Global Data Assimilation of
Virtual SWOT data in
CaMa-Flood

Tokyo Institute of Technology

Daiki Ikeshima

Apr. 17th, 2017 SWOT Teleconference1. SWOT DA Overview
2. Method for DA
3. Results and Discussions
4. Future Steps

1. SWOT DA Overview
River Model
CaMa-Flood

SWOT
Water Surface Elevation

Model Forecasted Discharge
etc…

Data Assimilation
River
Discharge
Get Better Estimation by merging “data” into “simulation”

1. SWOT DA Overview
SWOT

Daily SWOT Observation
Coverage is LimitedRiver Model CaMa-Flood
Simulation has
Full Area Forecast

Data Assimilation
362719day 1day 2day 3day 4

1. SWOT DA Overview
SWOT

Daily SWOT Observation
Coverage is LimitedRiver Model CaMa-Flood
Simulation has
Full Area Forecast

Objective

To Develop a Global Framework ofSWOT Data Assimilation
Evaluated the Effectiveness Before Launch2. Method of Data Assimilation
SWOT

River Model CaMa-Flood

Not launched until 2021

Used Virtual SWOT

(virtual observation data madefrom river model)

and did Virtual Experiment2. Method of Data Assimilation
Virtual Experiment
Virtual SWOT and Simulation must be apart
3627

SWOTAssimilation

19

day 1Simulation
day 2

day 3

day 4Virtual SWOT

Simulation

Virtual Observation Data
made from River Model

Artificially and Purposely Error
Corrupted Simulation

2. Method of Data Assimilation
Virtual Experiment
we did 2 Different Patterns (Experiment) for making them apart(A) -25% Experiment
Runoff (Input)

Reduce 25%

25% Reduced
Runoff (Input)(B) Blind Runoff Experiment1991 Runoff

Water Surface
Elevation

1991
Simulation

Virtual SWOT

Virtual SWOT
1990 Runoff-25%Simulation

1990
Simulation

Corrupted
Simulation

Corrupted
Simulation

2. Method of Data Assimilation
Virtual Experiment
we did 2 Different Patterns (Experiment) for making them apartal., 2007)(A) -25% Experiment (Andreadis(B)etBlind
Runoff Experiment

Runoff (Input)

1991 Runoff

CaMaTrue
Simulation
FloodTrue
Simulation

mask out
Planned
SWOT Orbit Data

SWOTadd
1990 Runoff
Coverage Mask

-25%Simulation

1991
Water
SurfaceElevation
Simulation
VirtualSWOT
VirtualSWOT

1990
Measurement
Error
Simulation
Corrupted
Simulation

2. Method of Data Assimilation
Virtual Experiment
we did 2 Different Patterns (Experiment) for making them apartal., 2007)(A) -25% Experiment (Andreadis(B)etBlind
Runoff Experiment

Runoff (Input)

1991 Runoff

CaMaTrue
Simulation
FloodTrue
Simulation

VirtualSWOT
VirtualSWOT

Reduce 25%

25% Reduced
Runoff (Input)

1991
WaterSurface
Elevation
Simulation-25%
Simulation
CaMaFlood

1990 Runoff-25%
Simulation

1990← Roughly
Simulation
25%
Smaller

Corrupted
Simulation

Corrupted
Simulation

2. Method of Data Assimilation
Virtual Experiment
we did 2 Different Patterns (Experiment) for making them apart(A) -25% Experiment
Runoff (Input)

Reduce 25%

25% Reduced
Runoff (Input)(B) Blind Runoff Experiment1991 Runoff

Water Surface
Elevation

1991Simulation

Virtual SWOT

Virtual SWOT
1990 Runoff-25%
Simulation

1990
Simulation

Corrupted
Simulation

Corrupted
Simulation

2. Method of Data Assimilation
Virtual Experiment
we did 2 Different Patterns (Experiment) for making them apart(A) -25% Experiment36

RunoffSimulation(Input)27

Reduce 25%SWOT

1991 Runoff

Water Surface
Elevation

1991
Simulation

Virtual SWOT

Virtual SWOT

1925% Reduced
Runoff (Input)(B) Blind Runoff Experiment1990 Runoff-25%
Simulation

1990
Simulation

Corrupted
seasonal difference
Simulation

Corrupted
Simulation

2. Method of Data Assimilation
Virtual Experiment
we did 2 Different Patterns (Experiment) for making them apart(A) -25% Experiment
Runoff (Input)

Reduce 25%

25% Reduced
Runoff (Input)(B) Blind Runoff Experiment1991 Runoff

Water Surface
Elevation

1991
Simulation

Virtual SWOT

Virtual SWOT
1990 Runoff-25%Simulation

1990
Simulation

Corrupted
Simulation

Corrupted
Simulation

2. Method of Data Assimilation
Virtual Experiment

Virtual SWOT

Corrupted
Simulation

Data Assimilation

Ensemble Kalman Filter (EnKF)
Implemented with LETKF (Local Ensemble
Transformation Filter)=+Assimilation Simulation Observation

Assimilated Estimation[×Ensemble Statistics]

2. Method of Data Assimilation
Virtual Experiment
Ensembleare needed→
Virtual SWOT

Data Assimilation

Corrupted
Simulation

Ensemble Kalman Filter (EnKF)

Corrupted
Implemented with LETKF (Local EnsembleSimulation
Transformation Filter)=+[×Assimilation Simulation Observation…Statistics
Ensemble]

Assimilated
EstimationMade
20 Ensemble Members (using Gaussian Noise)2. Method of Data Assimilation
Virtual Experiment
Ensembleare needed→
Virtual SWOT

Data Assimilation

Corrupted
Simulation

Ensemble Kalman Filter (EnKF)
Implemented with LETKF (Local Ensemble
Transformation Filter)=+Assimilation Simulation Observation

Assimilated Estimation[×Ensemble Statistics]

2. Method of Data Assimilation
Virtual Experiment

Virtual SWOT

CorruptedSimulation

LWater Surface
Elevation (WSE)

SfDrZ

SfSr

DfW

Ac
AfBAc

Many Variables

Af

This is possible because CaMa-Floodcalculates water dynamics based on WSE

Data Assimilation
Assimilated Estimation

3. Results and Discussions(A) -25% Experiment

absolute Error Rate of River Discharge
Assimilation

Corrupted Simulation

Error Decreased
the Amazon River3. Results and Discussions(B) Blind Runoff

Assimilation Index(AI): relative Assimilation Achievement

AI = 1

Assim. Corrup.
T rue Corrup.

1

AI=1: done well
AI=0: not wellwellnotwell

3. Results and Discussions

Frequent Observation(4~7 days interval)(B) Blind Runoff

3. Results and Discussions
high AI at
most part of river
Frequent Observation(4~7 days interval)(B) Blind Runoff

3. Results and Discussions

Less Observation(10~21 days interval)(A) -25% Experiment3. Results and Discussions(A) -25% ExperimentLess Observation(10~21 days interval)high AI only atDownstream

Assimilation
Index (AI)Upstream

Fluctuate(drop in 5~10 days)

Introduction Method

Downstream
Result/Discussion

Stay HighLess Observation(10~21 days interval)Less Observation(10~21 days interval)

Assimilated Discharge
Inflowed(6,000,000 km2)
high at Downstream

Lena (AI)Assimilation
Index
Corrupt.

Introduction Method

Seasonal Transition
Result/Discussion

Assim.

4.2 days
Virtual interval
Obs.

MATCH

Mekong

Corrupted
Assimilated•

Inflow from Upstream•Local and Upstream
Observation•Observation Frequency

10.4 daysinterval
ERROR

VirtualObs.

3. Results and Discussions
Amazon (Downstream)(B) Blind Runoff
Congo (Midstream)

Congo (Downstream)
Virtual Obs.

Discharge

Assimilated
Corrupted

Assimilation Index●: Observation

3. Results and Discussions
Mekong (Downstream)(B) Blind Runoff
Lena (Downstream)

Ob (Midstream)
Virtual Obs.

Discharge

Assimilated
Corrupted

Assimilation Index●: Observation

4. Future Steps
Enlarging Local Patch
Assimilation is possible only when there is Observation at that location

Originally, only target pixel iscalculated at Assimilation

Using informationof near-by pixelAssimilation Correction will bepossible when there is observation at
somewhere in the local patch

4. Future Steps
Improving Ensemble Spread
Ensemble
BundleUpstream•Usually, ensemble is bundled in daily step•This is often used in Atmospheric Model,
which Ensemble easily spread•

However Ensemble and Bundle at daily step is
too short for River Model to spread Ensemble
Downstream

Observation at
Downstream is notmuch effective

4. Future Steps
Improving Ensemble Spread
Ensemble
Bundle•Usually, ensemble is bundled in daily step•This is often used in Atmospheric Model,
which Ensemble easily spread•However Ensemble and Bundle at daily step is
too short for River Model to spread Ensemble•Only bundle when there is Observation &Assimilation (once in few days)•This will allow the Ensemble to Spread•

This will make assimilation at downstream
more effectiveEnsemble

Bundle