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Enhancing the Barcelona Supercomputing Centre

chemical transport model with aerosol assimilation

Enza Di Tomaso1, Nick Schutgens2, Oriol Jorba1, George Markomanolis1

1 Earth Sciences Department, Barcelona Supercomputing Centre2 Atmospheric, Oceanic and Planetary Physics, University of Oxford

WWOSC 2014, Montreal, August 20, 2014

The Barcelona Dust Forecast Center: a WMO initiative

Our Dust Model

NMMB/BSC-CTM

NMMB

BSC-CTM

DUST

Pérez et al., ACP, 2011

CHEM

Jorba et al., JGR, 2012

SEA-SALT

Spada et al, ACP, 2013

Current Operational Flow

model

00 IC +06 FC +12 FC +18 FC +24 FC

model

00 IC FC+06 +12 +18 +24

timeday 1 day 2 day 3 …

Data Assimilation Flow

model

00 IC +06 FC +12 FC +18 FC +24 FC

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00 IC FC+06 +12 +18 +24


06 obs 12 obs 18 obs 24 obs


model

00 IC +06 FC +12 FC +18 FC +24 FC

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model

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model

00 IC +06 FC +12 FC +18 FC +24 FC

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00 IC FC+06 +12 +18 +24


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DA

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DA DA


model

00 IC +06 FC +12 FC +18 FC +24 FC

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06 AN 12 AN 18 AN 24 AN

DA

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DA DA

Local Ensemble Transform Kalman Filter

FUNCTION 𝐴𝑝𝑝𝑙𝑦𝐿𝐸𝑇𝐾𝐹 … , 𝐘𝑏 𝑛𝑜𝑏𝑠,𝑀 , 𝐒𝜀 , 𝐲 − 𝐻 𝐱𝑏 , … ,𝐖 𝑀,𝑀

(function and figure by Takemasa Miyoshi, Ott et al. 2004, Hunt et al. 2005)

{ 𝐱(𝑚)= 𝐱𝑏 + 𝐗𝑏 𝐰 𝑚 ∶ 𝑚 = 1,… ,𝑀}

Dust Journey in the Model

Emission Scheme

(credits C. Perez)

Perturbations factor

Vertical mass flux of dust into a transport bin k

𝐹𝑘 = 𝐶 𝑆 1 − 𝑉 𝛼 𝐻

𝑖=0

3

𝑚𝑖 𝑀𝑖,𝑘 𝑘 = 1,⋯ , 8

Emission Scheme

(C. Perez et al. 2011)

source function

Observations

Observation uncertainty

Experiment setup

Experiment Assimilated Observations Perturbations

CTL none NA

Exp1 NRL MODIS 1 calibration factor

Exp2 selected NRL MODIS 1 calibration factor

Exp3 NRL MODIS calibration factors per bin

Exp4 NRL MODIS calibration factors per fine/coarse bin

Experiment setup


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vs

Control experiment

DA experiment

Validation

Control experiment

DA experiment

Validation

Control experiment

DA experiment

Validation

Ensemble analysis - background

First-guess departures

Experiment setup


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vs

Observations

Selected observations

Validation

Experiment setup


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Validation

Experiment setup


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Validation

Porting the DA code to OmpSs programming model

• Serial execution with various tasks (different colors, Paraver view)

• The usage of OmpSs on the ‘calcensstat’ subroutine (green color)

• By using two cores we improve two times the performance of the subroutine and we gain

17% of the total execution time

International Model Intercomparison: the Global Domain

= data assimilation

Dust optical depth: 2014 3 Apr FC+24

International Model Intercomparison: the Regional Domain

http://sds-was.aemet.es/forecast-products/dust-forecasts/compared-dust-forecasts

= data assimilation

Conclusions

Data assimilation with the LETKF scheme can help us

to better forecast atmospheric dust

Conclusions

A correct characterisation of the ensemble perturbations

has a great potential to deal with our model uncertainties

Conclusions

Once we will have the complete aerosol family in the BSC

chemical transport model, the assimilation of satellite aerosol

products will be more meaningful

Thanks to:

All the Principal Investigators and their staff for establishing and maintaining the AERONET sites

used in this investigation (www.aeronet.gsfc.nasa.gov)

NRL-UND for the MODIS AOD and FF L3 product (Zhang et al. 2006, 2008, Shi et al. 2011,

Hyer et al. 2011) (http://usgodae.org/docs/modis_l3.html)

The MODIS mission scientists and associated NASA personnel for the production of the AOD and

AE data used in this investigation (www.disc.sci.gsfc.nasa.gov/Giovanni)

Takemasa Miyoshi (RIKEN Institute, Japan) who developed the core of the LETKF scheme

(Ott et al. 2004, Hunt et al. 2005)

Acknowledgments

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