyond. The first product is the Flash Rate Density, a derived, deterministic lightning product implemented in some NCEP high-resolution NWP models. The second product, the SPC Calibrated Thunderstorm Probability, combines forecasts of measurable precipitation and favorable lightning environments determined from the Cloud Physics Thunder Parameter. Information about these products is presented in the ...
Available online: https://www.meted.ucar.edu/training_module.php?id=1272
Published by: The University Corporation for Atmospheric Research ; 2016
This lesson introduces two numerical weather prediction (NWP) lightning hazard products that forecasters can use during a convective meteorological watch and to assess lightning risk at Day 2 and beyond. The first product is the Flash Rate Density, a derived, deterministic lightning product implemented in some NCEP high-resolution NWP models. The second product, the SPC Calibrated Thunderstorm Probability, combines forecasts of measurable precipitation and favorable lightning environments determined from the Cloud Physics Thunder Parameter. Information about these products is presented in the context of a case study in which learners determine the potential for lightning to impact a large outdoor event. In the process, they learn how to use the lightning products with traditional near- and nowcast diagnostics, such as radar, satellite imagery, and hourly HRRR analyses of convective instability.
Disclaimer regarding 3rd party resources: WMO endeavours to ensure, but cannot and does not guarantee the accuracy, accessibility, integrity and timeliness of the information available on its website. WMO may make changes to the content of this website at any time without notice.
The responsibility for opinions expressed in articles, publications, studies and other contributions rests solely with their authors, and their posting on this website does not constitute an endorsement by WMO of the opinion expressed therein.
WMO shall not be liable for any damages incurred as a result of the use of its website. Please do not misuse our website.Language(s): English
Format: Digital (Standard Copyright)Tags: Weather forecasting ; Numerical weather prediction ; Lesson/ Tutorial ; NWP Skills and Knowledge for Operational Meteorologists
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Weather Forecast Uncertainty Information for Everyday Users - Presentation at 2015 Workshop on Communicating Uncertainty to Users of Weather Forecasts
Although previous research suggests that we are not very good at reasoning with uncertainty, the research described in this talk is far more encouraging. Unlike earlier work that compares peoples' decisions to a rational standard, these experiments compared decisions made by people with uncertainty information to decisions made by people without uncertainty information. The results suggest that including specific numeric uncertainty estimates in weather forecasts leads to better decisions. This talk was part of Meteorological Service of Canada's 2015 Workshop on Communicating Uncertainty to Us ...
Weather Forecast Uncertainty Information for Everyday Users - Presentation at 2015 Workshop on Communicating Uncertainty to Users of Weather Forecasts
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Available online: https://www.meted.ucar.edu/training_module.php?id=1250
Published by: The University Corporation for Atmospheric Research ; 2016
Although previous research suggests that we are not very good at reasoning with uncertainty, the research described in this talk is far more encouraging. Unlike earlier work that compares peoples' decisions to a rational standard, these experiments compared decisions made by people with uncertainty information to decisions made by people without uncertainty information. The results suggest that including specific numeric uncertainty estimates in weather forecasts leads to better decisions. This talk was part of Meteorological Service of Canada's 2015 Workshop on Communicating Uncertainty to Users of Weather Forecasts precursor to the 49th CMOS Congress & 13th AMS Conference on Polar Meteorology and Oceanography. This resource is made available courtesy of Dr. Susan Joslyn, Environment Canada, CMOS and The Eumetcal Project, and is not produced, owned or hosted by UCAR/COMET.
Disclaimer regarding 3rd party resources: WMO endeavours to ensure, but cannot and does not guarantee the accuracy, accessibility, integrity and timeliness of the information available on its website. WMO may make changes to the content of this website at any time without notice.
The responsibility for opinions expressed in articles, publications, studies and other contributions rests solely with their authors, and their posting on this website does not constitute an endorsement by WMO of the opinion expressed therein.
WMO shall not be liable for any damages incurred as a result of the use of its website. Please do not misuse our website.Language(s): English
Format: Digital (Standard Copyright)Tags: Weather forecasting ; Numerical weather prediction ; Lesson/ Tutorial ; Simulation ; NWP Skills and Knowledge for Operational Meteorologists
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Short-Range Ensemble Forecast Upgrade
The Short-Range Ensemble Forecast (SREF) system underwent a major upgrade in Fall 2015. The intended result of the upgrade was to improve the SREF ensemble spread and probabilistic skill, and to reduce a cool, wet bias in surface and near-surface temperatures and moisture. This 20-minute lesson addresses the changes to improve the SREF, including the increase in ensemble size, the increase in initial condition and model physics diversity, and drier land surface parameters to lessen the cool, wet bias. Each is introduced by comparing the old and new SREF forecasts for a potential winter storm f ...
Available online: https://www.meted.ucar.edu/training_module.php?id=1214
Published by: The University Corporation for Atmospheric Research ; 2016
The Short-Range Ensemble Forecast (SREF) system underwent a major upgrade in Fall 2015. The intended result of the upgrade was to improve the SREF ensemble spread and probabilistic skill, and to reduce a cool, wet bias in surface and near-surface temperatures and moisture. This 20-minute lesson addresses the changes to improve the SREF, including the increase in ensemble size, the increase in initial condition and model physics diversity, and drier land surface parameters to lessen the cool, wet bias. Each is introduced by comparing the old and new SREF forecasts for a potential winter storm from December 2014. The results from the case study and long-term seasonal results are used to show the extent to which changes to the SREF succeeded in improving its forecasts.
Disclaimer regarding 3rd party resources: WMO endeavours to ensure, but cannot and does not guarantee the accuracy, accessibility, integrity and timeliness of the information available on its website. WMO may make changes to the content of this website at any time without notice.
The responsibility for opinions expressed in articles, publications, studies and other contributions rests solely with their authors, and their posting on this website does not constitute an endorsement by WMO of the opinion expressed therein.
WMO shall not be liable for any damages incurred as a result of the use of its website. Please do not misuse our website.Language(s): English
Format: Digital (Standard Copyright)Tags: Weather forecasting ; Numerical weather prediction ; Lesson/ Tutorial ; NWP Skills and Knowledge for Operational Meteorologists
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SatFC-G: GOES-R Impacts on Satellite Data Assimilation
This five minute lesson presents a brief overview of how GOES-R observations are expected to support and potentially enhance NWP for various analysis and forecast applications. This lesson is a part of the NWS Satellite Foundation GOES-R Course.
Available online: https://www.meted.ucar.edu/training_module.php?id=1259
Published by: The University Corporation for Atmospheric Research ; 2016
This five minute lesson presents a brief overview of how GOES-R observations are expected to support and potentially enhance NWP for various analysis and forecast applications. This lesson is a part of the NWS Satellite Foundation GOES-R Course.
Disclaimer regarding 3rd party resources: WMO endeavours to ensure, but cannot and does not guarantee the accuracy, accessibility, integrity and timeliness of the information available on its website. WMO may make changes to the content of this website at any time without notice.
The responsibility for opinions expressed in articles, publications, studies and other contributions rests solely with their authors, and their posting on this website does not constitute an endorsement by WMO of the opinion expressed therein.
WMO shall not be liable for any damages incurred as a result of the use of its website. Please do not misuse our website.Language(s): English
Format: Digital (Standard Copyright)Tags: Weather forecasting ; Numerical weather prediction ; Data assimilation ; Lesson/ Tutorial ; NWP Skills and Knowledge for Operational Meteorologists ; Satellite Skills and Knowledge for Operational Meteorologists
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HiresW HREF Upgrade
This 20-minute lesson presents upgraded versions of the two NWP models used as High Resolution Window (HiresW), the Weather Research and Forecasting-Advanced Research WRF (WRF-ARW) and the Non-Hydrostatic Multiscale Model on the B-grid (NMMB). Domains include the CONtinental US (CONUS), Alaska, Hawaii, Guam, and Puerto Rico. The CONUS runs of the NMMB and WRF-ARW became part of a new High Resolution Ensemble Forecast (HREF) system in 2015, the first of its kind produced at the National Centers for Environmental Prediction. To familiarize the operational forecaster with the HREF, products from ...
Available online: https://www.meted.ucar.edu/training_module.php?id=1215
Published by: The University Corporation for Atmospheric Research ; 2016
This 20-minute lesson presents upgraded versions of the two NWP models used as High Resolution Window (HiresW), the Weather Research and Forecasting-Advanced Research WRF (WRF-ARW) and the Non-Hydrostatic Multiscale Model on the B-grid (NMMB). Domains include the CONtinental US (CONUS), Alaska, Hawaii, Guam, and Puerto Rico. The CONUS runs of the NMMB and WRF-ARW became part of a new High Resolution Ensemble Forecast (HREF) system in 2015, the first of its kind produced at the National Centers for Environmental Prediction. To familiarize the operational forecaster with the HREF, products from a surrogate ensemble system (the Storm Scale Ensemble of Opportunity from NCEP's Storm Prediction Center) with a similar configuration are used in a 2014 severe weather case study from upstate New York.
Disclaimer regarding 3rd party resources: WMO endeavours to ensure, but cannot and does not guarantee the accuracy, accessibility, integrity and timeliness of the information available on its website. WMO may make changes to the content of this website at any time without notice.
The responsibility for opinions expressed in articles, publications, studies and other contributions rests solely with their authors, and their posting on this website does not constitute an endorsement by WMO of the opinion expressed therein.
WMO shall not be liable for any damages incurred as a result of the use of its website. Please do not misuse our website.Language(s): English
Format: Digital (Standard Copyright)Tags: Weather forecasting ; Numerical weather prediction ; Lesson/ Tutorial ; NWP Skills and Knowledge for Operational Meteorologists
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Verification Methods in the NWS National Blend of Global Models
This lesson introduces learners to the methods used in verifying the various weather element forecasts included in Version 1.0 of the U.S. National Weather Service (NWS) National Blend of global Models (NBM). This Level 2 lesson is intended for forecasters and users of NWS forecast products; some prior knowledge of numerical weather prediction and statistics is useful. Learners will be introduced to the analysis of record used to verify the NBM. Learners will also explore single event, grid-to-observation, and grid-to-grid verification methods, as well as how to interpret the results using the ...
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Statistical Methods in the NWS National Blend of Global Models
This lesson introduces users to the statistics used in generating the various weather element forecasts included in Version 1.0 of the U.S. National Weather Service (NWS) National Blend of global Models (NBM). This Level 3 lesson is intended for forecasters and users of NWS forecast products; some prior knowledge of numerical weather prediction and statistics is useful. Learners will be introduced to the analysis of record used to calibrate the NBM’s bias and error estimates. Learners will also explore the downscaling, bias correction, and weighting procedures applied to the model products, an ...
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Communicating Forecast Uncertainty
This scenario-based lesson introduces the topic of communicating forecast uncertainty to decision-makers, such as emergency managers, related industry professionals, the public, and other end-users. In a case that spans the lesson, learners begin by developing a forecast discussion using deterministic data, refine it with probabilistic ensemble data, and evaluate how well it conveys uncertainty information. Then they assume several end-user roles, assessing how well the forecast discussion addresses their needs. From there, important research findings on communicating uncertainty are discussed ...
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Introduction to the NWS National Blend of Global Models
The National Blend of Global Models was developed to utilize the best available science and provide a consistent National Weather Service forecast product across the U.S. This lesson describes the background and motivation for the National Blend and includes comparisons of Blend forecasts with current guidance. The lesson also offers a short summary of future plans and training related to the National Blend.
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Assessing NWP with Water Vapour Imagery
You've seen it happen repeatedly. Forecasters have a tough forecast ahead of them. But how are they supposed to know which model data will be the best one to help them come to a conclusion about the situation? In situations like this, the first step should always be to assess the model data against a set of current observations that should show a 1-to-1 relationship with the model output. Which variable should be plotted? On which surface? Which current observations will make the most sense to assess against? If you know the answers to some, but not all of these questions, find these answers a ...
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Operational Models Encyclopedia
The availability of numerical guidance from NWP models has been an important component of operational forecasting for decades. For many, the output from this numerical guidance was produced by a mysterious “black box”. Rules for using and adjusting the guidance for operational forecasters were often subjective “Rules of Thumb” based on experience rather than based on quantitative analysis. To open up this “black box”, we produced this web-based “Operational Models Encyclopedia” linking both generic information on how NWP models work, and specifics on physical parameterizations, dynamics, and d ...
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NWP Essentials: NWP and Forecasting
This lesson introduces forecasters to the complex and multifaceted process for creating a forecast. It also discusses how NWP fits into that process. In addition, the lesson provides a broad overview of the basic components of NWP and how they combine to produce a model forecast.
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NWP Essentials: Structure and Dynamics
This lesson is focused on how a model forecast and the interpretation of that forecast, is affected by the basic design of the model. Topics include how meteorological variables are represented in grid point and spectral models, fundamental differences between hydrostatic and nonhydrostatic models, horizontal resolution of orographic and free-atmosphere features, vertical coordinate systems and how they affect the vertical resolution of features in the model forecast.
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Gridded Products in the NWS National Blend of Global Models
This lesson introduces users to the five different guidance products that will be included in Version 1.0 of the U.S. National Weather Service (NWS) National Blend of global Models (NBM). The primary audience for this lesson includes forecasters and users of NWS forecast products; some prior knowledge of numerical weather prediction is useful. Learners will explore how model guidance from the Global Forecast System, Global Ensemble Forecast System, Canadian Meteorological Centre Ensemble, Ensemble Kernel Density Model Output Statistics (MOS) and gridded GFS MOS is produced. The strengths and l ...
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Introduction to Tropical Meteorology, 2nd Edition: Chapter 6 Vertical Transport
This chapter examines vertical transport of heat, moisture, momentum, trace gases, and aerosols, including the role of tropical deep convection and turbulence. Diurnal and seasonal variations in surface fluxes and boundary layer depth are examined. The boundary layer is compared over the ocean, humid, and dry tropics, including its role in dispersing chemicals and aerosols. Boundary layer clouds are examined in terms of their connection to sub-cloud layer properties. Comparisons are made between heat and moisture transport under a variety of convective modes such as mesoscale convective system ...
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NWP Essentials: Model Physics
This lesson describes model parameterizations of surface, PBL, and free atmospheric processes. It specifically addresses how models treat these processes, how such processes can potentially interact with each other, and how they can influence forecasts of sensible weather elements. Topics covered include: soil moisture processes, radiative processes involving clouds, and turbulent processes in the PBL and free atmosphere.
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NWP Essentials: Precipitation and Clouds
Both the processes of convection and of rainfall formation are typically subgrid scale, and require parameterisation. This lesson examines two types of precipitation parameterisation used by models: Convective parameterisation Microphysics The lesson also discusses how to identify when these parameterisations are not performing well and steps to address the issues that arise.
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HYSPLIT Applications for Emergency Decision Support, 2nd Edition
This module helps forecasters provide decision support services during hazardous materials emergencies. Topics covered include: Types of weather data inputs required for short-range dispersion models typically used by emergency managers Types of inputs required to run the web version of the HYSPLIT model with the ALOHA source term, which is now available to NWS forecasters The types and scales of events that are appropriate and inappropriate for modeling by HYSPLIT Key uncertainties that can cause misleading dispersion model forecasts The processes and limitations of CAMEO/ALOHA and HYSPLIT Ho ...
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NWP Essentials: Data Assimilation
This lesson introduces the processes of model data assimilation. It also discusses the impacts of errors in the data assimilation on model forecasts and how a human forecaster can compensate for them.
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Ensemble Applications in Winter
This lesson provides an introduction to ensemble forecast systems using an operational case study of the Blizzard of 2013 in Southern Ontario. The module uses models available to forecasters in the Meteorological Service of Canada, including Canadian and U.S. global and regional ensembles. After briefly discussing the rationale for ensemble forecasting, the module presents small lessons on probabilistic ensemble products useful in winter weather forecasting, immediately followed by forecast applications to a southern Ontario case. The learner makes forecasts for the Ontario Storm Prediction Ce ...
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Introduction to Aircraft Meteorological Data Relay (AMDAR)
Introduction to Aircraft Meteorological Data Relay (AMDAR) provides national meteorological services worldwide, airlines, and aviation organizations with information about the World Meteorological Organization (WMO) aircraft-based observing system. The audience includes meteorological service managers and providers, observational development groups, the aviation industry, and others interested in benefiting from an aircraft-based observing system in their region. The content includes interviews with several experts to provide examples of AMDAR use for both meteorological and aviation applicati ...
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How Satellite Observations Impact NWP
Satellite observations have a huge impact on numerical weather prediction (NWP) model analyses and forecasts, with sounding data from polar orbiting and GPS-radio occultation satellites reducing model forecast error by almost half. All of this despite the fact that NWP models only assimilate 5% of all satellite observations! This lesson discusses the use of satellite observations in NWP and how model limitations prevent more of the data from being assimilated. The lesson begins by briefly describing the history of satellite observations in NWP and their impact on NWP model forecast skill. The ...
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Introduction to Ensembles: Forecasting Hurricane Sandy
This module provides an introduction to ensemble forecast systems with an operational case study of Hurricane Sandy. The module concentrates on models from NCEP and FNMOC available to forecasters in the U.S. Navy, including NAEFS (North American Ensemble Forecast System), and NUOPC (National Unified Operational Prediction Capability). Probabilistic forecasts of winds and waves developed from these ensemble forecast systems are applied to a ship transit and coastal resource protection. Lessons integrated in the case study provide information on ensemble statistics, products, bias correction and ...
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WRF-EMS Aviation Products
This lesson illustrates how numerical guidance from the Weather Research and Forecasting Model - Environmental Modeling System (WRF-EMS) can be added to surface observations, satellite graphics, and conceptual models of important aviation phenomena, to produce TAFs. Specifically, the lesson describes how visibility, cloud ceilings, and the flight categories variables provide values for aviation forecasts in Africa.
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Gridded Forecast Verification and Bias Correction
To become a better forecaster, it is not enough to simply know that a forecast did not verify. One must determine what happened and identify methods for improvement through forecast verification. The forecast verification process helps answer questions like: Is there a particular method that has been more effective in the past in similar circumstances? Is there guidance that is more accurate? Are there persistent biases in our forecasts? Do our forecasts perform better in certain regimes than others? In the era of gridded forecasts, grid-based verification provides more information about the s ...
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