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Dust Monitoring: Enhancements and Impacts on Weather Forecasts

Learn about the advancements in dust monitoring models like DREAM and their impact on numerical weather forecasts. Discover how dust feedback influences radiation and weather predictions. Explore the DREAM-Salt prediction system for sea-salt aerosols.

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Dust Monitoring: Enhancements and Impacts on Weather Forecasts

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  1. Sand and Dust Storm Monitoring:A) International Research Coordination, and B) Example of Dust Modelling Developments Slobodan Nickovic WMO Research Department snickovic@wmo.int NCEP, 9 Dec 2008

  2. DREAM: Dust Regional Atmospheric model • 4 out of 7 operational models in Africa/Europe SDS-WAS region are DREAM-based systems • 1993: First ever-done forecast made by DREAM • Used in more than 20 organizations for operations and/or research • Driven by the NCEP/Eta; most recently, by NCEP/NMM as well • From a single  4  8 particle size bins • All major dust processes included: • Dust emission, vertical mixing, advection, deposition NCEP, 9 Dec 2008

  3. Governing equation – mass conservation of dust concentration NCEP, 9 Dec 2008

  4. SURFACE CONDITIONS Vegetation data • 1 x 1 km USGS global data on vegetation - used to define the dust productive areas Soil types • FAO global soil types converted into model texture types- used to define particle size distribution NCEP, 9 Dec 2008

  5. How the model sees surface conditions Dust production function NCEP, 9 Dec 2008

  6. Surface concentration (Shao et al., 1993) • Surface fluxes – viscous sublayer (Janjic, 1994) • physical similarity with other mobile surfaces (e.g. sea, snow) • viscous sublayer operates in smooth and transitional, rough, and very rough turbulent regimes •  is the viscous diffusivity for dust concentration; KCsfc is the surface mixing coefficient, zC is the height of the viscous sublayer NCEP, 9 Dec 2008

  7. DREAM Operations at Barcelona Super-Computer Center NCEP, 9 Dec 2008

  8. NCEP, 9 Dec 2008

  9. Recent developments -Impact of Saharan dust on numerical weather forecasts Kischa et al., [2003]; Haywood et al., [2005] suggest that inclusion of radiative effects of dust could improve the weather prediction NCEP, 9 Dec 2008

  10. Dust Feedback On Radiation Can Improve Weather Forecasts In A Regional Model (Nickovic, 2004) Through negative feedback on winds “dust kills dust”. (Carlos et al., 2006) Ground cools down by ~5 C during strong SDS and air aloft warms slightly NCEP, 9 Dec 2008

  11. CTR RAD dust is considered as a dynamic tracer without interaction with atmospheric radiation interaction between short- and long-wave radiation and dust is included EXPERIMENTAL DESIGN 8-15 April 2002 major dust outbreak over the Mediterranean 2 sensitivity experiments • Cold Start on 5 April 2002 • 50 km horizontal resolution • 24 layers up to 15 km vertical NCEP, 9 Dec 2008

  12. APRIL 2002 DUST OUTBREAK MSL pressure 12 April at 12 UTC 20 m/s NCEP, 9 Dec 2008

  13. Napoli Raman Lidar 12 April 2002 NCEP, 9 Dec 2008

  14. However, 35-45 % reduction of the average AOD over the area covered by the main dust plume CTR RAD DUST NEGATIVE FEEDBACK • High dust spatial correlation between CTR and RAD: 0.95 • Strong negative feedback • upon dust emission by • dust radiative forcing NCEP, 9 Dec 2008

  15. NUMERICAL WEATHER PREDICTION Can we improve it? Sea-level pressure forecasts RAD-CTR RAD significantly improves the forecast NCEP, 9 Dec 2008

  16. Sea Salt version of DREAM NCEP, 9 Dec 2008

  17. DREAM-Salt prediction system at Tel-Aviv University from 2006. 3 1 2 DREAM-Salt based on the DREAM adapted for sea-salt aerosol instead of for desert dust; 8 particle size classes (1, 2, 3, 4, 5, 6, 7, and 8m); Sea-salt production scheme (Erickson et al., 1986) with introduced viscous sub-layer (Janjic, 1994); Ref.: Nickovic, S., Janjic, Z.I., Kishcha, P., and P. Alpert (2007), Model for Simulation of Sea-Salt Aerosol Atmospheric Cycle. In: Research Activities in Atmospheric and Oceanic Modeling, WMO, Geneva, CAS/JSC, WGNE, section 04, 19 – 20, 2007. NCEP, 9 Dec 2008

  18. DREAM-Iron model • Dust is a carrier of the embedded nutrients such as Fe (and phosphorus) • In remote oceans, new iron inputs are dominated by mineral dust • rather than by ocean upwelling • Iron is an essential micronutrient in marine environments, • if in a soluble form • Iron solubility at dust sources is low, but drastically increases • during the transport process over the ocean NCEP, 9 Dec 2008

  19. NCEP, 9 Dec 2008

  20. PRELIMINARY MODEL RESULTS The model simulates increase of Fe solubility with increased distance from soil sources in horizontal, while concentration decreases In the vertical, a similarity was foundwith behavior in the horizontal: at higher elevations distant from sources (ground), the solubility is high, while concentrations are low Obtained results are consistent with Baker and Jickells (2006) NCEP, 9 Dec 2008

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