OUR VISION, OUR RESEARCH
Atsushi Okazaki
atsushi.okazaki@chiba-u

IPCC AR6 SPM

Our world is anticipated to be less comfortable and less safeResources we can use are in decline= Options we can take will be limited

Reserves-toProduction
ratio = 50 years

100 million barrel / day= 37 billions / a?

The power of computation and the data size
are still growing

Raise of quantum computers

https://www.telegraph.co.uk/business/2023/07/02/googlequantum-computer-breakthrough-instant-calculations/Our vision in this sentence• Demand: Need to predict what will happen at various scale (hourly,daily, monthly, seasonal, interannual, decadal, centennial) to be well
prepared in an efficient way• Our mission: To create a new world with cutting-edge predictions andbeyond• Our vision: To optimize the world with the power of prediction anddata science“OPTIMIZE THE WORLD” with the power of prediction and data science
Early warning

Water resource management

Prediction
Exposure

Control

Exposure

Food security
Energy network design

HazardHazardVulnerability

Risk

Mitigate risk

Adaptation

Vulnerability

What’s necessary to realize the vision?
Prediction✘ Accurate prediction(hourly ~ cenntennial)✘ Observations

Control / Adaptation✘ Fast prediction✘ Control theory✘ High-resolution prediction✘ Estimation method (DA, ML)✘ System model✘ Long dataset- to understand the current state- as an input for ML✘ Risk / Impact estimation

Things that is important but we do not touch✘ Computation power✘ Development of actuators✘ Ethical, Legal, and Social Issues(ELSI) incl. risk communication

What’s necessary to realize the vision?
Prediction

Control / Adaptation
Assimilation and Control

Seasonal to Decadal Prediction✘ Accurate prediction(hourly ~ cenntennial)✘ Observations✘ Fast prediction✘ High-resolution prediction✘ Estimation method (DA, ML)✘ System modelObservations
incl. satellite& dataset✘ Control theory

Machine Learning✘ Long dataset- to understand the current state- as an input for ML✘ Risk / Impact estimation✘ Computation power✘ Development of actuators✘ Ethical, Legal, and Social Issues(ELSI) incl. risk communication

Hydrology

Observation

Hydrology

Optimization of observation network

Shiojiri-san & Saito-san proposed a method to optimize observation network 9Observation

Hydrology

Improve global precipitation estimation
with EnKF
Observations

PriorCPCLETKF [this study]
Rank correl(vs. APHRODITE)

LETKF

mm day-1

Muto-san developed a nicer dataset than ever!!

ERA5
CPC𝑖𝑖𝑖𝑖𝑖𝑖
𝑑𝑑𝑚𝑚𝑚𝑚𝑚𝑚km

Prediction Hydrology

Ensemble forecast with RRI

Fujimura-san proposed a method for flood prediction!

11Prediction
Assim & Ctrl

Quantum Annealing for 4DVAR
Average of 50 data assimilations(window: 2days, 40 obs, B is manually tuned)linearizedbinary expression

FGPretty fast!

QAs used 4 quantum bits (QBs) for a real
Simulated QA
Physical QA
Kawasaki-sandemonstrated
thepotential(GPU-based)(D-Wave)
number dueof
to QC!
the limitation of total QB.

Scipy BFGS w/wo outer loop
(w/wo TLM/ADJ updates)

ML Hydrology

RRI emulator development

GOAL: Develop anemulator that
estimates water depttimeseries

Peak discharge
Strongest
rainfall

RRI

Momoi et al.
(2023)

This study

AMomoi et al.
(2023)

This study

water depth at
Shimabukuro-san developed
a fast and detailed model!
point A 13

ML Hydrology

Precipitation super-resolution

WGAN

 Dowinscaling precipitation with CNN and WGAN WGAN is an algorithm that used Wasserstein Distance (WD)as the loss function

LR

Original data

SRCNN

ORGHR

Discriminator(Critic)WD

Traninig to Maximize WD

Traninig to Minimize WDGenerator

Wasserstein GANShiraishi-san is exploring forefront techniques, which inspires us a lot!!

Assim & Ctrl

MPC & CSE Experiments w/ Lorenz 63 model

to keep L63 in right-side wing

Miyoshi & Sun(2022)

An MPC for a prediction

Weather control study has just began but is rapidly growing thanks to Kawasaki-san!!

Kawasaki and Kotsuki, in review

Assim & Ctrl

Development of EnVAR based MPC algorithmSettings▍Model (L63; dt=0.01=1hr)
𝑑𝑑(𝑥𝑥+δ𝑥𝑥)= 𝜎𝜎 𝑦𝑦 + δ𝑦𝑦) − (𝑥𝑥 + δ𝑥𝑥) + 𝑢𝑢𝑥𝑥
𝑑𝑑𝑑𝑑𝑑𝑑(𝑦𝑦+δ𝑦𝑦)= 𝑥𝑥 + δ𝑥𝑥 𝜌𝜌 − 𝑧𝑧 + δ𝑧𝑧 − 𝑦𝑦 + δ𝑦𝑦
𝑑𝑑𝑑𝑑𝑑𝑑(𝑧𝑧+δ𝑧𝑧)= (𝑥𝑥 + δ𝑥𝑥)(𝑦𝑦 + δ𝑦𝑦) − 𝛽𝛽(𝑧𝑧 + δ𝑧𝑧)
𝑑𝑑𝑑𝑑

••
•

▍Data Assimilation••••

Ens. Size: 10 member
Constraints: x>0Estimate: δx, δy, δz, ux
DA method: EnMPC▍Horizons••

Prediction Horizon:
10steps (=10hr)
Control Horizon:
5 steps (=5hr)(unit: 1/dt)

Kurosawa-san added the essence of DA to weather control, which accelerates our work!
12

We’ve done a lot so far!!
Prediction✘ Accurate prediction(hourly ~ cenntennial)✘ Observations

Control / Adaptation✘ Fast prediction✘ Control theory✘ High-resolution prediction✘ Estimation method (DA, ML)✘ System model✘ Long dataset- to understand the current state- as an input for ML✘ Risk / Impact estimation✘ Computation power✘ Development of actuator machines✘ Ethical, Legal, and Social Issues(ELSI) incl. risk communication

Now it’s your turn!!

Takeshima-sanPrediction

Control / Adaptation
Assimilation and Control

Seasonal to Decadal Prediction✘ Accurate prediction(hourly ~ cenntennial)✘ Observations

Miyazawa-san✘ Control theory

Machine Learning✘ Fast prediction✘ High-resolution prediction✘ Estimation method (DA, ML)✘ Risk / Impact estimation
NewPDHydrology✘ System model

Observations
incl. satellite& dataset✘ Long dataset- to understand the current state- as an input for MLNewPD

Let’s keep this good momentum.

Lastly but not least…
Let me tell you the essence to be a successful scientist
That is VW; Vision and work hard
Robert W. Mahley
Vision without action is a daydream
Action without vision is a nightmare
Taikan Oki

You have to work at least 60 hours a week
when you’re young to be a scientist
Fuqing Zhang