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Наставление по Глобальной системе обработки данных и прогнозирования: Дополнение IV к Техническому регламенту ВМО
Наставление предназначается для следующих целей: a) способствовать сотрудничеству в отношении обработки данных и прогнозирования между странами-членами; b) определить обязанности стран-членов по осуществлению Глобальной системы обработки данных и прогнозирования (ГСОДП) Всемирной службы погоды (ВСП); c) обеспечивать единообразие и стандартизацию практики и процедур при выполнении пунктов (a) и (b) выше.GCRF African SWIFT
GCRF African-SWIFT is a programme of research and capability building, led by the National Centre for Atmospheric Science (NCAS), and funded by UK Research and Innovation Global Challenges Research Fund. The project aims to deliver a step change in African weather forecasting capability from hourly to seasonal timescales, and build research capability to continue forecasting improvements in Africa for the foreseeable future.
The GCRF African-SWIFT team works with forecast users across sectors from aviation to agriculture, energy, water and emergency response to understand how to ...GCRF African SWIFT
GCRF African-SWIFT is a programme of research and capability building, led by the National Centre for Atmospheric Science (NCAS), and funded by UK Research and Innovation Global Challenges Research Fund. The project aims to deliver a step change in African weather forecasting capability from hourly to seasonal timescales, and build research capability to continue forecasting improvements in Africa for the foreseeable future.
The GCRF African-SWIFT team works with forecast users across sectors from aviation to agriculture, energy, water and emergency response to understand how to ...
GCOS, 229. Plan for improving observations around Lake Victoria that support numerical weather predictions, climate services and adaptation
World Meteorological Organization (WMO) ; United Nations Educational, Scientific and Cultural Organization (UNESCO); Intergovernmental Oceanographic Commission (IOC); et al. - WMO, 2019The Forecast Process: Using the Forecast Funnel
This lesson was developed by meteorologist, Dr. Mick Pope, with sponsorship from the Australian Bureau of Meteorology (BoM). The lesson is a somewhat broad-brush review of the overall forecast process, but with specific application of the forecast funnel approach as used by Australia's Bureau of Meteorology (BoM). The forecast process components include decision support and communication, use of numerical weather prediction, and applying the forecast funnel approach. The forecast funnel is described in detail, along with the forecaster time pyramid, and it is applied using a BoM forecast polic ...CESM Distance Learning Course
The Community Earth System Model (CESM) is a fully-coupled, community, global climate model that provides state-of-the-art computer simulations of the Earth's past, present, and future climate states. The CESM Distance Learning Course is based on the CESM Tutorial held annually at the National Center for Atmospheric Research (NCAR) in Boulder, Colorado. This course consists of 12 lectures and 4 practical sessions on simulating the climate system and practical sessions on running Community Earth System Model (CESM), modifying components, and analyzing data. The course is targeted at the graduat ...Convection-allowing Models (CAMs): Winter Applications
In this lesson, forecasters will practice using guidance from different convection-allowing models (CAMs) over the short term. As they review and analyze the model guidance they will encounter some of the advantages and limitations of using CAMs for winter weather.Land Surface Analysis: An Introduction to the EUMETSAT LSA-SAF Products
This 45-minute lesson provides an overview of the satellite-derived products generated by the Satellite Application Facility on Land Surface Analysis (LSA-SAF) that may provide beneficial information to the agriculture community. Learners will practice reading and interpreting the LSA-SAF products to better understand the characteristics of vegetation. The lesson also discusses the application of satellite-derived products in regression analysis to model agricultural production, and uses a wine production case in the Portuguese Douro Valley to show learners how seasonal crop productions may be ...GOES-16/JPSS Case Exercise: Monitoring the Rhea Oklahoma Grassland Fire
The current GOES-R and JPSS meteorological satellites have improved capabilities for enhanced fire detection that include more effective monitoring of fire starts, evolution, and smoke. This lesson provides forecasters and others with the opportunity to become more familiar with both GOES-R and JPSS satellite products (including the longwave-shortwave IR difference, Fire Temperature RGB, GeoColor, GOES-R Fire Mask, JPSS Active Fire, and others) during the onset of a large grassland fire event, known as the Rhea Fire, that affected western Oklahoma from April 12-18, 2018. Interactions and quest ...Introducing the FORMOSAT-7/COSMIC-2 Satellite System - Next Generation Observations for Weather and Climate
The latest-generation Constellation Observing System for Meteorology, Ionosphere, and Climate (FORMOSAT-7/COSMIC-2) provides high-resolution observations of Earth's atmosphere, including the ionosphere. In this video, scientists and mission planners introduce the instrumentation used and describe the collaborations that made the COSMIC-2 mission possible. These experts describe how COSMIC uses a technique called radio occultation—making use of existing navigation satellite signals passing through the atmosphere to provide detailed measurements of temperature, pressure, and water vapor. They ex ...What's New in NBM v3.2
The U.S. National Weather Service (NWS) National Blend of Models (NBM) is scheduled to be upgraded to version 3.2 in November 2019. It includes the first probabilistic blended guidance for temperature, precipitation, snow, and ice. There are more blended forecast products for aviation, marine, water resources, fire weather, winter weather, and tropical weather. Version 3.2 uses more model components to improve guidance, and introduces a new Guam domain. For a transcript, see What’s New in NBM v3.2. (https://www.meted.ucar.edu/nwp/blend_v32_video/NBM_v32_script.pdf)Satellite Signals from Space: Smart Science for Understanding Weather and Climate
Want to know about COSMIC, and how satellite signals can provide information about Earth's atmosphere? This video provides anyone interested in the topic with a brief overview of the Constellation Observing System for Meteorology, Ionosphere, and Climate, called COSMIC. Targeted to students and teachers in Grades 5-9 but accessible to anyone, the video introduces the latest COSMIC mission (COSMIC-2), which uses satellites orbiting near Earth to measure how the atmosphere affects signals from global positioning system (GPS) satellites high above the surface. This technique is called radio occul ...Basic Satellite and NWP Integration
NWP is one of the most important forecasting tools in our toolbox. Yet identifying when/where it isn’t capturing reality is difficult. In the short-term forecasting range, it is important as a forecaster to identify when/where NWP output isn’t matching reality. Then you can make appropriate changes to the forecast output. To find those mismatches anywhere in the world, one of the best tools is satellite imagery. In this lesson, we will focus on a few cases using satellite imagery to help identify mismatched features/processes between the satellite imagery and the NWP. Anyone trying to add valu ...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.
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