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MACC-II analyses and forecasts of atmospheric composition and European air quality: a synthesis of observations and models. Richard Engelen & the MACC-II consortium. Exciting satellite observations. CO 2 , GOSAT, ACOS/JAXA/NIES. SO 2 , GOME-2, SACS, BIRA/DLR/EUMETSAT.
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MACC-II analyses and forecasts of atmospheric composition and European air quality: a synthesis of observations and models • Richard Engelen & the MACC-II consortium
Exciting satellite observations CO2, GOSAT, ACOS/JAXA/NIES SO2, GOME-2, SACS, BIRA/DLR/EUMETSAT Aerosol Optical Depth, MODIS, NASA NO2, OMI, KNMI/NASA
From combining observations and model forecast… IASI CO (LATMOS/ULB) MOPITT CO (NASA)
… 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 Brussels forecast O3 NO2 SO2 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. PM10
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)
Synthesis of model and observations Combining observations with models provides significant added value, when we want to forecast the evolution of pollution events for the next few days. 1-day model forecast observations Improved model emissions Using data assimilation (Huijnen et al., 2012)
Regional data assimilation CO Forecast CO increments CO Analysis 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.
Balance of model and observations MACC Reanalysis ERA-Interim Reanalysis Limb-sounding ozone data assimilated from August 2004 (MLS) are clearly improving stratospheric ozone. Switch to near-real-time version of MLS observations, which misses lowest layers. Chemical modelling is needed for correct representation of tropospheric ozone.
Combining many observations Ozone Near-real-time observations for a 12-hour period SCIA SBUV/2 NOAA-17 SBUV/2 NOAA-18 CO MOPITT IASI OMI MLS SO2 NO2 OMI GOME-2 OMI SCIA GOME-2
Using the Averaging Kernel We make use of the averaging kernel A in the observation operator by using the following: Observation Model simulated observation Difference 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.
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.
http://www.gmes-atmosphere.eu Thank you! NO2 Air-quality ensemble forecasts and (re-)analyses Stratospheric ozone records UV index Global forecasts Monthly methane emissions …And many more services.
