WEBVTT

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[Music]

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[Harry Cikanek, STAR Director]
STAR, the Center for Satellite Applications and Research, 

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is the organization within NOAA that's charged with bringing the power 

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of environmental observing satellites to NOAA's missions.

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NOAA's mission is to provide science, service, and stewardship.

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First and foremost is to protect life, property, and economic vitality.

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So STAR is critical and essential to NOAA's mission because it

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brings the satellite expertise necessary to translate raw observations into useful information. 

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How that's used is, for example, in forecast models. 

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The vast majority of data that's used to initialize them is satellite data;

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so they have to have very high quality data in order to be able to work successfully.

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I tend to class STAR's users into four categories: first, is the models;

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second is operational users that need data and information for situational awareness;

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downstream value-added product providers that will 

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take satellite-based data and combine it with other things to

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make a new information product, and then finally

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research users which are developing new

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environmental understanding and capabilities for use by operational agencies.

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[Scott Rudlosky]
So STAR serves as a model on how to effectively

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transition new science from new instruments into operational applications

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that benefit all aspects of society. 

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The geostationary lightning mapper (GLM) is the first instrument

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of its kind which launched two years ago.

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It's providing continuous total lightning observations over a near

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hemispheric field of view. 

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So we can observe the total lightning -- the inter-cloud and cloud-to-ground lightning 

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all the way from the west coast of Africa to New Zealand. 

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So the GLM allows forecasters to track embedded convection;

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it allows them to identify storm mode

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and how that evolves with time. 

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They are able to gain insights on whether storms

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are strengthening and weakening, and they're also able to

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characterize the storms as they transition offshore. 

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[Deidre Byrne]
Satellite altimetry is a measure of the height of a water surface,

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such as the ocean or a lake or a river, that's made from a satellite. 

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The ocean is not like a bathtub -- it's not 

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just a big blob of uniform water. It has

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cold spots and warm spots it has strong

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currents like rivers moving through part of it.

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And altimetry is really the only instrument that gives you, 

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in essence, a snapshot of that. 

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The information that we make could be used

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by a fisherman wanting to know if it's

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an appropriate time to go out and look

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for the type of fish he wants. It could

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be used by someone running an

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aquaculture farm wanting to know if

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harmful conditions are about to occur in

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the ocean just just outside of their farm.

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It could be used by someone trying

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to monitor a remote lake or river -- 

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is that river drying up, or alternatively is it

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about to flood and wipe away some village?

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[Satya Kalluri] 
Environmental satellites feed into numerical weather prediction models.

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We call this discipline satellite meteorology. 

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We use satellite observations of ocean, land and atmospheres 

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to feed into numerical weather prediction models, to provide

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both short-term as well as seasonal or

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long-term forecasts of the environment.

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There are three different types of products that study a variety

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of parameters for global change research.

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One of the most widely used is the sea surface temperature. 

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The sea surface temperature record goes back up to the

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early 80s till now. We also have temperature

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moisture soundings or profiles of the atmosphere

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from polar satellite that go back to the late 70s.

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Then the third type of data sets cover land. 

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We use a lot of vegetation indices

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which are used to study global vegetation dynamics which are related

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to aspects such as global food security,

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food production, net primary production,

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disturbances in land use and land cover,

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both due to anthropogenic as well as non-anthropogenic factors

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such as fires, deforestation, etc. 

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[Sid Boukabara]
The big data challenge, as we commonly call it,

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is already here. We have a lot more observations

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from satellites than we can handle, 

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and that's why STAR is looking at new alternatives

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of how to handle those data,

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including artificial intelligence,

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numerical approaches like machine learning, and so on. 

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AI is a transformational technique that we have

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been exploiting for a couple of years now,

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and it has been demonstrated to add efficiency

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and improvement in forecast skill.

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So we used it for calibration, 

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we used it for transformation of raw data

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into geophysical data, and we use it also

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hand-in-hand with the weather forecasting systems, 

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to help in those skills. 

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[Harry Cikanek]
There will always be a big need for satellite observations 

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and it will grow over time because 

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the unique characteristics of those, and the demands

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of our users. So STAR will continue to do

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what it has traditionally done, which is

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bring the satellite expertise to NOAA's mission.

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The vision is to move to basically a digital twin 

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of the Earth's environment, which the users can draw from,

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and which we can draw from to create

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the time-critical products that they need for their operations.

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[Music]