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E. Cuevas, SDS-WAS NAMEE (AEMET, Spain) J.M. Baldasano, SDS-WAS NAMEE (BSC-CNS, Spain) PowerPoint Presentation
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E. Cuevas, SDS-WAS NAMEE (AEMET, Spain) J.M. Baldasano, SDS-WAS NAMEE (BSC-CNS, Spain)

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E. Cuevas, SDS-WAS NAMEE (AEMET, Spain) J.M. Baldasano, SDS-WAS NAMEE (BSC-CNS, Spain)

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  1. SDS-WAS Research and Development activities 6th meeting of the WWRP JSC(WMO Headquarters, Geneva, 18-19 July 2013) E. Cuevas, SDS-WAS NAMEE (AEMET, Spain) J.M. Baldasano, SDS-WAS NAMEE (BSC-CNS, Spain) X. Zhang, SDS-WAS EA (CAMS, China) W. Sprigg, SDS-WAS As (Chapman University, USA) E. Terradellas SDS-WAS NAMEE (AEMET, Spain) S. Nickovic (Ex-WMO SDS-WAS, Serbia) WMO; AREP

  2. Impacts of Sand and Dust Human Health (Asthma, infections, Meningitis in Africa, Valley Fever in the America’s) Agriculture (negative & positive impacts) Marine productivity (negative & positive impacts) Improved Weather and Seasonal Climate prediction Aviation (air disasters) Ground Transportation (high speed rail) Industry (Semi-conductor, etc.) Energy (Thermal solar energy)

  3. SDS-WAS Mission To enhance the ability of countries to deliver timely and quality sand and dust storm forecasts, observations, information and knowledge to usersthrough an international partnership in research and operations

  4. WMO Research Program Components

  5. SDS-WAS research issues • Sources • physical, optical and chemical properties • interaction with radiation, clouds and precipitation • Model validation and intercomparison • Ensemble forecasting • Impacts of dust (health, transport, agriculture) • Improve weather, climate and air quality modelling

  6. History and Milestones in SDS-WAS • 2004: International Symposiumon SDS & a WMO Experts Workshop on SDS (CMA, Beijing-China). • 2005: a questionnaire on SDS to WMO Members indicated 40 interested countries. • 2006: Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS) proposed (CMA, Beijing-China). • 2007: the 14th WMO Congress endorsed the launching of the SDS-WAS • 2008: WMO SDS-WAS Implementation plan drafted (Athens, Greece). • 2010: First SDS-WAS hands-on training Workshop (BSC, Barcelona-Spain).

  7. SDS-WASa federated system NEW Preliminary steps Regional node West Asia Regional node Americas RC WMO SDS-WAS Regional node East Asia and Pacific Regional node Northen Africa, Midle-East and Europe Regional Center Regional Center Partner n Partner n Partner 1 …. Partner 1 …. Partner 2 Partner 5 Partner 2 Partner 5 Partner 3 Partner 4 Partner 3 Partner 4

  8. CollaborationMechanisms Flow of information between SDS-WAS system components for a regional node consisting of a consortium of partners supported by the Regional Steering Group and Regional Centre

  9. SDS-WAS Node structure

  10. AsianNode _Regional Centre http://www.sds.cma.gov.cn

  11. Na-ME-E Regional Centre; http://sds-was.aemet.es

  12. A Pan-American Center for the WMO SDS-WAS • Information relevant to airborne dust (as it impacts socio‐economic and environmental sectors including health, safety, water supplies, air quality, agriculture, fisheries, commerce and defense) is at the Center’s core. • The Center facilitates research & applications for North, Central and South America. • The Pan‐Am Center acts as a node to Exchange information, talent, & infrastructure across the Americas to facilitate progress in predicting, adapting to, and avoiding maladies of airborne dust, crossing scales of weather and climate, región & globe. William A. Sprigg Chapman University (USA)

  13. Future West Asia (Gulf Countries + Iran + Turkey) for the WMO SDS-WAS A support from the West Asian UNEP office • A short-term project to perform an assessment report on skills and needs for establishing a new WMO SDS-WAS node • A technical UNEP/WMO SDS conference was held in Abu Dhabi (May 2013) to discuss further steps

  14. Current research topics in SDS-WAS Regional Centers Model validation/evaluation High resolution modelling Data assimilation Long term forecasting Improvement/characterization of dust observations Dust impacts

  15. Model validation/evaluation

  16. Themodels • VARIABLES: Dust surface concentration – Dust Optical Depth at 550 nm • LEAD TIME: 0 – 72 hours, every 3 hours • GEOGRAPHICAL DOMAIN: 25ºW – 60ºE, 0 – 65ºN

  17. Jointvisualization. Dust AOD at 550 nm

  18. Jointvisualization. Surfaceconcentration

  19. Generation of multimodelproducts Model outputs are bi-linearly interpolated to a common 0.5ºlon x 0.5ºlat grid mesh. Then, different multi-model products are generated: • CENTRALITY: median - mean • SPREAD: standard deviation – range of variation

  20. AsianNode _Regional Centre http://www.sds.cma.gov.cn Homogenization forecasts in the “Asian Node” CUACE/Dust + MASINGAR + ADAM + . . . N model

  21. NRT evaluationusing AERONET data SANTA_CRUZ_TENERIFE TAMANRASSET_INM

  22. Modelvalidationwith AERONET Evaluations • Monthly • Seasonal • Annual • Bias Error • Root Mean Square Error • Correlation Coefficient • Fractional Gross Error • Near Real Time Observations • Common data processing and calibration procedures http://sds-was.aemet.es/forecast-products/forecast-evaluation/model-evaluation-metrics

  23. Evaluation of Saharan dust transport over the Atlantic Source: WHOI

  24. NRT evaluation of dust forecasts with MODIS deepblue: NMMB-BSC/Dust Since June 2013 RMSE BIAS FGE CORRELATION

  25. Validation of MACC-fszd reanalysis with PM10 from AMMA Cuevas et al. 2013 MACC 3h data were averaged to obtain daily mean surface concentration Daily mean values. AMMA PM10 where filtered by wind direction to assure dust conditions

  26. Comparative AOD climatology at selected AERONET sites 2007-2008 Cuevas et al. 2013

  27. Comparative spatial climatology of AOD Cuevas et al. 2013 WINTER 2007-2008

  28. Validation MACC-fszd extinction vertical profiles with lidars at M’Bour (Senegal) and Tenerife (The Canary Islands, Spain) Cuevas et al. 2013 M’Bour-Senegal lidar station from LOA (CNRS-Univ. Lille) WINTER 2007-2008

  29. LIDAR (LIRIC algorithm) – MODELS comparison BSC-DREAM8B_v2 NMMB-BSC/Dust DREAM8-NMME-MACC 60 – 80 dust events within the period Jan 2011 – Jun 2013

  30. High resolution modelling test footer

  31. Model nesting strategy

  32. 7:45 PM Phoenix as the dust storm neared. Phoenix (Arizona) Haboob, 5 July 2005 W.A.Sprigg, S. Nickovic, G. Pejanovic, J. Galgiani, A. Vukovic

  33. DUST SIMULATION – 6-km model 10m WIND MAGNITUDE Successful simulation of the Phoenix haboob (Chapman University dust modelling group) NASA Applied Science support led to this high-resolution forecast & simulation capability 10m WIND MAGNITUDE Phoenix Phoenix NASA Applied Science support led to this high-resolution forecast & simulation capability W.A.Sprigg, S. Nickovic, G. Pejanovic, A. Vukovic

  34. Data assimilation

  35. Operational Dust assimilation at CMA

  36. Assimilation MODIS AOD in ECMWF MACC WWRP JSC 21-24 February 2011

  37. Long-term forecasting

  38. (average=15) SDS Seasonal Prediction : DGAE/SSDS 2007 Yellow is anomaly from average number of SDS Light blue and dark blue represents the first and second level (13-14cases) respectively for less than average 2009 Light red, peach blow and red wine represents the first, second and third level (16 cases) respectively for more than average.

  39. Improvement/Characterization of dust observations

  40. CARSNET Mean AOD from 40 stations since 2002 Quality Assured AOD results

  41. Aerosol/dust climatologies Zhang XY et al., ACP, 2012

  42. Aerosol characterization in Northern Africa, Northeastern Atlantic, Mediterranean Basin and Middle East from direct-sun AERONET observations S.Basart, C. Pérez, E. Cuevas, J.M. Baldasano, and G.P. Gobbi (Atmos. Chem. Phys. October-2009)

  43. Increasing the observation capacity in North Africa Sand and Dust Storm WarningSystem Regional Center forNortherAfrica, Middle East and Europe Cairo (Egypt) Thala (Tunisia) Ouazarzate (Morocco) Tamanrasset- Argelia

  44. Dust impacts

  45. Improve Weather Forecast via Dust Radiative Forcing (RF) Feedback by CUACE/Dust Operational Forecasting System in Asian Regional Centre Forecasted Vertical T Forecasted surface T With dust RF feedback With dust RF feedback Wang et al., JGR, 2010

  46. 78% 75% Individual particle analysis show that 70% of aerosol particles in North China Plain are internally mixed with other two or three sourced aerosols, in which mineral dust play a key role. • S/Ca ratio • Normal: 0.73 ;Fog: 6.11 Li et al., 2009

  47. Impact of Saharan dust clouds over North Atlantic on trop-O3 Andrey et al., 2013 test footer

  48. Cuevas et al; 2011: Meningitis linked to mineral dust transport in the Sahel (MACC Report) Pérez et al; 2013: Soil dust aerosols as predictors of seasonal meningitis incidence in Niger (Environmental Health Perspectives (EPH))

  49. Capacity building • 8-12 Nov 2010: Training WeekonSatelliteMeteorology. Barcelona-Spain • 13 Nov 2010: LecturesonAtmospheric Mineral Dust and itsImpacton Human Health, Environment and Economy. Barcelona-Spain • 15-19 Nov 2010 Training Weekon WMO SDS-WAS products. Barcelona-Spain • 22-26 Feb 2011: Training onMeteorologicalServices, SDS Forecast and EarlyWarningSystem. Istanbul-Turkey • 21-25 Nov 2011: 2nd Training Courseon WMO SDS-WAS (satellite and groundobservation and modelling of atmosphericdust). Antalya-Turkey • 5-9 Nov 2012: II LecturesonAtmospheric Mineral Dust. Barcelona-Spain • 26-28 Nov 2012: Workshop on Meteorology, Sand and Dust Storm (SDS), Combating Desertification and Erosion. Ankara-Turkey WMO