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Sorted by (Issue date descending, Date of update descending, Date of creation descending, Number of collection descending) Add the result to your favourites Refine your searchSatellite 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.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 CourseInterpreting 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 ...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 ...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.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 ...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.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 ...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.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 ...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 ...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 ...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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