MACC-II analyses and forecasts of atmospheric composition and European air quality: a synthesis of observations and models Richard Engelen & the MACC-II consortium

SO 2, GOME-2, SACS, BIRA/DLR/EUMETSAT NO 2, OMI, KNMI/NASA Aerosol Optical Depth, MODIS, NASA CO 2, GOSAT, ACOS/JAXA/NIES Exciting satellite observations

From combining observations and model forecast… MOPITT CO (NASA) IASI CO (LATMOS/ULB)

… to global forecasting… MACC-II provides daily 5-day global forecasts with a horizontal resolution of 80km and a time resolution of 3 hours.

… to regional ensemble forecasts The global system drives the regional models providing European-scale air quality forecasts. The model ensemble provides a first-order error estimate of the forecast. Brussels forecast O3O3 NO 2 SO 2 PM 10

 MACC is a component of Europe’s Global Monitoring for Environment and Security (GMES) initiative  which provides services for atmosphere, land, ocean, emergency response and security  The atmospheric programme comprises  developing operational space- based observation of constituents (Sentinels)  strengthening the provision of in situ observations (GISC)  developing and operating associated data and information services (MACC-II)

(Huijnen et al., 2012) Synthesis of model and observations 1-day model forecastobservations Improved model emissions Using data assimilation Combining observations with models provides significant added value, when we want to forecast the evolution of pollution events for the next few days.

Regional data assimilation Lowest layers of atmosphere are most important for air quality monitoring and forecasting. This explains focus on using surface observations so far. MACC-II is now experimenting with data assimilation of satellite observations in regional models. Using MOPITT CO observations in the EURAD-IM data assimilation system provides significant increments in some areas. CO ForecastCO incrementsCO Analysis

Balance of model and observations MACC ReanalysisERA-Interim Reanalysis Limb-sounding ozone data assimilated from August 2004 (MLS) are clearly improving stratospheric ozone. Chemical modelling is needed for correct representation of tropospheric ozone. Switch to near-real-time version of MLS observations, which misses lowest layers.

OMI SBUV/2 NOAA-17 SBUV/2 NOAA-18 MLS SCIA MOPITTIASI GOME-2OMI Ozone CO NO2 GOME-2OMI SO2 SCIA Combining many observations Near-real-time observations for a 12-hour period

We make use of the averaging kernel A in the observation operator by using the following: Correct weighting of the vertical profile; influence of the a priori profile removed. A priori error assumptions are still contained in the averaging kernels themselves and we assume everything is linear within the bounds of these a priori assumptions. Observation Model simulated observation Difference Using the Averaging Kernel

Loss of ENVISAT The impact on the MACC-II NRT monitoring/forecasting system from the loss of ENVISAT is mostly coming from the absence of MIPAS data at high latitudes. MLS only provides profile information above about 68 hPa, which was complemented by the MIPAS information.

Past & future - O3

Air-quality ensemble forecasts and (re-)analyses UV index Stratospheric ozone records Monthly methane emissions Global forecasts NO 2 …And many more services. Thank you!