The HBM-PDAF assimilation system for operational forecasts in the North and Baltic Seas

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The HBM-PDAF assimilation system

for operational forecasts in the North and Baltic Seas

Lars Nerger, Svetlana Losa

Alfred Wegener Institute for Polar and Marine Research Bremerhaven, Germany

Thorger Brüning, Frank Janssen

Federal Maritime and Hydrographic Agency (BSH) Hamburg, Germany

EuroGOOS 2014, Lisbon, Portugal, October 28 – 30, 2014

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Lars Nerger et al. – HBM-PDAF Assimilation System

BSSC 2007, F. Janssen, S. Dick, E.

Kleine!

Grid nesting:

- 10 km grid - 5 km grid - 900 m grid

Starting point: Operational BSH Model (BSHcmod), V4

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BSSC 2007, F. Janssen, S. Dick, E.

Kleine!

Grid nesting:

- 10 km grid - 5 km grid - 900 m grid

Starting point: Operational BSH Model (BSHcmod), V4

Goal:

•  improve forecast skill of operational model using ensemble data assimilation

•  started with BSHcmod

•  updated to HBM for future operational use

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single program

state time

state

observations

mesh data

Indirect exchange (module/common) Explicit interface

Model

initialization time integration post processing

Filter

Initialization analysis re-initialization

Observations

obs. vector obs. operator

obs. error

Core of PDAF

Logical separation of assimilation system

Nerger, L., Hiller, W. Software for Ensemble-based DA Systems –

Implementation and Scalability. Computers and Geosciences. 55 (2013) 110-118

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Extending a Model for Data Assimilation

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Start

Stop Do i=1, nsteps

Initialize Model

generate mesh Initialize fields

Time stepper

consider BC Consider forcing

Post-processing

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Start

Stop Do i=1, nsteps

Initialize Model

Time stepper

Post-processing

Model Extension for

data assimilation

Enable ensemble forecast using parallelization

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Start

Stop

Initialize Model

Time stepper

Post-processing init_parallel_pdaf

Do i=1, nsteps init_pdaf

assimilate_pdaf

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Start

Stop

Initialize Model

Time stepper

Post-processing init_parallel_pdaf

Do i=1, nsteps init_pdaf

assimilate_pdaf assimilate_pdaf

plus:

Adapt writing of sponge files

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•  Model and observation specific operations

•  Elementary subroutines implemented like model routines

•  Called by PDAF routines through a defined interface

Link to model

!  initialize model fields from state vector

!  initialize state vector from model fields Observation handling

!  application of observation operator H to some vector

!  initialization of vector of observations

!  multiplication with observation error covariance matrix

User-supplied routines (call-back)

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PDAF: A tool for data assimilation

PDAF - Parallel Data Assimilation Framework

"  provide support for ensemble forecasts

"  provide fully-implemented filter algorithms

"  easily useable with (probably) any numerical model

(coupled also to NEMO, MITgcm, FESOM, ADCIRC)

"  makes good use of supercomputers

"  separate development of model and assimilation

algorithms

Open source:

Code and documentation available at http://pdaf.awi.de

L. Nerger, W. Hiller, Computers & Geosciences 55 (2013) 110-118

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Assimilated Data – Satellite, MARNET and profiles

11. Oct. 2007 MARNET

Satellite data: NOAA SST Scanfish and CTD: T,S profiles MARNET: T and S time series

!  Surface temperature: 12-hour composites

!  Strong variation of data coverage (clouds)

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Assimilation Methodology

!  12-hour forecast/analysis cycles

!  Ensemble size 8 (sufficient for good results)

!  Assumed data errors:

SST: 0.8^oC (gave best results) MARNET: 0.5^oC, 0.5 psu

Scanfish: 0.8^oC, 0.5 psu

!  Ensemble Kalman filter (local SEIK)

!  Localization:

•  Influence radius 100 km (tuned)

•  Weight on data errors

(Exponential, e-folding at 100 km)

!  Showing mainly results from BSHcmod (very similar to HBM)

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Deviation from NOAA Satellite Data

!  RMS errors for SST in 12-hour forecasts

!  Average over 1 year (10/2007 – 9/2008)

!  23% overall reduction

!  Mean error also reduced

No assimilation SST assimilation

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ACCEPTED MANUSCRIPT

ACCEPTED MANUSCRIPT

16/10/07 17/10/07 18/10/07 19/10/07 20/10/07 21/10/07 22/10/07

0.4 0.5 0.6 0.7 0.8 0.9 1

date

o C

RMS error evolution

Model without DA LSEIK forecast LSEIK analysis 120h LSEIK forecast

Figure 7: RMS error temporal evolution over the period 16 October 2007 – 21 October 2007 for simulated SST without DA (black curve); LSEIK analysis (red); mean of ensemble forecast based on 12-hourly analysis (blue) and 5 days forecast (green curve) initialized with the analysis state obtained on 16 October 2007.

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Forecast improvements

black: free model run

blue/red: 12h assimilation/

analysis cycles green: 5 day forecast

from 16/10

➜   Very stable 5-day forecasts

RMS error for SST over time

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Red: Assimilation 12h forecasts

Validation with independent data (only SST assim.)

MARNET station data

•  Reduction of

•  Bias

•  RMS error

Error estimates:

Bias: -0.55 -0.17 RMSe: 1.27 0.81

RMSe bias

free 0.87 0.3

satellite data 0.59 0.11 assimilation 0.55 0.08

1 year mean over 6 stations:

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Assimilation of MARNET data

!  Salinity: Significant improvement at surface and bottom

!  Localization parameters influence assimilation performance

11/10/07 21/10/07 31/10/07 10/11/07 20/11/07 30/11/07 10/12/07 20/12/07 30/12/07 09/01/08 10

15 20 25

S/psu

date

Marnet station Fehmarn Belt: Surface salinity

11/10/07 21/10/07 31/10/07 10/11/07 20/11/07 30/11/07 10/12/07 20/12/07 30/12/07 09/01/08 10

15 20 25

date

S/psu

Marnet station Fehmarn Belt: Bottom salinity

Marnet data

BSHcmod without DA

exp LSEIK MAR forecast, lr = 10gp exp LSEIK MAR forecast, lr = 0/4gp

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Summary

Ongoing work

!  Include coastal mesh for assim. (900m resolution)

!  Include ecosystem model ERGOM (@BSH)

!  Assimilation of clorophyll data

!  Switch to ESTKF filter (Nerger et al., MWR, 2012)

!  HBM-PDAF system provides improved forecasts

!  Very small increase of run time

!  Assimilation framework PDAF to implement assimilation systems (http://pdaf.awi.de)

Lars.Nerger@awi.de

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References

Nerger, L., Hiller, W. (2013) Software for Ensemble-based Data Assimilation Systems – Implementation and Scalability.

Computers and Geosciences. 55, 110-118

Losa, S.N. et al. (2012). Assimilating NOAA SST data into the BSH

operational circulation model for the North and Baltic Seas: Inference about the data. Journal of Marine Systems, 105-108, pp. 152-162

Losa, S. N. et al. (2014). Assimilating NOAA SST data into the BSH operational circulation model for the North and Baltic Seas: Part 2.

Sensitivity of the forecast's skill to the prior model error statistics.

Journal of Marine Systems, 129, 259-270

http://pdaf.awi.de www.demarine.de www.data-assimilation.net

The HBM-PDAF assimilation system for operational forecasts in the North and Baltic Seas