Today was the wedding anniversary of adorable an American couple Samuel & Michelle. To give surprise to each other, both impatiently were waiting for parcel delivery man to receive their wedding anniversary gifts. At last time has come when Wireless Door Bell rung up, so they both rushed to collect their parcel. After receiving, they both came into the living room, and gave each other gifts. During the unboxing Amazon parcel, Samuel got ruby stone red cufflinks and Michelle flower TKV necklace. They were so very excited to receive gift from each other, couldn't stop kissing and hugging each other. Suddenly, when alert message from Samuel smartphones interrupted their romance, both husband and wife paused for a while. It was Gmail alert received in inbox of Samuel, so he opened email and start reading tech news and headline was Professional weather predicting software is beaten by Google's AI.
GraphCast is an artificial intelligence model
that Google DeepMind researchers
trained to forecast the weather.
The outcome
exceeded expectations: in terms of forecast accuracy, speed, and
cost-effectiveness, it performed better than the current professional software.
The industry
standard utilized by the European Center for Medium-Range Forecasts, or HRES,
is a counterpart to GraphCast.
Consequently,
GraphCast used 1,380 tracking characteristics to predict the weather 90% of the
time. For instance, ten days prior to Hurricane Lee's arrival, AI forecast that
it would form on Long Island.
Numerical
prediction (NWP) is used by traditional software to forecast the weather. It is
predicated on well-chosen equations that are transformed into computer code and
run on supercomputers.
GraphCast
adopts a different strategy, using artificial intelligence (AI) to learn from
decades' worth of historical weather data, rather than using equations. AI can
therefore "see" into the future by analyzing patterns and studying
cause-and-effect linkages.
In addition
to being significantly more accurate, this method is far less expensive than
utilizing supercomputers.
GraphCast's
accuracy is largely due to conventional software because the AI could not train
as well without NWP data.
Apart from
typical meteorological conditions, artificial intelligence has the ability to
forecast natural disasters well in advance, providing a window of opportunity
for early evacuation of individuals from high-risk regions.
On a Google
TPU v4 computer, GraphCast generates a 10-day forecast in less than a minute,
demonstrating its extraordinary performance in this context. While requiring
significantly more processing power, HRES finishes this task in a few hours.
The fact
that the model's developers released the model's source code into the public
domain is particularly notable since it will enable meteorologists to create
systems that are far more accurate.
Furthermore,
an experiment utilizing GraphCast is already being carried out by the European
Center for Medium-Range Forecasts.
When Samuel
finished reading tech news, he resumed loving with life partner Michelle.
0 Comments