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NWP Comparisons: Total-column Variables
Another way to try to find mismatches between observed weather and NWP output is by using total-column variables. There are a few of them to choose from, and they make for a relatively simple comparison method for finding correctable mismatches. In this lesson, we'll address appropriate methods for making these comparisons and build to a point where we will focus on bigger picture atmospheric processes. This is the second in a series of video lessons that introduces three different methods for modifying NWP output to add human value to forecasts.
Available online: https://www.meted.ucar.edu/training_module.php?id=1617
Published by: The University Corporation for Atmospheric Research ; 2019
Another way to try to find mismatches between observed weather and NWP output is by using total-column variables. There are a few of them to choose from, and they make for a relatively simple comparison method for finding correctable mismatches. In this lesson, we'll address appropriate methods for making these comparisons and build to a point where we will focus on bigger picture atmospheric processes. This is the second in a series of video lessons that introduces three different methods for modifying NWP output to add human value to 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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PV Modification
You know what PV is, yet aren't quite sure how to modify it to make a better forecast. In this short lesson, we will discuss how to modify the PV surface to match water vapour imagery and how those adjustments affect the surface sensible weather. This is the fifth in a series of video lessons that introduces three different methods for modifying NWP output to add human value to forecasts. Pre-requisite Knowledge: Satellite Water Vapour Interpretation -- Short Course
Available online: https://www.meted.ucar.edu/training_module.php?id=1615
Published by: The University Corporation for Atmospheric Research ; 2019
You know what PV is, yet aren't quite sure how to modify it to make a better forecast. In this short lesson, we will discuss how to modify the PV surface to match water vapour imagery and how those adjustments affect the surface sensible weather. This is the fifth in a series of video lessons that introduces three different methods for modifying NWP output to add human value to forecasts. Pre-requisite Knowledge: Satellite Water Vapour Interpretation -- Short 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 ; Lesson/ Tutorial ; NWP Skills and Knowledge for Operational Meteorologists
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Interpreting and Communicating EPS Guidance: Germany Winter Event
This 45-minute lesson briefly introduces learners to the benefits of using probabilistic forecast information to assess weather and communicate forecast uncertainties. Learners will explore a winter weather event in Germany and practice synthesizing deterministic and probabilistic forecast guidance to better understand forecast uncertainties based on lead-time. Also, learners will decide how to best communicate the potential weather threats and impacts to local end users. The lesson is another component of the Forecast Uncertainty: EPS Products, Interpretation, and Communication distance learn ...
Available online: https://www.meted.ucar.edu/training_module.php?id=1407
Published by: The University Corporation for Atmospheric Research ; 2019
This 45-minute lesson briefly introduces learners to the benefits of using probabilistic forecast information to assess weather and communicate forecast uncertainties. Learners will explore a winter weather event in Germany and practice synthesizing deterministic and probabilistic forecast guidance to better understand forecast uncertainties based on lead-time. Also, learners will decide how to best communicate the potential weather threats and impacts to local end users. The lesson is another component of the Forecast Uncertainty: EPS Products, Interpretation, and Communication distance learning 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 ; Precipitation ; Snow ; Numerical weather prediction ; Freezing rain ; Forecast uncertainty ; Lesson/ Tutorial ; Germany ; NWP Skills and Knowledge for Operational Meteorologists
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Satellite Foundational Course for JPSS: SatFC-J (SHyMet Full Course Access)
The Satellite Foundational Course for JPSS (SatFC-J) is a series of short lessons focused on topics related to microwave remote sensing and Joint Polar Satellite System instruments and capabilities. Hosted by the Cooperative Institute for Research in the Atmosphere (CIRA), this resource provides access to the full set of course lessons, which were developed specifically for National Weather Service (NWS) forecasters. The lessons provide foundational training to help forecasters and decision makers maximize the utility of the U.S.’ new-generation polar-orbiting environmental satellites. The cou ...
Available online: https://www.meted.ucar.edu/training_module.php?id=1614
Published by: The University Corporation for Atmospheric Research ; 2019
The Satellite Foundational Course for JPSS (SatFC-J) is a series of short lessons focused on topics related to microwave remote sensing and Joint Polar Satellite System instruments and capabilities. Hosted by the Cooperative Institute for Research in the Atmosphere (CIRA), this resource provides access to the full set of course lessons, which were developed specifically for National Weather Service (NWS) forecasters. The lessons provide foundational training to help forecasters and decision makers maximize the utility of the U.S.’ new-generation polar-orbiting environmental satellites. The course is intended to help learners develop and improve their understanding of the value and anticipated benefits of JPSS, including improved monitoring of meteorological, environmental, and climatological phenomena and related hazards. The full listing of lessons is accessible through the SHyMet website. [Note that NOAA personnel should access the lessons through the Commerce Learning Center (CLC).] Training developers include VISIT/SHyMet staff from the Cooperative Institutes at CIMSS and CIRA; COMET; the Cooperative Institute for Mesoscale Meteorological Studies (CIMMS); the Short-term Prediction Research and Transition Center (SPoRT); and the NWS Office of the Chief Learning Officer (OCLO).
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: Meteorology ; Observations ; Satellite ; Weather forecasting ; Precipitation ; Remote sensing ; Training ; Lesson/ Tutorial ; Satellite Skills and Knowledge for Operational Meteorologists
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Frontal Diagnosis 1
In this lesson, we start by investigating the different types of fronts that are commonly analyzed. Next, we address two different types of cold fronts: classic (stacked), and katabatic. Then, we identify the main characteristics of these frontal types and what sets them apart from each other in conceptual models and in water vapour imagery. This is the first lesson in a two part series that addresses three different types of cold fronts and how to diagnose them.
Available online: https://www.meted.ucar.edu/training_module.php?id=1619
Published by: The University Corporation for Atmospheric Research ; 2019
In this lesson, we start by investigating the different types of fronts that are commonly analyzed. Next, we address two different types of cold fronts: classic (stacked), and katabatic. Then, we identify the main characteristics of these frontal types and what sets them apart from each other in conceptual models and in water vapour imagery. This is the first lesson in a two part series that addresses three different types of cold fronts and how to diagnose them.
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 ; Lesson/ Tutorial ; Satellite Skills and Knowledge for Operational Meteorologists
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GOES-R Geostationary Lightning Mapper (GLM) North America Examples
The Geostationary Lightning Mapper (GLM) aboard the GOES-R series satellites provides continuous lightning detection from space, giving forecasters a unique tool to monitor developing thunderstorms. This 45 minute lesson introduces learners to the benefits of using GLM gridded products, primarily Flash Extent Density (FED). Learners will explore several North American convective events and use Flash Extent Density, in combination with other satellite and radar data, to diagnose convective initiation, storm intensification, and areal extent of lightning activity. Helpful hints to keep in mind w ...
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Introduction to Modifying NWP Output
Surface observations are usually the first place we go when trying to find mismatches between observed weather and NWP output. We'll talk in this lesson about appropriate methods for making those comparisons and build to a point where we will focus on bigger picture atmospheric processes. This is the first in a series of video lessons that introduces three different methods for modifying NWP output to add human value to forecasts.
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Adjusting NWP: Direct Comparison
If there were a way to make direct comparisons between satellite imagery and NWP output, that would appear to be the best possible way to find mismatches between the observed weather and NWP output. In this lesson, we'll address possible methods for making direct comparisons, starting with pseudo or synthetic satellite imagery and building to a point where we focus on a relatively unused NWP output. This is the third in a series of video lessons that introduces three different methods for modifying NWP output to add human value to forecasts. Pre-requisite Knowledge: Satellite Water Vapour Inte ...
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Interpreting and Communicating EPS Guidance: Iberian Heat Wave
This 45-minute lesson briefly introduces learners to the benefits of using probabilistic forecast information to assess the weather and communicate forecast uncertainties. Learners will explore a heat wave event in Spain and practice interpreting EPS forecast products effectively to determine various forecast parameters based on lead-time. Also, learners will decide how to best communicate the potential weather threats and impacts information to local end users.
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What's New in the National Blend of Models version 3.1
Intended for U.S. National Weather Service forecasters, this short video describes changes to the NWS National Blend of Models when it was updated to v3.1. These changes include: More global, mesoscale, and ensemble components; Increased spatial resolution of some components; New and improved weather elements for aviation, QPF, winter, fire, and marine weather forecasting; Significant wave height for offshore waters and the Great Lakes; Improved bias correction; MOS-like text products; Shortened NBM forecast projections delivered at 19 UTC. For an illustrated transcript, see What’s New in NBM ...
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SatFC-J: The CrIS and ATMS Sounders
This lesson introduces the capabilities of NOAA’s next-generation infrared and microwave sounders, the Cross-track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS). Both fly on board the Suomi NPP satellite mission and constitute the foundation for NOAA’s operational space-based sounding capability on the next-generation JPSS polar-orbiting satellites. In addition to their complementary sounding duties, CrIS and ATMS provide capabilities and improvements for a variety of environmental products essential to weather forecasting and environmental monitoring. Some of th ...
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Mesoscale Model Components of the National Blend of Models Version 3.0
The National Weather Service National Blend of Models (NBM) was updated to version 3.0 on 27 July 2017. Changes include: Eight new components for the contiguous U.S. (CONUS) and Alaska, including four deterministic models, two ensemble systems, and two post-processed statistical components Five new components for Hawaii and Puerto Rico Expanded forecast domains for the CONUS and Alaska A “Time of Day” (ToD), rather than NWP model, initial time concept Hourly NBM forecasts, with short, day 2-4, and extended forecasts Updated NBM guidance available 50-60 minutes after hourly run time New weather ...
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GOES-R Series Multilingual Training Resources
This listing of multilingual training materials for the GOES-R series includes both foundational lessons and quick guides developed by various partners at the request of the U.S. National Weather Service and NESDIS. The selections included here represent materials translated to Spanish and Portuguese. Training contributors include COMET, RAMMB/CIRA, CIMSS, and SPoRT. Translation contributors/reviewers include the Servicio Meteorológico Nacional (SMN) in Argentina and the University of São Paulo in Brazil.
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Unified Terrain in the National Blend of Models
This lesson discusses errors associated with the use of inconsistent terrain in the analyses in the Real-Time and the Un-Restricted Mesoscale Analyses (RTMA and URMA, respectively), and in downscaling numerical weather prediction model data to the resolution of the U.S. National Weather Service National Blend of Models (NBM). The sources of these inconsistencies are examined, and the errors that result are discussed. A solution is to use a unified, consistent terrain in the analyses and the NBM. This solution is only partial however, as resolution of small, meteorologically significant feature ...
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GOES-16 and S-NPP/JPSS Case Exercise: Hurricane Harvey Surface Flooding
Satellite data are important tools for analyses and short-term forecasts of surface floodwater. This lesson will highlight the August 2017 flooding associated with Hurricane Harvey in southeastern Texas, one of the most costly weather disasters in U.S. history. Through the use of interactive exercises the learner will become familiar with use and interpretation of satellite imagery in regions with surface flooding. The lesson will use data from both the S-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) and the GOES-16 Advanced Baseline Imager (ABI). The satellite-derived flood map and th ...
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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.
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SatFC-J: The AMSR2 Microwave Imager
This short lesson describes the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the next-generation polar-orbiting satellite platforms. AMSR2’s primary mission is to improve scientists’ understanding of climate by providing estimates of precipitation, water vapor, cloud water, wind velocity, sea surface temperature, sea ice concentration, snow depth, and soil moisture. AMSR2 also advances weather forecasting through real-time imagery, value-added products, and input to numerical weather prediction. This lesson is part of the Satellite Foundational Course for JPSS (SatFC-J).
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SatFC-J: The VIIRS Imager
This lesson introduces the VIIRS imager on board the Suomi NPP and JPSS satellites. The lesson briefly describes the capabilities, improvements, and benefits that VIIRS brings to operational meteorology. Numerous images are shown that demonstrate a variety of applications available in the AWIPS weather display system. This lesson is part of the Satellite Foundational Course for JPSS (SatFC-J).
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SatFC-J: Orbits and Data Availability
This lesson presents a brief overview of NOAA's operational low Earth orbiting satellites, focusing on how their orbits define observational coverage and how ground receiving capabilities impact data latency from the observation time to product availability. This lesson is part of the Satellite Foundational Course for JPSS (SatFC-J).
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Rapid Scan Applications and Benefits
This lesson introduces the capabilities and benefits of rapid scan imaging from geostationary meteorological satellites with a special focus on the current Meteosat Second Generation satellites. The lesson begins with an overview of current rapid scan imaging strategies and the products made from those observations. It then addresses nowcasting applications that benefit from these products with a focus on convection and its evolution. Other application areas that benefit from rapid scan observation are mentioned including the monitoring of fog and low stratus, wildfires, tropical cyclones, and ...
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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 ...
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Operational Environmental Monitoring Applications using the Community Satellite Processing Package (CSPP)
This resource demonstrates the variety of satellite imagery and products accessible through the Community Satellite Processing Package (CSPP). Two videos, the first focused on imagery applications and the second on microwave applications, provide an overview of the types of weather and environmental information available through CSPP. Using CSPP, forecasters and others needing timely access to data can download and display imagery and products from Joint Polar Satellite System (JPSS) instruments. The resource provides some background information for obtaining and using the CSPP software, which ...
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SatFC-J: The VIIRS Day/Night Band
This lesson introduces the innovative Day/Night Band (DNB). Producing both daytime and nighttime visible images, the unique aspect of the DNB is its nocturnal low-light imaging capability. It views reflected moonlight from clouds and Earth's surface, surface light emissions from various natural sources (such as fires) and anthropogenic sources (such as city lights and gas flares), and even from certain atmospheric light emissions such as the aurora, airglow, and lightning flashes. The lesson describes the capabilities and benefits of the DNB, in particular using the Near-Constant Contrast (NCC ...
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GOES-16 GLM Case Exercise: Buenos Aires Tornado and Hail Event
The Geostationary Lightning Mapper (GLM) flies aboard the GOES-R series satellites and provides lightning detection data at a quality and resolution not previously available from space. The GLM's continuous lightning monitoring capability is a valuable asset to detecting and monitoring developing thunderstorms 24 hours a day. This 30 minute lesson introduces learners to the benefits of using Geostationary Lightning Mapper (GLM) observations in assessing convection. Learners will explore a severe weather event near Buenos Aires, Argentina, and practice using GLM observations to determine initia ...
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Гидродинамический прогноза погоды на территории Гвинеи
Agriculture is the largest employer in the world and is probably the most dependent on the climate of all human activities. In recent years there have been events that have put in evidence the vulnerability of global food security to major meteorological phenomena, both in global agricultural markets and the world economy. The food price crisis and the subsequent economic crisis reduced the purchasing power of large segments of the population in many developing countries, which seriously reduced their access to food and thus undermined their food security. During the years 2009 and 2010 in Ven ...
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