1. Operational assimilation of dust optical depth Bruce Ingleby, Yaswant Pradhan and Malcolm Brooks © Crown copyright 08/2013 Met Office and the Met Office logo are registered trademarks Met Office FitzRoy Road, Exeter, Devon, EX1 3PB United Kingdom Tel: 01392 885680 Fax: 01392 885681 Email: bruce.ingleby@metoffice.gov.uk 1. Introduction Mineral (desert) dust is a major atmospheric aerosol and has effects on the radiation balance, visibility (affecting aviation/travel and defence applications) and human health (meningitis). The highest dust concentrations are seasonally/synoptically varying but tend to be over North Africa and the Middle East with lower concentrations over parts of Asia and Australia. The uplift of dust depends on soil type and wetness and the surface wind speed. Once airborne dust (especially the finer particles) can be transported for several days – mostly within the atmospheric boundary layer - before wet or dry deposition. These mechanisms were introduced into the climate version of the Met Office Unified Model (MetUM) by Woodward (2001) and more recently have been included in the operational forecasting system. In April 2013 we started assimilating satellite dust information to improve the initial conditions of the forecasts. 2. Observations Aerosol Optical Depth (AOD) inferred from satellite provides an estimate of the vertically integrated total column aerosol amount. We are using AOD retrievals (mainly “Deep Blue” retrievals) from the MODIS instrument on the AQUA satellite – Figure 1. (We also have Met Office AOD retrievals from the SEVIRI instrument on Meteosat, but these are not currently assimilated.) Sparse surface-based AOD estimates from AERONET stations are used for validation of satellite retrievals (Figure 2) and dust forecasts. The satellite retrievals are for total AOD (including contributions from pollution, smoke and sea salt) – we choose those that are likely to be dominated by dust. AOD observations are limited to cloud-free daylight conditions (and currently we only use data over land). 3. Assimilation and forecast performance We have an “observation operator” to convert from dust concentrations (for two sizes of dust particles and 70 model levels) to AOD. The forecast AOD is then compared with observed AOD and the increments are projected back into model space (this uses 4D-Var to take account of advection over the 6-hour assimilation window). The partition between coarse and fine mode dust uses the proportions in the forecast (as in Benedetti et al, 2009). Early trials used a background error correlation length scale of 300 km, but the scales (~87 km) from the training data gave slightly better results and were adopted. Over North Africa assimilating MODIS AOD tends to reduce the dust amounts whereas over Asia the dust has been increased (Figures 3, 4). By comparison with AERONET data (squares) the results are generally better (although over parts of Asia pollution contributes to the aerosol). Trials using the SEVIRI AOD retrievals as well tended to give rather high dust concentrations over Africa (Figure 5). 4. Summary The Met Office has been producing operational global dust forecasts since July 2011, with assimilation included from April 2013. The assimilation improves the first few days of the forecast. http://www.metoffice.gov.uk/research/news/dust-forecasting http://www.metoffice.gov.uk/research/news/dust-forecasting Results over North Africa and the Middle East compare favourably with those from other centres, see SDS-WAS ( http://sds-was.aemet.es/ ). http://sds-was.aemet.es/ Met Office plans include use of more satellite data and the addition of extra aerosol species (sea salt and smoke from biomass burning). There will also be work to improve the description of soil properties in the main regions of dust storms. 5. References A. Benedetti et al, 2009: Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System. Part 2. Data assimilation. JGR 114, D13205 S Woodward, 2001: Modeling the atmospheric life cycle and radiative impact of mineral dust in the Hadley Centre climate model. JGR 106, pp 18155-18166 Figure 1. Top assimilated AOD reports for one day, bottom all MODIS reports. Figure 2. Comparison of SEVIRI, MODIS and AERONET AODs. MODIS and AERONET show better agreement. Figure 3. Top (no assim.), middle (MODIS assim.) and bottom the difference between them (six week mean). AERONET values in squares. Figure 4. Top (no assim.) and bottom (MODIS assim.) for 19 Dec 2011. A dust storm over south Afghanistan forced David Cameron’s plane to divert. Figure 5. MODIS vs background (6-hour forecast) statistics for North Africa. Adding SEVIRI AOD (blue/purple lines) increased the bias (top). The correlation (bottom) is better when assimilating MODIS (red/green) than not (black). Figure 6. Dust reports (orange/red) from Synops, 1 Dec 2011. Smoke – light blue, haze – grey.