Hui-su
1
APPEARANCES
1
PODCASTS
012
DEC 30
JAN 6
JAN 13
JAN 20
JAN 27
FEB 3
FEB 10
FEB 17
FEB 24
MAR 3
MAR 10
MAR 17
MAR 24
MAR 31
APR 7
APR 14
APR 21
APR 28
MAY 5
MAY 12
MAY 19
MAY 26
JUN 2
JUN 9
JUN 16
JUN 23
JUN 30
JUL 7
JUL 14
JUL 21
JUL 28
AUG 4
AUG 11
AUG 18
AUG 25
SEP 1
SEP 8
SEP 15
SEP 22
SEP 29
OCT 6
OCT 13
OCT 20
OCT 27
NOV 3
NOV 10
NOV 17
NOV 24
DEC 1
DEC 8
DEC 15
DEC 22
DEC 29
JAN 5
JAN 12
JAN 19
JAN 26
FEB 2
FEB 9
FEB 16
FEB 23
MAR 2
MAR 9
MAR 16
MAR 23
MAR 30
APR 6
APR 13
APR 20
APR 27
MAY 4
MAY 11
MAY 18
MAY 25
JUN 1
JUN 8
JUN 15
JUN 22
JUN 29
JUL 6
JUL 13
JUL 20
JUL 27
AUG 3
AUG 10
AUG 17
AUG 24
AUG 31
SEP 7
SEP 14
SEP 21
SEP 28
OCT 5
OCT 12
Jun 15, 2026
Using AI to predict the weather
7:27
8:15
8:26
8:34
8:52
9:00

Xiaofeng LiGUEST
If we issue too many false alarms, it reduces the trust in the forecast, but missing real events can be also dangerous.

Hui SuGUEST
Nowcasting is a weather forecasting method that predicts conditions for the immediate future, typically in the next zero to six hours.

Hui SuGUEST
Nowcasting relies on real-time data from radar or satellite to track the current movement of storms.

Hui SuGUEST
Unlike traditional methods that would struggle with storm growth and dissipation, and also some AI models tend to blur predictions of extreme events, our deep diffusion model for satellite data uses a two-step process.

Hui SuGUEST
that explicitly separates the predictable large-scale motion from chaotic stochastic evolution of storm.

Hui SuGUEST
The first step is to use a neural network to generate deterministic baseline prediction of future satellite images.
