Met Office dust forecasting Using the Met Office

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Met Office dust forecasting Using the Met Office Unified Model™ David Walters: Manager Global

Met Office dust forecasting Using the Met Office Unified Model™ David Walters: Manager Global Atmospheric Model Development, Met Office. (with M. E. Brooks, B. Ingleby, B. Johnson, J. Mulcahy, Y. Pradhan) © Crown copyright Met Office

Dust forecasting with the Met. UM © Crown copyright Met Office

Dust forecasting with the Met. UM © Crown copyright Met Office

Met Office Unified Model™ (Met. UM) A 'seamless' model used across all time and

Met Office Unified Model™ (Met. UM) A 'seamless' model used across all time and space scales. Met. UM framework includes: OPS, VAR, VER … © Crown copyright Met Office

Met Office Unified Model™ Atmospheric model components Dynamics: Physics: • Regular lat/lon grid. •

Met Office Unified Model™ Atmospheric model components Dynamics: Physics: • Regular lat/lon grid. • Spectral band radiation • Non-hydrostatic dynamics with a deep atmosphere. • Diagnostic or prognostic cloud • Semi-implicit time integration with 3 D semi-Lagrangian advection. • Mass flux convection • Atmospheric tracer advection • Mixed-phase ppn • Boundary layer • Gravity wave schemes • JULES land-surface model Aerosols/chemistry: • CLASSIC: SO 4, Soot, Biomass smoke, OCFF, x. NO 3, seasalt, dust • United Kingdom Chemistry Aerosol (UKCA) community model © Crown copyright Met Office

Dust in the Met. UM Woodward (2001, 2011) Friction velocity scheme using explicit U*

Dust in the Met. UM Woodward (2001, 2011) Friction velocity scheme using explicit U* over bare soil only: Horizontal flux: Kawamura (1951) Dry Ut* prescribed for each bin Iverson and White (1982) Soil moisture contribution to Ut* from top soil layer Fecan et al. (1999) Horizontal flux calculated for 9 size divisions Size division 1 2 3 4 5 6 7 8 9 Lower lim (mm) 0. 0316 0. 1 0. 316 1. 0 3. 16 10 31. 6 100 316 Upper lim (mm) 0. 1 0. 316 1. 0 3. 16 10 31. 6 100 316 1000 Horizontal and vertical flux © Crown copyright Met Office Saltation only

Dust in the Met. UM Crisis Area Model implementation Mrel, sand/silt/clay & soil properties

Dust in the Met. UM Crisis Area Model implementation Mrel, sand/silt/clay & soil properties from HWSD Nachtergaele et al (2008) e. g. grid-box clay fraction Vertical flux: © Crown copyright Met Office Gillette (1979)

Dust in the Met. UM Mapping from horizontal to vertical flux Using 6 size

Dust in the Met. UM Mapping from horizontal to vertical flux Using 6 size divisions: • Dynamically diagnosed from horizontal flux distribution: Currently used in climate research or • Prescribed following a fixed size distribution: Currently used in limited area NWP © Crown copyright Met Office

Dust in the Met. UM Mapping from horizontal to vertical flux Using 2 size

Dust in the Met. UM Mapping from horizontal to vertical flux Using 2 size divisions: Size division 1 2 3 4 5 6 7 8 9 Lower lim (mm) 0. 0316 0. 1 0. 316 1. 0 3. 16 10 31. 6 100 316 Upper lim (mm) 0. 1 0. 316 1. 0 3. 16 10 31. 6 100 316 1000 Horizontal saltation flux • Uses prescribed size distribution only Size division 1 2 Lower lim (mm) 0. 1 2 Upper lim (mm) 2 10 Vertical flux © Crown copyright Met Office Currently used in global NWP

Dust in the Met. UM Once dust is lifted: • Transported by SL advection

Dust in the Met. UM Once dust is lifted: • Transported by SL advection and • Interacts with radiation scheme via direct aerosol effect Can use climatological dust in radiation instead • Removed via dry/wet deposition Balkanski et al. (2007)

Operational dust models Global. Africa Southern North Model Asia Model • N 512 12

Operational dust models Global. Africa Southern North Model Asia Model • N 512 12 km (~25 km) 70 L (80 km 70 L lid)(80 km lid) • 4 D-Var 3 D-Var DA • 2 6 bin dust scheme since 2011 2008 • No dust DA • Radiation No dust inuses LBCsclimatology

Dust model validation © Crown copyright Met Office

Dust model validation © Crown copyright Met Office

Dust forecast validation

Dust forecast validation

Dust forecast validation • Dust Aerosol Optical Thickness (AOT) compared to AERONET

Dust forecast validation • Dust Aerosol Optical Thickness (AOT) compared to AERONET

Dust forecast performance ← → DUST ←… DUST

Dust forecast performance ← → DUST ←… DUST

Dust forecast validation Equitable Threat Score (ETS): fraction of observed events that were Demanding

Dust forecast validation Equitable Threat Score (ETS): fraction of observed events that were Demanding score for small correctly predicted, reduced by the numbers you would by chance. scale features asexpect correct prediction 0 – no skill means: • 1 exactly - perfect right place • right time (3 hour window)

Other validation methods • Subjective verification (including from theatre) • Verification of visibility against

Other validation methods • Subjective verification (including from theatre) • Verification of visibility against SYNOP/METAR • Model inter-comparison • Observational campaigns © Crown copyright Met Office

Global Model validation Pre-operational testing (April 2011) Met Office research news article on global

Global Model validation Pre-operational testing (April 2011) Met Office research news article on global dust forecasting: http: //www. metoffice. gov. uk/research/news/dust-forecasting

Global Model results Pre-operational testing Skill scores over SAM region: • beats the SAM

Global Model results Pre-operational testing Skill scores over SAM region: • beats the SAM for low severity events • capturing inflow into domain • misses high severity events • high res models still required? Model went operational in July 2011 Provide forecasts to SDS-WAS through MACC-II project from December 2011

Future developments © Crown copyright Met Office

Future developments © Crown copyright Met Office

Dust model developments • Validation show dust from areas with seasonal vegetation is important:

Dust model developments • Validation show dust from areas with seasonal vegetation is important: link dust emissions to “radiative bare soil fraction” • Dust DA currently being developed (to use SEVERI and MODIS AOD products) • Emissions sensitive to soil properties, soil moisture etc: further improvements required in these areas • Continuing to develop satellite products currently provided to WMO SDS-WAS Feedback on these products appreciated © Crown copyright Met Office

Summary Please take home the following points • Met Office dust forecasts operational since

Summary Please take home the following points • Met Office dust forecasts operational since 2008 • Model validation shows reasonable performance and has identified areas of potential improvement • Dust in 25 km global NWP since summer 2011 • Global model data to SDS-WAS from December 2011 © Crown copyright Met Office

Questions © Crown copyright Met Office

Questions © Crown copyright Met Office

Forecasting Replenishment Forecasting Replenishment Forecasting Forecasting Replenishment ForecastingForecasting Replenishment Forecasting Replenishment Forecasting Forecasting Replenishment Forecasting
forecasting longterm forecasting middleterm forecasting shortterm forecasting 2forecasting longterm forecasting middleterm forecasting shortterm forecasting 2
Dust storm dust dust storm everyone Everyone standsDust storm dust dust storm everyone Everyone stands
Dust Explosion Fundamentals Dust can explode 1 DustDust Explosion Fundamentals Dust can explode 1 Dust
The Dust Bowl The Dust Bowl Dust StormThe Dust Bowl The Dust Bowl Dust Storm
Dust To Dust Then shall the dust returnDust To Dust Then shall the dust return
Dust Lots of dust in spiral galaxies DustDust Lots of dust in spiral galaxies Dust
Dust storm forecasting at the UK Met OfficeDust storm forecasting at the UK Met Office
Introduction Definition Forecasting Technology Technological Forecasting Process ForecastingIntroduction Definition Forecasting Technology Technological Forecasting Process Forecasting
Forecasting Forecasting Problems and Methods New product forecastingForecasting Forecasting Problems and Methods New product forecasting
Forecasting Forecasting A Basis of Forecasting In businessForecasting Forecasting A Basis of Forecasting In business
Forecasting Forecasting A Basis of Forecasting In businessForecasting Forecasting A Basis of Forecasting In business
DEMAND FORECASTING DEMAND FORECASTING Demand forecasting means estimationDEMAND FORECASTING DEMAND FORECASTING Demand forecasting means estimation
Forecasting Models FORECASTING Forecasting is attempting to predictForecasting Models FORECASTING Forecasting is attempting to predict
Forecasting smoke and dust using HYSPLIT Smoke ForecastForecasting smoke and dust using HYSPLIT Smoke Forecast
CDEM1000 Coal Dust Explosibility Meter Coal Dust ExplosionsCDEM1000 Coal Dust Explosibility Meter Coal Dust Explosions
Cosmic Dust Enrichment and Dust Properties Investigated byCosmic Dust Enrichment and Dust Properties Investigated by
The Dust Bowl Those Terrible Dust Bowl DaysThe Dust Bowl Those Terrible Dust Bowl Days
Wood Dust and Asthma April 2010 Wood DustWood Dust and Asthma April 2010 Wood Dust
Dust Explosions Safe handling of solids Dust ExplosionDust Explosions Safe handling of solids Dust Explosion
DUST TO DUST ASHES TO ASHES OR WHYDUST TO DUST ASHES TO ASHES OR WHY