www.bsc.es Enhancing the Barcelona Supercomputing Centre chemical transport model with aerosol assimilation Enza Di Tomaso 1, Nick Schutgens 2, Oriol Jorba 1, George Markomanolis 1 1 Earth Sciences Department, Barcelona Supercomputing Centre 2 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
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
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
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 00 IC +06 FC +12 FC +18 FC +24 FC model day 1 day 2 day 3 time 00 IC FC+06 +12 +18 +24 model
Data Assimilation Flow 00 IC +06 FC +12 FC +18 FC +24 FC model day 1 day 2 day 3 time 06 obs 12 obs 18 obs 24 obs 00 IC FC+06 +12 +18 +24 model
Data Assimilation Flow 00 IC +06 FC +12 FC +18 FC +24 FC model day 1 day 2 day 3 time 06 obs 12 obs 18 obs 24 obs model 1 model m model M 00 IC FC+06 +12 +18 +24 model
Data Assimilation Flow 00 IC +06 FC +12 FC +18 FC +24 FC model day 1 day 2 day 3 time 06 obs 12 obs 18 obs 24 obs model 1 model 1 model m DA model m DA DA model M model M 00 IC FC+06 +12 +18 +24 model
Data Assimilation Flow 00 IC +06 FC +12 FC +18 FC +24 FC model day 1 day 2 day 3 time 06 obs 12 obs 18 obs 24 obs model 1 model 1 model m DA model m DA DA model M model M 06 AN 12 AN 18 AN 24 AN 00 IC FC+06 +12 +18 +24 model
Local Ensemble Transform Kalman Filter { x (m) = x b + X b w m m = 1,, M} FUNCTION ApplyLETKF, Y b nobs, M, S ε, y H x b,, W M, M (function and figure by Takemasa Miyoshi, Ott et al. 2004, Hunt et al. 2005)
Dust Journey in the Model
Dust Journey in the Model
Emission Scheme (credits C. Perez)
Perturbations factor Vertical mass flux of dust into a transport bin k F k = C S 1 V α H 3 i=0 m i M i,k k = 1,, 8
Perturbations factor Vertical mass flux of dust into a transport bin k F k = C S 1 V α H 3 i=0 m i M i,k k = 1,, 8
Emission Scheme source function (C. Perez et al. 2011)
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 Experiment Assimilated Observations Perturbations CTL none NA vs 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
Control experiment
DA experiment
DA experiment
DA experiment
Validation
Validation
Validation
Control experiment
DA experiment
DA experiment
Validation
Control experiment
DA experiment
DA experiment
Validation
Ensemble analysis - background
Ensemble analysis - background
Ensemble analysis - background
First-guess departures
First-guess departures
First-guess departures
First-guess departures
Experiment setup Experiment Assimilated Observations Perturbations CTL none NA Exp1 NRL MODIS 1 calibration factor vs Exp2 selected NRL MODIS 1 calibration factor Exp3 NRL MODIS calibration factors per bin Exp4 NRL MODIS calibration factors per fine/coarse bin
Observations
Selected observations
Validation
Experiment setup Experiment Assimilated Observations Perturbations CTL none NA Exp1 NRL MODIS 1 calibration factor Exp2 selected NRL MODIS 1 calibration factor vs Exp3 NRL MODIS calibration factors per bin Exp4 NRL MODIS calibration factors per fine/coarse bin
Validation
Validation
Validation
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 vs calibration factors per bin Exp4 NRL MODIS calibration factors per fine/coarse bin
Validation
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
International Model Intercomparison: the Global Domain = data assimilation
http://sds-was.aemet.es/forecast-products/dust-forecasts/compared-dust-forecasts International Model Intercomparison: the Regional Domain Dust optical depth: 2014 3 Apr FC+24 = data assimilation
http://sds-was.aemet.es/forecast-products/dust-forecasts/compared-dust-forecasts International Model Intercomparison: the Regional Domain Dust optical depth: 2014 3 Apr FC+24 = 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
Acknowledgments 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)