Enhancing the Barcelona Supercomputing Centre chemical transport model with aerosol assimilation

Transcription

1 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

2 The Barcelona Dust Forecast Center: a WMO initiative

3 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

4 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

5 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

6 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

7 Current Operational Flow 00 IC +06 FC +12 FC +18 FC +24 FC model day 1 day 2 day 3 time 00 IC FC model

8 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 model

9 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 model

10 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 model

11 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 model

12 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)

13 Dust Journey in the Model

14 Dust Journey in the Model

15 Emission Scheme (credits C. Perez)

16 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

17 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

18 Emission Scheme source function (C. Perez et al. 2011)

19 Observations

20 Observation uncertainty

21 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

22 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

23 Control experiment

24 DA experiment

25 DA experiment

26 DA experiment

27 Validation

28 Validation

29 Validation

30 Control experiment

31 DA experiment

32 DA experiment

33 Validation

34 Control experiment

35 DA experiment

36 DA experiment

37 Validation

38 Ensemble analysis - background

39 Ensemble analysis - background

40 Ensemble analysis - background

41 First-guess departures

42 First-guess departures

43 First-guess departures

44 First-guess departures

45 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

46 Observations

47 Selected observations

48 Validation

49 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

50 Validation

51 Validation

52 Validation

53 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

54 Validation

55 Validation

56 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

57 International Model Intercomparison: the Global Domain = data assimilation

58 International Model Intercomparison: the Global Domain = data assimilation

59 International Model Intercomparison: the Regional Domain Dust optical depth: Apr FC+24 = data assimilation

60 International Model Intercomparison: the Regional Domain Dust optical depth: Apr FC+24 = data assimilation

61 Conclusions Data assimilation with the LETKF scheme can help us to better forecast atmospheric dust

62 Conclusions A correct characterisation of the ensemble perturbations has a great potential to deal with our model uncertainties

63 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

64 Acknowledgments Thanks to: All the Principal Investigators and their staff for establishing and maintaining the AERONET sites used in this investigation ( NRL-UND for the MODIS AOD and FF L3 product (Zhang et al. 2006, 2008, Shi et al. 2011, Hyer et al. 2011) ( The MODIS mission scientists and associated NASA personnel for the production of the AOD and AE data used in this investigation ( Takemasa Miyoshi (RIKEN Institute, Japan) who developed the core of the LETKF scheme (Ott et al. 2004, Hunt et al. 2005)