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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 ...
Available online: https://www.meted.ucar.edu/training_module.php?id=1375
Published by: The University Corporation for Atmospheric Research ; 2018
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 elements for aviation and winter weather forecasting including precipitation type; and new QPF, cloud bases, ceilings, and visibilities New computation methods for wind direction and doubling of model components for ocean winds NBM v3.0 products have different characteristics from the previous v2.0. Coupled with the new “ToD” paradigm and new weather elements, the new NBM v3.0 results in big changes to forecast operations. This lesson starts with a five-minute video that describes these changes. Then to illustrate how forecast operations will be affected, we follow forecasters through important parts of a day shift, using NBM products to predict a winter storm.
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 ; Mesoscale ; Numerical weather prediction ; Lesson/ Tutorial ; Aviation ; NWP Skills and Knowledge for Operational Meteorologists
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Radio Wave Propagation
As a society we have become dependent on satellite communications, but satellites fail with alarming frequency. Before the advent of satellites, long distance communications were carried out with high frequency (HF) radio transmissions. This lesson examines the factors that control long-distance radio communications, with an emphasis on refraction in the ionosphere, frequency selection, and the effects of solar radiation.
Available online: https://www.meted.ucar.edu/training_module.php?id=1394
Published by: The University Corporation for Atmospheric Research ; 2018
As a society we have become dependent on satellite communications, but satellites fail with alarming frequency. Before the advent of satellites, long distance communications were carried out with high frequency (HF) radio transmissions. This lesson examines the factors that control long-distance radio communications, with an emphasis on refraction in the ionosphere, frequency selection, and the effects of solar radiation.
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: Radio wave ; Ionosphere ; Lesson/ Tutorial
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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.
Available online: https://www.meted.ucar.edu/training_module.php?id=1405
Published by: The University Corporation for Atmospheric Research ; 2018
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.
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: Satellite ; Weather forecasting ; Mesoscale ; Data assimilation ; Remote sensing ; Convection ; Lesson/ Tutorial ; Satellite Skills and Knowledge for Operational Meteorologists
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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 ...
Available online: https://www.meted.ucar.edu/training_module.php?id=1376
Published by: The University Corporation for Atmospheric Research ; 2018
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 features is still somewhat limited. Poorly resolved physical features will still result in analysis and forecast error in some circumstances.
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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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 ...
Available online: https://www.meted.ucar.edu/training_module.php?id=1397
Published by: The University Corporation for Atmospheric Research ; 2018
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 the data that go into the flood map will both be highlighted in the lesson. Examples of floodplain inundation, interbasin transfer, and water pooling in reservoirs will be shown along with issues related to spatial and temporal resolution.
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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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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Instrumentation and Measurement of Atmospheric Pressure
This lesson provides information about current science and technologies for measuring atmosphere pressure. The lesson begins by reviewing the key physical laws governing atmospheric pressure, including Dalton's Law of Partial Pressures. Then, it explores typical requirements and uncertainty parameters related to atmospheric pressure sensors and provides details about the components of pressure sensors, including fluidic, mechanical, and electronic transducers. The lesson is part of the Instrumentation and Measurement of Atmospheric Parameters course series.
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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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Física del caos en la predicción meteorológica
AEMET, 2018Over 100 experts in weather and climate modeling, numerical and operational forecasting, and related areas have come together to write this compendium of knowledge that addresses a diversity of maters such as history and foundations of meteorology, ensemble prediction systems, probabilistic forecasting and its applications, climatic change and social aspects, cases of study of special meteorological events, etc.
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