Forecast model bias correction in ocean data assimilation

G. Chepurin, Jim Carton, and D. Dee*Univ. MD and *GSFC

• Bias in ocean data assimilation• Two-stage bias correction algorithm

– Bias model– Results from a series of 30-yr assimilation

experiments

Manuscript available: {http://www.atmos.umd.edu/~carton/bias}

Bias is the difference between the state forecast and the true state

ftft

0ε 0

Time-mean bias along equator

20C“Cold tongue is too cold, while the thermocline in the central basin is too diffuse”

Annual cycle of mixed layer bias in subtropics (10N-30N)

Dec

June

“Too hot in summer, too cold in winter”

of TT

Time-evolution of forecast

error along equator

“Forecast error is episodic, linked to ENSO”

100m

Tim

e

Mixed layer

Two stage algorithm to correct systematic aspects of forecast error

)( ffofa ωββ HL

fofa ωωωω ~~ HK

Stage I

Stage II

Three-term bias forecast model

fk

tifk

kAe G )(ˆ

Time-

mea

n bi

as

Ann

ual c

ycle

bia

s

ENSO

-link

ed

bias

Correcting time-mean bias

along Pacific Eq

This is business as usual

This is what results when time-mean bias is modeled

20C

20C

of ww

Correcting time-mean biasCorr time-mean bias

Correcting annual cycle bias

Business as usual Annual cycle bias correction

Dec

June

Annual cycle of forecast error before correction

Annual cycle of forecast error after correction

BeforeAfter

Correcting ENSO biasbe

for e

afte

rCorEOF1,SOI = 0.7

Summary of the impact of bias

correction

time mean

+annual cycle

+ENSO variability

RMS (fcst-obs)

ML temp Thermocline depth

Conclusions

• Half of the {forecast – observation} differences in high variability regions are due to bias. The largest contribution is time-mean followed by annual cycle and interannual variability.

• Two-stage correction works well in addressing these.

Manuscript available: {http://www.atmos.umd.edu/~carton/bias}

The End