Dust modeling and forecasting at the Barcelona Supercomputing Center: activities and developments

Transcription

1 Dust modeling and forecasting at the Barcelona Supercomputing Center: activities and developments Carlos Pérez 1, José M. Baldasano 1,2, Pedro Jiménez-Guerrero 1, Oriol Jorba 1, Karsten Haustein 1, Emilio Cuevas 3, Sara Basart 3, Slobodan Nickovic 4 (1) Earth Sciences Department. Barcelona Supercomputing Center (BSC; Spain) (2) Laboratory of Environmental Modeling. Universitat Politècnica de Catalunya (UPC, Spain) (3) National Institute of Meteorology (INM; Spain) (4) AREP, World Meteorological Organization (WMO; Switzerland) carlos.perez@bsc.es WMO/GEO Expert Meeting on an International Sand and Dust Storm Warning System, Barcelona, 8 November 2007

2 Current activities and developments Dust forecast and validation system Air quality PM10 forecast in Europe (dust + anthropogenic) On-line dust-radiation: improving weather forecast 50-year regional dust climatology: validation and applications New model development: Global / Regional ESMF/NMM-b DUST

3 Dust REgional Atmospheric Model (DREAM) (Nickovic et al., 2001) Simulates all major processes of the atmospheric dust cycle. On-line model fully embedded as one of the governing prognostic equations in the atmospheric NCEP/Eta atmospheric model (Janjic 1994, 1996a,b, Janjic 1997) Dust production scheme with introduced viscous sublayer (Shao 1993; Janjic 1994). Soil wetness effects on dust production (Fecan et al, 1999). Dry (Georgi, 1986) and wet deposition. 2 model versions: 4 and 8 transport particle sizes Dust-radiation feedback (Pérez et al., 2006a,b) ( First model version in 1994 (Nickovic and Dobricic, 1996) operated at the WL research centre in Erice, Italy and in the INM, Tunisia Successively developed and operated at University of Athens (1995-onwards), ICOD Malta ( ) and Barcelona Supercomputing Center (2005-onwards)

4 Current operational domains of simulation North Africa and Mediterranean - 1/3 x 1/3 degree resolution (in the rotated semi-staggered Arakawa E grid) - 24 vertical levels hour daily forecasts - Initialized at 12UTC East-Asia - 1/2 x 1/2 degree resolution 24 vertical levels hour daily forecasts - Initialized at 00UTC

5 Operational forecast products SDS WS Operational products Model predictions (72-h): Horizontal distribution PM2.5, PM10, TSP at surface and height Total column mass (dust load) Dust aerosol optical depth Wet, dry, total deposition Visibility (soon available) Meteorological variables Vertical distribution Cross sections Fixed point/time profiles Fixed point (selected sites/cities) Dustgrams Meteograms Request-only basis: Numerical data Climatology

6 Dust forecast and validation system Model has shown very good agreement with observations in a number of studies of single events (e.g., Ansmann et al., 2003, Papayannis et al., 2005; Pérez et al., 2006a;b; Jiménez et al, 2006.) Lidars - EARLINET AERONET - ONLINE Meteosat Second Generation SeaWIFS

7 A European Aerosol Research Lidar Network to Establish an Aerosol Climatology: EARLINET Earlinet FP5 ( ) Earlinet-ASOS ( ) - Forecasting model for coordinated observations of dust events in Europe - Model verification during all the period BSC-DREAM Barcelona LIDAR Pérez et al. (2006a) BSC-DREAM

8 A European Aerosol Research Lidar Network to Establish an Aerosol Climatology: EARLINET Frontoso et al. (2007)

9 Aerosol Robotic Networks: AERONET / PHOTONS / RIMA On-line Verification / surveillance In collaboration with the Spanish Institute of Meteorology

10 Meteosat Second Generation: On-line qualitative verification / surveillance In collaboration with the Spanish Institute of Meteorology Dust monitoring SAF_NowCasting with MSG satellite 3x3 km 2 15 min step (day and night) Under development: SeaWifs, Modis, OMI

11 CALIOPE Air Quality Forecasting System for Europe (12 km resolution) and Spain (4 km) and 1km for hot spot regions PM10 product for Europe currently available Dust (DREAM)+ Anthropogenic (EMEP12km/WRF/CMAQ) 12 km resolution 4 km resolution Spain forecast 1km Hermes Emissions Surface level PM2.5 sulfates, Jun 2007 Jiménez-Guerrero, Pérez, C., Jorba., O, Baldasano, JM., 2007., Contribution of Saharan dust in an integrated air quality system and its on-line assessment. GRL accepted

12 CALIOPE Air Quality Forecasting System

13 WRF-EMEP-CMAQ, PM10 not including dust 18 km Obs Model Bias Model, Observations, Bias (ug m -3 ) Need for including the Saharan dust contribution to PM10 concentrations ene feb abr may jul sep oct dic-04

14 WRF-EMEP(2003)-CMAQ + DREAM, PM Obs Model Bias Model, Observations, Bias (ug m -3 ) Improvement of the bias of the model during Saharan dust outbreaks ene feb abr may jul sep oct dic-04

15 Statistical evaluation of PM10 with EMEP stations in southern Europe during the year 2004 Year 2004 PM Obs Model Bias RMSE MNBE MNGE Model, Observations, Bias, RMSE ( μg m -3 ) Observations Model Mean (μg m -3 ) Max (μg m -3 ) n 366 Min (μg m -3 ) r 0.85 Model Evaluation, PM10 MB (μg m -3 ) MNB (%) -4.5 % MNGE (%) 24.4 % MAGE (μg m -3 ) 4.45 NMB (%) -6.7 % NME (%) 25.2 % RMSE (μg m -3 ) 5.55 MFB (%) -9.2 % UPA (%) % MNBE, MNGE (%) ene feb abr may jul sep oct dic The European Directive 1999/30/EC defines an accuracy objective of 50% accuracy objective of 50% for annual average.

16 Distribution of OC+EC in the domain of study PM2.5 Station data taken from Querol et al. (2006), Atmospheric Particulate matter in Spain: levels, composition and source origin. Ed: CSIC and Ministerio de Medioambiente, Madrid, 39pp. OC+EC (μg m -3 ) PM2.5 < >15.0

17 Spatial distribution of PM2.5 composition in the location of EMEP stations

18 Validation for YEAR 2004 Current validation activities 4km reslution in colaboration with the Spanish Research Council (IJA-CSIC):

19 Dust-radiation interaction Nickovic et al. (2004) Pérez et al. (2006b) Current situation of weather forecasting models Use pre-specified O 3 and CO 2 profiles in radiation calculations NCEP regional models use the solar constant reduced by 3% anywhere anytime to represent aerosol influence X None of the operational atmospheric models use online predicted mineral dust concentration for radiation calculations Advances in dust modeling over the last decade permit accurate dust concentration input for dust-radiation interactions

20 Dust-radiation interaction COUPLING DUST-RADIATION Replaced ETA Geophysical Fluid Dynamics Laboratory (GFDL) radiation package by NASA Goddard Climate and Radiation Branch package Short-wave radiation [Chou et al., 1999] water vapor, O 3, O 2, CO 2, clouds and aerosols from µm to 10 µm Long-wave radiation [Chou et al., 2001] water vapor, CO 2, O 3, N 2 O, CH 4, CFC s, clouds and aerosols from 0 to 3000 cm -1 Dust optical properties as functions of spectral band, height, and bin are input parameters to the radiation routine.

21 Dust-radiation interaction 8-15 April 2002 major dust outbreak over the Mediterranean 2 sensitivity experiments CTR dust is considered as a dynamic tracer without interaction with atmospheric radiation Atmosphere Dust RAD interaction between short- and long-wave radiation and dust is included Atmosphere Dust Radiation Within every model time step both dust and atmospheric fields are updated through their mutual influences

22 Napoli Raman Lidar Dust-radiation interaction 11 April 2002

23 Napoli Raman Lidar Dust-radiation interaction 12 April 2002

24 Dust-radiation interaction 13 April 2002

25 Dust-radiation interaction NUMERICAL WEATHER PREDICTION Can we improve it? Atmospheric temperature forecasts RAD and CTR evaluated against objective analysis data Temperature bias in the dust-affected area 12 h Forecast 24 h Forecast 36 h Forecast 48 h Forecast

26 Dust-radiation interaction NUMERICAL WEATHER PREDICTION Can we improve it? RAD improves in most of the atmospheric layers Reduction of the low level warm bias Reduction of the upper level cold bias WHY? Reduction of sensible heat flux reduces PBL temperature Dust redistributes heat from the surface and near surface to higher levels of the atmosphere

27 Regional dust climatology Long term Saharan dust simulations (first experiments) (Pérez et al. 2007) Reanalysis data: NCEP/NCAR D fields of dust and meterology - Validated with observations (under progress) MareNostrum - Peak performance of 94,21 Teraflops IBM Power PC 970MP processors

28 Regional dust climatology Regional dust climatology Izaña Station (Tenerife) dust record Model Validation 12 h average total dust concentration Daily Dust Concentration (ug m -3 ) DAILY CORRELATION TOTAL Observations (Izaña) DREAM model /01/ /05/ /09/ /02/ /06/ /11/ /03/ /08/ /12/ /04/1999 Montly Dust Concentration (ug m -3 ) Observations (Izaña) Model (DREAM) /87 01/88 01/89 01/90 01/91 01/92 01/93 01/94 01/95 01/96 01/97 01/98 01/99 MONTHLY CORRELATION TOTAL Observations (Izaña) Model (DREAM) Seasonal Dust Concentration (ug m -3 ) DJF-1988 JJA-1988 DJF-1989 JJA-1989 DJF-1990 JJA-1990 DJF-1991 JJA-1991 DJF-1992 JJA-1992 DJF-1993 JJA-1993 DJF-1994 JJA-1994 DJF-1995 JJA-1995 DJF-1996 JJA-1996 DJF-1997 JJA-1997 SEASONAL CORRELATION TOTAL DJF-1998 JJA-1998 DJF-1999 JJA-1999

29 Regional dust climatology Winter Seasonal Dust Concentration (μg m -3 ) nm IZAÑA Observations DJF (Izaña) Model DJF (DREAM) 0, YEAR R=0.79 AVHRR 10-30W 15-30N R=0.62 Evan et al., 2006 JFM DREAM AOD 550nm - AVHRR dust [%] W N 8 0,7 DREAM AVHRR 7 DREAM AOD 550 nm 0,6 0,5 0,4 0,3 0, AVHRR dust [%] 0, YEAR

30 Regional dust climatology NOV NAO NOV dust Winter monthly correlations DEC NAO DEC dust JAN NAO JAN dust FEB NAO FEB dust

31 Interannual Mediterranean summer evolution matched Seasonal occurrence of dust aerosols (%) Antoine and Nobileau, 2006 DREAM AOD JJA 1998 JJA 1999 JJA 2000 JJA 2001 JJA 2002 JJA 2003 JJA 2004

32 Summer Atlantic Seasonal Dust Concentration (μg m -3 ) nm IZAÑA Observations JJA (Izaña) Model JJA (DREAM) DREAM AOD 550 nm 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 METEOSAT/TOMS DOT 10-30W 15-30N JJA DREAM AOD 550nm - TOMS/METEOSAT DOT W N DREAM TOMS/METEOSAT R=0.36???? 1,2 1 0,8 0,6 0,4 0,2 Satellite DOT YEAR R=0.66 Chiapello et al., YEAR

33 Model developments ( ) Implementation of the DREAM model into the most recent parallelized and non-hydrostatic version of the NMM/ NCEP model, with application either as a limited area or global model Increasing resolution Global model domain High resolution dust forecast MARENOSTRUM Eta model Regional model Hydrostatic Eta coordinate Arakawa E-gridE Convection parameterization ESMF/NMM-b b model Global/Regional model Non-hydrostatic Sigma coordinate Arakawa B-gridB Microphysical convection Haustein (2007) PhD proposal - Refining the dust production parameterisation - Introducing seasonal variability of dust source functions - Improving wet deposition - Including radiative effects

34 Current and potential users Current users of the system - Ocean community - Experimental campaigns (TROMPETA, SAMUM, ) - Observational Networks: Earlinet (European Lidar Network), AERONET - In-situ observations - Satellite community - Spanish administration: alert system Potential users - health - Administration: early warnings to population - Air transport activities

35 THANKS Carlos Pérez 1, José M. Baldasano 1,2, Pedro Jiménez-Guerrero 1, Oriol Jorba 1, Karsten Haustein 1, Emilio Cuevas 3, Sara Basart 3, Slobodan Nickovic 4 (1) Earth Sciences Department. Barcelona Supercomputing Center (BSC; Spain) (2) Laboratory of Environmental Modeling. Universitat Politècnica de Catalunya (UPC, Spain) (3) National Institute of Meteorology (INM; Spain) (4) AREP, World Meteorological Organization (WMO; Switzerland) WMO/GEO Expert Meeting on an International Sand and Dust Storm Warning System, Barcelona, 8 November 2007