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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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SatFC-G: Basic Principles of Radiation
This lesson is an abbreviated review of the scientific basis for using visible and infrared satellite imagery. The concepts and capabilities presented are common to most geostationary (GEO) and low-Earth orbiting (LEO) meteorological satellites. Basic remote sensing and radiative theory are reviewed using conceptual models to help organize scientific concepts. Some imagery is also included to illustrate concepts and relate them to sensor observations. This lesson is a part of the NWS Satellite Foundation GOES-R Course. More in-depth information on radiation and radiative transfer can be found ...
Available online: https://www.meted.ucar.edu/training_module.php?id=1239
Published by: The University Corporation for Atmospheric Research ; 2016
This lesson is an abbreviated review of the scientific basis for using visible and infrared satellite imagery. The concepts and capabilities presented are common to most geostationary (GEO) and low-Earth orbiting (LEO) meteorological satellites. Basic remote sensing and radiative theory are reviewed using conceptual models to help organize scientific concepts. Some imagery is also included to illustrate concepts and relate them to sensor observations. This lesson is a part of the NWS Satellite Foundation GOES-R Course. More in-depth information on radiation and radiative transfer can be found in the COMET lesson, Basics of Visible and Infrared Remote Sensing.
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 ; Radiative transfer ; Lesson/ Tutorial ; Satellite Skills and Knowledge for Operational Meteorologists
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SatFC-G: Near-IR Bands
This lesson introduces you to three of the four near-infrared imager bands (at 1.37, 1.6, and 2.2 micrometers) on the GOES R-U ABI (Advanced Baseline Imager), focusing on their spectral characteristics and how they affect what each band observes. For information on the 0.86 micrometer near-IR "veggie" band which is not included here, refer to the Visible and Near-IR Bands lesson. This lesson is a part of the NWS Satellite Foundation GOES-R Course.
Available online: https://www.meted.ucar.edu/training_module.php?id=1268
Published by: The University Corporation for Atmospheric Research ; 2016
This lesson introduces you to three of the four near-infrared imager bands (at 1.37, 1.6, and 2.2 micrometers) on the GOES R-U ABI (Advanced Baseline Imager), focusing on their spectral characteristics and how they affect what each band observes. For information on the 0.86 micrometer near-IR "veggie" band which is not included here, refer to the Visible and Near-IR Bands lesson. 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: Aerosols ; Weather forecasting ; Ice cloud ; Water cloud ; Lesson/ Tutorial ; Satellite 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.
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JPSS River Ice and Flood Products
This lesson introduces hydrologists, meteorologists, and the education community to two new JPSS (Joint Polar Satellite System) satellite capabilities for monitoring river ice and flooding. It begins by describing the need for information on river ice and flooding, the capabilities of the Suomi NPP and future JPSS VIIRS imagers to provide products for monitoring river conditions, and the new river ice and flood products. This is followed by several cases, notably the May 2013 Galena, AK flood event, that demonstrate the use and value of the products in monitoring river ice and related flooding ...
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Forecasting Sensible Weather from Water Vapour Imagery
Forecaster nowcasting at the synoptic scale is rapidly being replaced by the numerical weather prediction models. However, there are plenty of opportunities for you as a forecaster to improve on those forecasts with simple comparisons of water vapour hand analyses and surface hand analyses. The goal of this lesson is to improve your skills in water vapour and surface analyses to evaluate the three-dimensionality of the atmosphere and thus forecast the sensible weather better. This is the capstone for the entire Satellite Interpretation distance learning course.
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Satellite Foundational Course for GOES-R: SatFC-G (SHyMet Full Course Access)
The Satellite Foundational Course for GOES-R (SatFC-G) is a series of nearly 40 lessons designed specifically for National Weather Service (NWS) forecasters and decision makers to prepare for the U.S.’ next-generation geostationary environmental satellites. The course is intended to help learners develop or improve their understanding of the capabilities, value, and anticipated benefits from the GOES-R suite of instruments. These instruments and imagery offer improved monitoring of meteorological, environmental, climatological, and space weather phenomena and related hazards. The course will a ...
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SatFC-G: Tropical to Extratropical Transition
This lesson uses water vapor satellite imagery from Himawari-8 to describe the typical extratropical transition of a tropical cyclone. The Himawari-8 imager previews comparable capabilities coming online with the GOES-R ABI imager. The lesson also provides a brief overview of subtropical cyclones and their transition to tropical cyclones. This lesson is a part of the NWS Satellite Foundation GOES-R Course.
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SatFC-G: Impact of Satellite Observations on NWP
This lesson covers how satellite data inform numerical weather prediction models. From a basic overview of how satellite data is assimilated to how a new instrument's data might get into a model. This lesson is a part of the NWS Satellite Foundation GOES-R Course. More in-depth discussions and a quiz on the impacts of satellite observations on NWP can be found in the COMET lesson, How Satellite Observations Impact NWP.
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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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SatFC-G: Introduction to the GLM
This lesson describes the need for real-time lightning information and the capabilities of the Geostationary Lightning Mapper (GLM), which will fly on the next-generation GOES-R satellites as the first operational lightning detector in geostationary orbit. This lesson is a part of the NWS Satellite Foundation GOES-R Course. More in-depth discussions and a quiz on the lightning flash cycle and lightning applications can be found in the COMET lesson, GOES-R GLM: Introduction to the Geostationary Lightning Mapper.
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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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