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Giant solar flares: the sun producing super-storms and massive radiation bursts.
Pitris/Getty Images/iStockphoto
Cloud and systems


HOW NASA USES AWS TO PROTECT LIFE AND INFRASTRUCTURE ON EARTH


NASA IS USING UNSUPERVISED LEARNING AND ANOMALY DETECTION TO EXPLORE THE EXTREME
CONDITIONS ASSOCIATED WITH SOLAR SUPERSTORMS.

By Arun Krishnan
January 14, 2020
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In March 1989, strange things began to happen in the US and Canada. The
Hydro-Quebec electric grid collapsed within 90 seconds. A strong electric
current surged through the surface bedrock making all intervention impossible.
Over 6 million people were left without power for nine hours. At the same time,
over in the United States, 200 instances of power grid malfunctions were
reported. More worryingly, the step-up transformer at the New Jersey Salem
Nuclear Power Plant failed and was put out of commission.

These outages weren’t caused by an earthquake, a terrorist group or some other
terrestrial event. Instead, the culprit was a massive solar coronal mass
ejection (CME) that was 36 times the size of earth.

The sun emits flares in the form of heat and light on a regular basis. These
flares reach the earth in about eight minutes and, while ongoing, they interrupt
radio communications signals. The flares often come with a surge of high-energy
solar particles, which can travel at 80 percent of the speed of light and reach
our planet in a time range that can vary from 10 to 20 minutes.

A solar eruption on Sept. 26, 2014, seen by NASA's Solar Dynamics Observatory.
If erupted solar material reaches Earth, it can deplete the electrons in the
upper atmosphere in some locations while adding electrons in others, disrupting
communications either way.
Credit: NASA.gov

Earth’s magnetic fields protect us against much of the sun’s activity — but in
some circumstances that radiation can seep through our earth’s protective
atmosphere. On occasion, solar flares burst forth along an eruption called a
coronal mass ejection, or CME. CMEs are massive clouds of plasma and magnetic
fields, and can (when accompanying magnetic fields are oriented in the correct
direction) cause the magnetic fields around earth to begin oscillating like a
cosmic gong. When solar coronal mass ejections collide with the earth’s
magnetosphere, they can induce geomagnetic solar superstorms.

Superstorms such as the one in March 1989 are rare indeed, estimated to occur
only once every 50 years. Experts who study extreme events, like super-volcanoes
or asteroid impacts, frequently call these occurrences low frequency/high
consequence events. NASA scientists are involved in understanding what turns an
average solar storm into a superstorm, just as meteorologists have been able to
understand how a tropical storm over the ocean turns into a hurricane. The more
we understand about what causes such space weather, the more we can improve our
ability to forecast and mitigate their effects.



THE DIFFICULTY OF PREDICTING SUPERSTORMS



However, predicting superstorms, and developing early response systems to these
extreme events is a difficult endeavor. For one, given just how rare superstorms
are, there are very few historical examples that can be used to train
algorithms. This makes common machine learning approaches like supervised
learning woefully inadequate for predicting superstorms. Additionally, with
dozens of past and current satellites gathering space weather information from
different key vantage points around Earth, the amount of data is prodigious —
and the attempt to find correlations laborious when searched conventionally.

NASA is working with AWS Professional Services and the Amazon Machine Learning
(ML) Solutions Lab to use unsupervised learning and anomaly detection to explore
the extreme conditions associated with superstorms. The Amazon ML Solutions Lab
is a program that enables AWS customers to connect with machine learning experts
within Amazon.

With the power and speed of AWS, analyses to predict superstorms can be carried
out by sifting through as many as 1,000 data sets at a time. NASA’s approach
relies on classifying superstorms based on anomalies, rather than relying on an
arbitrary range of magnetic indices. More specifically, NASA’s anomaly detection
relies on simultaneous observations of solar wind drivers and responses in the
magnetic fields around earth.

Superstorms can be modeled as anomalous outlier events. Anomaly score values and
evolution can be used to identify patterns that distinguish superstorm events
from other solar storms. The anomaly scores offer an alternative method of
categorizing extreme events that does not rely on arbitrary ranges of
geomagnetic indices but rather on features of the storms themselves.

Emergent features that can be used for anomaly detection include the particle
density in our ionosphere – the lowest levels of space which overlap with our
upper atmosphere. Most of the ionosphere is electrically neutral. However, solar
radiation activity can cause electrons to be dislodged from atoms and molecules.
As a result, particle density can increase by orders of magnitude on the
sun-facing side of the planet during a superstorm. On the flip side, superstorms
create a hole in the ionosphere on earth’s dusk side, as sunlight is not
available to replenish the density of the ions after the ionosphere is lifted
upward during the event.



DETECTING ANOMALIES WITH AWS



NASA uses Amazon SageMaker to train an anomaly detection model using the
built-in AWS Random Cut Forest Algorithm (RCF) with heliophysics datasets
compiled from various ground- and satellite-based instruments. Anomalies are
easy to describe in that, when viewed in a plot, they are often easily
distinguishable from the more typical data. With each data point, RCF associates
an anomaly score. Low score values indicate that the data point is considered
normal. High values indicate the presence of an anomaly in the data.

The Amazon ML Solutions Lab uses a serverless streaming data pipeline
application to enable real-time monitoring. The pipeline processes in-situ
observations from spacecraft to detect and alert on anomalies in real-time. The
streaming pipeline leverages Amazon Kinesis Data Streams to determine anomaly
scores.

Amazon Kinesis Firehose, can stream real-time data into Amazon S3 is used for
record delivery and schema conversion to Parquet. In addition to real-time
alerting, the model results are persistently stored in Amazon S3 where they can
be further analyzed using Amazon SageMaker and visualized with Amazon
QuickSight, which allows for the creation and publishing of interactive
dashboards that include ML Insights.



With a robust set of anomalies to examine, researchers can search for what
causes them and linkages between them. NASA and AWS are developing a centralized
data lake which will allow researchers to access and analyze cosmic-scale data
with dynamic cloud compute resources. To date, the initiative has aggregated
observational data from 50+ satellite missions containing images, time-series
and miscellaneous telemetry data. Data is continually pre-processed and combined
to develop visualizations that drive future heliophysics research and
innovation.

> With Amazon, we can take every single piece of data that we have on
> superstorms, and use anomalies to improve the models that predict and classify
> superstorms effectively.

Janet Kozyra, Heliophysicist, NASA



To improve forecasting models, scientists can examine the anomalies and create
simulations of what it would take to reproduce the superstorms we see today.
They can amplify these simulations to replicate the most extreme cases in
historical records, enabling model development to highlight subtle precursors to
major space weather events.

“We have to look at superstorms holistically, just like meteorologists do with
extreme weather events,” says Janet Kozyra, a heliophysicist who leads this
project from NASA headquarters in Washington, D.C.

“Research in heliophysics involves working with many instruments, often in
different space or ground-based observatories. There’s a lot of data, and
factors like time lags add to the complexity. With Amazon, we can take every
single piece of data that we have on superstorms, and use anomalies we have
detected to improve the models that predict and classify superstorms
effectively."

Research areas
 * Cloud and systems
 * Machine learning
 * Sustainability

Tags
 * Amazon Web Services (AWS)
 * Anomaly detection

About the Author
Arun Krishnan
Arun Krishnan is a contributor to Amazon Science and the editor-in-chief for
Alexa Events.


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