Dust prediction models

Transcription

1 Dust prediction models Sara Basart 1 (sara.basart@bsc.es), E. Terradellas 2, E. Cuevas 3, O. Jorba 1, J.M. Baldasano 1 and F. Benincasa 1 1 Earth Sciences Department, BSC, Barcelona, Spain 2 AEMET, Barcelona, Spain 3 CIAI-AEMET, Tenerife, Spain

2 Dust cycle and associated processes Dust global distribution Annual mean frequency distribution of MODIS Deep Blue ( ) Dust Optical Depth (DOD) > 0.2 (brown colour scale), TOMS ( ) Aerosol Index (AI) > 0.5 (blue contour lines), and OMI ( ) AI > 0.5 (green contour lines). The isocontours of TOMS and OMI have been removed over oceans for clarity. Extracted from Ginoux et al. (2012, JGR) 2

3 Dust cycle and associated processes: Types of dust storms Synoptic dust storms (large scale weather systems) Pre-frontal winds Post-frontal winds Large-scale trade winds Mesoscale dust storms Downslope winds Gap flow Haboobs 3

4 Dust cycle and associated processes Dust Impacts Meteorology and climate Marine productivity Coral mortality Hurricanes formation Air Quality and Human Health Respiratory disease (asthma) Eye infections Meningitis in Africa Valley Fever in the Americas Aviation and Ground Transportation Low visibility (i.e. air disasters) Agriculture Image from WMO website ( Energy and industry 4

5 Dust forecasting models Dust models are a mathematical representation of atmospheric dust cycle. To complement dust-related observations, filling the temporal and spatial gaps of the measurements. To help us to understand the dust processes and their interaction with climate and ecosystems. To predict the impact of dust on surface level concentrations used as SHORT-TERM FORECASTING TOOLS (3-5 days ahead) Extracted from Shao (2008) 5

6 Dust forecasting models METEO DATA T, P, winds, SST, surface fluxes, soil moisture ATMOSPHERIC & LAND-SURFACE MODEL Initialization: Optimal analysis; self-nesting Dynamics: Advection & adjustment, IC/BC Physics: Diffusion, clouds & surface forcing Soil moisture & temperature Surface soil hydrology Energy & mass fluxes On-line coupled DUST MODULE Size resolved dust emission Dust injection Dust 4D concentration NUMERICAL INTEGRATION Wind speed, friction velocity, moisture, Roughness length, vegetation fraction GIS DATA Dust climate Surface topography and roughness Soil type & particle size distribution Veg. height cover & leaf-area index Land use Albedo DUST MONITORING Satellite remote sensing Synoptic network Lidar network Dust monitoring station samples DATA ASSIMILATION Dust initial conditions 6

7 Dust forecasting models METEO DATA T, P, winds, SST, surface fluxes, soil moisture ATMOSPHERIC & LAND-SURFACE MODEL Initialization: Optimal analysis; self-nesting Dynamics: Advection & adjustment, IC/BC Physics: Diffusion, clouds & surface forcing Soil moisture & temperature Surface soil hydrology Energy & mass fluxes On-line coupled DUST MODULE Size resolved dust emission Dust injection Dust 4D concentration NUMERICAL INTEGRATION Wind speed, friction velocity, moisture, Roughness length, vegetation fraction GIS DATA Dust climate Surface topography and roughness Soil type & particle size distribution Veg. height cover & leaf-area index Land use Albedo DUST MONITORING Satellite remote sensing Synoptic network Lidar network Dust monitoring station samples DATA ASSIMILATION Dust initial conditions 7

8 Barcelona Dust Forecasting Center (BDFC; First Specialized Center for Mineral Dust Prediction of WMO NMMB/BSC-Dust selected to provide operational forecasts at high resolution (10km) for NAMEE region 8

9 NMMB/BSC-Chemical Transport Model The main system is build on the meteorological driver NMMB Fully on-line coupling: weather-chemistry feedback processes 9

10 Dust forecast products 10

11 Arabian dust event February 2015 MODEL Model AERONET Obs. OBSERVATIONS

12 Canary Islands dust event March 2015 MODEL Model AERONET Obs. OBSERVATIONS MODIS composite 8 th March from EOSDIS World Viewer

13 Europe dust event May 2015 MODEL Model AERONET Obs. OBSERVATIONS MODIS composite 13 th May from EOSDIS World Viewer

14 Thank you Barcelona Dust Forecast Center Acknowlegde to Enza DiTomaso, Antonis Gkikas, Kim Serradell, Lluís Vendrell, Michele Spada, and Vincenzo Oviso. T H E S O U R C E O F S O M E O F T H E M O V I E S A N D I N F O R M A T I O N I N T H I S P R E S E N T A T I O N IS T H E C O M E T W E B S I T E A T H T T P : / / M E T E D. U C A R. E D U / O F T H E U N I V E R S I T Y C O R P O R A T I O N F O R A T M O S P H E R I C R E S E A R C H ( U C A R ), S P O N S O R E D I N P A R T T H R O U G H C O O P E R A T I V E A G R E E M E N T ( S ) W I T H T H E N A T I O N A L O C E A N I C A N D A T M O S P H E R I C A D M I N I S T R A T I O N ( N O A A ), U. S. D E P A R T M E N T O F C O M M E R C E ( D O C ) U N I V E R S I T Y C O R P O R A T I O N F O R A T M O S P H E R I C R E S E A R C H. A L L R I G H T S R E S E R V E D. S A T E L L I T E D A T A U S E D I N T H I S P R E S E N T A T I O N W E R E P R O D U C E D W I T H T H E E O S D I S W O R L D V I E W E R O N L I N E D A T A S Y S T E M, D E V E L O P E D A N D M A I N T A I N E D B Y T H E N A S A G E S D I S C. W E A C K N O W L E D G E T H E N A S A A N D A E R O N E T M I S S I O N S C I E N T I S T S A N D P R I N C I P A L I N V E S T I G A T O R S W H O P R O V I D E D T H E D A T A U S E D I N T H I S R E S E A R C H E F F O R T. 14

15 Feedback Survey for users Barcelona Dust Forecast Center 15

16 Survey We will circulate a short online survey to get user s feedback on: WMO SDS-WAS Regional Center for Northern Africa, Middle East and Europe (R&D) Barcelona Dust Forecast Center (Operations) 16

17 Barcelona Dust Forecast Center