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


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National Water Model, Part 1: Science and Products
This lesson provides an introduction to the benefits, important input (forcing data), and key products of the National Water Model. Both official and evolving products are presented. The lesson uses the flooding associated with Hurricane Harvey in August 2017 to demonstrate key products.
Available online: https://www.meted.ucar.edu/training_module.php?id=1296
Published by: The University Corporation for Atmospheric Research ; 2018
This lesson provides an introduction to the benefits, important input (forcing data), and key products of the National Water Model. Both official and evolving products are presented. The lesson uses the flooding associated with Hurricane Harvey in August 2017 to demonstrate key products.
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: Drought ; Flood ; Weather forecasting ; Numerical weather prediction ; Water cycle ; Flash flood ; Runoff ; Stream discharge ; Soil moisture ; Lesson/ Tutorial ; NWP Skills and Knowledge for Operational Meteorologists
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Interpreting and Communicating EPS Guidance: British Columbia Winter Storm
This 45-minute lesson provides an opportunity to use ensemble prediction system products to evaluate uncertainty in the forecast and then communicate that information effectively to a public audience. The lesson places learners in the role of a Meteorological Service of Canada forecaster who must assess forecast uncertainty and then issue early warning notifications to decision-makers regarding the winter storm. In a subsequent work shift during the event, the learner must effectively deliver forecast information via social media and respond to questions from the general public. The lesson is ...
Available online: https://www.meted.ucar.edu/training_module.php?id=1354
Published by: The University Corporation for Atmospheric Research ; 2018
This 45-minute lesson provides an opportunity to use ensemble prediction system products to evaluate uncertainty in the forecast and then communicate that information effectively to a public audience. The lesson places learners in the role of a Meteorological Service of Canada forecaster who must assess forecast uncertainty and then issue early warning notifications to decision-makers regarding the winter storm. In a subsequent work shift during the event, the learner must effectively deliver forecast information via social media and respond to questions from the general public. The lesson is intended for experienced forecasters knowledgeable about mid-latitude weather regimes, and is also suitable for the academic community.
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 ; Forecast uncertainty ; Lesson/ Tutorial ; NWP Skills and Knowledge for Operational Meteorologists
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Statistical Methods in the NWS National Blend of Global Models Part 2
This lesson introduces users to the statistics used in generating the various weather element forecasts included in version 2 and 3 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 bias correction, weighting, and post-processing procedures used to produce the forec ...
Available online: https://www.meted.ucar.edu/training_module.php?id=1299
Published by: The University Corporation for Atmospheric Research ; 2017
This lesson introduces users to the statistics used in generating the various weather element forecasts included in version 2 and 3 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 bias correction, weighting, and post-processing procedures used to produce the 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 ; Precipitation ; Numerical weather prediction ; Lesson/ Tutorial ; NWP Skills and Knowledge for Operational Meteorologists
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Web-Based Ensemble Tools: Ensemble Situational Awareness Table
The National Weather Service (NWS) Western Region (WR) has developed a Ensemble Situational Awareness Table (ESAT), which uses probabilistic NWP to bring attention to the potential for extreme events, especially in middle-range forecasts. The lesson, which is the first of two on the ESAT, describes the ESAT and how its data can be used to support assessment of extreme weather event forecasts. Additionally, statistical methods, including employment of reanalysis and NWP model climatologies (R-Climate and M-Climate, respectively) are described in reference to the products available in the ESAT.
Available online: https://www.meted.ucar.edu/training_module.php?id=1341
Published by: The University Corporation for Atmospheric Research ; 2017
The National Weather Service (NWS) Western Region (WR) has developed a Ensemble Situational Awareness Table (ESAT), which uses probabilistic NWP to bring attention to the potential for extreme events, especially in middle-range forecasts. The lesson, which is the first of two on the ESAT, describes the ESAT and how its data can be used to support assessment of extreme weather event forecasts. Additionally, statistical methods, including employment of reanalysis and NWP model climatologies (R-Climate and M-Climate, respectively) are described in reference to the products available in the ESAT.
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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Introduction to EPS Theory
This lesson introduces the concepts needed to understand and use ensemble prediction system (EPS) products. It describes basic statistical quantities and methods used to develop EPS products, such as probability distribution functions (PDFs) and cumulative distribution functions (CDFs). From there, it discusses ways of using EPS products compared to deterministic products. The final section briefly introduces nine common EPS products. The lesson is a prerequisite for the EPS Products Reference Guide.
Available online: https://www.meted.ucar.edu/training_module.php?id=1340
Published by: The University Corporation for Atmospheric Research ; 2017
This lesson introduces the concepts needed to understand and use ensemble prediction system (EPS) products. It describes basic statistical quantities and methods used to develop EPS products, such as probability distribution functions (PDFs) and cumulative distribution functions (CDFs). From there, it discusses ways of using EPS products compared to deterministic products. The final section briefly introduces nine common EPS products. The lesson is a prerequisite for the EPS Products Reference Guide.
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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Forecasting Aviation Convective Impacts with INSITE
The National Weather Service (NWS) has developed the INSITE tool (INtegrated Support for Impacted air-Traffic Environments) to improve NWS convective impact forecasts by providing functionality that enables forecasters to include more precise impact areas in aviation convective weather forecast products. The tool lets forecasters identify potential constraints to the National Airspace System by combining forecast weather and air-traffic data. Improved convective weather forecast products can reduce delays in air-traffic and increase efficiency in the National Airspace System (NAS). In this 45- ...
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Communicating Forecast Uncertainty, European Case
This lesson is a follow-on to COMET’s Communicating Forecast Uncertainty lesson, which introduces research findings on the effective communication of uncertainty information and enables learners to apply them to a North American case. This lesson focuses on a European winter weather case and provides an additional opportunity to evaluate end-user needs and formulate effective responses to their questions based on the research findings. Learners are strongly encouraged to take Communicating Forecast Uncertainty before starting this lesson. The lesson is aimed at experienced forecasters with kno ...
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Limitations of High-Resolution NWP Models
This scenario-based lesson examines how the limitations of high-resolution NWP forecasts affect their analyses and forecasts of winter and severe weather, and how best to use the output in light of the limitations. The lesson is structured around a case that occurred in Texas in December 2015 when winter weather and severe weather hit Amarillo and Dallas-Ft. Worth, respectively. As users go through the case, they learn how spin-up time, errors in initial conditions, and deficiencies in the modeling of mesoscale phenomena can impact high-resolution forecasts in the NAM nest and HRRR models.
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EPS Products Reference Guide
The EPS Products Reference Guide provides information about nine commonly used ensemble prediction system (EPS) products. Each has a description, tips for interpreting and using it effectively, a list of its strengths and weaknesses, and practice exercises. The Guide is meant to be used as reference material and does not have a quiz.
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Guidelines for Nowcasting Techniques
The purpose of the WMO nowcasting guidelines presented here is to help National Meteorological and Hydrological Services (NMHSs) by providing them with information and knowledge on how to implement a nowcasting system with the resources available to them and an understanding of the current state of science and technology.
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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 ...
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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 ...
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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 ...
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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.
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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 ...
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